Interventions to increase physical activity and healthy eating among overweight and obese children...

Interventions to Increase Physical Activity among HealthyAdults: Meta-Analysis of Outcomes

Vicki S. Conn, PhD, RN, FAAN, Adam R. Hafdahl, PhD, and David R. Mehr, MD, MS

AbstractObjective—This meta-analysis summarized effects of interventions designed to increase physicalactivity among healthy adults.

Methods—Comprehensive searching located 358 reports eligible for inclusion. Random-effectsanalyses were used to synthesize data. Potential moderator variables were examined with meta-analytic analogues of ANOVA and regression. Moderator variable robustness and publication biaswere explored.

Results—Meta-analytic results were computed from studies including 99,011 subjects. Theoverall mean effect size (d) for treatment vs. control groups comparisons was 0.19 (higher meanfor treatment than control subjects). This 0.19 is consistent with a mean difference of 496 steps/day between treatment and control subjects. Exploratory moderator analyses suggested that thecharacteristics of the most effective interventions included behavioral interventions instead ofcognitive interventions, face-to-face delivery versus mediated interventions (e.g. via telephone,mail, etc.), and targeted individuals instead of communities. Participant characteristics wereunrelated to physical activity effect sizes. Substantial between-studies heterogeneity remainedbeyond individual moderators.

Conclusions—These findings suggest that interventions designed to increase activity aremodestly effective. Findings suggest interventions should emphasize behavioral strategies overcognitive strategies to increase physical activity.

Adequate physical activity (PA) is linked with important health outcomes, including reducedcardiovascular disease,1 type 2 diabetes,2–3 some cancers,4–5 falls,6 osteoporotic fractures,7and depression,8 as well as improved physical function,9–11 weight management,12–15

cognitive function,16–17 and quality of life.18 Despite this compelling evidence, healthyadults commonly get inadequate PA.19

Extensive primary research has tested interventions to increase PA. Although many meta-analyses have addressed health outcomes of PA, few have examined PA behavior outcomes.The seminal 1996 meta-analysis of interventions to increase PA behavior reported amoderate effect size across 127 studies of healthy and chronically ill adults and children.20

Their moderator analyses documented larger effect sizes when interventions used behaviormodification, had face-to-face delivery vs. mediated delivery (e.g. telephone), focused on

Corresponding author: Vicki S. Conn, PhD, RN, FAAN, S317 School of Nursing, University of Missouri, Columbia, MO 65211,[email protected], 573 882 0231 (office), 573 884 4544 (fax).Human participant protection: The Institutional Review Board for the Protection of Human Subjects categorized the project asexempt from requiring individual subject consent.Author contributions: Dr. Conn conceived the project, developed methods, supervised study conduct, led interpretation of findings,wrote the first draft of the manuscript, and is the principal investigator of the grant which funded the work. Dr. Hafdahl participated indeveloping the research protocol, analyzed data, facilitated interpretation of findings, and actively participated in manuscriptdevelopment. Dr. Mehr participated in conceptual discussions of project development and design, he also actively participated inmanuscript development and revision.

NIH Public AccessAuthor ManuscriptAm J Public Health. Author manuscript; available in PMC 2012 April 1.

Published in final edited form as:Am J Public Health. 2011 April ; 101(4): 751–758. doi:10.2105/AJPH.2010.194381.

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healthy people, measured active leisure vs. structured exercise, measured low-intensityactivity, encouraged unsupervised PA vs. supervised PA, targeted subjects of diverse ages,and targeted groups vs. individuals. A recent comprehensive meta-analysis of worksiteprograms for healthy adults documented a d effect size (standardized mean difference) of0.21 but did not conduct moderator analyses to determine the intervention characteristicslinked with the largest PA increases.21 Other meta-analyses have integrated acrosschronically ill adults22 or focused on small specific interventions or populations, such asprimary-care-based referrals to PA programs,23 older adults,24 computer-basedinterventions,25 or environmental interventions.26 Many meta-analyses have been plaguedby small samples that hinder moderator analyses.27–28 For example, only 19 studies wereincluded in the most recent Cochrane review which aggregated randomized controlled trialswith at least six-month follow-up data following interventions to increase PA amongsedentary adults.27 Given the importance of PA and the proliferation of studies testinginterventions to increase PA, we sought to move this area of science forward by conductinga comprehensive meta-analysis to estimate the overall effect of interventions and moreimportantly to conduct moderator analyses to identify intervention characteristics associatedwith the best outcomes. The project addressed these questions: (1) What are the overalleffects of interventions designed to increase PA on PA behavior after completion ofinterventions? (2) Do interventions’ effects on PA behavior vary depending on intervention,methodology, or sample characteristics?

METHODSSearch Strategies

We used multiple comprehensive search strategies to avoid the bias resulting from narrowsearches.29–30 An expert reference librarian conducted searches in 13 databases (e.g.MEDLINE, Dissertation Abstracts, SCOPUS). The National Institutes of Health ComputerRetrieval of Information on Scientific Projects was examined for potential studies. Thirty-six research registers were searched.31 Broad terms ensured comprehensive searches.Ancestry searches were completed for review papers and eligible studies. Computerizeddatabase searching was conducted for senior authors and principal investigators of alleligible studies. Our staff hand-searched 82 journals from 1960 through 2007.31 Thisextensive searching yielded 54,642 papers to consider for inclusion.

