Systematic review to identify and appraise outcome measures used to evaluate childhood obesity treatment interventions: evidence of purpose, application, validity, reliability and sensitivity

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Chapter 1 Aims and objectives

This study aimed to perform a systematic review to identify and appraise existing outcome measures for use in the evaluation of childhood obesity treatment interventions. This aim was met via the following objectives:

1. Systematic review of the literature in order to produce a database of outcome measures that have been used (or developed for use) in childhood obesity treatment interventions.

2. Appraisal of outcome measures to identify and highlight those that have been developed and evaluated using high-quality, fully rigorous methods.

3. Creation of a childhood obesity outcome measures framework, categorised by (1) anthropometry/weight status; (2) diet; (3) eating behaviours; (4) physical activity (PA); (5) sedentary time; (6) fitness; (7) psychological well-being; (8) quality of life; (9) environmental measures; and (10) physiological outcomes. This framework was intended to guide researchers as to the best tool to use in their evaluation of childhood obesity treatment interventions and aimed to include:

   • outcome measure description (name, purpose, number of items and mode of administration)

   • outcome-specific issues (population intended for, theoretical orientation)

   • content (any evidence given for an underlying conceptual model; list of domains/scales covered)

   • measurement evaluation properties (development method, item reduction, validity, reliability, feasibility and responsiveness)

   • cost and practical considerations (details of licensing fees, duration of administration).

Chapter 2 Background

Many interventions to treat obesity are aimed at children but there remains a lack of high-quality evidence on effective childhood obesity interventions in the literature. 1 Existing systematic reviews aimed at comparing effectiveness of intervention programmes (particularly those conducting meta-analysis) are hampered by a lack of quality in the conduct and reporting of trials in this area. There has been some attenuation in the rising rates of childhood obesity in recent years, and it is therefore probable that many attempts to prevent and treat obesity in children have been of some success. 2 The problem, therefore, may lie in the methods used to evaluate and report interventions.

The degree to which weight management leads to improvements in a child’s health is reflected by measuring change in outcomes in clinical trials. Outcomes either directly measure a definitive clinical change (i.e. primary outcome of weight loss) or assess proximal/secondary outcomes (e.g. change in diet) that impact on the primary outcome. In the design phase of a trial, choosing the appropriate outcomes is essential. Use of inappropriate outcomes will result in data that are inaccurate or biased and that do not indicate the effectiveness of an intervention. Moreover, collection of data using poorly chosen outcomes is a waste of resources, both for the researchers and participants involved in the trial. 3 Inappropriate selection of outcomes in childhood obesity research is probably due to the uncertainty about which outcome domains are most relevant to children and their families. 4 Furthermore, there is a lack of knowledge on which can be most reliably measured.

Guidance tools are available to facilitate the design of high-quality research, including the Medical Research Council (MRC) guidance for the evaluation of complex interventions and, more specifically, the National Obesity Observatory Standard Evaluation Framework (NOO SEF) for childhood obesity evaluation (www.noo.org.uk/core/SEF). 5 The latter (commissioned by the Department of Health) was produced via consensus of prominent obesity researchers to aid clinicians in their evaluation of childhood obesity programmes. It now stands as a grounded tool to enable consistency with research design. The primary audience for the NOO SEF is those evaluating public health obesity programmes. However, much of the advice is of relevance to researchers conducting trial evaluations. For example, recommended outcomes are listed and described as ‘essential’ or ‘desirable’. This resembles the output of a core outcome set, although the inclusion of each outcome has not been based on formal consensus methodologies, such as those described by ‘COMET’ (Core Outcome Measures in Effectiveness Trials). Core outcome sets are a minimum set of outcomes that should be measured and reported within trials or other forms of research for a specific condition (www.comet-initiative.org/). The use of core outcome sets permits comparisons between trials that are agreed on by experts within each disease area. At present, there is not a core outcome set for obesity research – partly because of the complexity and variability in intervention targets (requiring potentially different outcomes). The NOO SEF therefore stands as a guide, rather than a minimum set of outcomes. Importantly, the NOO SEF does not provide advice or details of outcome measures that should be used within each outcome domain that it recommends. Although there have been reviews published on some individual measures and their general application (e.g. measurement of television exposure6), there has not been a review that has focused specifically on outcome measures for used in childhood obesity treatment intervention evaluations.

The lack of consensus in determining appropriate outcome measures for the reliable and valid assessment of childhood obesity interventions means that comparisons between interventions are consequently difficult, partly because of a shortage of validated outcome measures available, but also because the selected outcome measures differ between studies. Consequently, it is a challenge to identify which interventions are most effective. Such a lack of consistency and inadequacy impedes the progress of childhood obesity research.

Chapter 3 Methods

Design

Evidence synthesis

A systematic review that will guide the production of a childhood obesity outcome measures framework that will be crucial in guiding researchers aiming to assess the impact of obesity treatment interventions in children. Resulting outcome measures that were identified by this review were appraised by a two-stage process of internal and external appraisal. A summary of the design is shown in Figure 1.

FIGURE 1.

Summary of study design. FDA, Food and Drug Administration.

Appraisal of quality of outcome measures

Each outcome measure was appraised for quality in order to identify those that demonstrate rigorous methods in both development and evaluation procedures. Appraisal involved two stages: (1) internal appraisal and (2) external appraisal.

Internal appraisal

Principles of international guidelines9,10 were drawn on (where appropriate) to appraise rigour (i.e. development and measurement properties) of outcome measures meeting eligibility criteria. Measures within outcome domains were specifically appraised according to its construct and/or clinical context, as strict adherence to any individual guideline is not always appropriate. For example, many anthropometric and physiological outcomes are derived from standard clinical tests, and it would therefore be unlikely to find published data on measurement development in relation to childhood obesity; thus anthropometric outcome measures were not expected to have involved obese children in the development stage.

Specific international guidelines that were used in developing the data extraction and scoring systems were the Scientific Advisory Committee (SAC) of the Medical Outcomes Trust guidelines11 and Food and Drug Administration (FDA) guidelines on the development of patient-reported outcomes (PROs). 10 The SAC defines key attributes that should form part of the development and evaluation of instruments. With this, there are clear rules on what the committee considered to be important in the reporting of a reproducibility or validation study (e.g. a clear description of the methods of data collection and reporting of specific estimates and standard errors). In addition, standards for evaluation are provided, such as for assessment of reliability and some criteria for good measurement properties, including cut-off points for intraclass correlations. These criteria were used as a guide rather than explicitly regulating which measures were and were not considered as rigorously developed and evaluated. FDA guidance describes the best practice in the review, and evaluation of existing, modified or newly created PRO instruments. The criteria helped to guide appraisal procedures related to the conceptual framework (definition of the concepts being measured with description of relationships between items/domains and scores) and measurement properties (reliability, validity, ability to detect change) of each measure. Specific characteristics that were included in the CoOR appraisal method include concepts being measured, number of items, conceptual framework, intended use, population for intended use, data collection method, administration mode, response options, recall period, scoring, weighting, format and response burden.

A scoring system was also applied to the development and evaluation of each secondary outcome measure. Scores were based on quality in the conduct and results of evaluation where appropriate and ranged from ‘1’ to ‘4’ (with ‘1’ being the lowest). These were developed from criteria set by the international guidelines,9,10 in addition to previous research conducted by the lead applicant (MB). For example, in reporting the study sample, a maximum score of ‘4’ was assigned to manuscripts reporting a minimum of the four characteristics: age, gender, ethnicity and socioeconomic status (SES). Those describing three of these were assigned a score of ‘3’ and so on. Appendix 5 provides the data extraction form for the diet outcome domain in which the scoring system is fully detailed. In addition, Table 2 provides criteria that were applied in assigning scores.

TABLE 2 - Criteria used to allocate robustness scores for evaluation of quality

ANOVA, analysis of variance; AUC, area under the curve; CFA, confirmatory factor analysis; ES, effect size; ICC, intraclass correlation coefficient; MCID, minimal clinically important difference; N/A, not applicable; RNI, reference nutrient intake; RNSEA, root-mean-square error of approximation; ROC, receiver operating characteristic; SRM, standardised response mean.

The protocol for consideration of statistical tests that were not listed included consideration by the team statistician (JB).

Measurement development and reporting
The concept to be measured was clearly stated (rationale and description)		4 = strongly agree (concepts are named and clearly defined) 3 = agree (concepts are named and general described) 2 = disagree (concepts named only but not defined) 1 = strongly disagree (concepts are not clearly named or defined)
Was a theoretical or conceptual framework used or referenced?		4 = strongly agree (theory/framework used as a basis for development) 3 = agree (theory/framework named and incorporated) 2 = disagree (theory/framework named but not used) 1 = strongly disagree (no theory/framework described) 0 = N/A = (biochemical/anthropometry, direct measures/observations)
Populations that the measure was intended for were adequately described		4 = strongly agree (describes at least four characteristics, including age, gender, race/ethnicity and SES) 3 = agree (three characteristics reported) 2 = disagree (two characteristics reported) 1 = strongly disagree (no characteristics reported)
Were the populations for which the measure was intended involved in measurement development?		4 = strongly agree (at least three methods of involvement, including part of study team, steering committee, pilot testing, cognitive interviews/focus groups) 3 = agree (involved using at least two methods) 2 = disagree (populations minimally involved in one method) 1 = strongly disagree (populations not involved) 0 = N/A (biochemical/anthropometry)
Measurement evaluation
	Sample size	Appropriate statisticsa	Results/findings
IC	Five or more participants per item	Cronbach’s alpha	α = 0.7
		KR-20 (Kuder–Richardson coefficient)
		Split half
TRT reliability	≥ 50	Spearman	r = 0.4
		Pearson
		Kappa	κ = 0.4
		Agreement	Agreement (not used to score but reported for comparisons)
Inter-rater reliability	Study specific (depending on design)	Pearson/ICC/rho = kappa	r = 0.4
		K = Kripendorff’s alpha	κ = 0.40
FA	Five or more participants per item	Eigenvalue	Eigenvalue ≥ 1
		Factor loading	Factor loading = high > 0.6, low < 0.4
		%variance	CFA RNSEA < 0.06, RNI close to 1
Criterion validity	≥ 50 [less for objective such as DLW (≥ 20)]	Pearson	Pearson’s/Spearman ≥ 0.4
		Spearman
		Regression	Regression coefficient = p > 0.5 or r ≥ 0.50
		Agreement	Agreement
		t-test (not in isolation)	t-test p > 0.05, t-value > 1
		ANOVA
		Sensitivity/specificity	AUC > 0.7
Convergent validity	≥100	Pearson	Pearson/Spearman ≥ 0.4
		Spearman
		Regression	Regression coefficient = p > 0.5 or r ≥ 0.50
		Agreement	Agreement
		t-test (not in isolation)	t-test p > 0.05, t-value > 1
		ANOVA
		Sensitivity/specificity	AUC > 0.7
Construct validity	≥ 100	Pearson	Pearson’s/Spearman ≥ 0.4
		Spearman
		Regression	Regression coefficient = p > 0.5 or r ≥ 0.50
		Agreement	Agreement
		t-test (not in isolation)	t-test p > 0.05, t-value > 1
		ANOVA
		Sensitivity/specificity	AUC > 0.7
Responsiveness	≥ 100	MCID	MCID/SRM > 0.5
		SRM
		ROC AUC	ROC AUC > 0.7
		ES	ES > 0.5
		t-test	t-test p < 0.05

