Teixeira et al. BMC Medicine (2015) 13:84
DOI 10.1186/s12916-015-0323-6

Obesity: exploring the causes, consequences and solutions

RESEARCH ARTICLE

Open Access

Successful behavior change in obesityinterventions in adults: a systematic review
of self-regulation mediators
Pedro J Teixeira1*, Eliana V Carraça1, Marta M Marques1, Harry Rutter2, Jean-Michel Oppert3,4,Ilse De Bourdeaudhuij5, Jeroen Lakerveld6 and Johannes Brug7

Abstract
Background: Relapse is high in lifestyle obesity interventions involving behavior and weight change. Identifying
mediators of successful outcomes in these interventions is critical to improve effectiveness and to guide approaches toobesity treatment, including resource allocation. This article reviews the most consistent self-regulation mediators of
medium- and long-term weight control, physical activity, and dietary intake in clinical and community behavior changeinterventions targeting overweight/obese adults.
Methods: A comprehensive search of peer-reviewed articles, published since 2000, was conducted on electronic
databases (for example, MEDLINE) and journal reference lists. Experimental studies were eligible if they reported
intervention effects on hypothesized mediators (self-regulatory and psychological mechanisms) and the association
between these and the outcomes of interest (weight change, physical activity, and dietary intake). Quality andcontent of selected studies were analyzed and findings summarized. Studies with formal mediation analyses werereported separately.
Results: Thirty-five studies were included testing 42 putative mediators. Ten studies used formal mediation analyses.
Twenty-eight studies were randomized controlled trials, mainly aiming at weight loss or maintenance (n = 21). Targeted
participants were obese (n = 26) or overweight individuals, aged between 25 to 44 years (n = 23), and 13 studies
targeted women only. In terms of study quality, 13 trials were rated as “strong”, 15 as “moderate”, and 7 studies
as “weak”. In addition, methodological quality of formal mediation analyses was “medium”. Identified mediators for
medium-/long-term weight control were higher levels of autonomous motivation, self-efficacy/barriers, self-regulation
skills (such as self-monitoring), flexible eating restraint, and positive body image. For physical activity, significant
putative mediators were high autonomous motivation, self-efficacy, and use of self-regulation skills. For dietaryintake, the evidence was much less clear, and no consistent mediators were identified.
Conclusions: This is the first systematic review of mediational psychological mechanisms of successful outcomes inobesity-related lifestyle change interventions. Despite limited evidence, higher autonomous motivation, self-efficacy,
and self-regulation skills emerged as the best predictors of beneficial weight and physical activity outcomes; for weightcontrol, positive body image and flexible eating restraint may additionally improve outcomes. These variables represent
possible targets for future lifestyle interventions in overweight/obese populations.
Keywords: Lifestyle interventions, Obesity, Self-regulation, Weight control, Physical activity, Dietary intake, Maintenance,
Behavior change, Mediation analysis* Correspondence: pteixeira@fmh.ulisboa.pt
1Interdisciplinary Centre for the Study of Human Performance (CIPER), Facultyof Human Kinetics, University of Lisbon, Lisbon, Portugal
Full list of author information is available at the end of the article© 2015 Teixeira et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.

Teixeira et al. BMC Medicine (2015) 13:84

Background
Lifestyle treatment interventions for obesity typically target changes in diet and physical activity through strategies
like setting adequate goals and enhancing patients’ motivation, changing their beliefs and expectations, and providing guidance in the use of a variety of self-regulation skills(such as self-monitoring), all of which are thought to influence behavior change and maintenance [1-4]. A wide
variety of health behavior change theories has been
employed to provide conceptual organization of these determinants, including social cognitive theories such as thetheory of planned behavior [5], theories of motivationsuch as self-determination theory [6], theories distinguishing between motivational and post-motivational or volitional phases [7] such as the health action process
approach (HAPA) [8], and self-regulation models such as
control theory [9]. Since all these theories address theregulation of a person’s behavior in the service of somegoal or desired outcome, through intrapersonal factors, inthis paper we broadly refer to intervening variables in thisprocess as self-regulation factors.
Behavior modification in general, and “comprehensive
lifestyle interventions” in particular [10] are currently
the first recommended step in obesity management.
However, so far, randomized controlled trials evaluating
the effectiveness of programs that target lifestyle behavior have shown mixed effects and, if effective, they havegenerally resulted in only small changes in target behaviors [11-15]. In addition, the evidence shows that relatively little if any weight loss accomplished in treatment
programs is maintained over the long term [16]. Furthermore, few studies have analyzed why, or by which mechanisms, interventions are successful for some individuals
and not for others. Clearly, there is a need for research that
identifies causal predictors of long-term weight control, including successful weight loss and maintenance [17].
Despite the limited success of available interventions
in reversing the current trends in obesity prevalence, approaches focusing on individual behavior change remain
an important topic of interest in obesity research. Several reasons justify this assertion. First, these interventions typically focus on behaviors (for example, diet andphysical activity), which have widespread consequences
for health, with or without weight loss. Second, if andwhen individuals are able to successfully self-regulate
their behaviors, these effects tend to be sustainable,
which is essential for having a lasting impact on health;moreover, this successful self-regulation may also “transfer” to, and help change, other health behaviors [18].
Third, although some interventions targeting individuals
may be ineffective on their own, they might be able tocontribute to the effectiveness of strategies that integrate
multiple levels (that is, strategies that include individuallevel and environmental-level approaches) [19]. Finally,