Inclusion CriteriaWe included English-language reports of interventions to increase PA among healthy adults.PA was defined as any bodily movement that increased energy expenditure beyond basallevels. Diverse PA behavior change interventions were eligible (e.g. education sessions,supervised exercise practice sessions) if PA was measured separately from the intervention.To reduce biases, we included both published and unpublished studies.32–33 We alsoincluded small-sample studies and pre-experimental studies.

Data ExtractionA coding frame was developed from related meta-analyses, review articles, and extensiveexamination of primary studies.34 This captured studies’ results as well as characteristics ofthe source, participants, method, and intervention. Participant characteristics coded includedage, gender, overweight status, previous exercise, and minority status. Methodologicalfeatures coded were method of assigning subjects, attrition, PA measure, and intervalbetween intervention and PA assessment.

We coded 74 intervention characteristics. These included intervention social context(individual, group); social structure target (individual, community); theoretical framework

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for intervention (Social Cognitive Theory, Transtheoretical Model, other theories werereported too infrequently for analyses); behavioral target (PA only vs. PA plus other healthbehaviors); recommended PA (form, intensity, duration/session, frequency/week); andexercise session characteristics among studies with supervised PA (form, intensity, duration/session, frequency/week, total number of sessions). Specific intervention content wasrecorded: access enhancement, barriers management, competition, contracting,consequences/rewards, cues/stimulus control, decision making, education about the healthbenefits of PA, exercise prescription, feedback, goal setting, modeling, monitoring PAbehavior by research staff, motivational interviewing, problem solving, relapse preventioneducation, and self-monitoring. The presence of individually tailored interventions—intervention with specific content matched to individual subjects’ attributes such as itemsidentified as personal barriers to PA—was coded. Interventions that used a ‘train-the-trainer’(i.e. teach PA behavior change interventions to local community members or health careproviders so they can deliver the interventions to individuals) approach were noted. Wecoded special intervention targets including entire communities, worksites, and ambulatoryhealth care settings and we coded the mode of delivery (e.g. face-to-face, mass media,mediated by telephone, mail, email). Thirty studies were pilot coded.

Data were coded at a micro-level to enhance validity.35 To enhance reliability, twoextensively trained coders independently extracted all data from each report. All data werecompared between coders to achieve 100% agreement. A third coder verified effect-sizedata. Discrepancies were resolved by consulting the first author or another member of theresearch team. We extracted data on 564 pairwise comparisons from 358 reports.

Data AnalysisFour types of effect-size (ES) comparisons were calculated.36 Treatment vs. control post-intervention ESs refer to treatment group results compared to control group results afterinterventions. Treatment vs. control pre-post ESs were calculated as a comparison betweentreatment group pre-post ES and control group pre-post ES. Treatment pre-post ESs arewithin-group ESs calculated for studies which provided treatment group baseline andoutcome data. Control pre-post comparisons are the same but for control subjects. Astandardized mean difference (d) ES was calculated for each primary study comparison.Positive d reflects more favorable scores for the treatment group or at post-test. The fourtypes of comparisons were analyzed separately.

Main Analyses of Treatment vs. Control Data—Treatment vs. control post-intervention ESs were calculated as the treatment post-test mean minus the control grouppost-test mean, divided by the pooled post-test SD. A second two-group ES, treatment vs.control pre-post ES, was generated to address possible treatment changes from time-relatedeffects in addition to the intervention effect (e.g., maturation, testing, regression). These ESswere calculated as treatment group pre-post ES minus control group pre-post ES, whereeach pre-post ES was computed as post-test mean minus pre-test mean, divided by pre-testSD. ESs were adjusted for bias.37 Larger samples were given more influence in the analysisby weighting each ES by the inverse of its sampling variance (i.e., precision). Homogeneitywas assessed using a conventional heterogeneity statistic (Q) and I2, which is an index ofbetween-studies heterogeneity relative to within-study sampling error used to assess theimpact of (in)consistency among trials on meta-analytic results. Studies with two or moretreatment groups compared to a single group were included in the meta-analysis byaccounting for the dependence caused by the shared control group: A two-stage approachwas used where each study’s dependent ESs were combined into a single independent ES38

then submitted to standard univariate random-effects analysis. Estimates of mean PA ESswere converted to the original metrics of steps/day and minutes/week. To detect possible

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publication bias, we used multiple statistical procedures since all strategies havelimitations.39–43 Outliers were detected statistically, omitting each ES one at a time andchecking for large externally standardized residuals or substantially reduced measures ofheterogeneity.