It was not feasible to assign scores to all of the anthropometry (primary) outcome measure studies. The majority of manuscripts meeting criteria for eligibility evaluated multiple measures, which would mean that scores would have to be provided for an amount of studies that was beyond the capacity of this study (estimated to be > 300 studies). This was also deemed inappropriate, as multiple studies evaluated the same measures (generating multiple scores for the same measures). Instead, CoOR members grouped all manuscripts evaluating the same measure and reported the overall conclusions (reported by authors) of each paper as: (1) yes, authors advocate its use; (2) no, authors do not advocate its use; and (3) conclusions drawn by authors are unclear (?). The form used to record this information is provided in Appendix 17 for clarity. (Note: This also provides the findings.)

Internal recommendation of measures to include or exclude (degree“of certainty)

Two members of the CoOR internal team (MB and LA) classified each of the primary and secondary measures into one of three categories (by discussion and consensus) in relation to their confidence of whether or not each measure should be recommended for inclusion into the final CoOR outcome measures framework: (1) ‘certain, good evidence, fit for purpose’ (i.e. confident that the measure is robust and should be recommended for use); (2) ‘certain, poor evidence, not fit for purpose’; and (3) ‘uncertain, requiring further consideration’. Assignment of certainty considered the data extracted from each study alongside the scoring system. For example, a measure that was assigned a score of 3 out of 4 for quality of reliability testing was further investigated to determine why one point was lost. If lost because of poor reporting methodology, the team may have been more likely to deem a measure ‘uncertain’ rather than ‘unfit’ than lost points due to poor results or inadequate sample size. This was conducted separately for each domain in order to facilitate comparisons between measures (i.e. questionnaire-style outcome measures would be expected to include a measure of IC, which was not applicable in objective measures. Similarly, historical physiological measures, such as blood pressure, would not be expected to have included obese children in their development). Tools were placed into Category 1 or 2 only, providing that mutual agreement had been established. Category 1 was assigned only when the tool was clearly highly robust in terms of development and evaluation. Similarly, Category 2 was assigned only when the tools was very poorly developed and evaluated. Any disagreements were placed into Category 3 to be further discussed at the expert appraisal meeting.

Expert appraisal

Results of the systematic review and corresponding files from the internal appraisal were reviewed by experts with specific proficiency in each outcome, in addition to methodological experts. Each expert was asked to review all of the included outcome measures that met eligibility criteria of CoOR, as well as considering the internal appraisal decisions. Figure 2 shows the process in which external appraisal was conducted.

FIGURE 2.

Expert appraisal process.

In Phase I, experts were provided with all materials (via a web-based file share facility: Dropbox). Provided documents included:

1. A list of all included manuscripts (with information on the pathway in which each was included). This included manuscripts that did not fully meet eligibility criteria but which the internal team felt had potential for inclusion.

2. PDFs of all manuscripts meeting eligibility criteria (with copies of measurement questionnaire if available).

3. Summary tables providing details of all data that were extracted for each measure according to domain (see Appendices 6–15).

4. Tables providing internal scoring for development and evaluation of each measure (see Appendices 18–26).

5. Appraisal decision of certainty for each measure [see Appendix 17 (primary) and Appendix 28 (secondary)].

Experts were asked to look at material for all 10 domains. As part of Phase II, they were then asked to more closely examine documents within areas of their expertise (predefined by the CoOR team), so that they could lead discussion in these domains at a future face-to-face meeting. Experts involved were Susan Jebb and Carolyn Summerbell (diet and eating behaviours), John Reilly (anthropometry/weight status), Ashley Cooper and Ulf Ekelund (PA and sedentary time/behaviour), Lucy Griffiths and Andrew Hill (psychological well-being), Maria Bryant and Steven Cummins (environmental outcomes), Paul Kind (economics/quality of life), and Julian Hamilton-Shield (physiological outcomes). Two further consultants with expertise in outcome evaluation and clinical trial methodology reviewed the framework (Claudia Gorecki and Julia Brown, respectively). In addition, a specialist in public health evaluation from the NOO (Katharine Roberts) facilitated in consideration of measure applicability for public health interventions.

Experts were provided with instruction asking them to consider factors such as appropriateness of categorisation (i.e. ensure within correct outcome domain); obvious omissions not identified by search strategy (including knowledge of modified versions of outcomes); and personal and theoretical experience of use of outcome measures related to feasibility.

Phase III of the external appraisal involved a face-to-face meeting with all experts. A physical (rather than a remote) meeting was chosen because it was more likely to create a richer, in-depth discussion of the inclusion (or exclusion) of all outcomes. Experts were provided with a short presentation by the CI (MB) describing the study aims and methodology. They were then divided into two groups. Group A included experts for the domains: diet, eating behaviour, psychological well-being, economics/health-related quality of life (HRQoL) and environment. Group B included experts from the domains of anthropometry, PA, sedentary behaviour/time, fitness and physiology. Discussions began by determining expert agreement on the internal appraisal decisions ‘1’ (certain, fit for purpose) and ‘2’ (certain, unfit for purpose). Disagreements were resolved by discussion. Outcome measures that had been given an internal appraisal decision of ‘3’ (uncertain, requiring further consideration) were then more fully discussed. Justifications for decisions were provided at the meeting and final rulings of the tools were made based on consensus. This was recorded directly on to a predefined pro forma that permitted the recording of internal and external decisions (see Appendices 16 and 27), alongside any relevant discussion. In addition, discussions from both groups were recorded and transcribed.

After each group had made decisions regarding certainty, a final discussion was held by both groups together to review key decisions. All final decisions contributed towards the development of a provisional framework, which was then forwarded to each expert to secure their final agreement (Phase IV).

Note: At the time of the expert appraisal meeting, data from some of manuscripts had not been extracted. These included those that had to be ordered by The British Library and which had not yet been delivered to the team. The exact same methodology was later applied to these manuscripts; however, experts were asked to review them remotely. Outcome measures that were appraised using this approach are highlighted within Appendix 16. The exception to this was with manuscripts written in languages other than English. Where possible, data were extracted via translation of methodology papers. However, these were not appraised for quality.

Chapter 4 Results

Number and type of studies identified

Combined, searches 1 and 2, conducted in 11 databases, identified 25,486 manuscripts (after removal of 8674 duplicates). A further 25 were identified through hand-searching [grey literature, citations and references from relevant reviews (including manuscripts cited in 48 reviews)]. Of these, 14,419 were search 1 trial manuscripts and 11,092 were search 2 methodology manuscripts. Screening for eligibility at both the title and abstract stage and the full paper review resulted in the inclusion of 200 trial manuscripts from search 1. After data were extracted from these papers, 417 further manuscripts were identified that were citations linked to the outcome measures used by the trials. However, only 56 cited methodology manuscripts met eligibility criteria for inclusion as methodology papers. The majority of other citations were linked to a previous study using the outcome measure (i.e. not papers describing development or evaluation) or were completely incorrect citations (28 were duplicates, already found in search 2). Screening of search 2 methodology papers resulted in the inclusion of 320 manuscripts meeting eligibility criteria. Combined with search 1, a total of 376 manuscripts were identified that described 180 outcome measures (Figure 3).

FIGURE 3.

Summary of study selection and exclusion.

Note, although this study did not exclude manuscripts that were not written in English, there was no formal protocol for translation or extraction of papers. Eligible manuscripts written in languages other than English (n = 53) that were identified via search 1 are listed in Appendix 27 but data have not been extracted from them. Manuscripts written in languages other than English (n = 23) that were identified via search 2 (i.e. pertaining directly to development/evaluation of outcome measures) were included for data extraction. These are listed within study findings and the language is indicated in the detailed summary tables (see Appendices 5–14). However, as the level of extraction was not as detailed as with English papers, measures described by these papers were not considered in appraisal unless already included within another study manuscript written in English.

Study characteristics

Manuscripts describing childhood obesity treatment trials

Data were extracted from 200 manuscripts describing the evaluation of a childhood obesity treatment intervention (see Appendix 3). The majority (156 manuscripts) described a phase III evaluation of a childhood obesity treatment. Nine manuscripts described a feasibility study, 30 manuscripts described a pilot study and nine manuscripts were protocol papers for future RCTs. Publication dates ranged from 1960 to 2012, and included sample sizes ranging from 811 to 2112. 12 Most studies evaluated a lifestyle intervention, but there were also evaluations of cognitive interventions, drug and surgical interventions, drug/surgical interventions combined with lifestyle change and those that focused on reducing sedentary behaviours. Figure 4 shows the different types of primary outcome measures used by identified trials. The most common primary outcome was BMI [including those deriving body mass index standard deviation score (BMI-SDS) or %BMI]. However, measurement of weight was also popular, with 37 evaluations assessing absolute weight or percentage weight change as the primary outcome.

FIGURE 4.

Frequency (%) of primary outcome measures used in search 1 trials.

Eighty-two (41%) of trials included a measure of diet as a secondary outcome. Sixty-eight (34%) studies included a measure of PA, with the most popular measures being activity recalls and objective measures (e.g. accelerometers or pedometers). Seventy (35%) of the trials included an evaluation of psychological well-being, measuring a variety of concepts, including self-esteem, depression and body image. Physiological measurement was also popular, with 94 (47%) trials measuring outcomes such as blood pressure, insulin or blood lipids. Other secondary outcomes were used less frequently (Figure 5).

