A pedometer-based walking intervention in 45- to 75-year-olds, with and without practice nurse support: the PACE-UP three-arm cluster RCT

What are the benefits of physical activity for adults and older adults?

Physical activity (PA) leads to reduced mortality, a reduced risk of > 20 diseases and conditions, and improved function, quality of life and emotional well-being. 1 Physical inactivity is the fourth leading risk factor for global mortality;2 in 2008, it was estimated to have caused 9% of premature mortality and 5.3 million deaths worldwide. 3 Physical inactivity is also a major cost burden on health services. 1,4,5

What are the current physical activity guidelines and who is achieving them?

Adults and older adults are advised to be active daily and, for health benefits, should achieve at least 150 minutes (2.5 hours) weekly of at least moderate-intensity activity [moderate to vigorous PA (MVPA)] or 75 minutes of vigorous-intensity PA, or an equivalent combination, achieved in bouts of at least 10 minutes’ duration. 1,2 Muscle-strengthening activities are also recommended on at least 2 days weekly,1,2 but are not part of our intervention, which is focused on increasing walking. One effective way to achieve the aerobic PA recommendations is by undertaking 30 minutes of moderate-intensity PA on at least 5 days weekly. 1,6,7 Regular walking is the most common PA of adults and older adults, and walking at a moderate pace (3 miles or 5 km per hour) qualifies as moderate-intensity PA. 8 Time spent being sedentary for extended periods should also be minimised, as this is an independent disease risk factor,1 which increases steeply with age from 45 years. 9 There is an increasing awareness that as a dose–response relationship exists for PA and health benefits, getting inactive people to do a little more PA is also important, rather than just relying on trying to achieve PA recommendations. 10,11 Emphasising that the 30 minutes can be built up from 10-minute bouts is an important message for older adults and those with disabilities, enabling them to increase their MVPA gradually. Among adults in England aged ≥ 19 years, 66% of men and 56% of women self-reported meeting the recommended PA levels, whereas only 58% of men and 52% of women aged 60–74 years did so. 12 Lower socioeconomic groups9 and Indian, Pakistani, Bangladeshi and Chinese ethnic groups are significantly less likely to report meeting the recommended PA levels, whereas the activity levels of other ethnic groups (black Caribbean, black African and Irish) are similar to that of the general population. 13 Over one-third of adults worldwide are insufficiently active, but there is large geographical variation. 10,14 However, PA, including walking, is very unreliably recalled, so surveys overestimate PA levels. 15 Objective accelerometry found that only 5% of men and 4% of women aged 35–64 years and 5% of men and 0% of women aged ≥ 65 years achieved the recommended PA levels, which is a fraction of those who self-report achieving them. 9

What are the risks from increasing physical activity?

The risks from a sedentary lifestyle far exceed the risks from regular PA. 6,16,17 Moderate-intensity PA carries a low injury risk,18 mainly musculoskeletal injury or falls. 19 Walking is very low risk, ‘a near perfect exercise’. 8 Screening participants for contraindications before participating in light- to moderate-intensity PA programmes is no longer advocated. 6,20 An important safety feature of our study is that individualised goals can be set from the participant’s own baseline, in line with the advice that older adults in particular should start with low-intensity PA and increase the intensity gradually: the ‘start low and go slow’ approach. 16,17 This worked well with our previous PA trial in older adults, which employed a similar approach and showed no increase in adverse events (AEs). 21

How can adults and older adults increase their physical activity levels?

A systematic review of PA interventions reported moderately positive short-term effects, but the findings were limited by mainly unreliable self-report measures in motivated volunteers. 22 This review has recently been updated by three complementary Cochrane reviews assessing (1) face-to-face PA interventions,23 (2) remote (including postal and telephone) and web 2.0 interventions24 and (3) a direct comparison of these two approaches. 23 Evidence supports the effectiveness of both face-to-face interventions and remote or web 2.0 interventions for promoting PA. However, the reviewers called for future studies to provide greater detail of the components of face-to-face interventions and to assess the impact on quality of life, AEs and economic data,23 and to include participants from varying socioeconomic and ethnic groups. 24 Only one study25 met the review criteria to compare face-to-face interventions with remote or web 2.0 interventions (many trials were excluded as a result of having less than 1 year’s follow-up data or an inadequate control group); this study showed no effect on cardiorespiratory fitness25 and there were no reported data for PA, quality of life or cost-effectiveness. 23 The review concluded that there was insufficient evidence to assess whether face-to-face interventions or remote and web 2.0 approaches are more effective at promoting PA, and called for more high-quality comparative studies. 23 None of the studies included in the reviews provided objective PA measurement. 23,24 Other studies have concluded that exercise programmes in diverse populations can promote short- to medium-term increases in PA when interventions are based on health behaviour theoretical constructs, individually tailored with personalised activity goals and using behavioural strategies. 26,27 A critical review and a best-practice statement on older people’s PA interventions advised home-based rather than gym-based programmes, and behavioural strategies (e.g. goal-setting, self-monitoring, self-efficacy, support, relapse prevention training), rather than health education alone. 17,27 National Institute for Health and Care Excellence (NICE)’s guidance concluded that no particular behaviour change model was superior and that training should focus on generic competencies and skills, rather than on specific models. 28 More recent complementary NICE guidance specifically recommended that goals, planning, feedback and monitoring techniques should be included in behaviour change interventions. 29 Starting low, but gradually increasing to moderate intensity is promoted as best practice, with advice to incorporate interventions into the daily routine (e.g. walking). 17 A recent systematic review of walking interventions concluded that interventions tailored to people’s needs, targeted at the most sedentary people and delivered at the level of the individual or household, can be effective, although evidence directly comparing interventions targeted at individuals, couples or households was lacking. 30

Are pedometers helpful?

Pedometers are small, cheap devices, worn at the hip, providing direct step count feedback. A systematic review (of 26 studies) found that pedometers increased steps per day by 2491 steps [95% confidence interval (CI) 1098 to 3885 steps] and PA levels by 27%, with significant reductions in body mass index (BMI) and blood pressure. 31 A second review (of 32 studies) found an average increase of 2000 steps per day. 32 Step count goals and diaries were key factors. 31,32 Several limitations were recognised: study sizes were small and long-term effects were undetermined; many studies included several components (e.g. pedometer and support), so independent effects were difficult to establish; and the inclusion of older people and men was limited. 31,32 Recent studies have addressed some of these limitations. A pedometer plus behaviour change intervention increased PA at 3 months, but not at 6 months, in 210 older women, with pedometers providing no additional benefit. 33 Two trials in high-risk groups showed sustained step count increases at 12 months. 34,35 A recent study of 298 older adults found a significant increase in both step counts and time in MVPA at 3 months and 12 months from a practice nurse-delivered pedometer-based walking intervention, but did not separate out pedometer- and nurse support-related effects. 21 NICE recently updated its advice from advising the use of pedometers only as part of research36 to advising their use as part of packages, including support to set realistic goals, monitoring and feedback. 37

How do step count goals relate to physical activity recommendations?

Step count goals lead to more effective interventions, but no specific approach to goal-setting is favoured. 28 Goals are based on a fixed target (e.g. 10,000 steps per day)38,39 or on advising incremental increases from the baseline as a percentage (5% per week,40 10% biweekly41 or 20% monthly33) or on a fixed number of extra steps. Those advocating a fixed number of extra daily steps have developed step-based guidelines to fit with existing evidence-based guidelines with an emphasis on 30 minutes of MVPA on ≥ 5 days weekly. 42 Despite individual variation, moderate-intensity walking appears to be approximately equal to at least 100 steps per minute. 42,43 Multiplied by 30 minutes, this produces a minimum of 3000 steps per day, to be done over and above habitual activity, which is the ‘3000 in 30’ message. 43 Several studies have advocated adding in 3000 steps per day on most days weekly, either from the beginning34 or by increasing incrementally (initially an extra 1500 steps per day and increasing),44,45 or by increasing by 500 steps per day biweekly. 35 Studies that advised adding 3000 steps per day from the baseline produced significant improvements in step counts at 3 months and two measured outcomes at 12 months, and showed sustained improvements in step counts,34,35 waist circumference34 and fasting glucose levels,35 but no sustained improvements to date in MVPA levels. Although there is no evidence at present to inform a moderate-intensity cadence (steps per minute) in older adults, Tudor-Locke et al. 46 advocate using the adult cadence of 100 steps per minute in older adults (while recognising that this may be unobtainable for some individuals) and advising that the 30 minutes can be broken down into bouts of at least 10 minutes. This model was used in a primary care walking intervention in 41 older people, which found significant step count increases from baseline to week 12, which were maintained at week 24. 47,48

Could accelerometers be useful in a pedometer-based walking intervention?

Accelerometers are small activity monitors worn like pedometers, but are more expensive; however, they are able to provide a time-stamped record of PA frequency (step counts) and intensity (activity counts). They require computer analysis, function as blinded pedometers in objectively measuring baseline and outcome data, and provide objective data on time spent in different PA intensities, including time spent in MVPA and time spent being sedentary, two important public health outcomes. Pedometer studies without accelerometers have relied on self-reported measures of these outcomes. Accelerometers are valid and acceptable to adults9,49 and older adults. 9,21,50–53 Although both instruments measure step count and are highly correlated,50 pedometers usually record lower step counts, and accelerometers cannot reliably be substituted for pedometers at an individual level. 54 Thus, although we used the accelerometer to measure outcomes, including step count, MVPA and sedentary time, we used the blinded pedometer, worn simultaneously at baseline, to set individual step count targets.

Are pedometers cost-effective?

There is limited knowledge on the cost-effectiveness of pedometer-based interventions in the UK. Recent systematic reviews that considered the economic outcomes of pedometer-based interventions found no evidence,55,56 partly because of an insufficient number of data. 57 However, a recent study assessed the cost-effectiveness of giving an individualised walking programme and pedometer with or without a PA consultation alongside a community-based trial of 79 people. 58 The incremental cost-effectiveness ratios (ICERs) per persons achieving an additional 15,000 steps per week were £591 and £92 with and without the consultation, respectively. However, even with this highly selected sample, no data on quality of life were collected, and the impacts on long-term outcomes were not estimated.

What is the role of primary care in promoting physical activity?

Primary care centres (general practices) in the UK provide health care and health promotion, free at the point of access, to a registered list of local patients (for many of whom PA will be of benefit), using disease registers to provide annual or more frequent chronic disease reviews via a multidisciplinary health-care team providing continuity of care. NICE guidance found that brief interventions in primary care are cost-effective, and it therefore recommends that all primary care practitioners should take the opportunity to identify inactive adults and provide advice on increasing PA levels. 36 New 5-yearly NHS health checks include adults aged 40–74 years and incorporate advice on increasing PA, often from primary care nurses. 59 Primary care nurses are effective at increasing PA, particularly walking, in this age group. 60 Not only can PA advice through consultation with health professionals be individually tailored61 and have more impact than other PA advice,62 but this is particularly the case for older adults,63 especially given the uncertainty about the effectiveness of exercise referral schemes from primary care. 64 Exercise-prescribing guidance in primary care reinforces the importance of follow-up to chart progress, set goals, solve problems, and identify and use social support;65 this will be an important feature of the nurse PA consultations in this trial. Evaluation of the UK Step-O-Metre programme, delivering pedometers through primary care, showed self-reported PA increases, but advised investigation with a randomised controlled trial (RCT) design. 44 Two trials, both in older primary care patients, have assessed the effectiveness of pedometers plus primary care PA consultations; one small trial (n = 41) showed a significant effect on step counts at 3 months, which was maintained at 6 months,47,48 and the other was our recent Pedometer Accelerometer Evaluation-LIFT (PACE-LIFT) trial21 (n = 298), which showed differences in both step counts and time in MVPA in bouts of at least 10 minutes, at 3 and 12 months for the nurse intervention compared with the control group. Neither trial separated out pedometer effects from the support provided. 21,47,48

Theoretical base, piloting and preparatory work to develop the intervention

The pedometer-based intervention is based on work cited above,31,32 showing that pedometers can increase step counts and PA intensity. 31,32 It extends current understanding by also including older adults and men, having a 12-month follow-up and ensuring that the pedometer and support components could be evaluated separately. The patient handbook, diary (both available on the journals library website: www.journalslibrary.nihr.ac.uk/programmes/hta/103202/#/) and practice nurse PA consultations use behaviour change techniques (BCTs; e.g. goal-setting, self-monitoring, feedback, boosting motivation, encouraging social support, addressing barriers or relapse anticipation). These techniques have been successfully used by non-specialists in primary care after brief training,66 and are emphasised in the Improving Health: Changing Behaviour: NHS Health Trainer Handbook,67 based on evidence from a range of psychological methods and intended for NHS behaviour change programmes, with local adaptation. 67 They also include techniques specifically recommended to be included in more recent NICE guidance (goals, planning, feedback and monitoring). 29 We adapted the Improving Health: Changing Behaviour: NHS Health Trainer Handbook67 for use in this trial into Pedometer And Consultation Evaluation-UP (PACE-UP) nurse and patient handbooks, to focus specifically on PA using pedometers. The BCTs were classified in accordance with the refined taxonomy of BCTs for PA interventions by Michie et al. 68 Diary recording of pedometer step counts provides clear material for PA goal-setting, self-monitoring and feedback, and should fit well with this approach. We have adopted the approach used by others44,45 of advocating adding in 3000 steps per day to an individual’s baseline on most days weekly in an incremental manner, and of advising on gradually increasing PA intensity to achieve more time in MVPA, with the message that 3000 steps in 30 minutes will help people to achieve PA guidelines. 43 Relevant pilot and preparatory work includes observational work using pedometers and accelerometers in primary care53 and a successful trial with older primary care patients developing the PA consultations and pedometer-based walking intervention (the PACE-LIFT trial; ISRCTN4212256121,69). The PACE-LIFT trial demonstrated that tailored support from practice nurse PA consultations combined with a pedometer-based walking programme (plus accelerometer feedback on PA intensity) led to an increase in both step counts and time in MVPA compared with the control group at both 3 and 12 months in 60- to 75-year-old primary care patients. The trial was limited in terms of both ethnic and socioeconomic diversity, has not yet published on sedentary time or cost-effectiveness and, as mentioned, was unable to separate out the effects of the pedometer (and accelerometer feedback) from the effects of nurse support. 21

Study aims

The main hypotheses to be addressed were as follows:

1. Does a 3-month postal pedometer-based walking intervention increase PA in inactive 45- to 75-year-olds at the 12-month follow-up point?

2. Does providing practice nurse support through dedicated PA consultations provide additional benefit?

The study also aimed to assess the cost-effectiveness of both interventions, whether or not any factors modified the intervention effects and the effect of the interventions on patient-reported outcomes, anthropometric measures and primary care-recorded AEs.

Primary objectives (relating to the primary outcome of step counts)

In inactive adults aged 45–75 years, the primary objectives were to:

• confirm that tailored support from practice nurse PA consultations combined with a pedometer-based walking programme can promote an increase in step counts compared with the control group at 12 months (pedometer plus nurse support vs. control)

• determine whether or not the simple provision by post of pedometers plus written instructions for a pedometer-based walking programme can promote an increase in step counts compared with the control group at 12 months (pedometer by post vs. control)

• estimate the effect of tailored support from practice nurse PA consultations combined with a pedometer-based walking programme compared with the postal pedometer-based walking programme, on step counts at 12 months (pedometer plus nurse support vs. pedometer by post).

Secondary objectives (relating to secondary outcomes of time in moderate to vigorous physical activity in bouts, sedentary time and cost-effectiveness)

In inactive adults aged 45–75 years, the secondary objectives were to:

• confirm that tailored support from practice nurse PA consultations combined with a pedometer-based walking programme can promote an increase in steps counts at 3 months and time spent in MVPA in ≥ 10-minute bouts at 3 and 12 months, and a decrease in sedentary time at 3 and 12 months compared with control (pedometer plus nurse support vs. control)

• determine whether or not the simple provision by post of pedometers plus written instructions for a pedometer-based walking programme can promote an increase in step counts at 3 months, an increase in time spent in MVPA in ≥ 10-minute bouts at 3 and 12 months and a decrease in sedentary time at 3 and 12 months compared with the control group (pedometer by post vs. control)

• estimate the effect of tailored support from practice nurse PA consultations in addition to the pedometer-based walking programme alone on step counts at 3 months, and time spent in MVPA in ≥ 10-minute bouts and sedentary time at 3 and 12 months (pedometer plus nurse support vs. pedometer by post)

• determine the cost-effectiveness of these alternative approaches to increasing PA levels at both 12 months and from a lifetime perspective from the viewpoint of the NHS and participants (see Chapter 4).