Estimated ESs we report here are based on the random-effects model with the betweenstudies variance component, , estimated by weighted method of moments, unlessotherwise designated. The random-effects model assumes individual ESs vary as a result ofboth subject-level sampling error and sources of study-level error.44 The random-effectsmodel is appropriate when study implementation is heterogeneous. Inclusion criteriavariations, intervention differences, dose variations, and study execution differencescontribute to heterogeneity.45–47 We expect heterogeneity in behavior change research andused four strategies to manage it. First, findings of the random-effects model, whichassumes heterogeneity, are presented. Second, both a location parameter and variabilityparameter are reported. Third, potential study-level moderators were explored to understandsources of heterogeneity. Fourth, findings were interpreted in light of heterogeneity. Thesestrategies are important because they help us interpret the extent to which heterogeneityaffects meta-analysis conclusions.45

Analyses of Alternative Designs—Treatment group pre-post comparisons includedstudies designed as single-group projects, those with multiple treatment groups and nocontrol group, and studies designed as treatment vs. control comparisons that also providedpre-intervention data so treatment pre-post comparisons were possible. Control pre-postcomparisons were calculated from baseline and outcome control group scores. Each single-group ES was calculated as the post-minus pre-intervention mean divided by the baselineSD. Correlations between pre- and post-intervention scores were solicited from primarystudy authors to calculate sampling variances. Adjustment for bias, sample size weighting,detection of outliers, random-effects models, estimation of publication bias, and assessmentof heterogeneity as described above were applied to these single-group ESs. Findings fromsingle-group comparisons are presented as ancillary evidence to the more internally validtwo-group comparisons. Within-group control subject findings are presented as empiricalevidence to address the common concern that control subjects may experience some benefitfrom participation in study procedures.

Moderator Analyses—Exploratory moderator analyses were conducted with treatmentvs. control post-intervention comparisons. A mixed-effects meta-analytic analogue ofregression was used for moderator analyses. This incorporates between-studiesheterogeneity into the estimate and test of the moderator’s relationship with mean ES. Forcontinuous moderators this estimates and tests the (unstandardized) regression slope, β. Fordichotomous moderators, a special case of regression estimates and tests the differencebetween two mean ESs. The moderator’s effect is tested with a heterogeneity statistic, eitherfor the model (QModel) or between groups (QBetween). These moderator analyses should beinterpreted as hypothesis generating, given the lack of consistent previous findings to form afirm basis for hypothesis testing. Moderator pairs were analyzed to address a givenmoderator’s robustness and generalizability in the presence of other moderators bydetermining how much its effect changed when each other moderator was controlled andhow much it interacted with each other moderator. Robustness was summarized as excellent,good, mixed, mediocre, and poor, based on the extent of agreement in rankings ofinteraction significance, interaction size, and size of change in main effect.

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RESULTSPrimary Study Characteristics

We calculated ESs from about 99,011 subjects’ data. Treatment vs. control post-interventionanalyses included 74,852 subjects (206 comparisons). Treatment pre-post comparisons useddata from 43,701 subjects (498 comparisons). Study characteristics are described in Table 1.The median of mean age was 44 years. Median sample size was 72 participants (range 5 to17,579). Women were well-represented with a median of 74% females, but the median forminority participants was only 14% among studies that reported such data. Interventionsranged from a single motivational education session to extensive supervised exercisesessions occurring over many weeks. The median duration of supervised exercise was 45minutes. The median number of sessions was 27 supervised exercise encounters.Motivational interventions’ median duration was 60 minutes delivered in a median of 5sessions.

Effect of InterventionsTable 2 shows the effects of interventions on PA behavior outcomes. We found a mean ES(d) estimate of 0.19 for both treatment vs. control post-intervention comparisons and fortreatment vs. control pre-post comparisons. A mean ES (d) of 0.33 was documented fortreatment pre-post comparisons. These ESs indicate that, on average, interventions didincrease the overall PA after completion of the intervention. In contrast, control subjects didnot experience increased PA by participating in studies, as evidenced by a mean ES of .00(d). Findings from heterogeneity analyses (Q and I2) suggest substantial variation in true ESamong studies. The two-group comparison mean ES of 0.19 is consistent with a meandifference of 14.7 minutes/week of PA or 496 steps/day between the treatment and controlgroups. If we assume true ESs are normally distributed with mean 0.19 and SD 0.17 (seeTable 2), then the middle 95% of true ESs fall between −0.14 and 0.53. Expressing thisinterval in an original metric gives (−11.0, 40.3) min/wk or (−371, 1363) steps/day. That is,for instance, a randomly selected study’s true mean difference could plausibly range from 11min/week less for treatment subjects to 40 min/week more for treatment subjects. Evidencesuggested possible publication bias among ESs for treatment vs. control post-intervention,treatment vs. control pre-post, and treatment pre-post ESs. No publication bias was apparentfor control pre-post ESs.

Moderator AnalysesTables 3 and 4 present the results of dichotomous and continuous moderator analyses oftreatment vs. control post-intervention ESs. Tabled results from the analyses of multiple-dfcategorical moderators and moderator pairs are available from the first author. Analyseswith fewer studies providing information on a characteristic (smaller k) should beinterpreted more cautiously than comparisons with more comparisons (larger k). Moderatoranalyses should be considered exploratory.

Report, Sample, and Methodological Moderators—Neither publication nor fundingstatus was related to PA ESs (see QB in Table 3). Studies published more recently had largermean ESs (Qmodel in Table 4). Although the dichotomous moderator analyses suggestedstudies of samples who exercised prior to the intervention reported lower ES (0.14) thanstudies of sedentary samples (0.27), these findings were not robust in joint moderatoranalyses. ESs were unrelated to sample characteristics (e.g. age), to random versusnonrandom assignment, or to method of measuring PA. The attrition difference betweentreatment and control subjects was related to ES in both the individual moderator analysesand the joint moderator analyses to assess robustness of findings. Specifically, studies withsmaller treatment-group attrition rates as compared to control-group attrition rates reported

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larger ESs. The days between intervention and outcome measurement was unrelated to ES inthe continuous moderator analyses (Table 4) or in the joint moderator analyses.