FIGURE 5.

Frequency (%) of trials using each type of secondary outcome.

Four hundred and seventeen citations that were linked to primary and secondary outcome measures within all of the 200 included manuscripts were located. However, only 56 of these referred to manuscripts that described the development and/or evaluation of outcome measures. Incorrect citations were linked to the majority of outcome measures, most commonly linking to a previous study that had used the same measure.

Manuscripts describing the development/evaluation of outcome measures

A total of 379 manuscripts that describe the development or evaluation of 180 measures met inclusion criteria to CoOR. Fifty-six of the included manuscripts were derived from searching citations of the trials (from search 1) and the remaining 323 were identified directly from search 2. Of these, 24 were written in a language other than English. Efforts were made to translate these (and gain information from English abstracts), resulting in the inclusion of all except for three studies. 13–15 A further paper that was not translated describes a measure that has already been included within the eating behaviour domain. 14 It has been included in the summary table (see Appendix 8), but no data have been extracted from this paper. Table 3 provides detail on the number of manuscripts and corresponding measures (excluding the three written in non-English that could not be translated). Some manuscripts evaluated more than one measure (hence there is a discrepancy between the number of manuscripts and the number of studies). In addition, some measures have multiple manuscripts describing their evaluation, thus the number of manuscripts and number of measures are not equal.

TABLE 3 - Number of eligible manuscripts with corresponding measures by outcome domain

Outcome domain	No. of manuscripts	No. of studies	No. of measures
Anthropometry	162 (including 15 non-English)	258	38 (none exclusively non-English)
Diet	40	44	22
Eating behaviour	39 (including one non-English)	40	22 (none exclusively non-English)
PA	35	45	24
Sedentary time/behaviour	5	6	6
Fitness	14	14	13
Physiology	28 (including two non-English)	28	12 (none exclusively non-English)
HRQoL	25 (including three non-English)	25	16 (including three non-English)
Psychological well-being	19	20	17
Environment	9	10	10
Total	376	490	180

Findings of the systematic review

The following text summarises data extraction of measures pertaining to evaluation of reliability and validity within outcome domains. Key findings are provided with ’in-text’ citations for some manuscripts. However, given the volume of included manuscripts, not all are cited within the text. However, full details of data extracted from every manuscript are provided in the corresponding Appendices 5–14 and within the reference list.

Anthropometry

Data from a total of 162 papers with 38 tools were extracted (see Appendix 6). Of these 162 manuscripts, 15 were written in a language other than English. Data were extracted only from abstracts (which were available in English); however, all non-English papers described further evaluation of outcome measures that were also described in multiple other papers written in English. Appraisal decisions to ‘recommend’ or ‘not recommend’ tools were therefore not based on non-English papers.

Of the 162 papers, only eight evaluated the validity of primary outcomes against a gold standard measure of body composition using either the four-compartmental (4C) model16–19 or total body water (TBW) by deuterium dilution. 20–24 Each of the four papers using the 4C model as a gold standard describes the validation of DXA [with Gately et al. 17 also validating air displacement plethysmography (ADP) and total body water]. Wells et al. 16 and Gately et al. 17 validated DXA in 174 and 30 overweight and obese adolescents, respectively. Findings were similar, with Gately et al. 17 finding that the total error and mean difference [± 95% limits of agreement (LOA)] compared with the 4C model were 2.74 kg and 1.9 kg (± 4.0 kg), respectively, and Wells et al. 16 finding similar LOA at ± 4.2 kg, with overestimations of fat mass by DXA of 0.9 kg. However, interpretation of these results differs by authors, with Wells et al. 16 applying more caution to the validity of DXA. Additionally, Wells et al. 16 showed that the bias in fat mass was significantly related to the magnitude of fat mass (so that greater inaccuracies were seen with increasing fat mass). Further longitudinal analysis was conducted by Wells et al. 16 in a subsample of 66 children. Although average bias was not found to differ significantly from zero for ‘change’ in both lean mass and fat mass, the LOA in individuals were described as ‘large’ (± 3 kg) compared with an average weight change of 1.7 kg (lean mass) or 0.6 kg (fat mass). Combined with problems encountered in actually using the equipment in very obese children, authors conclude that further work (including investment by companies manufacturing DXA machines to develop technology capable of measuring obese participants) may be required to enhance measurement accuracy. Variability in accuracy in DXA according to other factors was also found by Williams et al. 18 in a study that compared groups of obese children, ‘normal’ weight children and children with cystic fibrosis. Bias in measurement was found according to the sex, size, degree of adiposity and disease state of the subjects, indicating that DXA is unreliable for studies of persons who undergo significant changes in nutritional status between measurements (comparisons with obese children were based on 28 children). The final paper identified by CoOR in which DXA was validated against the 4C model also highlighted limitations of the method, although concluded that it remains of use in longitudinal population comparisons. 19 Comparisons were made in per cent body fat in a sample of children and adolescents and show a mean difference between DXA and 4C of −3.5% (p = 0.171), with LOA at +5% to −12%.

Further comparisons by Gately et al. 17 were made between the 4C model and other anthropometrie measures of ADP and TBW, finding strong correlations for all measures (r ≥ 0.95, p < 0.001; standard error ≤ 2.14). The anthropometric measurement demonstrating the highest validity in this study was ADP (total error 2.5, mean difference 1.8 kg, 95% limit of agreement ± 3.5 kg for ADP with Siri equations, and total error 1.82, mean difference 0.04 kg, ± 3.6 for ADP with Loh equations).

Many studies evaluated BIA, but only two reported comparisons against the gold standard methodology of TBW by deuterium dilution. 21,24 Wabitsch et al. 24 was also one of the few studies to measure the ability of a measure to detect change following an intervention. In comparisons between BIA and TBW, cross-sectional comparisons showed good agreement between BIA and TBW. However, correlations were poor (r = 0.21) with change, where BIA was not accurate at predicting small changes in TBW. Rush et al. 21 also used the deuterium dilution method to compare BIA and BMI in their study of 172 children and adolescents, although the focus of the paper was actually to develop predication equations in three ethnic groups. A further study made comparisons with a three-compartmental (3C) model25 and found that BIA (using Tanita equations) overestimated fat-free mass by 2.7 kg (p < 0.001), although new equations by the authors improved correlations.

Fifty-five papers tested the use of BMI as a valid measure of change in body fat by deuterium dilution. 22,23 Findings from these suggest that fat mass (from TBW) is well correlated with BMI across ethnic groups (Caucasians r = 0.81, p < 0.001; Sri Lankans r = 0.92, p < 0.001)22 and genders (girl r = 0.82, p < 0.001; boy r = 0.87, p < 0.001), but that BMI cut-offs often fail to detect obesity as defined by the gold standard methods. Use of self-reported BMI, however, often failed to produce correlations that were sufficient to suggest that they are of use for individual-level assessment, although they may be adequate to study trends on a population basis. This type of evaluation was common in the CoOR review, with 39 papers describing comparisons between self-report (or parental report) and measured height and weight.

Evaluation of SFT was also common in manuscripts identified by CoOR, with 24 studies reporting validating various types of skinfold measurements. Of these, just four studies26–29 present strong validity to advocate its use. However, none of these four studies validated against gold standards of the 4C model or TBW. Of the 20 studies evaluating WC reviewed here, 10 reported an adequate level of validity for WC. However, none of these made comparisons with gold standards of the 4C model or TBW.

Diet

A total of 44 studies (within 40 manuscripts) describing 22 different types of dietary assessment methodologies were extracted (see Appendix 7). These included 16 different food frequency questionnaires (FFQs), plus other methodologies described in Figure 6.

FIGURE 6.

Diet methodologies included in appraisal.

Summary findings are shown in Appendix 7. Sixteen FFQs/checklists were described in 21 manuscripts,30–51 of which 10 assessed TRT reliability, with results varying across studies (r = 0.16–0.74). In general, however, most were classed as adequate. Convergent validity was tested in 13 studies, comparing FFQ data with 24-hour recalls and food records (weighed and estimated). Correlations ranged from 0.23 to 0.66, and kappa statistics ranged from 0.08 to 0.67. Criterion validity, comparing against the ‘gold standard’ of direct observation, measure of habitual energy expenditure by doubly labelled water (DLW) or other biomarkers was conducted in four FFQs, with correlations ranging from 0.01 to 0.91. Worryingly, large LOA were often evident in these studies. IC was tested for two FFQs, with both showing strong alpha coefficients ranging from 0.84 to 0.88. 30–33 Construct validity was evaluated in three papers – with comparisons between the FFQs and (1) screen time,34 (2) BMI,52 and (3) diet quality35,52 – showing variable, but generally significant, correlations (see Appendix 7).

Diet history methods were described in three identified manuscripts. 53–55 Reliability testing was not reported in any of these papers. Each assessed criterion validity against a gold standard method, but results indicated an impact of BMI on validity. No other evaluation was reported for diet history methods.

Diet diaries were evaluated in 11 of the identified papers. 56–64 Of these, one56 tested inter-rater reliability using a tape-recorded method, with correlations ranging between 0.68 and 0.96. None of the papers assessed TRT reliability. Criterion validity was evaluated in 10 diet dairy papers, with many reporting significant effects of weight, BMI or other measures of adiposity on validity. 55,57–63 The only paper that reported no misreporting by body weight was O’Conner et al. 64 in their study of 45 children. This paper64 reports low relative bias [mean difference, energy intake (EI) – total energy expenditure (TEE) = at 118 kJ/day] but with wide LOA (bias plus or minus two standard deviations of the difference) at 118 ± 3345 kJ/day. Bias was associated most strongly with reported fat intake.