Other objectives

• To determine the effect of the interventions on anthropometric measures (BMI, waist circumference and body fat) at 12 months.

• To determine the effect of the interventions on patient-reported outcomes (self-reported PA levels, anxiety and depression score, exercise self-efficacy, quality of life, pain, AEs) and on primary care-recorded AEs at 3 months and 12 months.

• To determine whether or not age groups (< 60 years vs. ≥ 60 years), sex, taking part as a couple, socioeconomic group, disability, pain, BMI and exercise self-efficacy modify the effect of the intervention on increasing step count at 3 months and 12 months (ethnic group was originally intended to be included as an effect modifier, but there was inadequate power for this analysis because of the low number of non-white participants; see Changes from the published protocol).

• To compare the age, sex, socioeconomic group and ethnicity of those taking part in the trial with those invited but not participating, and to explore the reasons for not participating (see Chapter 5).

• To assess the fidelity and quality of the intervention implementation over time, by the evaluation of patient diary step count goals and recorded step counts for both intervention groups at the 3-month assessment, and the number and timing of recorded practice nurse contacts for the nurse support group (see Chapter 6).

• To explore the intervention’s acceptability to practice nurses and inactive adults, the reasons for dropout and the durability of effects, by qualitative interviews with participants after the 12-month follow-up, and a focus group with the nurses on study completion (see Chapter 7).

Practice inclusion criteria

General practices were recruited through the Primary Care Research Network – Greater London. Practices were required to be in the south-west London cluster, have a practice list size of > 9000, give a commitment to participate over the duration of the study, have a practice nurse interested and with time to carry out the PA interventions and trial procedures, and have the availability of a room for the research assistant to recruit participants and carry out baseline and follow-up assessments.

Participant inclusion criteria

Participants were patients aged 45–75 years, who were registered with one of the recruited south-west London general practices, were able to walk outside the home and had no contraindications to increasing their MVPA levels.

Participant exclusion criteria

• Physical activity based (by screening question on invitation letter). In order to maximise the benefits of the intervention to individuals and the NHS, the trial focused on less-active adults, using a single-item validated questionnaire measure of self-reported PA as a screening question to identify them. 60 Those who reported achieving a minimum of 150 minutes of MVPA weekly1 on their response letter were excluded (participants who, on subsequent baseline accelerometer assessment, were found to be above this PA level were not excluded, as they would be included if this intervention were to be rolled out in primary care).

• Health based [either by the Read code from primary care records or by general practitioner (GP)/practice nurse opinion, or from the telephone or face-to-face baseline assessment with the research assistant] for the following reasons:

  • housebound or living in a residential or nursing home

  • three or more falls in the previous year, or one or more falls in the previous year requiring medical attention

  • terminal illness

  • dementia or significant cognitive impairment

  • registered blind

  • new-onset chest pain, myocardial infarction, a coronary artery bypass graft or an angioplasty within the last 3 months

  • a medical or psychiatric condition that the GP (or practice nurse) considered to exclude the patient (e.g. acute systemic illness such as pneumonia, acute rheumatoid arthritis, unstable/acute heart failure, significant neurological disease/impairment, unable to move about independently, psychotic illness)

  • pregnancy.

Practice recruitment

The Primary Care Research Network – Greater London identified practices that fitted the above practice inclusion criteria. Practice recruitment was challenging for a number of reasons, including difficulties in finding practices with sufficient space to accommodate a research assistant on a regular basis, finding practices with nurses willing and with sufficient time to be engaged in delivering the intervention and finding practices that were prepared to provide administrative support. The Primary Care Research Network – Greater London provided us with strong support to recruit practices. Initially, six practices were recruited, with an additional practice added half-way through to boost recruitment. This was necessary, as recruitment at that point was running at just below 10% and we were concerned that we would not achieve the target recruitment from the original six practices within 12 months. The practices were selected to include a range of sociodemographic factors and geographical circumstances based on the practice postcode Index of Multiple Deprivation71 (IMD) scores (at least one practice from each quintile).

Participant recruitment

Practice staff identified patients aged 45–74 years on their primary care electronic patient record system, and, using Read codes and local care home knowledge, excluded ineligible patients (patients were aged 45–74 years when selected, but some were aged 75 years by the time of recruitment or randomisation). A list of potentially eligible patients was produced and ordered by household, with a unique household identifier number. An anonymised list was then used by the research team to create at least four random samples of 400 individuals at each practice. A maximum of two people per household were selected (we were aiming to select couples). If a household had two individuals, one was selected at random, and if the second individual had an age difference of ≤ 15 years, they were also selected; if they fell outside this age range, they were not included. If a household had more than two individuals, one was selected at random, and if there was a second individual aged within ≤ 15 years they were also selected; if there was not a second individual aged within ≤ 15 years, this became an individual household. Each sample list was examined by practice nurses or GPs to ensure trial suitability prior to invitation (see Participant exclusion criteria). Participants were recruited between September 2012 and October 2013, and follow-up was completed by October 2014.

Non-responders and non-participants

See Chapter 5 for more details.

Informed consent

Patients were sent an invitation letter from their own practice, along with a participant information sheet and a screening question on self-reported PA. A reminder invitation was sent if no reply was received after 6 weeks. A log was kept of the response rates from each practice. The decision regarding participation in the study was entirely voluntary. Those interested in participating returned the reply slip, including a response to a single screening question about their usual PA levels. If the participant self-reported as not achieving the PA guidelines,1 the research assistant arranged a baseline appointment for them and ran through the participant information sheet, and handled any questions or concerns that they had. If they were happy to proceed, they signed the study consent form; this form included consent to be contacted for qualitative interviews and consent for their general practice records for the year of the trial to be downloaded after trial follow-up was completed. Participants who had difficulty understanding, speaking or reading English were accompanied by a family member or friend during the research assistant appointment. Participants within a couple could attend together or separately.

Procedure for the postal intervention group

The participants received (by post) a pedometer (Yamax Digi-Walker, SW-200 model; Yamasa Tokei Keiki Co., Ltd, Tokyo, Japan), instructions and a 12-week step count diary for the 12-week walking intervention (see Figure 1). The research assistant contacted the participant to check that the pedometer had been received and to resolve any difficulties with the equipment. At the end of the 12-month follow-up period, the postal group were offered a single practice nurse PA appointment, if they wanted it.

Procedure for the nurse intervention group

Three dedicated PA consultations (week 1, week 5 and week 9) were arranged with the practice nurse, to individually tailor and support the 12-week pedometer-based walking programme (see Figure 1). At their first appointment, participants were given the same pedometer, diary and handbook that the postal group received. Participants were asked to wear a pedometer and keep a diary record of daily steps for 4 weeks between appointments, in order to review targets and goals at their next appointment. Participants were seen individually or as a couple.

Procedure for the control group

The participants were advised to continue their usual activity levels and were not offered the 12-week walking intervention, but were free to participate in any other PA, just as they would if they were not enrolled in the trial. At the end of the 12-month follow-up period, the control group was offered to receive a pedometer and the PACE-UP 12-week walking programme handbook and diary, either by post or as part of a single PA practice nurse appointment, as preferred.

Primary and secondary outcome measures

These were selected to reflect the needs of the target population, helping adults and older adults to increase their PA, particularly through walking, and to inform UK public health policy. The primary outcome was the change in average daily step count, measured over 7 days, between baseline and 12 months, assessed objectively by accelerometry (GT3X+; ActiGraph LLC, Pensacola, FL, USA). Secondary outcomes were as follows: changes in step counts between baseline and 3 months; changes in time spent weekly in MVPA in ≥ 10-minute bouts between baseline and 3 months, and between baseline and 12 months; and time spent sedentary between baseline and 3 months, and between baseline and 12 months. All of these secondary outcomes were also assessed objectively by accelerometry. Cost-effectiveness was also a secondary outcome in our protocol [incremental cost per change in step count and per quality-adjusted life-year (QALY)]; this is presented in Chapter 4.

Ancillary outcomes

• Change in self-reported PA, measured over the same 7 days as accelerometry using the short International Physical Activity Questionnaire (IPAQ)72 and the General Practice Physical Activity Questionnaire [(GPPAQ)73 as part of the 7-day PA questionnaire; see Appendix 1].

• Change in other patient-reported outcomes (from the health and lifestyle questionnaires at baseline, 3 months and 12 months; see Appendix 1): confidence in ability to do PA, as measured by exercise self-efficacy;74 anxiety and depression, as measured by the Hospital Anxiety and Depression Scale (HADS);75 perceived health status (health-related quality of life), as measured by the EuroQol-5 Dimensions, five-level version (EQ-5D-5L);76 and self-reported pain, measured by two items from the Medical Outcomes Study 36-item short-form health survey. 77

• Change in anthropometric measurements (weight, BMI, waist circumference, body fat; see Ascertainment of outcomes, Anthropometry, for measurement details).

• Adverse outcomes [falls, fractures, injuries, exacerbation of pre-existing conditions, major cardiovascular events and deaths from serious AEs (SAEs)] were collected as part of safety monitoring for the trial, by questionnaire self-report items designed by us at 3 and 12 months, and from primary care records after the 12-month follow-up period, for those giving consent.

• Health service use for those giving consent to primary care record access for the 12-month trial period, numbers of the following occurrences were collected for health economic evaluations (see Chapter 4): primary care consultations, accident and emergency (A&E) attendances, emergency and elective hospital admissions and outpatient referrals.

Accelerometry

Participants were asked to carry on with their usual PA levels and to wear an accelerometer (GT3X+, ActiGraph LLC) on a belt over one hip, during waking hours (from rising until going to bed) for 7 days, only removing it for bathing, at baseline, 3 months and 12 months. Participants were offered the option of text messaging to remind them to wear the accelerometer each day and to return it after the 7 days. A diary was provided to record what activities were done and for how long. The monitor, belt and diary were posted back on completion. Once returned, the participants received a £10 gift voucher.

Anthropometry

At the baseline and 12-month face-to-face assessments, the following measurements were taken: height (measured in bare feet to the nearest 0.5 cm using a stadiometer), weight (measured to the nearest 0.1 kg), body fat, bioimpedence [using the Tanita body composition analyser BC-418 MA (Tanita Corporation, Tokyo, Japan)] and waist and hip circumference (using a standard technique and tape measure with a clear plastic slider).

Questionnaire measures

Questionnaire measures were collected using validated tools (detailed under Outcome measures, Ancillary outcomes), as part of self-completed questionnaires at 3 months and 12 months.

In terms of the self-reported PA in the previous week, for the IPAQ72 we used the measure of MVPA (total minutes of vigorous and moderate PA weekly) and the measure of walking (total minutes of walking weekly). The GPPAQ73 provides a PA index (PAI), which is calculated from a combination of PA from both work and leisure activities. Active individuals are those who self-report ≥ 3 hours of MVPA per week on the PAI. However, walking is not included in the calculation of the PAI, although it is asked about in the questionnaire. Analysis of similar GPPAQ data compared with objective accelerometry in our earlier PACE-LIFT trial21 demonstrated that a modified PAI, which also included walking at a brisk pace for at least 3 hours per week, improved validity and repeatability compared with the standard GPPAQ. 78 A modified index, GPPAQ-Walk, was therefore generated.

In addition, the following were also recorded at baseline: demographic information, based on 2011 census questions79 (e.g. marital status, ethnic group, occupation, employment, household composition, home ownership); a list of common self-reported chronic conditions (e.g. heart disease, lung disease, arthritis, stroke, diabetes mellitus, depression); disability, as measured by the Townsend score;80 limiting long-standing illness;79 current medications; smoking; and alcohol consumption.

Several other questionnaire variables were collected at all three time periods, but were not considered to be ancillary trial outcomes in the trial protocol:70 loneliness, measured by a single item;81 risk of falls, measured using the Falls Risk Assessment Tool,82 was assessed using a combination of both self-reported items and direct observation of the ability to rise from a chair without using arms; and self-reported usual PA, as measured by the modified Zutphen Physical Activity Questionnaire. 83 Data from these variables are not presented in this report.

All study groups were asked about falls, injuries, fractures, exacerbation of any pre-existing conditions and the costs of any treatments in the 3- and 12-month questionnaires. Questions on the financial costs of participating in walking and other PAs were asked in the 3- and 12-month questionnaires.

Primary care computerised record measures

For participants who gave written consent, the following data were collected from their electronic primary care records, for the 12-month duration of the trial, after the 12-month follow-up:

• adverse events potentially relating to trial participation [Read codes relating to falls, fractures, injuries, cardiovascular events (myocardial infarction, coronary artery bypass graft, coronary angioplasty, transient ischaemic attack, stroke) and death]

• health service use – GP consultations, practice nurse consultations (excluding those for the trial), A&E attendances, emergency and elective hospital admissions and outpatient referrals.

These data were downloaded and pseudoanonymised before removal from the practice.

Baseline data collection

At baseline, a face-to-face assessment with the research assistant occurred at the participant’s general practice, and questionnaire and anthropometric data were collected (see Ascertainment of outcomes). Participants were then given a belt with an accelerometer (GT3X+, ActiGraph LLC) and a blinded pedometer (Yamax Digiwalker CW200) on it, and asked to wear this for 7 days. The CW200 pedometer model was used to enable the baseline target-setting of the pedometer step count, because of its 7-day memory of consecutive daily steps. However, it is bulky to wear and complicated to use, so this model was not used for the intervention.

Follow-up data collection

Follow-up data collection was conducted in the same way for all trial groups (Figure 2): (1) 3 months (postal) after randomisation (questionnaires and accelerometry) and (2) 12 months (face to face) after the baseline assessment (questionnaires, accelerometry and anthropometry). Participants were also contacted by the research assistant at 6 and 9 months after randomisation by telephone or e-mail to check on falls for trial safety reporting and contact details. For those in the intervention groups, a replacement pedometer or batteries were offered at each contact point, if required. The intervention groups were asked to return their 12-week step count diary following the intervention at 3 months. This was then photocopied and sent back to participants.

FIGURE 2.

Schedule of outcome assessment measures used in the PACE-UP trial.

Randomisation and concealment of allocation

Following completion of the baseline assessment (including providing accelerometry data on ≥ 5 complete days of ≥ 9 hours/540 minutes), each participant was allocated to a trial group using the King’s College London clinical trials unit internet randomisation service, to ensure independence of the allocation. If participants were unable to provide at least 5 days of ≥ 540 minutes wear time on accelerometry, they were asked to wear the accelerometer for a further 7 days, or they were excluded if this was not possible. Randomisation was at a household level. Randomisation of a group household took place only after both members of the household had completed the baseline assessment. Block randomisation was used within the practice with randomly sized blocks (2, 4 or 6) to ensure balance in the groups and an even nurse workload. Participants were informed by telephone which group they had been allocated to.

Contamination

Contamination could occur between partners in a household; we minimised this by ensuring that, if two household members were recruited, they were allocated to the same group (i.e. randomisation was at a household level). Contamination would have occurred if the control group used a pedometer to increase their walking during the 12-month trial follow-up. We tried to discourage participants in the control group from buying a pedometer, by ensuring that they knew that they would receive one at the end of follow-up. A question was included in the 12-month questionnaire to ask if they had used a pedometer during the course of the trial.

Treatment masking

Participants were randomised only after the successful return of accelerometers with 5 days’ recording. It was not possible to mask participants to their intervention group. The research assistants who carried out follow-up assessments were not masked to group allocation for pragmatic reasons alone: the study was funded to support only enough researchers to carry out recruitment and follow-up simultaneously. However, the main outcome was assessed objectively through accelerometry, and the assessment of the quality of the outcome data was done blind to intervention group: days with < 9 hours of data were excluded. Weight and body fat were also assessed objectively using the Tanita scales, which provided electronic printouts of results, and other outcomes were assessed using standardised measures (e.g. patient-reported outcomes from questionnaires). The statistician carrying out the primary analyses was masked to group allocation as far as possible.