Intervention Moderators—When considered individually, 13 of the dichotomousmoderators tested were associated with differences in PA outcomes (Table 3). Studies thatdid not use Social Cognitive Theory reported significantly larger ES (0.20) than studies thatdid (0.12). Studies without the Transtheoretical Model reported larger ES (0.21) than studieswith the model (0.15). These findings regarding better outcomes among studies withoutSocial Cognitive Theory or without the Transtheoretical model were robust in the jointmoderator analyses. A comparison of ES between studies using the two models did notreveal a statistically significant difference. Multiple-df analyses documented the largest ESfor studies using neither model (0.23). The pattern of findings suggested Social CognitiveTheory was more detrimental to ES values than the Transtheoretical Model.

Although the dichotomous moderator analyses suggested studies including exerciseprescription reported larger ESs for PA (0.30) than studies without prescription (0.17), thesefindings were not robust. The presence of supervised exercise in the intervention wasassociated with better PA outcomes (0.29 vs. 0.17) in dichotomous moderator analyses, butthis was not supported in joint analyses. The joint moderator analyses revealed mixedsupport for the finding that studies where research staff modeled exercise behavior wereassociated with larger ESs (0.38) than studies without modeling (0.17).

The joint analyses supported the finding that interventions which included a train-the-trainerapproach were less effective (0.09) than interventions with research staff providinginterventions directly to subjects (0.21). The finding that standardized interventions (0.20)were more effective than individually tailored interventions (0.04) received mixed support inthe joint moderator analyses. The dichotomous moderator finding that interventionsincluding relapse-prevention strategies (0.34) were more effective than interventions without(0.17) was not confirmed in the joint analyses.

Both the dichotomous and joint moderator analyses confirmed that interventions whichtargeted entire communities (0.09) were less effective than interventions aimed atindividuals (0.19). The finding that studies with mass media approaches (0.08) were lesseffective than studies using other strategies to increase PA (0.19) was confirmed in the jointanalyses. Interventions with mediated delivery of interventions (e.g. mail, telephone), hadsmaller ESs (0.15) than interventions that were delivered face-to-face (0.29) in the singlevariable analysis. The joint moderator analyses did not confirm the better ES for face-to-faceinterventions. Worksite- and primary-care-based interventions (0.21) did not report differentESs compared to interventions without these characteristics (0.18).

We grouped interventions into approaches that were either behavioral (e.g. goal setting,contracting, self-monitoring, cues, rewards) or cognitive (e.g. decision making, healtheducation, providing information). Interventions that exclusively used behavioral strategies(0.25) were more effective than other interventions (0.17). Multiple-df analyses confirmedthat the largest ESs were for interventions that focused entirely on behavioral interventions.The joint moderator analyses confirmed that the superiority of behavioral approaches was arobust finding.

Interventions often recommended PA form, intensity, or duration following interventions.None of these were significantly linked with ESs for PA. Neither the number of interventionstrategies nor the total minutes of intervention content (including total minutes of supervisedPA) was associated with PA outcomes.

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DISCUSSIONThis comprehensive meta-analysis found a moderate mean ES (d=0.19) across diversestudies designed to increase PA among healthy adults. Moderator analyses identified severalrobust and moderately robust ES moderators associated with larger PA ES: behavioralinterventions (vs. cognitive interventions which target knowledge, attitudes, beliefs),interventions delivered directly to individuals (vs. mass media interventions andinterventions targeting entire communities), interventions delivered by project staff (vs.train-the-trainer models), modeling PA behavior by research staff, standardizedinterventions (vs. individually tailored interventions), and absence of interventions based onSocial Cognitive Theory or the Transtheoretical Model.

The ES from these studies of healthy adults is smaller than the ES reported for chronicallyill adults22 (d=0.45) and the ES for chronically ill and healthy adults and children(d=0.72).20 These results are similar to the ES reported for older adults (d=0.26)24 in whichinterventions targeting specific disease patient populations elicited larger PA behaviorchanges than interventions not targeting such groups. The presence of chronic illness maycause patients to be more responsive to interventions. Our smaller ES than that reported byDishman20 could have resulted from our greatly expanded search strategies locating moreobscure studies with smaller ESs.

The magnitude of PA behavior change is modest. The achieved steps per day do not meetpublic health goals of 10,000 steps per day.48–49 It is unclear if entirely sedentary peoplegain incremental health benefits when they add even small amounts of PA. Futureintervention research should report outcomes in terms of understandable amounts of PAincreases such as steps per day or minutes per week.

The moderator analysis finding that behavioral strategies are superior to cognitive strategiesis consistent with meta-analytic findings for chronically ill adults22 and older adults.24

Behavioral strategies include goal setting, self-monitoring, PA behavior feedback,consequences, exercise prescription, and cues. Health care providers and public healthprograms often emphasize PA’s health benefits, but we found that health education did notincrease ES. Perhaps the public already is convinced of PA’s health benefits, so programsusing behavioral strategies to change PA behavior may be most effective. Future researchcomparing behavioral interventions to cognitive interventions in large randomizedcontrolled trials would help confirm these findings. Further primary research comparingspecific types of behavioral interventions (e.g. contracting, self-monitoring, cues, rewards)could identify the most effective behavioral intervention components. Public health workersdesigning interventions should emphasize behavioral strategies over cognitive approaches.