Recall methodologies were evaluated in six papers, of which findings for reliability and validity testing were variable. Two papers reported evaluating TRT reliability. 65,66 Edmunds et al. 66 compared ‘A Day in the Life’ questionnaire (a 24-hour recall method) collected twice, 2 days apart, and found non-significant differences overall, indicating good TRT reliability. Baxter et al. 65 conducted general-linear-model repeated-measures analysis in which diet was recorded over three time periods and included comparisons between different weight status groups. The effect of time period (i.e. repeated measures) was significant, indicating poor repeatability, with a significant interaction by weight status (with greater inaccuracy in overweight children). Comparisons of each of these methods was against direct observation of eating episodes. Two diet recall evaluation papers described different forms of inter-rater reliability,56,66 both providing strong evidence. Van Horn et al. 56 compared child report with parental report and show correlations of r = 0.75 (range 0.65–0.93). Edmunds et al. 66 made comparisons between coder and reported a kappa range of between 0.82 and 0.92. 66 Five of the six recall papers evaluated criterion validity, four of which made comparisons with direct observations33,65–67 and one with DLW. 68 Findings from comparisons with direct observation are difficult to compare, as each was conducted using different analytical approaches. In general, however, criterion validity using this type of comparator indicates moderate agreement (see Appendix 7). Johnson et al. 68 compared 3-day dietary recalls to data from DLW in 24 children and reported a poor correlation between reported EI and that estimated by DLW (r = 0.25, p = 0.24). LOA were −4612 ± 3356 kJ/day, with a mean difference of −225.1 kJ/day. Precision, however, was not correlated to body weight.

Three other dietary assessment methodologies meeting eligibility criteria were included. The first describes the measurement of biomarkers insulin-like growth factor 1 (IGF-1), insulin-like growth factor binding protein 1 (IGFBP-1) and insulin-like growth factor binding protein 3 (IGFBP-3),69 and reports that, as an indicator of construct validity, overweight children had higher serum levels of IGF-1 and IGFBP-3 but lower levels of IGFBP-1. Consequently, biomarker measurement (especially IGF) is advocated by the authors. The second paper describes the development and preliminary evaluation of a dietary observation method for use within child-care settings70 in which trained researchers attend centres to view dietary consumption by children. This paper reports excellent inter-rater reliability between observers of 100% agreement for most food items observed. One further paper71 describes the use of a mixed-method approach, including 24-hour recall, FFQ and nutrition, and PA behaviours. The primary purpose of this paper was to compare self-reported EI across weight status groups. However, further evaluation is reported within the methods section (see Chapter 3) specifically for evaluation of the recall component (linked to previous abstracts). Findings indicate good reliability [TRT agreement of 77% overall (range = 62–87%) and inter-rater reliability between self-report and dietitian report of r = 0.55–0.70]. However, comparisons with direct observations (for 24-hour recall data) indicate a systematic under-reporting of dietary intake by gender and weight status.

Eating behaviour

A total of 40 studies (within 39 manuscripts), describing 22 measures of eating behaviours, met the eligibility criteria. A description of data that was extracted from all studies is presented in Appendix 8. Of these, one manuscript was written in Portuguese. 14 It was not possible to extract data from this manuscript but the measure that it describes was evaluated by two other manuscripts. 72,73 It is included in Appendix 8 for reference purposes only.

Broadly speaking, eating behaviour questionnaires included those that targeted feeding styles/behaviours or those that measured affect/emotions related to eating, although some measures included both. Feeding questionnaires [e.g. Infant Feeding Questionnaire (IFQ) and Preschool Feeding Questionnaire (PFQ),74 Child Feeding Questionnaire (CFQ),75 Infant Feeding Style Questionnaire (IFSQ)76] included constructs such as concern, control, difficulties in feeding, pressure, restriction, etc. Measures of emotional eating [e.g. the Emotional Eating Scale for Children (EES-C),77 Dutch Eating Behaviour Questionnaire (DEBQ),78,79 Eating in the Absence of Hunger-Children (EAH-C)80] included constructs of eating in response to emotions, enjoyment of food, satiety, external eating, etc. However, there was little consistency across measures in the terms/names provided to describe similar constructs. Eligible studies also included those that described the development/evaluation of measures that screened for disordered eating, many of which had been included because they had been previously used in childhood obesity treatment trials. The suitability of these measures was questioned at the point of review, but decisions were left to the expert collaborators group (see Chapter 3, Expert appraisal).

Internal consistency assessment was common in eating behaviour questionnaires and was tested in 30 studies (alpha range = 0.54–0.90). Of these, 21 were considered acceptable (α > 0.70). TRT reliability was performed in 12 studies,73,77,80–89 also demonstrating high correlations (r = 0.58–0.81). Results from inter-rater reliability testing, however, were less strong; this was assessed in five studies,81,90–93 three of which compared child self-report with parent report. 90–92 Johnson et al. 68 reported a poor agreement of 41% (κ = 0.19) for their evaluation comparing findings from the Questionnaire of Eating and Weight Patterns (QEWP) reported by adolescents (QEWP-A) and the QEWP reported by parents (QEWP-P). This was later repeated by Steinberg et al. ,91 again demonstrating discordance between reports, with children reporting more disordered eating (sensitivity = 24%, specificity = 82% for diagnosis of overeating; sensitivity = 20%, specificity = 80% for diagnosis of eating disorders). Relatively low correlations were also observed by Braet et al. 92 in comparisons between child-reported DEBQ (DEBQ-C) and parent-reported DEBQ (DEBQ-P), with a range in correlations of between r = 0.35 and r = 0.45. Agreement between parents may be more similar and this was found by Haycraft et al. 93 in their evaluation of the CFQ (r = 0.66, range = 0.53 to 0.78). Similarly, better inter-rater reliability was observed in correlations between interviewers, with Decaluwé and Braet81 reporting highly correlated responses (mean r = 0.96, range = 0.91–0.99). Implications of the poor agreement between child and parent responses may be irrelevant if using these measures as trial outcomes, however, provided that the same reporter is used at baseline and follow-up in all trial arms. Authors would also need to clarify details of reporting to permit cross-trial comparisons.

Internal validity was evaluated in 22 eating behaviour papers, with the total variance ranging from 33% to 67%, and factor loadings ranging from 0.17 to 1.51. Of these, eight papers73,74,77,79,80,85,94 had all factors classed as acceptable (> 0.40) (see Appendix 8). Where appropriate, findings were used to make alterations to items and/or scales. Criterion validity was assessed in only one study evaluating the CFQ using the ‘gold standard’ of direct observation as a comparator. 93 Results indicated that fathers (r = 0.33) had a greater interpretation of child’s eating behaviour than mothers (r = 0.15); however, these results are based on a sample size of 46. Convergent validity was more frequently evaluated but was generally restricted to diagnostic measures of eating disorders. Other measures in which convergent validity was assessed included the EES-C77 and the Toddler Snack Food Feeding Questionnaire (TSFFQ). 82 The EES-C was compared with data from the QEWP (Spitzer 199295). Authors reported good convergent validity and show that those with loss of control (LOC) (from QEWP) had higher eating in response to anger, anxiety and frustration and higher depressive symptoms than people without LOC [although results based test for difference (analysis of covariance – ANCOVA), p < 0.05]. Convergent validity for the TSFFQ was weak, with correlations with the CRQ of r = 0.20 (in toddlers) and r = 0.21 (in preschool children) (range = 0.02–0.43). Implications of these findings when the measures are used to assess change is potentially less important and will be based on the choice of comparator measure.

Construct validity was evaluated in 18 manuscripts,72,77–80,82–85,90,91,96–101 comparing eating behaviour measures to weight,72,77,83,84,96–98 weight concerns99,100 and health-related behaviours,77–80,82,85,90,91,101 with correlations ranging from 0.03 to 0.59. Of those making comparisons to weight or weight status, correlations were weak: r = 0.13 (DEBQ-C83); r = 0.14 (CFQ96); r = 0.28 [Children’s Eating Attitudes Test (ChEAT)101]; and r = 0.07 (un-named measure of control in parental feeding practices84). Higher correlations were seen with weight concerns: r = 0.59 for the Youth Eating Disorder Examination-Questionnaire (YEDE-Q)99 and r = 0.4 for a further study evaluating ChEAT. 100

Physical activity

A total of 45 studies (within 35 manuscripts), describing 24 PA measures, were extracted. A summary of all of these studies is included in Appendix 9. Of these, two did not fully meet eligibility criteria (pathway 4) but were deemed relevant by experts during the subsequent external appraisal process (see Chapter 4, Results of expert appraisal)102,103 and have been added retrospectively.

Objective PA measures included pedometers, accelerometers, monitors and direct observations. Objective methods are considered optimal for quantification of PA and are advantageous over subjective methods through avoidance of reporting bias. 104 Criterion validity was assessed in 12 of the objective studies105–114 (with correlations ranging from r = 0.47 to r = 0.82). Four out of the five studies evaluating accelerometers measured criterion validity (compared with direct observation105,106); VO₂ (oxygen uptake);107 or heart rate (HR);108 with strong correlations observed for all (range: r = 0.71–0.86). Criterion validity of pedometers was also common with a range in correlations between r = 0.47 and r = 0.85 in studies comparing steps to accelerometers112–114 and direct observation. 109 One study, assessing per cent error, however, found that a high degree of error indicated under-reporting. 110 Authors commented on the range in validity of measures relating to the type of equipment used; for example, in the assessment of criterion validity of a SenseWear band (BodyMedia^® SenseWear, Pittsburgh, PA, USA),111 they reported the greatest validity with one specific model (SWA5.1) when comparing against DLW. Convergent validity was evaluated in three studies evaluating objective measures with moderate findings: accelerometers compared with Actiwatch (Actigraph^®, Pensacola, FL, USA) data r = 0.36;105 accelerometers compared with activity diaries r = 0.38,108 and SenseWear model SWA5.1 compared with the SWA6.1 model [showing statistically greater estimates of metabolic equivalents (METs) in boys than girls with the SWA5.1 model than in those with the SWA6.1 model]. 111

With regards to external reliability of objective measures, four studies conducted TRT reliability. 102,110,112,113 With pedometers, one study112 reported high validity (r = 0.77) but another113 found very poor reliability (r = 0.08). Another evaluation of pedometer TRT reliability reported a mean difference of 10% between measures. 110 The last of the four objective measures evaluating TRT reliability was the System for Observing Children’s Activity and Relationships during Play (SOCARP). 102 Findings from this study report per cent agreement ranging from 85% to 93%. Inter-rater reliability analysis was conducted in evaluation of the two direct observation tools. Both reported high levels of reliability with comparison between observers: (r = 0.96, κ = 0.8784) (89% agreement102).