Withdrawals and losses to follow-up

Participants could withdraw from the trial at any point. Participants who withdrew following informed consent, and prior to randomisation, were replaced with another participant. Participants who withdrew after randomisation were not replaced and were asked if they were prepared to contribute to further data collection on outcomes at 3 and 12 months. Participants were made aware that withdrawal from the trial would not affect future care and that information on those who withdrew or were lost to follow-up that had already been collected would still be used, unless consent for this was withdrawn.

Procedure for accounting for missing data

Only days with at least 540 minutes of registered time on an accelerometer on a given day were used, which was consistent with previous work (the PACE-LIFT trial,21 Trost et al. 90 and Miller et al. 91). Participants were randomised only if they provided at least 5 such days of accelerometer data at baseline. A multilevel linear regression model was used, taking account of repeated days within individuals to estimate the baseline average daily step count for each subject, adjusted for the day of the week and the day order of wearing the accelerometer. The same approach was used to estimate the average daily step count at 3 months and 12 months. The main covariates – age, sex, practice, month of baseline accelerometry and whether or not participants were taking part as a couple – were known for all participants, and most patients had complete data for other measures. To lessen attrition bias, the primary analysis included all participants with at least 1 satisfactory day of accelerometry recording at 12 months (i.e. a wear time of ≥ 540 minutes). The main analysis assumed that, depending on the model covariates, outcome data were missing at random. This was likely to be true for missing data as a result of accelerometer failure, and was plausible for missing days and participants who did not return accelerometers. However, an alternative plausible assumption is that participants who failed to provide outcome data were less active. Multiple imputation was used to impute values for those with no accelerometer data at 12 months (see Statistical methods, Sensitivity analyses). Further sensitivity analyses examined the impact of assuming that missing step counts at 12 months in the control group were equal to their baseline values and, in the two intervention groups, varied between 1500 steps lower and 1500 steps higher than their baseline values.

Primary analysis

The primary outcome measure was change in average daily step count from baseline to the 12-month follow-up point measured over 7 days. However, to overcome Lord’s paradox,92 the analytic approach regressed 12-month outcomes on baseline measures, thus allowing for regression to the mean. Eligibility was defined on the basis of ≥ 5 days with ≥ 540 minutes’ activity at baseline. If the participant was asked to wear the accelerometer for a second time, the second 7 days was used in the analysis. If there were > 7 days’ wear on the accelerometer, then the first 7 days were used and later readings were discarded. The primary analysis used all participants providing at least 1 day of ≥ 540 minutes accelerometry wear time at 12 months (i.e. a complete-case analysis).

All analyses were carried out using Stata, version 12. The xtmixed procedure was used for regression models. A two-stage process was used for accelerometry data. Stage 1 estimated the average daily step count at both baseline and 12 months, using a multilevel model in which daily step counts were regressed on day of the week and day order of wearing the accelerometer (from day 1 to day 7) as fixed effects, and with day within individual as the random effect (i.e. level 1 was the day within individual and level 2 was individual). In stage 2, average daily step count at 12 months was regressed on baseline average daily step count, sex, age, general practice, month of baseline accelerometry and treatment group as fixed effects, and household as the random effect, to allow for clustering at a household level (i.e. level 1 was individual and level 2 was household). This method effectively measured the change in step count from baseline to 12 months, minimising bias and maintaining power. Adjusting for baseline steps controlled for many factors that predict the number of steps in cross-sectional analyses (e.g. BMI, socioeconomic group, health status). The reference group for the intervention group comparisons was the control group. The post-estimation command pwcompare was used to obtain the estimates of change with 95% CIs and p-values for the difference in change in steps for the postal group versus the control group, the nurse-support group versus the control group and the nurse-support group versus the postal group. This last comparison provided information on whether or not the nurse intervention promoted a worthwhile increase in activity compared with a pedometer alone. It should be noted that, although this estimate can be obtained from the difference of the first two estimates, pwcompare also provided 95% CIs for this comparison. Checks were carried out to confirm that the distribution of residuals from the regression model for change in steps was normally distributed.

Secondary and ancillary outcome analyses

Secondary PA outcome measures from accelerometry were total weekly minutes of MVPA in ≥ 10-minute bouts, average daily sedentary time at 12 months and steps, MVPA in bouts and sedentary time at 3 months. These data were processed and analysed in the same way as described for the step counts (see Primary analysis). MVPA was highly positively correlated with step counts and sedentary time was negatively correlated with step counts.

Other ancillary outcomes were changes in exercise self-efficacy, anxiety, depression, perceived health status (health-related quality of life, as measured using the EQ-5D-5L), self-reported pain, anthropometric measures (weight, BMI, waist circumference, body fat) and self-reported PA from the IPPAQ and GPAQ questionnaires. Changes in these outcomes from baseline to 3 and 12 months were analysed using identical models to stage 2, as described in Primary analysis (i.e. level 1 was individual and level 2 was household).

Adverse event analyses

The number of participants who suffered an AE between 0 and 3 months or between 0 and 12 months (a spontaneously reported SAE or a systematically reported SAE from the 3- or 12-month questionnaire, or a SAE collected from the primary care record data) was compared between groups using exact tests for categorical tables.

Subgroup analyses

Sex, age groups (< 60 years or ≥ 60 years), taking part as a couple, socioeconomic subgroups, BMI, disability, pain and exercise self-efficacy were examined as potential effect modifiers by adding interaction terms to the regression model for the primary outcome, which was changes in step counts at 12 months.

Sensitivity analyses

Sensitivity analyses were carried out for the primary outcome (change in step counts from baseline to 12 months). The effects of using different criteria for defining satisfactory wear at 12 months were examined as follows: (1) at least 5 days of ≥ 540 minutes’ wear time, (2) ≥ 1 day of ≥ 600 minutes’ wear time and (3) ≥ 5 days of ≥ 600 minutes’ wear time. The effect of adjusting for change in wear time between baseline and 12 months was also examined.

Additional sensitivity analyses assessed whether participants lost to follow-up or who failed to provide a single adequate day’s recording might have introduced bias. This was first done by assuming that outcome data were missing at random, depending on the model covariates, using the Stata procedure mi impute. The first model used the standard model covariates to impute missing step counts at 12 months (treatment group, baseline steps, sex, age, general practice, month of baseline accelerometry and household as a random effect) and the second model added in the National Statistics Socio-economic Classification (NS-SEC), self-reported pain and fat mass as additional covariates. Further analyses explored the possible impact of outcomes not being missing at random, using the following assumptions: among those with missing data in the control group, the change in mean steps from baseline to 12 months was 0, and among those with missing data in each of the intervention groups, the change in mean steps from baseline to 12 months was –1500, 0 or +1500.

Three-month (interim) outcomes

There were significant differences for the change in average daily step counts from baseline to 3 months between intervention groups and the control group: additional step counts (steps per day) were 692 steps (95% CI 363 to 1020 steps; p < 0.001) for the postal group and 1173 steps (95% CI 844 to 1501 steps; p < 0.001) for the nurse-support group, and the difference between the intervention groups was statistically significant (481 steps, 95% CI 153 to 809 steps; p = 0.004). Findings for the change in time in MVPA levels showed a similar pattern: additional MVPA in bouts of ≥ 10 minutes (minutes per week) was 43 minutes (95% CI 26 to 60 minutes; p < 0.001) for the postal group and 61 minutes (95% CI 44 to 78 minutes; p < 0.001) for the nurse-support group, and the difference between intervention groups was 18 minutes (95% CI 1 to 35 minutes; p = 0.04). There was no difference between the groups for the change in sedentary time. Summary data for the 3-month PA outcomes are shown in Appendix 2, Table 24.

Twelve-month (main) outcomes

Both intervention groups increased their step counts between baseline and 12 months compared with the control group: additional step counts (steps per day) were 642 steps (95% CI 329 to 955 steps; p < 0.001) for the postal group and 677 steps (95% CI 365 to 989 steps; p < 0.001) for the nurse-support group, with no statistically significant difference between intervention groups (36 steps, 95% CI –277 to 349 steps; p = 0.82). Time spent in MVPA in bouts showed a similar pattern, that is, both intervention groups increased at 12 months compared with the control group: additional MVPA in bouts (minutes per week) was 33 minutes (95% CI 17 to 49 minutes; p < 0.001) for the postal group and 35 minutes (95% CI 19 to 51 minutes; p < 0.001) for the nurse-support group, with no statistically significant difference between the two intervention groups (2 minutes, 95% CI –14 to 17 minutes; p = 0.83). Again, there was no difference between the groups for the change in sedentary time. Summary data for the 12-month PA outcomes are shown in Appendix 2, Table 24.

Residuals from the 12-month models for steps and weekly MVPA in ≥ 10-minute bouts were plotted, and the distribution of residuals from both models was normally distributed (see Appendix 2, Figure 18).

Methods

Results

Table 9 summarises the data on costs, EQ-5D-5L utility scores and quality of life (see Appendix 3, Tables 31–35). At 3 months, the average cost per participant was highest in the nurse group (£249), followed by the postal group (£122) and the control group (£107). The mean cost and distribution is affected considerably by the inclusion of health service use, which resulted in the control group costing £36 more per participant than the postal group and £12 more than the nurse-support group. QALYs varied marginally, with the gap being the greatest between the control and postal groups at +0.002. At 12 months, the average cost per participant was highest in the nurse group (£603), followed by the control group (£461) and the postal group (£375). The inclusion of health service use resulted in the control group costing £86 more per participant than the postal group, but £142 less than the nurse-support group. QALYs were marginally higher in the postal group (0.843 QALYs) than in the control group (0.837 QALYs) and the nurse-support group (0.836 QALYs) group.

The main results (Table 10), which are adjusted for baseline differences, show that, at 3 months, the cost of the nurse-support group was statistically significantly higher than that of the control group (£135, 95% CI £99 to £171), whereas this was not the case for the postal group (£15, 95% CI –£15 to £45). Table 10 also shows that there was a statistically significant increase in daily steps and minutes of MVPA in ≥ 10-minute bouts for the intervention groups compared with the control group. The ICER per additional minute of MVPA in ≥ 10-minute bouts was £0.35 for the postal group and £2.21 for the nurse-support group, compared with the control group. However, both intervention groups accrued slightly fewer QALYs than the control group (postal group: –0.0005 QALYs; nurse-support group –0.0004 QALYs), although this difference was not statistically significant. Nevertheless, it contributed to the dominance (lower costs and more QALYs) of the control group compared with both intervention groups at 3 months.

Comparing the two interventions at 3 months (see Table 10) shows that the nurse group achieved 481 more steps (95% CI 153 to 809 steps) and 18 more minutes of MVPA (95% CI 1 to 35 minutes) per person than the postal group, at a statistically significant additional cost of £120 (95% CI £95 to £146). The estimated cost per additional step and additional MVPA minute (in bouts of ≥ 10 minutes) was £0.25 and £6.67, respectively. The nurse-support group had slightly fewer QALYs, although this was not significantly different (0.0004 QALYs, 95% CI –0.0026 to 0.0018 QALYs); however, it contributed to the nurse-support group being dominated (higher costs and fewer QALYs) by the postal group.

The main results at 12 months (see Table 10) were somewhat different from those at 3 months. Although the mean costs were lower for the postal group (–£91, 95% CI –£213 to £33) and higher for the nurse-support group (£126, 95% CI –£37 to £290) than the control group, neither was statistically significantly different. However, the increase in the cost of moving from postal delivery to nurse-support delivery was statistically significantly higher (£217, 95% CI £81 to £354). Although both interventions were associated with a statistically significant increase in both step count and weekly minutes of MVPA in ≥ 10-minute MVPA bouts compared with the control group, the difference between intervention groups was not statistically different at 12 months. The postal group took more steps on average (+ 642 steps) and cost less on average (–£91) than the control group, and dominated the control group in terms of the PA outcomes. Compared with the control group, the nurse-support group cost an additional £0.19 per step and £3.61 per additional minute of MVPA in bouts of ≥ 10 minutes. None of the small decrements in QALYs at each incremental comparison (–0.0009 QALYs for the postal group vs. the control group, –0.0007 QALYs for the nurse-support group vs. the control group and –0.0004 QALYs for the nurse-support group vs. the postal group) was statistically significantly different. Compared with the control group, the postal group had fewer QALYs (although this was not statistically significantly different) and lower costs (also not statistically significantly different). However, the magnitude of the cost-savings is such that they outweigh the forgone QALYs at a threshold of £20,000 per QALY, and would be considered cost-effective. Using QALYs, the nurse-support group is dominated by both the control group and the postal group.

Comparing the two interventions at 12 months (see Table 10) shows that the estimated cost per additional step and additional MVPA minute was £6 and £109, respectively, and that, in terms of QALYs, the nurse-support group was still dominated by the postal delivery group.

The cost-effectiveness planes (Figures 5 and 6; see also Appendix 3, Figure 19) broadly confirm the findings that the postal delivery group had a strong likelihood of lower costs, but also fewer QALYs, and that the nurse-support group tended to have fewer QALYs and higher costs than the control group. However, different levels of uncertainty surround these mean estimates, as reflected in the CEACs (Figure 7; see also Appendix 3, Figure 20). At £20,000 per QALY, the postal delivery group has a 50% chance of being cost-effective compared with the control group, which falls to 42% at £30,000 per QALY. This is because, as the willingness-to-pay threshold increases (and, therefore, the higher the value that is placed on forgone QALYs), the value of QALYs lost begins to outweigh the cost-savings (see Figure 9). This is reflected in the CEAC, on which the probability moves towards zero. The nurse-support group had only a 5.5% chance of being cost-effective compared with the control group at a willingness-to-pay threshold to gain, or give up, a QALY of £20,000, and this fell to 4.9% when compared with the postal delivery group.

FIGURE 5.

Cost-effectiveness plane for the postal delivery group vs. the control group at 12 months.

FIGURE 6.

Cost-effectiveness plane for the nurse-support group vs. the control group at 12 months.

FIGURE 7.

Cost-effectiveness acceptability curve for the postal delivery and nurse-support groups (vs. the control group) at different willingness-to-pay-per-QALY thresholds.

The deterministic sensitivity analyses (see Appendix 3, Table 36) show that, with the exceptions of (1) using health service use including only self-reported serious adverse effects, (2) excluding all health service costs, (3) changing perspective (including all participant costs) and (4) the worst-case ‘combined scenario’, the sensitivity analyses produced results that were consistent with the base-case findings. For the exceptions, the postal delivery group was dominated by the usual-care control group at 12 months.

Methods

An existing Markov model,130 designed to assess the cost-effectiveness of PA interventions, was used. This model has evolved from assessing the cost-effectiveness analysis of exercise referral schemes131–133 to brief interventions129 and beyond. 134 It has been used by NICE to update national guidance (PH44 on brief interventions, PH2 on exercise referral schemes guidance)36,135 and is the basis for the NICE Return on Investment Tool136 used by local authorities.

The model is driven by evidence of the impact that PA interventions have on the proportion of people meeting the recommended PA levels, short-term quality-of-life gains (associated with meeting the recommended PA levels) and the impact of PA on future rates of CHD, stroke and type 2 diabetes mellitus. Figure 8 shows the pathways within the model. In the original Markov model,129 a cohort of 100,000 33-year-old people were followed in annual cycles over their lifetime. At the end of the first year of the model, the cohort would be either ‘active’ (doing 150 minutes of MVPA per week) or ‘inactive’, and could have had one of three events (non-fatal CHD, non-fatal stroke, type 2 diabetes mellitus), remain event free (i.e. without CHD, stroke or diabetes mellitus) or die, either from cardiovascular disease (CVD) or from non-CVD-related causes. Active individuals have a better life expectancy and quality of life, attributable to lower risks of developing CHD, stroke and type 2 diabetes mellitus. People who become active in the first year (irrespective of the trial arm) accrue a one-off utility gain associated with achieving the recommended level of PA. QALYs reflect the health outcomes from a reduced risk of disease. A discount rate of 3.5% per annum is used for costs and QALYs, as recommended by NICE, and the analysis is conducted from a NHS perspective. Full details of the model are provided in Appendix 3 and elsewhere. 130

FIGURE 8.

Illustration of pathways within the long-term cost-effectiveness model. 130

The model by Anokye et al. 130 was adapted for use in five ways:

1. The population begins with the mean age of the trial population (i.e. 59 years rather than 33 years) and was followed to 88 years (average life expectancy at 59 years in the UK). 137 This change was also reflected in the age-specific estimates used.