The pattern of findings across mediated delivery (e.g. delivered via email, telephone), massmedia (e.g. delivered via television or newspaper), social structure target (individual vs.community), and train-the-trainer (i.e. teach PA behavior change interventions to localcommunity members or health care providers so they can deliver the interventions toindividuals) approaches suggest that delivering interventions face-to-face to individuals ismost effective. This finding moves beyond trends suggested in previous meta-analyses thatdid not achieve statistical significance.22,24 Audience attention to the message may behigher in individually delivered face-to-face interventions and may seem more important torecipients. Moreover, behavioral interventions may be easier to deliver in face-to-faceindividual encounters.

This meta-analysis was limited by the studies retrieved and the available information instudy reports. Primary study quality varies widely. Many quality aspects, such as treatmentfidelity and allocation concealment, are poorly reported and could not be examined in

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moderator analyses. Publication bias suggests studies with negative or low ESs remaininaccessible. Meta-analyses are unable to assess publication bias favoring studies withparticular characteristics. The comprehensive nature of this meta-analysis, and resultingheterogeneity among studies, is both a strength and limitation. The overall effect size shouldbe interpreted in light of discovered heterogeneity; not all interventions are equally effective.The results of the moderator analyses should be used to interpret findings: These effect sizecomparisons may be more important than the overall ES. The moderator analyses findingsshould be interpreted in the context of associations among moderators, substantial residualbetween-studies heterogeneity, and lack of hypotheses from the literature.

In conclusion, our comprehensive meta-analysis found that PA interventions producemoderate statistically significant increases in PA behavior and that behavioral interventionsappear more effective than cognitive interventions. These findings suggest interventions toincrease PA should emphasize behavioral components such as self-monitoring, stimuli toincrease PA, rewards, behavioral goal setting, and modeling PA behavior in standardizedinterventions delivered to individuals. Future research should explore which components ofbehavioral interventions are most effective.

AcknowledgmentsFinancial support provided by a grant from the National Institutes of Health (R01NR009656) to Vicki Conn,principal investigator. The content is solely the responsibility of the authors and does not necessarily represent theofficial views of the National Institutes of Health.

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48. Le Masurier GC, et al. Accumulating 10,000 steps: Does this meet current physical activityguidelines? Res Q Exerc Sport. 2003; 74:389–394. [PubMed: 14768840]

49. Tudor-Locke C, et al. How many steps/day are enough? Preliminary pedometer indices for publichealth. Sports Med. 2004; 34:1–8. [PubMed: 14715035]

Conn et al.


NIH


NIH


NIH


NIH


NIH


NIH


Conn et al.

Tabl

e 1

Cha

ract

eris

tics o

f Prim

ary

Stud

ies I

nclu

ded

in P

A M

eta-

Ana

lyse

s

Cha

ract

eris

tick

Min

Q1

Mdn

Q3

Max

Mea

n ag

e (y

ears

)16

418

4044

5288

Sam

ple

size

per

stud

y35

85

3372

207

1757

9

Perc

enta

ge a

ttriti

on fr

om c

ompa

rison

gro

up82

05

1222

77

Perc

enta

ge a

ttriti

on fr

om tr

eatm

ent g

roup

153

06

1629

73

Perc

enta

ge a

ttriti

on fr

om to

tal s

ampl

ea85

05

1322

62

Perc

enta

ge fe

mal

e20

70

5674

100

100

Perc

enta

ge m

inor

ity88

07

1490

100

Min

utes

of s

uper

vise

d ex

erci

se p

er se

ssio

n25

1830

4560

60

Tota

l num

ber o

f sup

ervi

sed

exer

cise

sess

ions

286

1627

4815

6

Min

utes

of e

duca

tion/

mot

ivat

ion

per s

essi

on60

324

6069

120

Tota

l num

ber o

f edu

catio

nal/m

otiv

atio

nal s

essi

ons

165

11

512

59

Num

ber o

f wee

ks in

terv

entio

n w

as d

eliv

ered

205

01

1026

313

Not

e. In

clud

es a

ll pr

imar

y st

udie

s tha

t con

tribu

ted

one

effe

ct si

ze fo

r any

type

of c

ompa

rison

. Ind

epen

dent

sam

ples

with

in st

udie

s agg

rega

ted

by su

mm

ing

sam

ple

size

s and

usi

ng w

eigh

ted

mea

n of

oth

erch

arac

teris

tics (

wei

ghte

d by

sam

ple

size

). k

= nu

mbe

r of s

tudi

es p

rovi

ding

dat

a on

cha

ract

eris

tic; Q

1 =

first

qua

rtile

, Q3

= th

ird q

uarti

le.

a Incl

udes

onl

y st

udie

s with

bot

h tre

atm

ent a

nd c

ompa

rison

sam

ples

.


NIH


NIH


NIH


Conn et al.

TAB

LE 2

Ran

dom

-Eff

ects

Phy

sica

l Act

ivity

Beh

avio

r Out

com

e Es

timat

es a

nd T

ests

ES

type

kμ̂ δ

p (μ̂

δ)SE

(μ̂δ)

μ δ 9

5% C

Iσ̂ δ

Qp

(Q)

I2

Trea

tmen

t vs.

cont

rol p

ost-i

nter

vent

ion

com

paris

ona

206

0.19

<.00

10.