The remaining measures were subjective (questionnaires, recalls, diaries, etc.). Twenty-one of the manuscripts describing subjective PA measures reported evaluating criterion validity, with a resulting range in correlation from r = 0.04 for correlations between the Children’s Leisure Activities Study Survey (CLASS) and accelerometery115 and r = 0.53 for correlations between the Previous Day Physical Activity Recall and accelerometers. 116 Overall, criterion validity was lower than that observed by objective measures, with 11 of these studies obtaining correlations of less than the adequate standard of 0.4, and some findings dependent on weight status of the children. 117 Convergent validity was assessed in 11 of the subjective studies118–125 and correlations were slightly higher (r = 0.22–0.88) but most were compared against other subjective methods and thus the high correlations may not necessarily suggest a robust instrument (i.e. it could be interpreted as the tools being equally as poor) (see Appendix 7). Construct validity of self-reported measures was poor118,119,126,127 (correlations ranging from r = 0.07 to r = 0.33). Of these, two studies failed to report findings for non-significant correlations and thus the lower end of the range may be less. 126 Two construct validity studies made comparisons with body weight/weight status. 126,127 Goran et al. 127 report correlations of 0.24 and 0.33 for findings from two substudies comparing the Physical Activity Questionnaire (PAQ) for Pima Indians with fat mass data from bioimpedance measurement. Moore et al. 126 also report low correlations of r = 0.10 for comparisons between the Physical Activity Questionnaire for Older Children (PAQ-C) and percentage body fat, which were also assessed by bioimpedance.

Reliability results of subjective PA measures were generally better than validity findings. Six studies126,128,129 reported IC, with a range of alpha values of between 0.66 and 0.84 (with just one reporting alpha values of < 0.70126). Results of TRT reliability (conducted in 14 studies) were more variable, with correlations ranging from r = 0.24 (for child-reported activity in CLASS115 and 0.98 (for the Previous Day Physical Activity Recall120). One study128 also reported a generalisability coefficient of 0.88. Inter-rater reliability was evaluated by five studies. 102,103,115,120,130 Again, results are highly variable with correlations as high as r = 0.99 for inter-rater reliability of the Previous Day Physical Activity Recall120 and as low as r = 0.19 for reliability of CLASS. 115 Results for inter-rater reliability evaluation may be dependent on the type of activity been assessed. For example, Telford et al. 115 reported a strong agreement of 87.5% for assessment of soccer, but just 8% agreement for tennis. This type of evaluation may also be dependent on obesity status. 130

Sedentary behaviour/time

A total of five manuscripts,130–134 describing six measures of sedentary time/behaviour met the eligibility criteria for CoOR (see Appendix 10).

Of the six measures, three were measures of sedentary time (i.e. time spent being inactive)131,132 using activity monitors. The remaining three133–135 assessed sedentary behaviours (i.e. frequency or duration spent doing specific low-energy behaviours such as screen time). Measurement of sedentary time in the included studies was by objective measurements compared with those assessing sedentary behaviour, which were all self-reported.

Studies by Reilly et al. 131 and Puyau et al. 132 (Study 1) both assessed criterion validity of accelerometers for the measurement of sedentary time using direct observations and room calorimetry, respectively. Both report high validity. Sample sizes for these were low (52 for Reilly et al. 131 and 26 for Puyau et al. 132) but not unusual given the type of measurements used for criterion assessment. Puyau et al. 132 also assessed convergent validity of the accelerometer against another monitor; the Mini-Mitter Actiwatch monitor, with an average correlation of r = 0.86 (range = 0.82–0.89). A further study (reported in the same paper) by Puyau et al. 132 (Study 2) also evaluated the Mini-Mitter Actiwatch monitor for criterion validity using room calorimetry and reported a mean correlation between activity and energy expenditure of r = 0.79 (range = 0.82–0.89). Other criterion methods of HR monitoring and microwave activity were also used for both the accelerometers and Actiwatch, with good overall findings (r = 0.57–0.72 for accelerometers and r = 0.66–0.83 for the Actiwatch).

Measures of sedentary behaviour also assessed criterion validity,133–135 although comparison was not made against direct observation or measured energy expenditure. Ridley et al. 133 made comparisons between the Multimedia Activity Recall for Children and Adolescents and accelerometry, and reported an overall correlation of r = 0.39 (range = 0.35–0.45). Dunton et al. 134 also used a criterion of accelerometry in their evaluation of the Electronic Momentary Assessment (EMA): a self-report survey on mobile phones – a method by which behaviours are captured in real time by use of mobile phones. Results indicate that the number of steps taken was significantly higher for the EMA surveys reporting active play, sports or exercise than any other type of activity [adjusted Wald test: F = 22.16, degrees of freedom (df) = 8, p < 0.001]. Epstein et al. 135 also evaluated criterion validity of a measure of Habit books with index cards against a criterion of accelerometers and report correlations of r = 0.63 (for average METs) and r = 0.60 [for per cent time in moderate to vigorous physical activity (MVPA)]. This study was not the primary aim of the manuscript (which reported trial evaluation results) and was conducted with only 41 participants. TRT reliability was evaluated in only one study133 finding high correlations (r = 0.92), although it was also conducted in a small sample of 32 children and adolescents.

Fitness

A total of 14 manuscripts136–149 were identified that described 13 fitness outcome measures. A summary of the data extracted for these studies is provided in Appendix 11.

The majority (12) of measures described in the included manuscripts assessed aerobic capacity (defined as the maximal amount of physiological work that an individual can do measured by oxygen use). Two136,137 assessed general fitness, of which one,137 ‘Fitnessgram^®’, also includes measurement of aerobic capacity, in addition to measures of muscular strength; muscular endurance and flexibility; and body composition. This measure was designed as an educational assessment tool for school populations (i.e. it was not designed for obesity research). However, it has been used an outcome, which is why it met inclusion criteria here.

Seven included measures136–142 determined TRT reliability, with correlation results ranging from r = 0.65–0.91, kappa statistics ranging from κ = 0.59–0.81 and per cent agreement ranging from 88% to 91%. Thus, all demonstrated at least moderate TRT results, indicating that they can be reliability assessed over multiple time periods.

Inter-rater reliability was evaluated in the Fitnessgram study,137 which compared teacher with expert agreement in recording children’s fitness scores. Results in agreement (84–87%) and kappa statistics (0.67–0.73) identified adequate robustness of results.

Criterion validity was assessed in 10 studies (r = 0.03–0.81) in which comparisons were made with measures against a gold standard of measured oxygen consumption [via VO_2max (maximum oxygen uptake to the point in which oxygen demands plateau) or VO_2peak (highest value of oxygen uptake from a particular test which is limited by tolerance level)]. 138–140,143–149 Of these, four had a sample size of < 50. 144–146,149 In the remaining six studies138–140,143,147,148 that measured criterion validity, two were evaluations of the 20-m shuttle run;139,140 one assessed basal metabolic mass estimates with fat-free mass;143 one assessed the 6-minute walk test; 139 one, the adjustable height step test;149 and one, bioelectrical impedance-derived VO_2max148 Correlations for these were mostly moderate but ranged between r = 0.03147 and 0.81. 148 One study140 reported higher validity in obese children (based on stratified analysis of 126 children). Conversely, Roberts et al. 147 assessed bioelectrical impedance-derived VO_2max and reported a weight-dependent correlation with measured VO_2max (VO_2max ml/kg/minute) of r = 0.03. Non-weight-dependent correlations (VO_2max l/minute) in this sample of 134 obese and overweight adolescents were considerably higher at r = 0.48. Thus, although the majority of studies report moderate to high levels of criterion validity, results are varied, with some dependent on weight status and also some conducting analysis on small samples.

Convergent validity was assessed by two studies. 136,141 Of these Loften et al. 141 compared different modes of calculating measured VO_2peak from either cycle or treadmill, thus it is a rare study within those identified by CoOR that evaluated the actual gold standard measure. Comparisons between the two approaches reported correlations ranging from r = 0.48 to r = 0.77. Tests for differences were all non-significant (p > 0.05) but the validation study was conducted in only 21 overweight/obese children/adolescents. This study also demonstrated strong TRT correlations – again, in a small sample size. Overall findings indicated that the cycle performed marginally better than the treadmill. However, importantly, children reported higher acceptability of the cycle than the treadmill. The other study assessing convergent validity was a comparison between the International Fitness Scale (IFIS)136 and what was described as ‘measured fitness’. However, the measure of fitness was based on a 20-m shuttle run (i.e. not measured VO_2max or VO_2peak), and has therefore been considered as a convergent validity (not criterion) by CoOR. Authors reported significant positive linear relationships with increased self-report and ‘measured’ fitness. This study also collected a number of cardiovascular outcomes as a means to test construct validity of the IFIS. Findings suggest that obesity was negatively associated with levels of fitness in the IFIS, except for measurement of muscular strength.

Construct validity was assessed in one further paper evaluating aerobic cycling power with insulin and reported a correlation of r = 0.37. 146 Similar to many other evaluations of fitness measurement, assessment was conducted in only a small sample of 35 obese adolescents.

No fitness measures conducted a formal assessment of the ability to measure change (responsiveness).

Physiology

A total of 28 papers, describing 12 outcome measures, met inclusion criteria for the physiology domain. A summary of all papers extracted are available in Appendix 12. Two included manuscripts were written in languages other than English. 150,151 Data were partially extracted from each of these, which is included in Appendix 12 for reference. However, appraisal of these was not conducted (one150 describes evaluation of ‘indices of insulin sensitivity’, which is evaluated in multiple other included manuscripts).

Of the 28 included manuscripts, the majority described the evaluation of insulin and/or glucose150,152–165 or energy expenditure or metabolic rate. 166–173 Of those assessing criterion validity of measures of insulin or glucose, six made comparisons with the gold standard of the euglycaemic–hyperinsulinaemic clamp (EHC) test152,154,156–158,162 reporting correlations ranging from r = 0.4–0.78 for varying indices of insulin sensitivity in sample sizes ranging from 31156,157 to 323. 162 Criterion validity was evaluated in all of the studies evaluating energy expenditure/metabolic rate, by making comparisons with measures such as direct and indirect calorimetry, but none used the gold standard of DLW. Moderate to high correlations were generally reported, but the primary focus of these studies was usually the development or comparisons of equations used to predict energy expenditure in obese children and adolescents. Except for one study,173 sample sizes were high (with 12 studies154,158,159,162,166–171 including samples of > 100).