2. The intervention was either pedometer plus nurse support or a posted pedometer.

3. Within-trial costs were used, with a second year of annuitised values added for the intervention arms (£5.03 per person for the postal delivery group and £4.14 per person for the nurse-support group – the number of pedometers to the postal delivery group was relatively higher as a result of more replacements), as the trial analysis had assumed pedometers lasted 2 years.

4. Effectiveness estimates from the PACE-UP trial were used as follows: the probability of moving from an inactive to an active state was based on estimated relative risks (RRs) for achieving 150 minutes of MVPA in ≥ 10-minute bouts over 7 days at 12 months. RRs of achieving ≥ 150 minutes of MVPA in ≥ 10-minute bouts at 12 months were estimated from ORs using the formula OR/[(1 – P_ref) + (P_ref × OR)], in which P_ref is the proportion of all subjects achieving 150 minutes of MVPA in ≥ 10-minute bouts at baseline (i.e. 218/1023 = 0.21). The OR was derived from a logistic regression model in which achieving 150 minutes of MVPA in bouts of ≥ 10 minutes at 12 months was regressed on baseline minutes of MVPA in bouts of ≥ 10 minutes, month of baseline accelerometry, age, sex, general practice and treatment group, with household as a cluster.

5. The short-term psychological benefits associated with achieving 150 minutes of MVPA per week used trial data; incremental EQ-5D-5L scores (at 12 months) for active people were regressed, via beta regression, on EQ-5D-5L scores at baseline, ethnicity, education, employment status, intervention group, practice and disability.

Other parameters were informed by estimates from the original model. Cost and utility estimates for disease conditions were originally sourced from literature reviews of economic evaluations conducted for NICE on CVD and diabetes mellitus. 5 Estimates for the health impacts of PA were taken from national/international evidence-based guidance on PA and health, such as the US Physical Activity Guidelines Advisory Committee Report, 2008. 138 Appendix 3, Table 37 details all parameter values.

The probabilistic sensitivity analysis (PSA) addressed the uncertainties around all parameters in the model (except for baseline mortality, as the mortality census data have little uncertainty). A total of 10,000 Monte Carlo simulations were used to generate stable estimates. Given that the within-trial sensitivity analysis showed the decisional influence of some assumptions, a deterministic sensitivity analysis using the lifetime model explored two alternative, conservative scenarios:

• Scenario 1 – combined exclusion of all health service use costs during the trial period (year 1 of the model), with no short-term QALY gain associated with achieving the recommended level of PA. This was considered as a result of the uncertainty around short-term changes to health service use, and because previous studies found the exclusion of short-term QALY gain associated with being physically active to the recommended level to be decisional. 5,129

• Scenario 2 – scenario 1 plus user costs related to participation in PA and the interventions. This combination represented a ‘worst-case’ scenario in the trial. Although this perspective is not one adopted by NICE, it represents the most conservative scenario based on this evidence.

Results

Table 11 shows that the postal delivery group dominated both usual practice and the nurse-support group, as the lifetime costs were lower and the number of QALYs was greater. The stochastic uncertainty associated with the mean ICER (Appendix 3, Figure 21) indicates that these findings are robust, as there is a 100% likelihood, at a willingness-to-pay threshold of £20,000 per QALY, that the postal delivery group is cost-effective compared with the control group and with the nurse-support group (Figure 9). This is consistent with the net monetary benefit estimates, which show that, although we can be 95% confident that the postal delivery group is better than the control group and the nurse-support group, we cannot be 95% confident that the nurse-support group is better than the control group.

FIGURE 9.

Cost-effectiveness acceptability curve showing the probability of lifetime cost-effectiveness for the postal delivery group and the nurse-support group (vs. the control group) at different willingness-to-pay threshold levels.

The results for scenario 1 of the sensitivity analyses for the 100,000 cohort were as follows:

• postal delivery group versus control group – postal delivery moved from a dominant position to being a more expensive option (+£4M) with greater QALY gains (+609 QALYs) and an ICER of £6100

• nurse-support group versus control group – the ICER increased from £16,000 to £26,000 (+£14M, +538 QALYs)

• nurse-support versus postal delivery group – the nurse-support group remained dominated by the postal delivery group (+£10M, –87 QALYs).

For scenario 2, the sensitivity analyses for the 100,000 cohort showed the following:

• postal delivery group versus control group – the postal delivery group moved from a dominant position to being more expensive (+£16M) with greater QALY gains (+609 QALYs) and an ICER of £26,600

• nurse-support group versus control group – the ICER increased from £16,000 to £25,400 (+£13.7M; +538 QALYs)

• nurse-support group versus postal delivery group – the nurse-support group moved from a dominated position to a cost-effective position (–£2M, –87 QALYs).

Strengths and weaknesses

To our knowledge, this is the first published study of short- and long-term costs and effects of a pedometer intervention. Its strengths are the use of detailed individual-level cost and effectiveness data from a well-designed population-based RCT, which had nearly complete (93.4%) follow-up data to 1 year, to estimate the cost per QALY at 1 year, and also to input into a lifetime model of cost-effectiveness. It also included both provider and user perspectives in costing service provision and participation, which allowed the impact of perspective to be investigated, and which provides a basis for further investigation of the role of cost in its association with participation in PA. The estimates of uncertainty extended commonly used techniques to account for increased precision in the context of clustered data. The sensitivity analysis pushed both the short- and long-term analyses to very conservative outcomes given the trial data and, therefore, provides a good indication of the robustness of findings based on the current evidence.

The weakness of the within-trial cost-effectiveness study connects to the cost of health service use over the period of the trial; no information was available on the severity or procedures used for hospital admissions, or cause of admission to the A&E department. We relied on the principal investigator’s ‘best guess’ or ‘nearest appropriate code’ (while blind to the treatment group) and on averaging across elective/non-elective admissions and we therefore explored alternative assumptions in our sensitivity analyses. There was considerable variation in costs in each trial arm, and the trial was not powered to detect a difference. Data were also not collected on costs to participants for months 4–9, and the last 3 months were multiplied to represent the missing data. These may have over- or underestimated participant costs and, if significantly underestimated, could be decisionally important. With respect to the long-term modelling, the model assumes that people would revert to PA patterns observed in long-term cohort studies. This estimate could be improved with longer-term trial data. All the challenges set out in previous work130 are also relevant here (e.g. some diseases, such as cancer and AEs, are not accounted for, which could lead to either over- or under-estimation of cost-effectiveness).

The study feeds into an area sparse of primary data139,140 populated only by small studies. 95,96 Leung et al. 97 showed a 95% likelihood that pedometers would be a cost-effective addition to green prescriptions (in New Zealand) at 12 months, which is much higher than the 50% likelihood that we found. Our study also provides long-term estimates based on the population-level primary data for comparison with the larger body of cost-effectiveness estimates134 from decision models. 97,98,141,142 Some have identified cost-savings and an improved quality of life at a population level from pedometers in the long term. 98,137 Others97,138 have indicated high probabilities that pedometers will be cost-effective in the long term, with Brennan et al. 142 indicating that, even with long-term support at £25 per year (for monitoring and support), ICERs fell well below £10,000 per QALY gained. Our study provides further support to indicate that pedometer-based programmes are a cost-effective method of improving health.

Data collection for quantitative comparisons

The sex, age and IMD score of all those invited were collected from general practice records. The IMD score is an anonymised measure of deprivation based on postcode. 71 To avoid the possibility of individuals being identified, aggregated practice-recorded ethnicity was exported from the practice in 10-year age bands for all batches where everyone was mailed, less exclusions (n = 10,155). Those not wanting to participate in the trial were asked in their invitation letter if they were willing to complete a shortened version of the trial baseline questionnaire, including questions on demographics, health, PA levels and a question on reasons for not participating. The following categories were offered, based on previous research, with space to add other reasons for trial non-participation: (1) I do not have time, (2) I cannot increase my PA, (3) I am not interested in increasing my PA, (4) I am already very physically active, (5) I am not interested in research and (6) I do not want to be put in a group by chance.

Comparison groups

Individuals whose invitation letters were ‘returned to sender’ were excluded from the analyses before calculating response rates. ‘Responders’ are defined as those who replied to the invitation letter, regardless of whether or not they wanted to take part. Individuals could respond by post, e-mail or telephone. ‘Participants’ are those who completed the baseline assessment, although not all were randomised as some provided inadequate accelerometry data. ‘Non-participants’ are those who completed a questionnaire but did not wish to participate in the trial (Figure 10).

FIGURE 10.

Flow chart to show the recruitment process in the PACE-UP trial. All percentages are out of all of those whose age and sex were matched with GP records (n = 10,927).

As the PACE-UP trial targeted inactive adults, participants who attended a baseline appointment were selected on the basis of their low PA levels. Non-participants were not selected in this way. In order to minimise selection bias, the quantitative analysis of participants and non-participants was therefore restricted to those categorised as ‘not active’ according to a self-reported primary care PA questionnaire, the GPPAQ,73 which was the only PA measure available for both groups.

Statistical analysis

Age- and sex-standardised rates were used to compare IMD quintiles for responders. Similarly, sex-standardised rates were used to compare age groups and age-standardised rates were used to compare sexes. The full population of invitees was used as a standard population throughout. No further analysis on non-responders was possible because they did not provide any questionnaire data on ethnicity or other factors.

Practice ethnicity data were available in 10-year age bands for 10,155 invitees, which was effectively a random sample of the 11,015 people invited to participate. The proportion of patients belonging to each ethnicity category within each age band and within each practice was calculated, and the number of invitees in each ethnicity in each practice and age band was estimated. Overall, 1903 invitees had ethnicity recorded as ‘unknown’. These are assumed to be missing at random in the main results, but sensitivity analyses were performed assuming that these were all white people or all non-white people. Age-standardised participation rates for not-active participants and non-participants completing questionnaires were calculated, assuming that invitees gave the same ethnicity on the questionnaire as was recorded in their practice records. Participation rates by age, sex and IMD score were calculated for not-active participants versus not-active non-participants completing questionnaires, as in the analysis of responders.

Not-active participants and non-participants completing the questionnaire were compared for additional demographic and social characteristics, and health and lifestyle factors, using logistic regression. All data came from the questionnaires. Models were adjusted for clustering by practice and household, by including fixed effects for practice and using robust standard errors for household.

Methods for the interview study of non-participants

This is fully described elsewhere. 148 Non-participants completing the questionnaire were asked if they could be contacted to discuss their reasons in more detail. A purposive sample of those willing to be contacted was selected to provide men and women of varying ages, ethnicities and employment status from the initial six participating practices. To maximise participation, we used focused telephone interviews; permission was gained for interviews to be audio-recorded. The topic guide was developed from the literature, from the previous PACE-LIFT trial qualitative evaluation21,149 and discussions between the authors, and is published148 and provided in Appendix 4. Approximately 30 interviews were planned, with recruitment continuing until no new themes were identified and a demographically balanced sample had been achieved. Open questions were asked about what influenced their decision not to participate and their opinions on the trial information received. Responses given on the completed questionnaires were used as a starting point to further explore their reasons. They were asked general questions about the perception of the trial design and were invited to make any final comments.

Methods for analysing interviews

Audio-recordings were transcribed verbatim and checked for accuracy. After 10 interviews, researchers (Rebecca Holmes, TH and CV) read the transcripts and discussed the interviews. The interview technique was then modified slightly to ensure that interviewees understood the trial randomisation process, as several participants had appeared not to understand the question about whether or not being put in a group by chance had influenced their decision not to participate. On completion of the interviewing, the transcripts were read and re-read for familiarisation by the researchers, who assigned codes (RN and TH), before a thematic framework was produced. 150 Coding discrepancies between researchers were resolved by discussion. The framework produced was informed both by a priori issues, mostly related to trial design, and by emerging themes. Themes were refined further by discussion between authors (Rebecca Holmes, TH, RN and CV), and broader categories, encompassing several subthemes, were generated. Reasons for declining given by all non-participants were also compared with those given at interview, to put our findings in a wider context and assess the generalisability to all of those actively declining.

Results from quantitative comparisons

Of the 12,625 individuals selected for screening (see Figure 10), 1421 (11.3%) were excluded by practice staff and 189 (1.5%) had invitation letters that were returned, as they had moved away; both of these groups were classified as ‘not invited’. In the 44 households where one person refused the invitation and the other did not respond, it was impossible to match the response to individual invitees within the household, so age and sex are unknown. These 88 people have been excluded from further analyses. Of the remaining 10,927, 4572 (42%) responded to the invitation letter, mainly by post, and 1150 (11%) completed the baseline assessments.

Of all invitees, 5229 (48%) were aged 45–54 years. Although all quintiles of deprivation were represented, only 7% were in the most deprived quintile. Response rates were higher in older people, women and those living in less deprived areas (Table 12). As individual ethnicity was available only for the participants and non-participants who completed a questionnaire, it was not possible to estimate response rates by ethnicity for all responders.

Although the GPPAQ was not used to assess PA levels for trial inclusion, it was the only PA measure available for both participants and non-participants. A total of 118 participants and 388 non-participants were classified as active by the GPPAQ, and 134 people did not complete the GPPAQ. These people were excluded from further analysis, leaving 924 participants and 715 non-participants.

Similar to the response rates, participation rates were higher in older people, women and those living in less deprived areas. (Table 13). Ethnicity was extracted from the practice for 10,155 invitees. Of these, 5991 were recorded as white (59%), 893 (9%) were recorded as Asian or British Asian and 915 (9%) were recorded as black Caribbean, black British or black African. A total of 1903 (18.7%) were recorded as ‘unknown’. The percentage of people with ‘unknown’ ethnicity varied by practice from 3% to 48%. Table 13 shows the estimated number in each ethnicity category for all 10,927 invitees, assuming that those for whom ethnicity was recorded as ‘unknown’ and those for whom we were not able to collect ethnicity have the same ethnicity distribution as the group with known ethnicity.

Of the white invitees, 709 (8.7%) agreed to participate in the trial and were not active, and a further 638 (7.9%) completed a non-participant questionnaire and were not active. Both Asian and black invitees had very low non-participant questionnaire completion (2.4% and 1.9%), but black invitees were as willing to participate as white invitees (8.5% vs. 8.7%), whereas only 5.4% of Asian invitees participated. The sensitivity analyses assuming that all ethnicities recorded as ‘unknown’ were white or non-white people showed similar results, and the same patterns were also seen in practices with nearly complete ethnicity coding.

Those providing questionnaire data were more likely to be working part-time, married or living with a partner, and less likely to have finished their education aged ≤ 16 years (Table 14). Participation was associated with recent primary care contact and with some degree of health problems (general health, long-standing illness and comorbidities), although those more severely affected were less likely to participate (see Table 14). This is consistent with the EQ-5D-5L (health-related quality-of-life) domains, whereby participants were more likely to have problems with pain and mobility, but less likely to have problems with self-care, which is likely to indicate greater disability. Forty-five per cent of the sample gave insufficient time (n = 327) or already being physically active (n = 325) as reasons for non-participation, even though those classified on the GPPAQ as active were excluded from this analysis. Less commonly, 152 (21%) people answered that they could not or were not interested in (n = 122, 17%) increasing their PA. Randomisation was cited as a reason for non-participation only by 88 respondents (12%).

Results from qualitative comparisons

Fifty-five trial non-participants were telephoned from March to July 2013: 21 could not be contacted and four declined to be interviewed. Thirty trial non-participants representing the six initial participating practices were interviewed. Data saturation was achieved prior to completing 30 interviews, but we continued to 30 to ensure a more ethnically diverse sample. The demographic details of interviewees and their main reasons for non-participation are published. 141 Those interviewed were not selected on the basis of inactivity, and a slightly higher percentage (67%; 20/30) reported being too active as a reason for non-participation compared with those who were included in the main quantitative analysis.