019

(0.1

5, 0

.23)

0.17

554.

4<.

001

.67

Trea

tmen

t vs.

cont

rol p

re-p

ost c

ompa

rison

b14

60.

19<.

001

0.02

1(0

.15,

0.2

3)0.

1529

9.7

<.00

1.5

2

Trea

tmen

t pre

-pos

t com

paris

on49

80.

33<.

001

0.01

4(0

.30,

0.3

5)0.

2629

45.3

<.00

1.8

3

Con

trol p

re-p

ost c

ompa

rison

115

0.00

.792

0.01

7(−

0.04

, 0.0

3)0.

1228

1.2

<.00

1.5

9

Not

e. T

reat

men

t vs.

cont

rol p

ost-i

nter

vent

ion

com

paris

on re

fers

to tr

eatm

ent p

ost-t

est m

ean

min

us c

ontro

l pos

t-tes

t mea

n. T

reat

men

t vs.

cont

rol p

re-p

ost c

ompa

rison

refe

rs to

trea

tmen

t gro

up p

re-p

ost E

Sm

inus

con

trol g

roup

pre

-pos

t ES.

Tre

atm

ent p

re-p

ost c

ompa

rison

ESs

refe

rs to

trea

tmen

t gro

up p

ost-t

est m

ean

min

us b

asel

ine

mea

n. C

ontro

l pre

-pos

t com

paris

on E

S re

fers

to c

ontro

l gro

up p

ost-t

est m

ean

min

us b

asel

ine

mea

n. k

=num

ber o

f com

paris

ons. μ̂ δ

= e

stim

ated

mea

n ES

. . Q

= h

eter

ogen

eity

stat

istic

. Und

er h

omog

enei

ty (H

0: δ

i = δ

) Q is

dist

ribut

ed a

s chi

-squ

are

with

df =

k −

1, w

here

k is

the

num

ber o

f (po

ssib

ly d

epen

dent

) obs

erve

d ef

fect

size

s; th

is a

lso

test

s H0:

. I

2 =

quan

tific

atio

n of

impa

ct o

f het

erog

enei

ty. P

oten

tial o

utlie

rsex

clud

ed b

ased

on

stan

dard

ized

rand

om-e

ffec

ts re

sidu

als f

or tr

eatm

ent v

s. co

ntro

l pos

t-int

erve

ntio

n (1

4 [6

% o

f com

paris

ons)

, tre

atm

ent v

s. co

ntro

l pre

-pos

t (7

[5%

of c

ompa

rison

s]),

treat

men

t pre

-pos

t (43

[8%

of c

ompa

rison

s]),

and

cont

rol p

re-p

ost (

13 [1

0% o

f com

paris

ons]

).

a Acc

omm

odat

ing

mul

tiple

-trea

tmen

t dep

ende

nce

due

to 2

3, 6

, and

1 m

ultip

le-tr

eatm

ent p

airs

, trip

lets

, and

qua

drup

lets

, res

pect

ivel

y.

b Igno

ring

depe

nden

ce d

ue to

18

and

3 m

ultip

le-tr

eatm

ent p

airs

and

trip

lets

resp

ectiv

ely.


NIH


NIH


NIH


Conn et al.

TAB

LE 3

Trea

tmen

t vs.

Con

trol P

ost-I

nter

vent

ion

Com

paris

ons:

Dic

hoto

mou

s Mod

erat

ors,

Mix

ed-E

ffec

ts A

naly

ses

Mod

erat

ork 0

k 1μ̂ δ 0

μ̂ δ1

SEdi

fQ

Bσ̂ δ

aI2

Rob

ustn

ess

Sour

ce c

hara

cter

istic

s

Publ

icat

ion

stat

us25

179

0.17

0.19

0.06

80.

10.

17.6

4

Fund

ing

7712

90.

210.

170.

038

1.1

0.17

.63

Parti

cipa

nt c

hara

cter

istic

s

Prev

ious

exe

rcis

ers

8811

80.

270.

140.

036

13.3

***

0.16

.60

poor

Mos

tly o

verw

eigh

t22

580.

210.

180.

062

0.2

0.15

.51

Res

earc

h m

etho

ds c

hara

cter

istic

s

Ran

dom

ass

ignm

ent

8512

10.

160.

210.

036

1.9

0.17

.63

Obj

ectiv

e vs

. sel

f-re

porte

d PA

mea

sure

4416

20.

230.

180.

050

1.1

0.17

.63

Epis

odic

vs.

over

all P

A m

easu

reb

1581

0.08

0.18

0.05

92.

50.

13.4

6

Post

-inte

rven

tion

lag

befo

re m

easu

re15

104

0.39

0.18

0.08

36.

3*0.

14.4

5po

or

Inte

rven

tion

char

acte

ristic

s

Soci

al c

ogni

tive

theo

ry (S

CT)

169

370.

200.

120.

045

3.5†

0.17

.63

exce

llent

Tran

sthe

oret

ical

mod

el (T

M)

146

600.

210.

150.

038

2.8†

0.17

.64

good

SCT

vs. T

M41

180.

140.

080.

065

0.9

0.14

.54

poor

Acc

ess e

nhan

cem

ent

193

130.