Convergent validity was assessed in four studies,152,155,161,174 of which three compared insulin with blood lipids,152 glucose tolerance155 and fasting insulin,161 and one examined relationships between glycated haemoglobin (HbA_1c) and fasting glucose. 174 Findings for convergent validity of indices of insulin sensitivity were generally high, with correlations ranging between r = 0.60 and r = 0.81 for insulin. Convergent validity for HbA_1c used accuracy testing [receiver operating area under the curve (AUC)], which reported a range of 0.60–0.81 in AUC in 1156 obese adolescents. However, results were influenced by weight status. This study also evaluated the relationship between HbA_1c and diabetic status, and demonstrated poor validity with this construct [κ = 0.2 (95% confidence interval 0.14 to 0.26)]. One further study175 evaluated construct validity in its assessment of ghrelin in 100 obese children. Results suggest that ghrelin is statistically associated with obesity and cardiovascular outcomes, although correlations are generally weak (ranging from r = 0.1 to r = 0.5). This study also reported ghrelin pre and post intervention. Tests indicate that it is able to detect change but that changing values levelled off after a period (advocating testing immediately post intervention if used). One other study170 that met criteria for inclusion to CoOR reported measuring the ability of the measure to detect change. This study170 evaluated predicted resting energy expenditure and reported a mean difference of 7.45% in resting energy expenditure after weight loss. Prediction equations for resting energy expenditure in this study170 involved inclusion of fat-free mass. As weight loss is associated with change in fat-free mass, the authors advocate assessment to be made only during periods of weight stability.

Only 2176,177 out of the 26 included studies conducted reliability testing. TRT reliability was evaluated by Libman et al. 176 in a study that compared measurement of glucose via fasting and 2-hour samples in 60 overweight/obese adolescents. Results indicated that fasting glucose (r = 0.73) had higher reliability than 2-hour glucose (r = 0.37) testing. Inter-rater reliability was assessed in one other study177 comparing radiologists working in three ultrasound units. This study177 reported high correlations between radiologists (κ ≥ 0.8) in ultrasound analysis of liver echogenicity, although the sample size was small (n = 11).

Economic evaluation

The original aim of the CoOR study was to include measures of economic evaluation as one of its outcome domains. However, review of identified manuscripts failed to find any manuscripts that described the development or evaluation of measures used that can assess utility and therefore estimate quality-adjusted life-years (QALYs). The National Institute for Health and Care Excellence (NICE) advocates the conduct of cost–utility analysis using utility measures (with the QALY as the health-related outcome measure for economic evaluation). No such measures were found for use in an obese childhood or adolescent population in this review, although the team are aware of some that are currently under development. Given that the existing CoOR review strategy did not include terms related to measurement of QALYs, a separate ‘scoping’ search was conducted. A copy of this search can be found in Appendix 16. This did not reveal any further appropriate measures of utility. Alternative measures of cost-effectiveness could be considered (although would not fit within NICE guidance), but assessment of cost (of intervention) per unit of weight loss is usually preferred (i.e. those described in the anthropometry domain). HRQoL measures are often used, but CoOR has viewed these as a separate domain, given that they cannot be used to estimate QALYs. Unless the research is focused on quality of life/psychological well-being, such measures are not essential. As measures of HRQoL were identified in the CoOR review from those already used as outcome measures and those that have specifically developed for childhood obesity research, a further domain of HRQoL has been included. This was possible, as the CoOR search did not include specific search terms relative to each outcome domain (i.e. it was designed to be sensitive enough to detect any kind of outcome measure).

Health-related quality of life

A total of 25 papers describing 16 measures were extracted for the HRQoL domain. Of these, four were written in languages other than English,15,178–180 which describe measures that have not been evaluated by any other included manuscript. Data have been extracted for three of these. 178–180 All have been included within the summary table in Appendix 13 but were not eligible for appraisal.

Seven HRQoL measures were developed specifically for use in a paediatric obese population: (1) Impact of Weight on Quality of Life (IWQoL);15,181,182 (2) Sizing Me Up;183 (3) Sizing Them Up (a parent-reported version of Sizing Me Up);184 and the Youth Quality-of-Life Instrument-Weight Module;185 plus three German HRQoL measures that were developed specifically for obese children. 178–180 Akin to most of the HRQoL measures, multiple forms of evaluation were conducted on many of these tools. Except for measures described in non-English papers, all assessed IC, reporting alphas ranging from 0.74184 to 0.92. 181,185 All report using FA to develop or refine the questionnaires, and all assess convergent validity by comparing against other questionnaires aimed at assessing similar constructs. Comparisons with the Paediatric Quality of Life questionnaire were made with three of the weight specific measures,181,183,184 of which the highest correlations were reported with the IWQoL questionnaire (r = 0.75). 181 Comparisons between Sizing Them Up and the IWQoL questionnaire reported weaker correlations of r = 0.27. 184 Additionally, TRT reliability was conducted on each measure, with each demonstrating at least moderate to high reliability (ranging from r = 0.67 to r = 0.82), although sample sizes were low for two studies. 182,185 Given that these measures were developed specifically for obese children, correlations with BMI (i.e. construct validity) were surprisingly lower than in other forms of validity, ranging from r = 0.16 for Sizing Me Up184 to r = 0.44 for the Youth Quality-of-Life Instrument-Weight Module. 181 Finally, two measures – weight specific – evaluated responsiveness. These were the only studies assessing responsiveness of all included HRQoL measures in CoOR. In evaluation of 80 children and adolescents, Kolotkin et al. 182 report a standardised response mean (SRM) of 13.43 [effect size (ES) of 0.75]. A smaller, but significant, SRM of −5.4 was reported in responsiveness testing of Sizing Me Up in 220 obese children and adolescents,184 both well within acceptable (moderate) levels (described by CoOR as having a SRM of > 0.5) to support their ability to assess change.

Of the remaining studies assessing generic HRQoL measures, a similar level of evaluation was conducted, with many reporting findings from multiple types of evaluation. Average IC findings were high in each of the nine studies186–194 conducting this evaluation, with a range of r = 0.72186 to 0.86187 in those that presented ‘means’ (and not only ranges). In fact, all of the included measures demonstrated a reasonably high level of reliability and validity, with some variability in findings of convergent validity (see Appendix 13). Two measures may be considered redundant, given that newer (or more appropriate) versions are now available. For example, the Paediatric Cancer Quality of Life measure188,195 would be less appropriate than a non-cancer version in the evaluation of childhood obesity treatments. Additionally, an older version (V1.0) of the Paediatric Quality of Life questionnaire191 can be substituted for newer versions. 190,191,196

Psychological well-being

A total of 20 papers, describing 17 measures were eligible for data extraction. A summary of all papers extracted are available in Appendix 14.

Given the nature of these self-reported survey questionnaires, assessment of criterion validity was not anticipated, where ‘gold standard’ measures are unlikely. Some authors reported conducting criterion validity, which was defined as ‘construct’ validity by CoOR (e.g. comparisons with body weight). One study,197 however, did make comparisons between self-report and direct observations in their evaluation of the Self-Control Rating Scale (SCRS).

Eight of the included psychological well-being studies included evaluation of convergent validity,197–204 each making comparisons against different psychological measures of differing constructs (often comparing with more than one other measure). Comparisons of the correlations between these is therefore limited, however, with a range of between r = 0.06 in the evaluation of convergent validity of the SCRS against the Delay of Gratification scale197 and r = 0.66 in the evaluation of the Body Esteem Scale against the Piers–Harris Children’s Self-Concept Scale. 204 Three studies evaluating convergent validity reported results with a correlation of < 0.40 for all of the included comparator measures. 197,198,202

Construct validity was assessed in nine studies. Six of these made comparisons to weight or weight status in children,198,200,204–207 of which findings varied between r = 0.07 (comparing the Body Shape Questionnaire to WHR206 to r = 0.55 (comparing the Body Esteem Scale to weight204). Stein et al. 207 report significant differences in scores for the Children’s Physical Self-Concept Scale (CPSS) between normal weight and overweight children (F = 33.91, p < 0.001). Percentage agreement of 90.5% (in obese children) was also reported by Probst et al. 208 for comparisons between the video distortion measure and BMI.

Test–retest reliability was conducted in 12 studies, with correlations ranging from r = 0.52 to r = 0.91. 195,197,199,201,205–211 Thus, all met the criteria (r > 0.4), suggesting that psychological well-being measures have strong TRT reliability.

Responsiveness testing was not reported in any of the studies evaluating psychometric well-being that were identified by the CoOR review.

Environment

A total of nine manuscripts,212–219 described 10 measures of the environment, met eligibility criteria for the environment domain. A summary of all papers extracted are available in Appendix 15. Two environmental measures assessed child-care environments212,213 and seven measured home physical and/or social constructs within the home environment. 214–219 A further was a measure capturing ‘perception’ of the built environment. 220

Reliability testing in the form of IC was implemented in six studies,215–218,220 all of which demonstrated high levels of internal reliability (α = 0.75–0.83). Similarly robust results for TRT reliability were evident in one measure of child-care settings212 and seven measures of the home environment,214,215,217–220 with mean correlations ranging from r = 0.59 of the home PA equipment scale219 to r = 0.85 of the Family Eating and Activity Habits Questionnaire215 (FEAHQ) (with mean κ = 0.57–0.66). Results for inter-rater reliability testing in six studies212,213,215,218,219 were also strong (r = 0.47–0.88). Thus, the outcome domain of environmental measures demonstrates high levels of multiple indicators of reliability, with no studies performing no form of reliability.

Internal validity was assessed in two studies,216,220 with total variance ranging from 7% to 47%, and factor loadings ranging from 0.31 to 0.88, of which one study220 reported all loadings to be above the acceptable limit of 0.40. However, providing that necessary amendments are made to questionnaires, this should not preclude the use of measures in which some factor loadings are low. Criterion validity was evaluated in two studies212,213 in which the gold standard method was direct observations by researchers. Benjamin et al. 212 evaluated criterion validity of their child-care setting measure, the Nutrition and Physical Activity Self-Assessment for Child Care (NAPSACC), by comparing items to researcher-measured items reported in the Environment and Policy Assessment and Observation (EPAO)213 also included in the CoOR review. Results were variable by item, with kappa ranging from 0.11 to 0.79 (mean κ = 0.37). The comparator gold standard method by Ward et al. 213 (EPAO) conducted a study of inter-rater reliability and reported moderate to high correlations (although also variable by item) (r = 0.63, range = 0.05–1.0). Bryant et al. 214 compared a parent report home environment measure, the ‘Healthy Home Survey’ (HHS) to researcher-conducted survey completion in the home and also reported variable findings, with a range in correlations of r = 0.3 to r = 0.88 (mean r = 0.62) and a range in kappa of 0–0.96 (mean κ = 0.55). This measure appears to be robust, along with strong findings for TRT reliability (r = 0.72, κ = 0.66); however, authors report concern related to the collection of some open-response items (e.g. food availability in the home) and are currently working on a new version – ‘HomeSTEAD’.