Most interviewees gave one primary reason for declining participation, consistent across sex, ethnicity and age groups. The majority (n = 18) said that they were too active either because they felt that their activity exceeded the trial’s target levels or because others would benefit more. Less frequently cited main reasons included existing medical problems (n = 4), travel from home (n = 3), work/other commitments (n = 3), concerns about potential equipment problems (n = 1) and reluctance to be randomised (n = 1). To further understand the reasons for non-participation, we categorised the emerging themes into three domains: internal, external and trial related. Short quotations illustrating all of these reasons are shown in Table 15 and more detailed quotations are given in our published paper. 148

Internal reasons for non-participation included already being active, medical problems (pain, heart conditions, stroke and multimorbidity), no wish to increase activity, no interest in walking, feeling incorrectly ‘targeted’ and altruistic reasons. The dominant reason in this category was a belief in already being sufficiently active. When explored in more depth, it seemed that, on self-report, many were achieving, with some significantly exceeding, the recommendations. This is supported by the finding that non-participants were found to be more active on the GPPAQ. Of those citing medical reasons, it was less clear whether or not these problems constituted a definite contraindication, especially as those with predefined medical conditions contraindicating an increase in walking should have been excluded. A small number of people suggested that they did not enjoy PA, were not interested in walking or suggested a different activity or a team sport.

The external theme related to factors ‘external’ to the potential participant, including work and other commitments, travel problems, being a carer and advice from others. Work and work-related travel were frequently given as reasons for not participating and many feared that they could not commit to the trial. Family and home life commitments were also important barriers to increasing activity, including being a full-time carer for family members. We were interested in finding out whether or not advice from friends or family affected the decision not to participate. Very few interviewees discussed participation; for those who did, it did not influence their decision, except for one interviewee whose daughter strongly agreed that she should decline.

Reasons related to trial design included programme length, trial material, equipment problems, being randomised, the venue, the walking environment, the nurse interaction and the overall trial design. For some interviewees, the trial duration, at 3 months, was too long and it was difficult to commit for this period. One interviewee reported a previous negative experience with pedometers as her primary reason for declining. Several felt that not being able to choose their allocated group was a disadvantage. Two interviewees expressed concern about the trial promoting walking as an exercise, because the local walking environment was ‘boring’, and another stated that it was ‘the wrong season’ for walking outdoors; however, most interviewees thought that walking was an appropriate and inclusive activity. Some expressed interest in a group intervention rather than one to one with a nurse, feeling that this would improve motivation and sociability; however, others felt that interacting directly with a nurse was preferable to being in a group. Most interviewees approved of the choice of their GP surgery as the location for a PA intervention, describing their surgery as ‘lovely’, ‘pleasant’, ‘convenient’ and ‘appropriate’. Many interviewees expressed a positive attitude towards PA and research, and regretted not being able to participate.

Summary of the findings

The PACE-UP trial recruited 11% of patients aged 45–75 years who were invited by post from their registered general practice, although not all were randomised because of failure to provide adequate accelerometry data. Those not replying were younger and more likely to be male and from deprived postcode areas. Asian patients were less likely to participate. Participation was associated with mild or moderate health impairment, although those with more severe problems were less likely to participate. Not having enough time and being already physically active were the most common reasons for non-participation, even among patients who were classified as not active. Interview findings supported the questionnaire findings and gave more detail about the reasons behind the lack of time (work, travel, family, caring commitments, etc.) and the type of health impairments that stopped people from taking part. Despite not wanting to participate, almost all interviewees were positive about the trial, aware of the benefits of PA and the importance of research, and supported primary care as a venue for such programmes. The design of the trial and intervention was not stated as a key reason for declining to participate.

Strengths and limitations

The PACE-UP trial is a large trial recruiting from a clearly defined invited population, based on GP lists, enabling us to assess the potential reach of the intervention in terms of age, sex and deprivation. Our estimate of 11% participation may underestimate the true rate, particularly in areas of high mobility where patients may have moved away and not informed the practice, inflating the number of patients counted as invited. Although based on limited data, the PACE-UP trial offers a rare opportunity to examine demographic differences between participants and non-participants. We were able to estimate participation within different ethnicities using pooled data. However, we were not able to match at an individual level and some participants may have categorised themselves in a different ethnic group to that on the GP register. Ethnicity was also poorly recorded in some practices and we needed to make assumptions about whether or not those with ‘unknown’ ethnicity were similar to those with recorded ethnicity. In a sensitivity analysis, even under extreme assumptions, the difference between Asian and black ethnic groups persisted. The trial excluded individuals who self-reported being active, but the non-participants were not selected in this way. Our quantitative analysis attempted to mitigate this difference by restricting analysis to all those who self-reported as not being active. However, some residual bias may remain.

The interview study represents an innovative attempt to systemically explore the reasons for non-participation with a purposive sample of those who were invited but declined. Our aim was to further understand the perceived barriers to participation to enhance recruitment to future trials and exercise programmes. We were also able to explore non-participants’ perception of the trial design and research in general. This sample spanned six out of seven of the practices involved and included both sexes, a range of ages, ethnicities, employment and educational backgrounds. The telephone interviews allowed in-depth exploration of the barriers to participation not possible from a questionnaire alone and allowed us to compare the interview findings with the non-participant questionnaire responses. The main limitation of the interview study is that this was based on a self-selected group of people who both returned the non-participant questionnaire and agreed to be interviewed. It is possible that some of our sample would have been excluded in any case on the basis of their pre-existing activity levels and, therefore, their decision to decline may have been entirely appropriate. In addition, despite our attempt to sample interviewees from non-white British backgrounds, these groups were under-represented when compared with the ethnic diversity of the population invited.

Comparisons with previous work

A systematic review of 47 studies of walking interventions151 showed that recruitment methods and participation rates were poorly reported. Of 25 RCTs, participation rates could be calculated for only five. We recruited by post to reduce the burden on practice staff and to obtain response rate data. Postal invitations are used in primary care for other preventative activities, making this a pragmatic approach. 152 Other walking intervention trials using postal invitations in primary care33,48,144,153 had similar response rates of 10–20%; those with higher rates (37% and 39%)154,155 recruited individuals who were older and more frequent attenders in primary care, and invited individuals in the primary care consultation, as well as by post. Our previous trial21 used similar recruitment strategies to the PACE-UP trial, but, unlike other trials, included active people. This trial had a recruitment rate of 30%, but was conducted among older people in an affluent setting with few non-white residents.

Non-responders were followed up with one reminder letter, but because of data protection constraints we were unable to contact patients by telephone. Although only 1% of invitation letters were returned to sender, this may underestimate those who did not receive the letter as we did not use registered post. A previous London study using registered post found that 26% of letters were not delivered. 156 Our findings of greater participation in women,157,158 older people158 and those in affluent areas157 are supported by other studies. Attwood et al. 158 found no association with deprivation or ethnicity, but this trial was based in an area of high deprivation with few non-white patients. Among Asian patients, our response rate was similar to postal invitations in the PODOSA (Prevention of Diabetes and Obesity in South Asians) trial (5.2%), in which community-based approaches,159 through partnership with local South Asian groups, were found to be more effective. Wilbur et al. 160 found that social networking was the most effective method for recruiting African American women from low-income areas.

The finding that declining participation in this PA trial was attributable to interviewees considering themselves to be already sufficiently active is in line with other literature. 157,161–163 Importantly, objective measurement of PA reveals that most people overestimate their activity levels,9 and that their assessment of their personal activity levels is likely to be influenced by a social context. 161 However, this interview series allowed activity levels to be explored in more detail and revealed that, at least on self-report, this was a relatively active cohort for some of whom the trial may not have been appropriate.

Our finding that participation was associated with some degree of health problems, but that severe impairment reduced participation, is more nuanced than previous work, which has suggested that declining participation in PA programmes or trials is attributable to medical problems, including pain,157,161,162,164 particularly in studies with older participants. 165 Lack of time because of work and other commitments has also been identified as an important barrier,157,161–163,166,167 particularly in younger and middle-aged participants. 165 A lack of interest in PA has also been reported in the literature,157,161–163,166 but travel away from home has not been reported prominently. This may reflect the high proportion of our interviewees still in full- or part-time work and the seasonal migration of the diverse south-west London population.

Implications

Guidelines published by NICE36 concluded that more research was needed to determine which interventions are effective and cost-effective in increasing activity levels among lower socioeconomic and high-risk groups, and that there is little evidence on differential effects of interventions. In our trial, those groups for which more evidence is required tended to be those with the lowest recruitment rates, such as Asians and those in more deprived areas. 143 It has been suggested168 that specific cultural groups may respond better to interventions directly targeted at their needs, rather than to universal interventions. Reasons for non-participation were often related to individuals not wanting to increase activity or feeling that they were sufficiently active. It is likely that such resistance will similarly apply to any intervention roll-out and may apply more widely to other public health interventions. Low participation rates mean that policy-makers should be cautious about the intervention’s potential reach and the possibility that it could increase activity inequalities, but these are not a reason not to implement an intervention shown to be effective in 11%93 of the population. We were successful in recruiting older people, women and those with comorbidities or some degree of health limitation. These groups have lower PA levels and are likely to benefit more from increased PA. However, those with more severe disability, people who have had falls and those with a fear of falling were not over-represented, indicating a rational choice by individuals.

Only 12% of non-participants cited randomisation as a factor for not participating, whereas 45% cited time constraints. The nurse intervention required three additional visits to the practice on top of the three data collection visits, which may deter working people or those with child-care and other commitments. However, the PACE-UP trial showed that both the nurse-support and postal delivery groups performed similarly at the main 12-month outcome. 93 An intervention offering pedometers with brief advice, without the need to provide research data, may be more acceptable.

Both the PACE-LIFT and the PACE-UP trials recruited to target, achieved follow-up rates of over 90% and demonstrated that the interventions were effective in increasing PA levels. 21,93 However, considerably more research effort was required (e.g. more contacts from research assistants) per randomised participant in the PACE-UP trial than in the PACE-LIFT trial, resulting from a lower uptake rate. In spite of the effort, we still had limited power to investigate ethnic and socioeconomic subgroups. Trials with greater reach are likely to be more expensive in terms of recruitment, and gains in generalisability need to be balanced with greater costs. Our findings have important implications for those planning PA trials, as well as for those commissioning community PA programmes. As the cohort we interviewed appeared to be relatively physically active, it may be necessary to tailor some interventions to maintaining, rather than increasing, activity; this may also be important to mitigate the decrease in PA levels that often occurs with ageing. Equally, education about the levels of activity that optimise health gain may prevent potential participants from declining because of overestimation of their actual levels of activity. Measurements using pedometers or accelerometers provide an easy approach to validating PA levels.

Lack of time was an important barrier, so it may be helpful to reiterate that activity can be broken up into 10-minute bouts throughout the day (this can also help those individuals limited by pain or disability). Tailoring interventions to an individual’s travel and work commitments and for their specific health problems may also increase uptake. Promotional material should be inclusive and explicitly state that pre-existing medical conditions do not necessarily prevent participation and dispel myths about the risks of moderate-intensity exercise. Information about the value of PA, particularly walking, for many different health conditions1 should be emphasised in the invitation to participate. RCTs inevitably involve randomisation, but emphasising that, in some trials (including the PACE-UP trial), the control group can receive the intervention after the trial may help recruitment. Most interviewees felt that primary care was an appropriate, convenient location for delivering a walking-based PA intervention, indicating that further primary care-based trials and programmes are likely to be well received.

Why is process evaluation necessary in the PACE-UP trial?

The PACE-UP RCT is a complex intervention comprising multiple interacting components (pedometer, handbook, diary, practice nurse PA consultations and BCTs – as part of both written materials and consultations). Although the RCT design is able to establish the effectiveness of the intervention (see Chapter 3), it does not provide information on how it works, or whether or not there are contextual factors that could be associated with variation in outcome in different settings. 169 Conducting a process evaluation of the PACE-UP trial enables a detailed examination of the mechanisms of change by gaining an understanding of how the intervention was delivered and received and how this may have affected the variation in outcomes. The process evaluation investigates the relationship between the fidelity and quality of implementation, the context of the intervention and the main trial outcomes. The evaluation also helps to draw conclusions on the replicability and generalisability of the intervention. The main findings from the process evaluation are published170 and are reproduced here under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0).

Medical Research Council’s guidance on process evaluations in complex interventions

In 2014, the Medical Research Council (MRC) published new guidance for process evaluations of complex interventions. 169 The guidance draws on the experiences of researchers and wider stakeholders who have conducted process evaluations within trials of complex public health interventions. We have used the guidance to provide the framework for the process evaluation of the PACE-UP trial. Process evaluation is accomplished through investigating aspects such as implementation, mechanisms of impact and context, and the relations between these, as described in Figure 11.

FIGURE 11.

The key functions of process evaluation and the relationships between these. Green boxes represent the components of process evaluation, which are informed by the causal assumptions of the intervention, and inform the interpretation of outcomes. Adapted from Moore et al. 169 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.

Adapted from Moore et al. 169 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.

Implementation refers to the structures, resources and methods through which delivery is realised, and comprises the following factors: implementation process, reach, fidelity, dose and adaptations. The implementation process describes how the delivery is achieved, through training, support, resources, etc. Reach refers to coverage and the degree to which the intervention is delivered to those for whom it was intended, that is, who receives the intervention. The other aspects of implementation are related to what is delivered. Fidelity is the degree to which the intervention was delivered as intended (content) and includes assessment of the quality of the intervention. Dose denotes the quantity of the intervention implemented. Adaptations are participant and implementer adjustments, which may impede or strengthen the intervention and which arise in response to the intervention itself.

Mechanisms of impact refer to how the intervention activities and participants’ responses to them cause change and adaptations.

Context refers to external factors which may influence, and be influenced by, implementation mechanisms and outcomes.

The first stage of designing the process evaluation was to describe the intervention and to clarify casual assumptions, which was accomplished through the use of a logic model, shown in Figure 12.

FIGURE 12.

Logic model for the PACE-UP (Pedometers and Consultation Evaluation – UP) PA trial.

Implementation process

Reach

The overall trial recruitment rate was 10% (1023/10,467). Details on how practices and participants were selected and recruited is described in Chapter 2. The methods for assessing trial representativeness and generalisability, by comparison of non-responders, non-participants and participants, and by interviews of non-participants, are described in Chapter 5.

Fidelity (content and quality)

Quality

Nurse and patient alliance questionnaire

Following the intervention, both the nurse and the participant independently completed a 12-item nurse and patient alliance questionnaire (see Appendix 5) covering different intervention aspects (e.g. working together and goal-setting, number of appointments). The questionnaires were developed by BCT trainers and trial investigators, and questions 1–11 were adapted from the Working Alliance Inventory,171,172 which is a validated alliance measure used in cognitive–behavioural therapy-based studies, and the Outcome Rating Scale. 173 Item 12 was added to specifically ask about the number of PACE-UP trial appointments. The questionnaire was posted to the participant and returned to the researcher, so that the nurse was blind to participant responses. Three directly comparable items (questions 1, 3 and 4) from both the patient questionnaire and the nurse questionnaire provided data on the quality of intervention delivery (Table 17). Seven directly comparable items directly relate to participant responsiveness (questions 5, 6, 8, 9, 10, 11 and 12). Two items (questions 2 and 7) were discounted as they did not relate to quality of participant responsiveness.

TABLE 17 - Quality of delivery and participant responsiveness data from the nurse and patient alliance questionnaires

Notes

Missing items were excluded from the percentage calculations.

Q9 on the nurse alliance questionnaire was printed blank on the Likert scale for answers, so a high number of responses were missing.

Delivery and responsiveness	Questionnaire
	Patient alliance			Nurse alliance
	N	‘Agree’ or ‘strongly agree’, n (%)	Missing items	N	‘Agree’ or ‘strongly agree’, n (%)	Missing items
Quality of delivery
Q1: the patient and I worked together on setting goals that were important to the patient	287	231 (80)	8	250	221 (88)	1
Q3: the patient felt heard, understood and respected	287	259 (90)	8	249	234 (94)	2
Q4: in our meetings together, the patient discussed everything they wanted to discuss	285	267 (94)	10	249	234 (94)	2
Participant responsiveness
Q5: the patient understands how to make lasting changes in activity levels	289	259 (90)	6	249	215 (86)	2
Q6: the approach to making change suited the patient	287	247 (86)	8	249	182 (73)	2
Q8: the patient feels confident to continue to make positive changes in PA on their own	288	238 (83)	7	246	191 (78)	5
Q9: the patient feels confident about overcoming obstacles to increasing activity levels in the future	257	169 (66)	38	190	124 (65)	70
Q10: the pedometer used in the PACE-UP study was helpful to the patient	288	260 (90)	7	246	209 (85)	5
Q11: the diary used in the PACE-UP study was helpful to the patient	284	229 (81)	11	247	203 (83)	4
Q12: the number of appointments with the PA nurse was just right . . .	278	232 (83)	17	241	178 (74)	10

The questionnaires were completed by 295 out of 346 participants (85%) in the nurse-supported intervention group and by the nurses for 251 out of 346 nurse-support group participants (73%).