190.

100.

070

1.6

0.17

.64

Bar

riers

man

agem

ent

157

490.

190.

160.

042

0.7

0.17

.64

Com

petit

ion/

cont

ests

198

80.

180.

240.

089

0.5

0.17

.63

Con

tract

ing

197

90.

190.

080.

082

1.7

0.17

.64

Con

sequ

ence

165

410.

180.

200.

046

0.2

0.17

.63

Dec

isio

n m

akin

g19

511

0.19

0.15

0.08

00.

20.

17.6

4

Exer

cise

pre

scrip

tion

176

300.

170.

300.

053

6.2*

0.17

.63

poor

Feed

back

165

410.

190.

180.

043

0.0

0.17

.63

Fitn

ess t

estin

g19

97

0.18

0.30

0.10

61.

30.

17.6

3

Goa

l set

ting

151

550.

170.

210.

039

1.2

0.16

.61

Hea

lth e

duca

tion

144

620.

190.

170.

039

0.6

0.17

.63


NIH


NIH


NIH


Conn et al.

Mod

erat

ork 0

k 1μ̂ δ 0

μ̂ δ1

SEdi

fQ

Bσ̂ δ

aI2

Rob

ustn

ess

Mas

s med

ia19

511

0.19

0.08

0.05

83.

8†0.

16.5

8go

od

Mod

elin

g18

521

0.17

0.38

0.06

411

.4**

*0.

16.5

9m

ixed

Mon

itorin

g17

036

0.17

0.24

0.04

91.

90.

17.6

3

Mot

ivat

iona

l int

ervi

ewin

g19

79

0.18

0.20

0.08

40.

00.

17.6

3

Prob

lem

solv

ing

184

220.

180.

200.

057

0.1

0.17

.63

Rel

apse

pre

vent

ion

190

160.

170.

340.

066

6.4*

0.17

.62

poor

Self-

mon

itorin

g13

769

0.18

0.20

0.03

90.

30.

17.6

4

Stim

ulus

con

trol

191

150.

190.

140.

067

0.6

0.17

.64

Supe

rvis

ed e

xerc

ise

172

340.

170.

290.

055

4.8*

0.17

.62

poor

Stan

dard

ized

vs.

indi

vidu

ally

tailo

red

196

100.

200.

040.

071

4.6*

0.17

.63

mix

ed

Targ

eted

174

320.

190.

170.

046

0.1

0.17

.64

Trai

n-th

e-tra

iner

app

roac

h17

333

0.21

0.09

0.04

56.

9**

0.17

.63

good

Any

beh

avio

ral i

nter

vent

ion

6514

10.

180.

190.

038

0.1

0.17

.62

exce

llent

Beh

avio

ral i

nter

vent

ions

onl

y15

155

0.17

0.25

0.04

34.

0*0.

17.6

3m

ixed

Beh

avio

ral o

nly

vs. w

/cog

nitiv

e86

550.

160.

250.

045

4.3*

0.16

.53

Any

cog

nitiv

e in

terv

entio

n10

010

60.

220.

160.

036

2.5

0.17

.63

Cog

nitiv

e in

terv

entio

ns o

nly

186

200.

190.

170.

059

0.1

0.17

.64

Cog

nitiv

e on

ly v

s. w

/beh

avio

ral

8620

0.15

0.17

0.05

70.

00.

15.5

3m

ixed

Mul

tiple

beh

avio

rs v

s. PA

onl

y ta

rget

115

870.

200.

170.

037

1.0

0.17

.63

Indi

vidu

al v

s. gr

oup

soci

al c

onte

xt84

122

0.21

0.17

0.03

71.

70.

17.6

2

Targ

ets i

ndiv

idua

ls v

s. co

mm

uniti

es19

412

0.19

0.09

0.05

73.

2†0.

16.5

8go

od

Med

iate

d de

liver

y (e

.g. t

elep

hone

)71

135

0.29

0.15

0.04

111

.7**

*0.

16.6

1po

or

Wor

ksite

pro

gram

157

490.

180.

210.

043

0.8

0.17

.63

Link

ed to

prim

ary

care

173

330.

190.

160.

048

0.3

0.17

.64

Rec

omm

end

spec

ific

PA12

185

0.18

0.20

0.03

70.

40.

17.6

3

Rec

omm

end

wal

king

for P

A14

838

0.17

0.23

0.04

81.

20.

18.6

6

Rec

omm

end

PA in

tens

ity15

490.

220.

180.

076

0.2

0.17

.61


NIH


NIH


NIH


Conn et al. N

ote.

k0

= nu

mbe

r of E

S es

timat

es w

ith th

e m

oder

ator

var

iabl

e ab

sent

. k1

= nu

mbe

r of E

S es

timat

es w

ith th

e m

oder

ator

var

iabl

e pr

esen

t. μ̂ δ

0=mea

n ES

for s

tudi

es w

ithou

t int

erve

ntio

n co

mpo

nent

. μ̂δ 1

=mea

n ES

for s

tudi

es w

ith in

terv

entio

n co

mpo

nent

pre

sent

. QB

= b

etw

een

grou

ps h

eter

ogen

eity

stat

istic

(dis

tribu

ted

as c

hi-s

quar

e on

df =

1 u

nder

H0:

μδ 0

=μδ 1

).