Convergent validity was assessed in only one study,216 in which the Parenting Strategies for Eating and Activity Scale (PEAS) was compared with data from the CFQ. 62 Findings were low with a mean correlation of r = 0.22 (range r = 0.02–0.65) in 91 children. Construct validity was evaluated in six studies. 216–220 Of these, four studies216,217,219 assessed correlations with BMI or obesity. Findings are difficult to compare because of inconsistencies in the analytical approaches used. Larios et al. 216 reported very weak correlations between PEAS and BMI z-score in a sample of 714 children (r = 0.03, range = 0.03–0.21). McCurdy et al. 217 conducted independent samples t-tests to determine whether scores on the Family Food Behaviour Survey (FFBS) varied by child weight status, and found that overweight was related to increased maternal control (p = 0.052) and that children were more likely to be of normal weight if there was increased maternal presence at meal and snack times (p = 0.01). Sample size for this study, was small, however, with only 28 children included. Both studies by Rosenburg219 to assess two brief scales that measure PA and sedentary equipment in the home assessed correlations with BMI z-score using linear regression models. Findings suggest that the electronic equipment scale (specifically, having a television in the bedroom) was significantly and positively associated with BMI z-score.

Responsiveness was assessed in one study, in which Golan et al. 215 reported the ability of the FEAHQ to detect change following a weight loss intervention. Change in child body weight was found to be associated to change in scores from the ‘exposure’ and ‘eating style’ scales of the questionnaire in both intervention and the control, with the change in score explaining 27% variance in weight reduction.

Chapter 5 Discussion

How to use the Childhood obesity Outcomes Review outcome measures framework

Figure 7 provides guidance as to how the CoOR outcome measures framework should be used by researchers. Importantly, researchers need to first consider which (if any) secondary outcome domains are most closely aligned to the targets of the intervention under investigation, including those that are expected to change, those that are expected to mediate this change (if appropriate), and any that may indicate an adverse event (if appropriate). Researchers are then advised to view the recommended outcome measures within each of the chosen outcome domains in the framework. Any selected measure needs to be aligned to the intervention targets, developed for use in a similar population and feasible to implement. In deciding the similarity between populations, validation of a measure is relevant to only really the population in which it was evaluated. However, given that is unlikely that there will be a tool that has been developed for use, and evaluated within all populations, researchers should make informed decisions regarding whether the characteristics of their populations are sufficiently close to the population in which the tool was developed. For example, it would not be advisable to use a tool that was developed within a white middle-class population of America in a South Asian lower-class population of the UK. Similarly, tools that were developed to be self-completed by adolescents are unlikely to be relevant for completion by parents.

FIGURE 7.

Using the CoOR outcome measures framework.

Further details on each measure within the framework can be accessed from the CoOR summary tables (see Appendices 6–15). If a measure that fulfils these criteria does not exist then the researcher may also choose to locate an alternative measure from the summary tables, with the caveat that these were not recommended for inclusion to the framework.

Chapter 6 Conclusions

The CoOR outcome measures framework provides clear guidance to researchers regarding recommended measures for use in their evaluations of childhood obesity treatment interventions. This should encourage a greater adoption of well-validated tools and ensure comparability between different studies or treatment interventions. Details of the validity of each of the recommended outcome tools provide an evidence base on which to base more accurate reporting of these measures in future studies. In addition, further details of other measures that may be appropriate for other settings are provided to inform decision-making.

It is recommended that further research should be conducted in the development and evaluation of preference-based measures for cost–utility analysis in line with NICE guidance. The CoOR team are aware of some measures currently being developed. Further research is also recommended to ascertain responsiveness of the recommended measures. This would be possible to conduct as part of future trials of childhood obesity treatments. Ascertainment of a MID is also recommended and should be based on consensus by clinical and academic experts and by children and their parents. Finally, there is also a lack of consistency within measures used in the evaluation of treatment of obesity in adults, and it is suggested that similar work to CoOR is conducted to fill this gap in evidence.

Acknowledgements

The CoOR team are extremely grateful to the following expert collaborators who gave their time and expertise in deciding which outcome measures to recommend for inclusion to the CoOR outcome measures framework:

• Professor John Reilly, Professor of Paediatric Energy Metabolism, Royal Hospital for Sick Children, Glasgow.

• Professor Ashley Cooper, Professor of Exercise and Health Science, University of Bristol.

• Professor Paul Kind, Honorary Professor of Economics, University of Leeds.

• Professor Carolyn Summerbell, Professor of Human Nutrition, School of Medicine & Health, and Fellow of the Wolfson Research Institute, Durham University Queen’s Campus.

• Professor Julian Hamilton-Shield, Professor in Diabetes and Metabolic Endocrinology, University of Bristol.

• Professor Ulf Ekelund, Professor of Physical Activity and Public Health, MRC Epidemiology Unit, Cambridge.

• Professor Andrew Hill, Professor of Medical Psychology, University of Leeds.

• Dr Lucy Griffiths, Senior Research Fellow at the Institute of Child Health, University College London.

• Professor Steven Cummings, Professor of Population Health & National Institute for Health Research Senior Fellow, London School of Hygiene and Tropical Medicine.

• Dr Claudia Gorecki, Research Fellow in Psychometrics, University of Leeds.

We are also very grateful to colleagues, at the University of Leeds, who volunteered to extract data from manuscripts written in languages other than English, including Elizabeth Mawer (Clinical Trials Research Unit), Ge Yu (Institute of Health Sciences), Roberta Longo (Institute of Health Sciences) and Sandy Tubeuf (Institute of Health Sciences).

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Appendix 1 Search 1 search strategy

Appendix 2 Search 2 search strategy

Appendix 3 Search 1 references (included childhood obesity treatment trials)

The following list of references includes eligible search 1 trials, from which citations of outcome measures used were obtained.

1. Adamo KB, Rutherford JA, Goldfield GS. Effects of interactive video game cycling on overweight and obese adolescent health. Appl Physiol Nutr Metab 2010;35:805–15.

2. Albala C, Ebbeling CB, Cifuentes M, Lera L, Bustos N, Ludwig DS. Effects of replacing the habitual consumption of sugar-sweetened beverages with milk in Chilean children. Am J Clin Nutr 2008;88:605–11.

3. Andelman MB, Jones C, Nathan S. Treatment of obesity in underprivileged adolescents. Comparison of diethylpropion hydrochloride with placebo in a double-blind study. Clin Pediatr (Phila) 1967;6:327–30.

4. Aragona J, Cassady J, Drabman RS. Treating overweight children through parental training and contingency contracting. J Appl Behav Anal 1975;8:269–78.

5. Atabek ME, Pirgon O. Use of metformin in obese adolescents with hyperinsulinemia: a 6-month, randomized, double-blind, placebo-controlled clinical trial. J Pediatr Endocrinol 2008;21:339–48.

6. Bacon GE, Lowrey GH. A clinical trial of fenfluramine in obese children. Curr Ther Res Clin Exp 1967;9:626–30.

7. Barkin SL, Gesell SB, Poe EK, Ip EH. Changing overweight Latino preadolescent body mass index: the effect of the parent–child dyad. Clin Pediatr (Phila) 2011;50:29–36.

8. Bathrellou E, Yannakoulia M, Papanikolaou K, Pehlivanidis A, Pervanidou P, Kanaka-Gantenbein C, et al. Parental involvement does not augment the effectiveness of an intense behavioral program for the treatment of childhood obesity. Hormones 2010;9:171–5.

9. Bauer S, de Niet J, Timman R, Kordy H. Enhancement of care through self-monitoring and tailored feedback via text messaging and their use in the treatment of childhood overweight. Patient Educ Couns 2010;79:315–19.

10. Bean MK, Mazzeo SE, Stern M, Bowen D, Ingersoll K. A values-based Motivational Interviewing (MI) intervention for pediatric obesity: study design and methods for MI values. Contemp Clin Trials 2011;32:667–74.

11. Berkowitz RI, Fujioka K, Daniels SR, Hoppin AG, Owen S, Perry AC, et al. Effects of sibutramine treatment in obese adolescents: a randomized trial. Ann Intern Med 2006;145:81–90.

12. Berkowitz RI, Wadden TA, Gehrman CA, Bishop-Gilyard CT, Moore RH, Womble LG, et al. Meal replacements in the treatment of adolescent obesity: a randomized controlled trial. Obesity (Silver Spring) 2011;19:1193–9.

13. Berkowitz RI, Wadden TA, Tershakovec AM, Cronquist JL. Behavior therapy and sibutramine for the treatment of adolescent obesity: a randomized controlled trial. JAMA 2003;289:1805–12.

14. Berry D, Savoye M, Melkus G, Grey M. An intervention for multiethnic obese parents and overweight children. Appl Nurs Res 2007;20:63–71.

15. Boutelle KN, Cafri G, Crow SJ. Parent-only treatment for childhood obesity: A randomized controlled trial. Obesity (Silver Spring) 2011;19:574–80.

16. Bravender T, Russell A, Chung RJ, Armstrong SC. A ‘novel’ intervention: a pilot study of children’s literature and healthy lifestyles. Pediatrics 2010;125:e513–17.

17. Brownell KD, Kelman JH, Stunkard AJ. Treatment of obese children with and without their mothers: changes in weight and blood pressure. Pediatrics 1983;71:515–23.

18. Burgert TS, Duran EJ, Goldberg-Gell R, Dziura J, Yeckel CW, Katz S, et al. Short-term metabolic and cardiovascular effects of metformin in markedly obese adolescents with normal glucose tolerance. Pediatr Diabetes 2008;9:567–76.

19. Carrel AL, Clark RR, Peterson SE, Nemeth BA, Sullivan J, Allen DB. Improvement of fitness, body composition, and insulin sensitivity in overweight children in a school-based exercise program: a randomized, controlled study. Arch Pediatr Adolesc Med 2005;159:963–8.

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21. Chang C, Liu W, Zhao X, Li S, Yu C. Effect of supervised exercise intervention on metabolic risk factors and physical fitness in Chinese obese children in early puberty. Obes Rev 2008;9(Suppl. 1):135–41.