There was strong agreement between the participant and nurse results for all of the items relating to quality and 80% or more of both nurses and participants agreed or strongly agreed with all of these statements, suggesting high quality of delivery.

The following are some examples of participant and nurse comments relating to quality from the questionnaires:

Nurse was encouraging, supportive. Encouraged me to set goals that were achievable for me and not to put too much pressure on myself.

Female, aged 47 years, nurse-support group

My nurse was lovely and encouraged me all the way through, even when some days I couldn’t do what I needed, we discussed alternatives. My nurse was a diamond. Thanks to PACE-UP and the nurse my walking has really improved.

Female, aged 47 years, nurse-support group

Client pleased with programme. Learning curve. Would recommend to others.

Practice nurse

Enjoyed patient, good discussions and understanding around increasing exercise.

Practice nurse

Audio-recordings from nurse sessions

Nurses were asked to audio-record a sample of their sessions so that these could be listened to by the BCT trainer and rated according to their skill in six different communication skill competencies. Ratings were made by the BCT trainer against a primary care consultation rating scale (range 0–6) in six domains (Figure 14) and used for both fidelity (quality) evaluation and supervision purposes. The rating scale used was developed from the Cognitive Behavioural Therapy Techniques for Palliative Care Practitioners Rating Scale174 and the Department of Health and Social Care’s The Ten Essential Shared Capabilities – A Framework for the Whole of the Mental Health Workforce. 175 The nurses were each asked to provide three audio-recorded sessions, one each for sessions 1, 2 and 3. They were asked to try to ensure that one of the recorded sessions was from a session where a couple were seen together. The mean scores and ranges for all nurses are shown across all domains in Table 18.

FIGURE 14.

Behaviour change technique competency level.

TABLE 18 - Fidelity: quality scores of performance for audio-recordings of nurse sessions by BCT trainer

Scores	Communication skill competency
	1. Framing, pacing, focus and use of time	2. Empowering explanations	3. Collaboration and active listening; interpersonal effectiveness	4. Setting goals, agreeing actions and motivational techniques	5. Feedback, reviewing and summarising	6. Building self-efficacy
Average (mean) score	5.3	4.7	5.0	5.2	5.3	5.2
Range of scores	3–6	3–6	4–6	4–6	5–6	4–6

The range of scores illustrates that even the lowest ratings were competent, and the highest scores were expert, across all six competencies. The lowest scoring competency was given for empowering explanations (mean score 4.7), whereas all the other competencies had mean scores above 5, demonstrating proficiency, with very good features.

Qualitative perspective

Semistructured individual interviews with participants and focus groups with nurses provided a qualitative perspective of the intervention, including the quality of delivery; this is presented in detail in Chapter 7. Overall, the nurses and participants described the intervention in a positive manner, as highlighted by the following quotations:

. . . they kept saying how well I was doing, and all this sort of thing, so it made me want to continue. I think it was . . . a part motivation, yes, because I knew I had to face somebody and I didn’t want to fail.

Female, aged 63 years, nurse-support group

. . . if you had, in your drawer, you had like a set . . . a package, programme, you could do, and if through the NHS Health Check you identified someone who was suitable, you could then discuss it with them and say, ‘Would this be something you’d be wanting to look at? . . . and go from there.

Practice nurse

Dose

For the purpose of the PACE-UP trial, the dose delivered to the postal delivery group was fixed, as they all received the same handbook, diary and pedometer. The dose could vary for the nurse group according to the number of sessions attended and the length of each of the sessions. Nurses were required to complete checklists at the end of each session (see Appendix 5, Table 39), providing details on attendance and the duration of sessions. The duration of sessions was also captured from the audio-recorded sample of intervention sessions; this allowed a comparison with session durations calculated from nurse checklists. Overall, three-quarters of participants in the nurse-support group attended all three sessions. Ninety-five per cent of participants (330/346) attended session 1, 86% (296/346) attended session 2 and 76% (263/346) attended session 3. The relationship between the number of sessions attended and trial PA outcomes was also explored (see the association between process evaluation measures and trial outcomes at the end of Methods and results).

Trial protocols detailed the following approximate duration of each nurse intervention session: 30 minutes for session 1 and 20 minutes each for sessions 2 and 3. A summary of nurse intervention session durations from nurse self-report and audio-recordings is available in Appendix 5, Table 42 (19 recordings, relating to 22 participants, as some were couples). There was good agreement between the planned protocol session length and the nurse self-report durations: mean of 30 minutes (SD 4 minutes) for session 1, mean of 24 minutes (SD 3 minutes) for session 2 and mean of 22 minutes (SD 4 minutes) for session 3. The duration of consultations from audio-recordings was based on much smaller numbers (n = 22 participants) but had a shorter mean duration: mean of 21 minutes (SD 6 minutes) for session 1, mean of 21 minutes (SD 7 minutes) for session 2 and mean of 14 minutes (SD 5 minutes) for session 3.

Adaptations

Details of nurse and participant adaptations made to the intervention were provided from nurse training session records. There were many examples presented relating to step count target adaptation and tailoring the intervention to individual circumstances. Adjustments were made to the intervention to accommodate religious observances, such as Ramadan and Christmas. Step count targets were adapted to be more achievable to reflect participants’ reduced energy/activity levels in advance of holidays, when there were expected to be reductions or increases in PA, during periods of participant illness and pain and in response to changing weather conditions. Nurses also explained the need for flexibility with participants who experienced difficulties with equipment use; for example, a small number of participants who did not like using the pedometer were advised to use time to measure their walking, rather than measuring step count (e.g. extra walks of 10–15 minutes per day, rather than an extra 1000–1500 steps per day). At the second training session, it became clear that the nurses did not find the optional handouts provided for use in consultations to supplement the patient handbook helpful and, as a consequence, were not using them. From these discussions, it was decided that these materials would be discontinued. Another adaptation revealed by the nurses was adapting targets and advice for participants taking part as a couple, particularly if they had very different PA levels and targets; this sometimes related to one individual of the couple having health problems that had an impact on mobility. Nurses adapted the intervention to encourage both participants to meet their targets, often encouraging a mixture of walking together and walking apart to achieve this.

Physical activity diaries also provided data on adaptions to the prescribed intervention through alterations of participants’ walking targets (see Appendix 5, Table 40). Few participants in the postal intervention group altered their targets in the diary (1%; 4/339), whereas 32% (89/346) of the nurse-supported intervention group altered their walking targets, mainly by decreasing the target [29% (80/346)].

Additional details of nurse and participant adaptations to the intervention by both intervention groups, obtained as part of the qualitative evaluations of the trial, are presented in Chapter 7. Some examples are given here.

The nurse quotations below illustrate the flexibility in intervention delivery during Ramadan and also during bad weather:

. . . they couldn’t walk or increase on their walking at that time because they hadn’t eaten and then they weren’t feeling too good, and all that, so we did it a different way then, and what I did with them was we relaxed it and then I said to them, when Ramadan’s over, we made the appointment so that their actual trial went on a bit longer.

Practice nurse

. . . if the weather was bad, or it was cold, or there were obstacles that got in the way . . . they would do things like, you know . . . activities indoors where they could not always go outside.

Practice nurse

Participant responsiveness

Seven items reflecting participant responsiveness were identified in both the patient alliance questionnaire and the nurse alliance questionnaire (see Table 17). There was a good degree of consistency between participant and nurse responses for all of the items relating to participant responsiveness and high levels of agreement with all of the statements. For example, 90% of participants said that the pedometer was helpful and 83% said that the number of nurse appointments was just right, suggesting that there was a good level of participant responsiveness to the intervention.

Some examples of both participant and nurse comments relating to participant responsiveness from the alliance questionnaires are shown below:

PACE-UP has changed my life. I use the car less when I go about. Although I drive to work I park about 1 km away from work, then walk all the way to and from.

Female, aged 47 years, nurse-support group

Wearing the pedometer really raised my awareness of how far I walked each day. I will continue to use it.

Female, aged 68 years, nurse-support group

Positive, liked study book. Was a very reflective/honest person regarding his exercise. Positives/negatives easily identified by patient.

Practice nurse

Patient has own excel monitor of readings/pedometer count. Also converts to miles daily.

Practice nurse

Information about participant responsiveness also came from the qualitative evaluations of participants from both the postal delivery and the nurse groups (from individual interviews) and from practice nurses (from a focus group and individual interviews), and is presented in Chapter 7.

The following quotations from the qualitative evaluation illustrate participant responsiveness and engagement from a nurse and participant perspective:

. . . the only other thing I’d say about the diary is that the people that really liked filling it in found it a really good motivator. When they came to the last appointment, they wanted another one.

Practice nurse

There’s nothing like the fact that you know you’re going to meeting someone and talk about it to make you do it, you know, . . . It’s basically the routine of being checked up on by someone else . . .

Male, aged 61 years, nurse-support group

. . . well having something which counts the steps makes one conscious of it and filling out a little booklet every day, likewise, it just creates some personal pressure.

Male, aged 59 years, postal delivery group

Nurse session attendance and physical activity outcomes

In the nurse group at 3 months and 12 months there was a positive association between the number of sessions attended and the PA outcomes. Participants attending all three sessions increased their step count and their time in MVPA in bouts at 3 and 12 months by significantly more than those attending between 0 and 2 sessions.

Diary return and physical activity outcomes

In the postal delivery group, there was a strong positive association between returning a diary and on both change in steps and MVPA in bouts at both 3 and 12 months compared with those in the postal delivery group, who did not return a diary. In the nurse-support group, there was a positive association between returning the diary and on change in step count and MVPA at 3 months, compared with those in the nurse-support group who did not return a diary. However, by 12 months, there was no significant association for either PA outcome of returning a diary within the nurse-support group.

Pedometer use and physical activity outcomes

In the postal delivery group, reported use of a pedometer every day or most days during the 12-week intervention period was associated with a significant change in step count at 3 months and 12 months, and with change in MVPA at 3 months (borderline effect at 12 months).

Within the nurse-support group, there were no significant associations between regular pedometer use during the 12-week intervention and change in step count or MVPA at 3 months and 12 months. This lack of significant effect could be explained by the very small numbers of participants in the nurse-support group who reported not having used a pedometer regularly during the 12-week intervention (n = 34; 11%).

Overall, the analysis of the association between process measures and PA trial outcomes exhibits a clear pattern of positive associations (i.e. increased nurse appointments, diary return and pedometer use were associated with increased objective PA levels). This provides clear evidence of the engagement with the trial process and outcomes, but cannot be interpreted as causality.

Main findings

Figure 13 summarises the key findings from the PACE-UP trial process evaluation, which followed the MRC guidance for process evaluation of complex interventions. 169 We gathered a number of positive data on implementation, suggesting good-quality intervention delivery and adherence to the protocol, despite the low reach of the trial. Nurse training was an important element of the trial, with nurses receiving approximately 16 hours of training, predominantly around delivery of BCTs. We demonstrated good coverage of the proposed session content by the nurses and also good-quality delivery, with the audio-recording of nurse sessions demonstrating high levels of competency in communication skills. High-quality delivery was also reflected in comments from participants who felt heard, understood and respected. Three-quarters of the nurse group attended all three PA consultations, and around 80% of both the nurse-support and the postal delivery groups engaged with the self-monitoring aspects of the trial and returned completed step count diaries at 3 months. In terms of the mechanisms of impact, we demonstrated high levels of participant responsiveness. Context was important and factors affecting the implementation of the walking intervention were suggested by nurses and participants from both intervention arms, and included the effects of weather, the environment, health issues, pain, employment, observance of religious events and social factors. The nurses worked with participants to help them to make adaptations, either to the intervention or their individual targets, or to encourage participants to come up with solutions when possible, when there were contextual challenges. Several process evaluation measures (number of nurse sessions attended, return of a completed diary and regular pedometer use) showed significant associations with PA outcomes at 3 and 12 months.

Strengths and limitations

Comparison with other complex intervention process evaluations

A number of studies have examined intervention implementation fidelity, with a large variety of process structures and methods; therefore, it is difficult to draw direct comparisons. Process evaluations have become increasingly important, but the purposes and design of studies have been mixed. Many process evaluations are completed independently of trial data collection and are observations of a random subsample of participants or practitioners. 176,177 The process evaluation of the PACE-UP intervention provides both participant and nurse perspectives and identifies a link between contextual factors and adaptations in intervention delivery and acceptance. The study allowed us to look at both perceived and observed behaviour change in the nurse intervention delivery and participant responsiveness; this is unlike many other studies, which have tended to focus on only one perspective, which is most often the person delivering the intervention. 176,178–181 Nurse comments collected at training, individual session checklists and nurse and participant comments from the qualitative work illustrate the intervention delivery and adaptations in context. The intervention was designed to bring about change at an individual level when delivered in a primary care environment; we have observed that context influences the delivery and implementation of the intervention through adaptation. This is similar to findings from other PA and dietary complex intervention studies with process evaluation. 182,183 Specifically, Fitzgerald et al. 182 identified that negotiation and flexibility play an integral role in overcoming the barriers and resistance to change in a dietary intervention. Previous studies have collected data at an organisational or practice level;176,181 there are few studies that have captured the evaluation of behaviour change at an individual level and from two perspectives. A previous study that did look at both patient and practitioner perspectives, however, reported much greater variations in dose and adherence to protocols than seen in the PACE-UP trial, therefore making it difficult to establish which elements of the intervention were effective. 184 Berendsen et al. 184 reported that many health-care professionals deviated from the protocol of a lifestyle intervention to accommodate individuals and reduce fallout, which was associated with increased patient satisfaction for the intervention sessions. This perhaps suggests that adaptations and tailoring of an intervention have a strong influence on retention, adherence and, possibly, effectiveness in lifestyle and behaviour change interventions. The PACE-UP intervention allowed nurses to adapt the sessions as necessary to each individual, while maintaining the key deliverables in each nurse-led session; although we have not looked at the effect of adaptation on retention, we have seen that dose (nurse session attendance) was associated with effectiveness of the intervention. This, in turn, promotes the consideration of building adaptations and flexibility into intervention design. Our finding of an association between the return of a completed step count diary and a change in PA outcomes is consistent with the findings of a systematic review,31 which suggested that the use of a step count diary was common to many successful pedometer interventions.

Implications of the process evaluation for the interpretation of the PACE-UP trial

We have demonstrated that the PACE-UP trial had good adherence to the protocol, the intervention was acceptable and was rated positively by both the nurse-support group participants and the postal delivery group participants, and both groups engaged in self-monitoring using the pedometer and step count diary. It is not possible to infer causality directly from the process evaluation data, but the high level of engagement with pedometers and diaries by both intervention groups suggests that these were important factors in helping people to make the PA changes observed. This is supported by the associations demonstrated between increased PA levels and the following process measures: number of consultations attended, return of a completed step count diary and pedometer use. The careful description and documentation of the trial processes, the collection of additional data for the process evaluation and the publication of the resources used as appendices mean that our intervention and process evaluation would be easy for others to replicate, from training through delivery, to follow-up and evaluation. Use of the MRC framework gave a logical and coherent structure for reporting,169 which is also easy for others to follow. The PACE-UP trial had a positive and significant effect on PA outcomes, but had this not been the case, the positive process evaluation with high levels of fidelity would have enabled us to have confidence that any negative trial effect would not have been because of poor trial implementation. The trial demonstrated a stronger effect on the main PA outcomes and on exercise self-efficacy at 3 months in the nurse-support group than in the postal delivery group, although the effects on PA outcomes were similar between the groups at 12 months (self-efficacy remained higher at 12 months in the nurse-support group). The process evaluation demonstrated that the nurses were delivering BCTs in their PA consultations in accordance with the protocol and with high levels of competence (in addition to the BCTs provided in the handbook and diary for both groups). This suggests that the nurse-delivered BCT elements of the intervention have strong short-term effects on PA levels (and, possibly, longer-term effects on self-efficacy). The possible effects on longer-term maintenance are examined in Chapter 8. The implications of the low reach of the trial for generalisability and the public health impact have been discussed in Chapter 5 and will be considered further in the main discussion in Chapter 9. The process evaluation demonstrated important contextual factors that had an impact on participants’ ability to engage with a walking intervention; these should be considered in any future roll-out of the programme, particularly regarding how the programme may need adaptations to be made in these circumstances.