. I2

= qu

antif

icat

ion

of im

pact

of h

eter

ogen

eity

. Ana

lysi

s rep

orte

d if

k 0 ≥

3 a

nd k

1 ≥

3. R

obus

tnes

s ass

esse

d in

join

t mod

erat

or a

naly

ses.

† p <

.10,

* p <

.05,

**p

< .0

1,

*** p

< .0

01 (f

or Q

B).

a For a

ll ta

bled

mod

erat

ors a

test

of H

0:

yie

lded

p <

.000

01.

b Excl

udes

com

paris

ons b

ased

on

fitne

ss m

easu

res (≥

6 m

onth

s afte

r sup

ervi

sed

PA tr

eate

d as

PA

beh

avio

r out

com

e).


NIH


NIH


NIH


Conn et al.

TAB

LE 4

Line

ar a

nd C

ubic

Mix

ed-E

ffec

ts M

oder

ator

Ana

lyse

s for

PA

Beh

avio

r Out

com

es fo

r Tre

atm

ent v

s. C

ontro

l Pos

t-Int

erve

ntio

n C

ompa

rison

s

Mod

erat

ork

MSD

Lin

ear β̂

Lin

ear

I2

Qm

odel

σ̂ δ

LC

LC

Publ

icat

ion

year

(−19

60)

206

39.4

16.

26−0.008

.63

9.9*

*10

.8*

0.17

0.17

Parti

cipa

nt c

hara

cter

istic

s

Mea

n sa

mpl

e ag

e13

647

.31

10.5

50.

002

.48

1.2

3.0

0.15

0.15

Prop

ortio

n fe

mal

e16

30.

600.

250.

027

.57

0.2

1.4

0.16

0.16

Prop

ortio

n et

hnic

min

ority

520.

250.

310.

030

.58

0.1

1.2

0.18

0.18

Mea

n ye

ars o

f edu

catio

n13

12.8

91.

490.

117

.54

2.5

3.3

0.25

0.30

Prop

ortio

n at

tritio

n, C

ontro

l13

00.

170.

150.

138

.47

1.0

2.9

0.14

0.14

Prop

ortio

n at

tritio

n, T

reat

men

t13

10.

180.

14−0.131

.46

0.9

1.2

0.13

0.14

Prop

ortio

n at

tritio

n, a

ll12

60.

170.

140.

041

.47

0.1

1.6

0.14

0.14

Diff

eren

ce in

pro

p. a

ttriti

on12

60.

000.

09−0.583

.46

6.6*

*8.

9*0.

140.

14

Log

odds

ratio

of a

ttriti

on96

0.01

0.72

−0.071

.49

4.5*

6.2

0.14

0.14

Log 1

0 day

s sin

ce in

terv

entio

n10

42.

090.

61−0.016

.44

0.1

4.6

0.14

0.14

Inte

rven

tion

char

acte

ristic

s

Num

ber b

ehav

iora

l stra

tegi

es20

61.

041.

340.

012

.62

0.8

3.1

0.17

0.17

Num

ber c

ogni

tive

stra

tegi

es20

60.

700.

93−0.004

.63

0.0

4.6

0.17

0.17

Num

ber s

trate

gies

206

3.17

3.07

0.00

5.6

30.

81.

90.

170.

17

Log 1

0 rec

. min

utes

/wk

462.

180.

13−0.183

.60

0.4

1.3

0.19

0.20

Log 1

0 rec

. sup

ervi

sed

PA27

3.25

0.51

0.13

3.5

40.

72.

00.

280.

28

Log 1

0 rec

. mot

ivat

e/ed

ucat

e50

1.97

0.72

−0.002

.57

0.0

4.3

0.18

0.19

Log 1

0 rec

. tot

al m

inut

es59

2.01

0.76

0.04

3.5

50.

84.

70.

190.

19

Not

e. k

= n

umbe

r of (

poss

ibly

dep

ende

nt) E

S es

timat

es. E

ach

mod

erat

or’s

wei

ghte

d m

ean

(M) a

nd st

anda

rd d

evia

tion

(SD

) com

pute

d fr

om a

ll av

aila

ble

case

s. M

oder

ator

cen

tere

d at

M. P

olyn

omia

l mod

els

with

deg

ree

m =

1 o

r 3: L

= li

near

, C =

cub

ic. H

eter

ogen

eity

stat

istic

s: Q

tota

l = to

tal,

igno

ring

mod

erat

or; Q

mod

el =

due

to a

ll po

lyno

mia

l ter

ms o

f mod

erat

or (x

) in

linea

r (β 1

x) o

r cub

ic (β

1x +

β2x

2 +

β 3x3

) mod

el, d

istri

bute

d as

chi

-squ

are

on d

f = m

und

er H

0: β

= 0

, whe

re β

is β

1, o

r [β 1β 2β 3

]T, r

espe

ctiv

ely,

for l

inea

r or c

ubic

mod

el. I

2 =

quan

tific

atio

n of

deg

ree

of h

eter

ogen

eity

. Ana

lysi

s rep

orte

d if

k≥

m +

5.

† p <

.10,


NIH


NIH


NIH


Conn et al. * p

< .0

5,

**p

< .0

1,

*** p

< .0

01 (f

or Q

mod

el).


Interventions to increase physical activity and healthy eating among overweight and obese children...

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