22. Chanoine JP, Hampl S, Jensen C, Boldrin M, Hauptman J. Effect of orlistat on weight and body composition in obese adolescents: a randomized controlled trial. JAMA 2005;293:2873–83.

23. Clarson CL, Mahmud FH, Baker JE, Clark HE, McKay WM, Schauteet VD, et al. Metformin in combination with structured lifestyle intervention improved body mass index in obese adolescents, but did not improve insulin resistance. Endocrine 2009;36:141–6.

24. Coates TJ, Jeffery RW, Slinkard LA, Killen J, Danaher BG. Frequency of contact and monetary reward in weight loss, lipid change, and blood pressure reduction with adolescents. Behav Ther 1982;13:175–85. URL: www.mrw.interscience.wiley.com/cochrane/clcentral/articles/912/CN-00183912/frame.html.

25. Coppins DF, Margetts BM, Fa JL, Brown M, Garrett F, Huelin S. Effectiveness of a multi-disciplinary family-based programme for treating childhood obesity (The Family Project). Eur J Clin Nutr 2011;65:903–9.

26. Daniels SR, Long B, Crow S, Styne D, Sothern M, Vargas-Rodriguez I, et al. Cardiovascular effects of sibutramine in the treatment of obese adolescents: results of a randomized, double-blind, placebo-controlled study. Pediatrics 2007;120:e147–57.

27. Danielsson P, Janson A, Norgren S, Marcus C. Impact sibutramine therapy in children with hypothalamic obesity or obesity with aggravating syndromes. J Clin Endocrinol Metab 2007;92:4101–6.

28. Davis AM, James RL, Boles RE, Goetz JR, Belmont J, Malone B. The use of TeleMedicine in the treatment of paediatric obesity: feasibility and acceptability. Matern Child Nutr 2011;7:71–9.

29. Davis JN, Tung A, Chak SS, Ventura EE, Byrd-Williams CE, Alexander KE, et al. Aerobic and strength training reduces adiposity in overweight Latina adolescents. Med Sci Sports Exerc 2009;41:1494–503.

30. Demol S, Yackobovitch-Gavan M, Shalitin S, Nagelberg N, Gillon-Keren M, Phillip M. Low-carbohydrate (low and high-fat) versus high-carbohydrate low-fat diets in the treatment of obesity in adolescents. Acta Paediatr 2009;98:346–51.

31. Diaz RG, Esparza-Romero J, Moya-Camarena SY, Robles-Sardin AE, Valencia ME. Lifestyle intervention in primary care settings improves obesity parameters among Mexican youth. J Am Diet Assoc 2010;110:285–90.

32. Doyle AC, Goldschmidt A, Huang C, Winzelberg AJ, Taylor CB, Wilfley DE. Reduction of overweight and eating disorder symptoms via the Internet in adolescents: a randomized controlled trial. J Adolesc Health 2008;43:172–9.

33. Duckworth LC, Gately PJ, Radley D, Cooke CB, King RF, Hill AJ. RCT of a high-protein diet on hunger motivation and weight-loss in obese children: an extension and replication. Obesity (Silver Spring) 2009;17:1808–10. URL: www.mrw.interscience.wiley.com/cochrane/clcentral/articles/987/CN-00718987/frame.html

34. Duffy G, Spence SH. The effectiveness of cognitive self-management as an adjunct to a behavioural intervention for childhood obesity: a research note. J Child Psychol Psychiatry 1993;34:1043–50.

35. Duggins M, Cherven P, Carrithers J, Messamore J, Harvey A. Impact of family YMCA membership on childhood obesity: a randomized controlled effectiveness trial. J Am Board Fam Med 2010;23:323–33.

36. Dunshea-Mooij C, Wall C, King C. ‘Games Galore’; a feasibility study to investigate the effect of a physical activity and a nutrition education programme for 10–14 year old New Zealand overweight and obese children. Proc Nutr Soc New Zeal 2003;28:71–4.

37. Ebbeling CB, Leidig MM, Sinclair KB, Hangen JP, Ludwig DS. A reduced-glycemic load diet in the treatment of adolescent obesity. Arch Pediatr Adolesc Med 2003;157:773–9.

38. Edwards C, Nicholls D, Croker H, Van Zyl S, Viner R, Wardle J. Family-based behavioural treatment of obesity: acceptability and effectiveness in the UK. Eur J Clin Nutr 2006;60:587–92.

39. Elloumi M, Makni E, Ounis OB, Zbidi A, Lac G, Tabka Z. Six-minute walking test to assess exercise tolerance in Tunisian obese adolescents over two-months individualized program training. Sci Sports 2007;22:289–92.

40. Elmahgoub SM, Lambers S, Stegen S, Van Laethem C, Cambier D, Calders P. The influence of combined exercise training on indices of obesity, physical fitness and lipid profile in overweight and obese adolescents with mental retardation. Eur J Pediatr 2009;168:1327–33.

41. Epstein LH, Paluch R, Kilanowski CK, Raynor HA. Effects of family-based behavioral treatment on obese 5-to-8-year-old children. Behav Ther 1985;16:205–12.

42. Epstein LH, McKenzie SJ, Valoski A, Klein KR, Wing RR. Effects of mastery criteria and contingent reinforcement for family-based child weight control. Addict Behav 1994;19:135–45.

43. Epstein LH, Nudelman S, Wing RR. Long-term effects of family-based treatment for obesity on non-treated family members. Behav Ther 1987;18:147–52.

44. Epstein LH, Paluch RA, Beecher MD, Roemmich JN. Increasing healthy eating vs. reducing high energy-dense foods to treat pediatric obesity. Obesity (Silver Spring) 2008;16:318–26.

45. Epstein LH, Paluch RA, Gordy CC, Dorn J. Decreasing sedentary behaviors in treating pediatric obesity. Arch Pediatr Adolesc Med 2000;154:220–6.

46. Epstein LH, Paluch RA, Gordy CC, Saelens BE, Ernst MM. Problem solving in the treatment of childhood obesity. J Consult Clin Psychol 2000;68:717–21.

47. Epstein LH, Paluch RA, Kilanowski CK, Raynor HA. The effect of reinforcement or stimulus control to reduce sedentary behavior in the treatment of pediatric obesity. Health Psychol 2004;23:371–80. URL: www.mrw.interscience.wiley.com/cochrane/clcentral/articles/322/CN-00490322/frame.html.

48. Epstein LH, Paluch RA, Raynor HA. Sex differences in obese children and siblings in family-based obesity treatment. Obes Res 2001;9:746–53.

49. Epstein LH, Roemmich JN, Stein RI, Paluch RA, Kilanowski CK. The challenge of identifying behavioral alternatives to food: clinic and field studies. Ann Behav Med 2005;30:201–9.

50. Epstein LH, Valoski AM, Vara LS, McCurley J, Wisniewski L, Kalarchian MA, et al. Effects of decreasing sedentary behavior and increasing activity on weight change in obese children. Health Psychol 1995;14:109–15.

51. Epstein LH, Wing RR, Koeske R, Andrasik F, Ossip DJ. Child and parent weight loss in family-based behavior modification programs. J Consult Clin Psychol 1981;49:674–85.

52. Epstein LH, Wing RR, Koeske R, Valoski A. Effects of diet plus exercise on weight change in parents and children. J Consult Clin Psychol 1984;52:429–37. URL: www.mrw.interscience.wiley.com/cochrane/clcentral/articles/924/CN-00193924/frame.html

53. Epstein LH, Wing RR, Penner BC, Kress MJ. Effect of diet and controlled exercise on weight loss in obese children. J Pediatr 1985;107:358–61.

54. Estabrooks PA, Shoup JA, Gattshall M, Dandamudi P, Shetterly S, Xu S. Automated telephone counseling for parents of overweight children: a randomized controlled trial. Am J Prev Med 2009;36:35–42.

55. Figueroa-Colon R, Franklin FA, Lee JY, von Almen TK, Suskind RM. Feasibility of a clinic-based hypocaloric dietary intervention implemented in a school setting for obese children. Obes Res 1996;4:419–29.

56. Figueroa-Colon R, von Almen TK, Franklin FA, Schuftan C, Suskind RM. Comparison of two hypocaloric diets in obese children. Am J Dis Child 1993;147:160–6.

57. Flodmark CE, Ohlsson T, Ryden O, Sveger T. Prevention of progression to severe obesity in a group of obese schoolchildren treated with family therapy. Pediatrics 1993;91:880–4.

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60. Garcia-Morales LM, Berber A, Macias-Lara CC, Lucio-Ortiz C, Del-Rio-Navarro BE, Dorantes-Alvarez LM. Use of sibutramine in obese mexican adolescents: a 6-month, randomized, double-blind, placebo-controlled, parallel-group trial. Clin Ther 2006;28:770–82.

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62. Gately PJ, King NA, Greatwood HC, Humphrey LC, Radley D, Cooke CB, et al. Does a high-protein diet improve weight loss in overweight and obese children? Obesity (Silver Spring) 2007;15:1527–34.

63. Ghayour-Mobarhan M, Sahebkar A, Vakili R, Safarian M, Nematy M, Lotfian E, et al. Investigation of the effect of high dairy diet on body mass index and body fat in overweight and obese children. Indian J Pediatr 2009;76:1145–50.

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Appendix 4 Data extraction form for search 1

1. Ref Man ID: ……………

2. Reviewer initials: ……………

3. Authors: …………… [put * next to contact author]

4. Year: ……………

[Unless otherwise stated, tick relevant box(s)]

5. Study design:

6. Type of intervention:

7. Intervention delivered to:

8. Sample size (final):

9. Ethnicity (continents and subcategories):

10. Ethnicity:

11. Sample age:

12. Primary outcome measure:

13. Secondary outcome measures (if more than one type of measure within each outcome, report name of tool and first author for each measure)

14. Comments: ……………

Appendix 5 Data extraction form for search 2: dietary assessment

1a. Ref Man ID: ……………

1b. Manuscript type:

Primary development paper □ Original used and evaluated □ Modified and evaluated □

1c. Category of measurement tool.

• Questionnaires/surveys with scales or categories with pre-defined terms □

• Diaries, recalls, direct observations or monitors with open responses/recall/observation □

• Biochemical or anthropometric measures or assays □

2. Reviewer initials: ……………

3. First author: …………… [put * next to contact author]

4. Year: ……………