Perceived value of nurse consultations

The nurse-led consultations were well accepted by the participants in that part of the trial, with 74% (255/346) attending all three sessions. Overall, those who were allocated to the nurse-supported intervention arm were very happy with their meetings with the nurse, as illustrated thus:

Yes, the nurse was very helpful . . . it was really good for me.

ID12

Conversely, those who had not been in the nurse-support group were, overall, confident that they did not need the support of the nurse:

I think I was happy doing it on my own. I don’t think . . . I mean it was very . . . it was very easy to follow your instructions and what you wanted us to do and so I don’t think meeting a nurse . . .

ID13

No, I don’t think it [visiting the nurse] would have been useful really.

ID17

Some were sceptical of what advice the nurse could give:

No, not really. What would she say, walk a bit quicker, eat a bit less? It’s common sense, I knew in the first place.

ID23

I know what I should be doing.

ID8

Others were confident in their own internal motivation:

I think if I’ve agreed to do something, then I will try and achieve that target, whether somebody tells me face to face or by post, so I think it’s dependent on the individuals maybe, individual choice.

ID38

There were two key caveats to the confidence of the postal delivery group in increasing their PA without the support of a nurse. These were existing health problems and overcoming barriers. Several participants in the postal delivery group observed that if they had had a long-standing health problem, they may have preferred the security and support of the nurse, as illustrated in this comment:

If I did have health problems, I may have wanted to see a nurse, and say, look, I’ve been doing this and I’ve had an ache and a pain here, shall I stop or . . . you know, if it was that situation, and I think yes, you might need to speak to someone you know who could advise you medically, but I didn’t need it.

ID13

In addition, some participants in the postal delivery group thought that being able to see the nurse might have helped them to overcome the difficulties and challenges they experienced when trying to increase their PA, for example:

I would have found that better [to see a nurse] because, if I’d have talked to her about the steps, she might have been able to umm introduce something else . . . [participant was concerned that the target step count was unachievable]. I think if I’d have been seeing the nurse regularly then, during that summer, umm, we would have found another way I feel, you know.

ID19

Behaviour change techniques

Our intervention included over 20 distinct behaviour change activities, as defined by Michie et al. ,68 embedded within the PACE-UP trial handbook and diary (received by both the postal delivery group and the nurse-support group) and, additionally, within the protocol for the nurse consultations. Reference to these specific BCTs were then extracted from the interview transcripts to determine what elements of behaviour change were viewed as being most helpful by the participants. 162 There were 152 examples of these factors in our interviews: 54 in the postal delivery group and 98 in the nurse-support group. With the exception of self-monitoring, the BCTs were more frequently noted in the nurse-support group compared with the postal delivery group. The elements of Michie et al. ’s68 typology that were especially evident in our participants’ comments were (1) the provision of information, (2) monitoring and feedback and (3) strategies for relapse prevention/overcoming challenges. Rewards, an important element of the typology, were not seen as being of great importance by our participants. Both of our intervention groups appreciated receiving information about the link between behaviour and health. However, a key and important type of information provided, especially in the nurse-support group, was specifically tailored and personalised information about how, where and when to increase walking, for example:

She gave me a printout of umm . . . some . . . walks that you could do, group walks and things like that.

ID18

In terms of monitoring and feedback, nurses played an important role for their group, as exemplified by comments such as the following:

She was very encouraging.

ID39

Oh I felt really happy [with the nurse] and she was very happy too with me and I did really like my nurses, yes. That was one of my reasons because, each week I go, I ask her if they think I am doing well, am doing well, yes [laughs].

ID12

Importantly, the nurses were seen to provide motivation and encouragement when the ‘novelty’ of the intervention was waning and participants were at risk of lapsing, for example:

I think there was a point where they sort of said, you know, don’t give up now, or something like that, you know, at the point where . . . the novelty might have worn off . . .

ID35

The postal delivery group largely felt that they could self-monitor their activity:

I felt like it was enough to know [the step count] . . . I think it was fine, just to sort of keep . . . I was very good about filling the diary in and it was sort of for me that was enough to keep me going really.

ID43

Adapting the trial protocol versus fidelity

Given that this was a trial, it was essential that the nurses delivered the intervention as per the protocol; therefore, before the trial started, we provided extensive training to the nurses and emphasised the importance of adherence to the prescribed protocol:

The equipment was excellent, the pedometers, the accelerometers, excellent, excellent, excellent.

However, given that the trial ran over 12 months, pragmatic adaptations were made by the nurses (in consultation with the team) in response to the specific circumstances of participants, for example, working around holiday periods (e.g. Christmas) or periods of religious observance, such as Ramadan:

. . . forget about the 2 months around Christmas . . . you can’t get appointments and they don’t want to wear it [pedometer].

This serves to remind us that delivering behaviour change interventions in real-world practice requires ‘fine tuning’ in the delivery to reflect the complexity of people’s daily lives. We assessed fidelity by audio-recording a minimum of three consultations for each nurse, as described in detail in Chapter 6. However, this also enabled us to provide specific feedback to the nurses on their use of the BCTs employed. This was universally welcomed and improved their practice during the trial, but also provided them with enhanced skills to take into practice beyond and after the trial:

I actually changed my practice from thereon, so yes, it was exceptionally helpful.

Trial materials and equipment

Both nurses and participants remarked on the materials that supported the trial, namely a pedometer, a PA diary and a series of optional handouts, as described in Chapter 2. From the participants’ perspective, each element of the trial had both advantages and disadvantages. However, the focus of the comments about equipment from the participants’ perspective was upon the accuracy (or otherwise) of the pedometer. Typical of the more critical comments of the pedometer was:

That day we went for a really long walk round the common, I was really disappointed when I come back, it didn’t seem to register very many steps. I’ve obviously done a lot more than . . . registered about ,000 or something, well walked around the whole of Tooting, is a bit more than that I think.

ID7

There were fewer comments about the diary, which was seen as being motivating:

I was very good about filling the diary in and it was sort of for me that was enough to keep me going really.

ID43

However, for others it was chore:

Like I say, it is an effort, it’s umm . . . you have to know what you are in for, and then really maintain it. I had to record it every day, yes, you are busy, sometimes you are out and you go for dinner and then you come home and then you had a few drinks and you can’t remember what day it is.

ID1

Actually writing down the activities and things, it . . . after about the first day, I got bored with that.

ID3

Nurse satisfaction

One feature of the trial noted by the nurses (but not the participants) was the time that they had to devote to the PA consultations compared with their normal activities, as this comment illustrates:

You know, we don’t have any protected time for health promotion . . . the health promotion is the add-on. It’s giving us the time, because we don’t have the time.

From the nurses’ perspective, this engagement provided considerable job satisfaction in seeing their participants embrace change and become more physically active. A feature not experienced prior to the trial by most of the nurses was delivering the intervention to couples rather than individuals. This presented a unique challenge for some of the nurses as, in normal practice, it is unusual to be working with two patients simultaneously. Sometimes the dynamic worked well:

. . . most couples, they enjoy doing it together because they’d go . . . they could go out walking together and, even if it was through the winter, at least if they were both going, they had each other . . . they use to encourage each other. So if one didn’t want to go the other one would encourage them and they’d make sure they went.

At other times, the problems encountered were a significant barrier:

I’m not actually overly sure how couples worked. I don’t know if I had, I don’t know if it caused more issues sometimes, in the fact with the pedometers, because they got so focused sometimes on the fact that their pedometers didn’t match up.

Ethics approval and research governance

Ethics approval for the 3-year follow-up was granted to the trial from London, Hampstead Research Ethics Committee (reference number 12/LO/0219). NHS local research and development approval was granted to cover all of the practice sites.

Participants eligible for the 3-year follow-up

All trial participants who had not withdrawn from the trial were eligible to be followed up, even if they had not provided 3- or 12-month follow-up data. Lists of eligible participants were organised by practice and practices were asked to check whether any participants had died, moved away or developed a terminal illness or dementia since trial participation. These patients were then excluded.

Contacting participants

Eligible participants were contacted with a letter explaining the trial 3-year follow-up. A participant information sheet, consent form and a Freepost return envelope were also included, as was information on the main 12-month trial results. The letter explained that a research assistant would contact the participants by telephone in around 1 week’s time to discuss the 3-year follow-up. If they were happy to take part without further discussion, they were invited to post back the signed consent form.

Informed consent

The research assistant contacted participants by telephone approximately 1 week after sending out the letter about the 3-year follow-up to discuss any questions they might have after reading the participant information sheet. Part of the consent form included consent to contact participants for an interview to discuss their current PA levels in more detail. If the participants were happy to proceed with the 3-year follow-up, they signed and dated the informed consent sheet and returned the top copy to us.

Data collection

Once the informed consent for follow-up was agreed, the research assistant arranged a time to post out the accelerometer (GT3X+) to participants to measure their current usual PA levels for 1 week. Instructions about how to wear the accelerometer were included (on a belt, over one hip) and participants were asked to wear it for 7 consecutive days, from getting up until going to bed, as they had done previously. A diary was also provided to record what activities were done and for how long. They were also sent a health and lifestyle questionnaire to complete (see Appendix 7; this was similar to that completed previously) and a short questionnaire about self-reported PA levels to complete (the 7-day PA questionnaire used previously) after they had finished wearing the accelerometer. They were provided with a Freepost return envelope to send the accelerometer and both questionnaires back. If the accelerometer did not record at least 5 days with at least 540 minutes per day, participants were asked to rewear it for a further week. The set of recordings with the greatest number of days with at least 540 minutes per day was included in the analysis. Once accelerometers with adequate data were received, participants were posted out a £10 gift voucher.

Outcome measures

The main outcome measures (all accelerometry) used to evaluate the 3-year follow-up were as follows:

1. change in average daily step count, measured over 7 days between baseline and 3 years

2. change in time spent weekly in MVPA in ≥ 10-minute bouts between baseline and 3 years

3. change in time spent sedentary weekly between baseline and 3 years.

Although patient-reported outcome measures were collected from the health and lifestyle questionnaire (e.g. depression,75 anxiety,75 quality of life,76 self-efficacy,74 pain,77 disability80) and from the 7-day PA questionnaire (IPAQ72 and GPPAQ73), these have not been assessed further as part of this report.

Accelerometer data reduction

ActiGraph data were reduced, as described previously in Chapter 2, for the main trial. Analysis summary variables were also identical to those used in the main trial, described fully in Chapter 2.

Procedure for accounting for missing data

Only days with at least 540 minutes of registered time on the accelerometer on a given day were used. The main analysis of effect included all subjects with at least 1 satisfactory day of recording at 3 years.

Statistical methods

Statistical methods for the analysis of the 3-year follow-up are largely as described in Chapter 2 for the primary analysis at 12 months. Average daily step count at 3 years was computed from a random-effects model, allowing for day of the week and day order of wearing the accelerometer as fixed effects and participant as a random effect. The average daily step count at 3 years was then regressed on average daily step count at baseline with treatment group, age, sex, practice and month of baseline accelerometry as fixed effects and household as a random effect, in a multilevel model. The same analyses were carried out for MVPA in ≥ 10-minute bouts and daily minutes of sedentary time.

The primary analyses used the 681 participants who provided accelerometry data at 3 years. Sensitivity analyses were carried out to assess the effect of missingness:

1. Multiple imputation methods were used to impute outcome data for those missing at 3 years, assuming that outcomes were missing at random, conditional on variables in the model. We used the Stata procedure mi impute.

2. Missing-not-at-random analyses were used when it was assumed that changes in the control group from baseline to 3 years were missing at random, but the change in each of the intervention groups was ± 500 and ± 1000 steps from their missing-at-random estimate.

For this analysis, we used a mean score method,191 which has been implemented in a Stata module, rctmiss, available from Statistical Software Components at https://ideas.repec.org/s/boc/bocode.html (accessed 25 May 2018).

Sampling

Participants consented to be contacted for a telephone interview at the same time as they consented to take part in the 3-year follow-up. In February 2016, the research assistant produced three lists of participants who had already provided 3-year accelerometry data and who had given consent to be interviewed (one list for each arm of the trial – nurse support, postal delivery, control). The trial statistician randomly sorted these three lists ready for the qualitative researchers to start approaching participants for interviews. The interviewers were blinded to participants’ previous and current PA levels.

One of the aims of the qualitative evaluation was to explore the success, or otherwise, of the minimalist intervention provided to the control group after the main trial; therefore, the following participants were excluded from this qualitative evaluation:

• control group participants who had attended a nurse consultation after the main trial

• control group participants who opted not to receive the postal pedometer after the main trial.

At 12 months, participants in the postal delivery group were also offered a nurse consultation. Again, those who attended the nurse consultation were excluded from the sampling procedure, so that all those sampled from the postal delivery group had received just the postal intervention.

The aim was to interview approximately 15–20 people from each arm of the trial (45–60 people in total), but continuing further if required, until saturation was reached. As two researchers (CB and CW) were interviewing participants, they met regularly to discuss the sampling, interview schedule and any emerging themes. Interviews were conducted until saturation of new information was reached. By looking at the participants’ demographic information it was possible to ensure that the study group included both males and females and also represented a range of different ages and ethnicities, to ensure that a wide range of views were explored.

Recruitment and informed consent

Participants were initially contacted via e-mail to arrange an appropriate time to contact them for a telephone interview. Participants without an e-mail address were called and the interview was either conducted then or arranged for later. To assess response, a detailed record was kept of when each participant was contacted, including information on who agreed, who refused and who could not be contacted. Charlotte Wahlich and Carole Beighton conducted the guided interviews with participants using a topic guide (see Appendix 7). Before the interview commenced, the participants were reminded of their initial consent to be approached for an interview; if the participants were happy to go ahead, their consent was then sought for the interview to be audio-recorded. Once the recording had started, the qualitative researchers stated the participant’s ID number to ensure confidentiality and anonymity in the subsequent transcript. On interview completion, the participants were offered a £10 high-street gift voucher to thank them for their time.

Transcribing

Interviews were promptly transcribed verbatim by an external source. Once the transcripts were received back, they were double-checked against each audio-recording by the qualitative researchers. Transcripts were also circulated to the research team to ensure consistency between the interviewers and to help assess when theme saturation had been reached.

Interview schedule

The interview schedules were developed through discussions with Tess Harris, Charlotte Wahlich, Carole Beighton, Christina Victor and Rebecca Normansell. Slightly different questions were used for participants in the intervention groups (postal delivery and nurse support) and participants in the control group (see Appendix 7). For those in the intervention groups, the aim was to explore participants’ views about PA maintenance and whether or not a ‘top-up’ intervention was required, whereas for those in the control group, the aim was to explore their views about the minimalist intervention. The interview schedules were revised slightly during data collection to ensure that the questions were clear and to include additional questions to gain a better understanding of the participants’ experiences.

Analysis

All verbatim transcripts were read repeatedly by Charlotte Wahlich and Carole Beighton. Initial line-by-line coding was conducted independently to assign conceptual ideas to important episodes within the data. Through discussion with Rebecca Normansell, Tess Harris and Christina Victor, any discrepancies were resolved; this helped to ensure that the interpretation and categorisation of the data were valid. After further discussions between Charlotte Wahlich and Carole Beighton, these codes were then refined and grouped into emergent and anticipated themes.

Follow-up rate

Of the 1023 original trial participants, 32 had withdrawn by the end of the 12-month follow-up, a further two had died between the 12-month and the 3-year follow-up and one was excluded by their practice for health reasons. Therefore, we approached 988 participants and 681 provided adequate accelerometry data (≥ 1 day with ≥ 540 minutes wear time) for analysis, giving a 3-year follow-up rate of 69% (681/988). However, in relation to the initial trial participants providing 3-year outcome data, the 3-year follow-up rate was 681 out of 1023 (67%). The CONSORT flow diagram with 3-year follow-up data is shown in Figure 15, by randomised group.

FIGURE 15.

The PACE-UP CONSORT flow diagram with 3-year follow-up data.

Data completeness

Table 20 shows that 92% of participants (625/681) overall provided ≥ 5 days of accelerometry data at 3 years (88% of control participants, 94% of postal delivery participants and 93% of nurse-support group participants).

Objective physical activity findings

Table 20 shows the summary measures for all three groups at each time point and Table 21 shows the estimates of effect for the different groups. For the main trial outcome of steps per day, both intervention groups were still doing more than the original trial control group: 627 steps (95% CI 198 to 1056 steps) in the postal delivery group and 670 steps (95% CI 237 to 1102 steps) in the nurse-support group. The nurse-support and postal delivery groups combined did 648 steps per day (95% CI 272 to 1024 steps). The pattern was similar for the total weekly MVPA in bouts (minutes per week): 28 minutes (95% CI 7 to 49 minutes) in the postal delivery group and 24 minutes (95% CI 3 to 45 minutes) in the nurse-support group. The nurse-support and postal delivery groups combined did 26 minutes (95% CI 8 to 44 minutes). There was no difference between the groups at 3 years for sedentary time or daily wear time.

Missing data analyses

Imputation analyses (see Table 22) presents the results for missing at random, using imputations based on the different assumptions detailed in the methods section. The imputation analyses show that making adjustments for missing values has only a small effect on the primary outcome, that is, the step count estimate, and does not change the interpretation.

The missing-not-at-random analyses make a bigger impact, but only when we assume that there is a strong differential departure between the non-random effects in the control and treatment groups (solid lines in Figure 16). Even then, it is only when we assume that the missing data in the treatment groups are 1000 steps below their missing-not-at-random values, while the values in the control group are at their missing-not-at-random values, that the treatment effects become non-significant; even then, the CI is still largely positive.

FIGURE 16.

Sensitivity analyses for different values of missing step counts. (a) Postal delivery group vs. control group; (b) nurse-support group vs. control group; and (c) postal delivery and nurse-support groups combined vs. control group. The figures show how different values for the missing step counts changes the treatment effects. The starting point where all missing step counts are replaced by ‘missing-at-random’ estimates in each group is represented by the zero difference estimate. Estimates in the different groups are then altered differentially over a range of scenarios. Control group – missing step counts are the same as the 3-year missing-at-random estimates. Treatment groups – missing step counts are 500 or 1000 steps lower than the 3-year missing-at-random estimates. Control group – missing step counts are 500 or 1000 steps higher than the 3-year missing-at-random estimates. Treatment group – missing step counts are the same as the 3-year missing-at-random estimates. Missing step counts are 500 or 1000 steps lower or higher than the 3-year missing-at-random estimates in all control and treatment groups (see White191 for methods). The vertical lines are the 95% CIs for the estimated treatment effects. The treatment effect becomes statistically not significant when the CI crosses the horizontal line at zero.

Factors affecting physical activity levels and maintenance at 3 years

A key theme that emerged from our interviews was the impact that the PACE-UP trial had on participants. Most participants, regardless of what group they were in, reported an increased awareness of PA. Participants described an increased understanding about the importance of PA for health, as well as an awareness about the amount of PA required to meet their daily step count target:

It’s made me more aware of the need to actually commit to doing some exercise a day, just strolling around the house, and going to the shops occasionally doesn’t really make much difference. It doesn’t meet the sort of threshold that you need to reach to ensure that you lead a healthy lifestyle.

ID3Y27M56P

Participants felt that taking part in the PACE-UP trial and using a pedometer had ‘kick-started’ regular activity:

It was the PACE-UP trial that helped get me started and I think that did make a huge difference to me.

ID3Y47F51N

Participants highlighted different barriers to, and facilitators of, being able to stay physically active in the longer term. These barriers and facilitators were often the opposite of each other; for example, some participants saw good weather as a motivator to engage in PA, whereas others saw bad weather as a barrier to being physically active. Other important facilitators and barriers included health, self-motivation, ageing and social support. These factors were considered to be important by participants, regardless of which group they were in. For some participants, they felt that engaging in regular PA helped them to manage their health condition:

The more active I am, the better the arthritis is.

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Conversely, others felt that having a health condition was a contraindication to PA:

I’ve got an ongoing problem where I get pain, so there’s no way I’m going to be going out walking if I don’t have to.

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Self-motivation was seen to be an important determinant to PA:

I think it’s got to come from inside.

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Some participants attributed the PACE-UP trial to providing them with this motivation:

Before the PACE-UP trial, I had no incentive. And that really did help me. That put me/gave me the first steps as it were, got me on the right track.

ID3Y47F51N

On the other hand, a few participants felt that since the PACE-UP trial had ended, their PA levels had decreased as a result of a lack of self-motivation:

I’m not covering anything like what I was covering when I was on the programme, but I don’t know how . . . to put myself in that mind set . . .

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Some participants chose to engage in PA as a way to stay young and slow down the ageing process:

I’ve got nieces and a nephew . . . I need to be active to keep up with them because they are young. I just need to keep up with everyone else really. I don’t want to slow down and become old. Unfortunately, I’m not really motivated by anything else.

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Other participants felt that their age had become a barrier to PA and were less likely to do as much as they did when they were younger:

If anything I’m getting a bit older and I’m beginning to find it a little bit more of a strain.

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Many participants spoke about the importance of having friends and family to motivate them to participate in PA. Support from, and accountability to, family and friends were therefore seen as common facilitators of PA:

Maybe my motivation is not only my health, but having somebody to do it with . . . to maybe be paired up with somebody who was like-minded . . . if I’ve promised/only promised myself, then I might find excuses not to do it.

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Lack of social support meant that some participants did not engage in PA:

I haven’t got anyone to walk with.

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Lack of time was the most frequently cited barrier to maintaining PA. Unlike the other factors previously mentioned, ‘having time’ to engage in PA was not mentioned as a facilitator. The reasons for a lack of time included having ‘family responsibilities’ (looking after children, caring for older relatives), ‘work commitments’ or simply being ‘too busy’:

I’ve got plenty of things that encourage me, it’s just the time I find because I work full time, I just find it difficult to come home, sort of prepare meals, go to the gym, go for a walk . . . I don’t think I need any more actual motivation, I just need a bit more time!

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Some participants spoke about strategies they adopted to overcome the barrier of lack of time, either by incorporating PA into their daily routine or by building up their daily PA in short bouts of activity:

I walk up the escalators to get my little bit of exercise.

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As opposed to the mind set of, oh, I’ve got to do an hour in the gym. Actually 10 minutes solid walking somewhere, several times a day, actually builds stuff up.

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At the end of the interview, participants were asked for their views on what additional support could be provided to aid PA maintenance. Participants were offered examples to comment on, which included regular text messages, online resources, annual nurse appointments and walking groups. Participants had varying preferences over which additional resources they would find the most beneficial. Although some participants liked the idea of a regular text message or ‘jolly little reminders’ (ID3Y47F51N) encouraging PA, others felt that these would be ‘too intrusive’ (ID3Y27M56P). Similarly, some participants liked the idea of having PA resources online to ‘open at their own time’ (ID3Y9F62C), whereas others felt that obtaining this information online would require ‘a more proactive’ (ID3Y47F51N) approach. On the whole, walking groups and nurse appointments were considered to be favourable. Some participants felt that walking groups would provide ‘more motivation to go out’ (ID3Y17M68C) and a regular nurse or other appointment would provide ‘external accountability’ (ID3Y52M66N).

As well as providing feedback on suggested possible resources, we proposed that participants also come up with additional suggestions. These suggestions included holistic appointments with a nurse to discuss both diet and PA, and more opportunities for older people:

There is not much for people over 60, there’s no real places that are easy on the doorstep.

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One participant spoke about being afraid to increase their PA levels, as they were unsure of whether or not it was safe. This participant sought more guidance around riskless ways to increase PA levels:

I’m doing quite well with what I’m doing, so what’s the point of sort of having a risk of a heart attack or something like that, suddenly break in to a stride and start running, so maybe a bit of guidance on what’s going to happen to you if you do step up your exercise plan.

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The effect of the minimal intervention on physical activity levels of participants in the control group

Of the 20 control group participants interviewed, 17 received the pedometer, handbook and diary after 12 months. Thirteen of these participants reported using these resources when they first received them; however, at 3 years, only four participants were continuing to monitor their steps [two with a pedometer, one with a Fitbit (Fitbit, San Francisco, CA, USA) and one with a mobile phone].

Although most of the control group participants who were interviewed felt that the PACE-UP trial had not increased their PA levels, they still stated that the PACE-UP trial had increased their awareness:

It’s made me aware that I do not do as much as I should be doing.

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Some, however, did talk about changes they had made to their daily lives as a result of their increased awareness:

I’m more likely to walk to work now, rather than going on the bus.

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For those who did not utilise the pedometer, some reported difficulties in using it:

I could not work the pedometer . . . could not get it going.

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A few others who used the pedometer stated that discontinued use was either because it had a negative impact on them psychologically or because they had fallen out of the habit of using it regularly:

It’s quite distressing to see how little I do.

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. . . there are lots of other things [that] intrude and you tend to slip back to old patterns.

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There was also a suggestion that, as well as the pedometer, you needed to have someone to report back to:

It always needs to have someone keeping you aware I think.

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Main quantitative findings from the 3-year follow-up

We followed up just over two-thirds of the original trial cohort with accelerometry outcome data at 3 years (and over 90% provided ≥ 5 days of data). Compared with baseline, those in the original nurse-support and postal delivery groups were still doing significantly more steps per day and weekly time in MVPA in bouts at 3 years than the control group. There were no significant differences in outcomes between the postal delivery and nurse-supported intervention groups (as was also the case at 12 months), and there were no significant differences between the three groups in terms of wear time or sedentary time. Our sensitivity analyses looking at the potential impact of missing outcome data at 3 years suggest that it is highly unlikely that missing data have substantially biased our results. Fairly extreme departures from the missing-at-random analyses were needed to result in non-significant effects and, even then, the 95% confidence limits were largely positive. This suggests that the trial interventions had a persistent effect on objectively measured PA levels at 3 years, with no difference between intervention groups. The fact that the minimal intervention given to the control group at 12 months was not effective at increasing the participants’ PA levels suggests that the additional support given to the original trial postal intervention group (with a follow-up telephone call after 1 week and encouragement to return the completed PA diary after 3 months) was an important component of this group’s success. Both the follow-up telephone call and the encouragement to return the completed PA diaries after the intervention were not part of the intended intervention package, but, rather, were research measures as part of the process evaluation to ensure fidelity of the intervention delivery. However, this minimal support, which was not provided face to face or by a health-care professional, seems to have been important to the success of the postal intervention. The original trial postal delivery group also received the postal pedometer intervention when they had just been recruited to the PA trial, when motivation may have been higher, and they had step count targets set for them based on their baseline blinded pedometer use, whereas those receiving the materials at 12 months needed to wear the pedometer again for 1 week to set their target step count. These factors may also have been important to the success of the trial postal intervention group.

Main qualitative findings from the 3-year follow-up interviews

A key finding was that most participants discussed their increased awareness of PA, irrespective of which group they were in and regardless of whether or not they thought that the PACE-UP trial had actually increased their PA levels. Key barriers to, and facilitators of, maintaining PA were reported that were often the inverse of one another and included health, weather, self-motivation, ageing and social support. Lack of time was the most frequently cited barrier. Some participants were able to overcome lack of time by incorporating PA into their daily routine or by breaking PA down into smaller, more manageable bouts throughout the day. Participants gave us mixed feedback on how useful they thought text messages and online resources would be to help to inform future interventions to increase and maintain PA, but walking groups and nurse or other appointments to provide external accountability were broadly welcomed. Additional suggestions provided included more holistic appointments with a nurse and more opportunities for PA for older people. Participants had differing opinions over the resources they would find most beneficial, emphasising the importance of individual tailoring of some aspects of PA interventions. Only a few of the control group participants interviewed were continuing to use the pedometer provided at 12 months. Some participants were not sure how it worked, and others felt that, as well as using the pedometer, it was important to have someone to report back to. This highlights the importance of the extra contact that participants in the postal delivery group received as part of the main trial: a follow-up telephone call to check that they knew how to work the device and encouragement to send back their completed PA diaries with step count recordings after the 12-week programme.

Further discussion of the strengths and weaknesses of both the quantitative and qualitative approaches, and the implications of the findings for health care and future research, are detailed in Chapter 9.

Study strengths

The PACE-UP study had many important strengths. It was large and population based with primary care sampling, allowing response and any bias in response to be assessed, rather than relying on recruiting volunteers. It was designed to have household randomisation, which allowed two members of a couple to take part together if they wished to, enabling a comparison of individual and couple effects. It had three trial arms, allowing the separation of nurse support and pedometer/handbook/diary effects. The intervention was pragmatic, using practice nurses who worked in the practices to deliver the nurse PA consultations, rather than external researchers or exercise specialists. There was a very good uptake rate of nurse appointments and return of completed step count diaries, showing participant engagement with the interventions. The main PA outcomes were objectively measured and were relevant to the PA guidelines. AEs were measured in a number of ways to minimise bias, both self-reported from questionnaires and objectively from primary care records. The trial achieved a follow-up rate of over 90% with complete primary outcome data. The trial also included embedded process, qualitative and economic evaluations, with the economic evaluations using trial results in a simulation of long-term cost-effectiveness. An extended 3-year follow-up allowed maintenance of any intervention effects beyond 12 months to be studied.

Study limitations

There were also some important study limitations. The 10% recruitment into the trial is considered in detail below, in Generalisability. At the baseline assessment, 218 out of 1023 participants (21%) achieved the PA guideline targets based on their accelerometry. These participants were not excluded from the trial because if the intervention were to be rolled out in primary care, self-reported PA levels would define participation. Our nurse-supported intervention group had slightly higher baseline PA levels; however, the trial results were not biased, as analyses were based on individual change, controlling for baseline PA level. It was impossible to mask participants and nurses to the intervention group and, pragmatically, research assistants recruited and followed up the same participants, so were unmasked to group at the outcome assessment. However, all the primary and secondary PA outcomes were assessed objectively by accelerometry. It is possible that participants might have tried harder with their PA when monitored, but this would also have affected control participants and would be reduced by using a 7-day protocol for data collection. 31 In addition, our intervention groups increased their MVPA in bouts of ≥ 10 minutes, implying that participants made changes suggested by the programme. Despite recruiting to target and having excellent follow-up, our CIs for the difference between intervention groups cannot rule out a small 12-month difference. Interpretation of our 3-year follow-up findings was potentially limited by the fact that two-thirds of the control group participants received a pedometer, handbook and diary, and 20% of them also received a single nurse appointment after their 12-month follow-up. However, any contamination appears to be minimal, as there was no evidence of a change in the control group and the intervention estimates at 3 years were very similar to those at 12 months. These findings are of potential importance as, in combination, they suggest that the minimal contact with the postal delivery group after participants were sent their pedometer packs (telephone contact after 1 week and encouragement to return their completed step count diaries at 3 months) was important in stimulating an effect. Timing may have been important too; as the control group was offered the intervention 12 months after the participants had initially expressed an interest in participating in the trial, they would have had to have worn the pedometer to measure their step count and set targets, whereas the trial postal delivery group had targets set based on wearing a blinded pedometer at baseline. These added factors may have also been important to the success of the trial postal delivery intervention group.

Implications for health care

• A primary care pedometer-based walking intervention, delivered by post with minimal support, could provide an effective and cost-effective approach to addressing the public health physical inactivity challenge.

• The ‘3000 steps in 30 minutes’ formula neatly captures intensity and could become a useful new public health goal, particularly as many people can measure steps easily with their mobile phones.

• The PACE-UP 12-week pedometer-based walking intervention could be considered for inclusion into the NHS Health Check programme, aimed at a similar age group (40- to 74-year-olds), and/or the NHS Diabetes Prevention Programme.

Recommendations for research

• The PACE-UP trial generalisability is limited by the 10% overall recruitment rate and a lower recruitment rate in Asian and socioeconomically deprived patients. Further research into different recruitment methods is needed, as is research assessing the recruitment rate achievable if this programme were offered outside a trial setting over a more prolonged time period.

• Although the overall postal intervention outcomes were as effective and more cost-effective than the nurse-supported intervention outcomes, further research is required to understand who would benefit most from the individual tailoring offered by a nurse-supported intervention.

• There has been a recent dramatic increase in the use of wearables to monitor personal PA levels, including through smartphones, wrist-worn devices, online monitoring and mobile apps. Further research into how the PACE-UP 12-week PA programme could be integrated into the use of these devices (with/without a pedometer) is needed.