Page 2 of 16

the potential for dissemination of individual-level intervention approaches is considerable, given that a sizable
number of overweight and obese persons will seek professional help at some point in their lives. Consequently,
improving the efficacy of such interventions has substantial clinical as well as public health relevance.
One recent development in studies testing lifestyle interventions for obesity is their ability to identify themechanisms or processes by which interventions induce
meaningful and lasting change in their (most successful)participants. These mechanisms can generally be named
predictors (or determinants) of success, and some studies
have gone one step further to evaluate the extent towhich they may be causal mediators of intervention effects. Testing of mediation, using appropriate methods,
is a critical step in this field; it provides the strongest
possible inference for the identification of elements ininterventions which are causally “responsible” for achieving desired outcomes [20].
Success and failure in the self-regulation of health behaviors involve multiple psychological and behavioral aspects.
The aim of this review was to identify and summarize psychological self-regulation mediators of successful weightchange, or change in energy balance-related behavior(physical activity and diet), in clinical and community behavior change obesity interventions. Because eventual
weight regain is frequent after behavior and/or weightchange interventions, particular attention was given to
studies reporting long-term outcomes, that is, one year or
more after the beginning of the intervention.

Methods
This systematic review was conducted in accordancewith the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [21].
Eligibility criteria

Studies were included in this review if they were intervention studies published since 2000 in the English language,used experimental designs, and referred to clinical orcommunity behavior change interventions with overweight/obese adults (≥18 years old) aiming to reduceoverweight/obesity. This review was limited to “lifestyleinterventions” defined as interventions that promotechange in energy balance-related behaviors (such as dietand physical activity, as the outcomes) and self-regulatory
factors (such as motivation and self-monitoring, as the potential mediators) relevant for overweight/obesity treatment, typically in settings involving personal contact
between interventionists and participants. There were no
restrictions with respect to the format and duration of theintervention. To be eligible, studies should also report outcomes assessed at least 6 months after the start of theintervention; include a quantitative assessment of change

Teixeira et al. BMC Medicine (2015) 13:84

in weight/BMI, physical activity (for example, selfreported or accelerometer-derived minutes of moderate
and vigorous physical activity, daily pedometer steps, attendance to PA sessions), or dietary intake (for example,
energy intake, fat intake, fruit and vegetable intake) as well
as a quantitative assessment of potential mediators of successful behavior change. We decided not to distinguishpredictors of weight loss and predictors of weight loss
maintenance, choosing instead to divide the studies according to the length of measurement periods (shorter
versus longer than 12 months). While it is possible thatpredictors of those two processes differ, to appropriately
evaluate predictors of weight loss maintenance, we would
have to rely on studies of successful weight losers, andpreferably including psychological measures before andafter the maintenance period. Only one intervention study
fit both criteria.
An a priori list of mediators was used for study inclusion/exclusion, based on previous work in this area (forexample, [2,22]). Only mediators representing individuallevel self-regulatory processes were considered (that is,
those related to skills, motivation, competence, coping
mechanisms, beliefs, physical self-perceptions, and eating
regulation factors such as disinhibition, restraint, and perceived hunger). Mediators associated with personality factors, social support, and health-related outcomes (such as
psychological distress, quality of life, and well-being) were
excluded. Finally, eligible studies were required to report
the effect of the intervention on hypothesized mediator(s)and the association of the putative mediator with the outcomes of interest.
Search strategy and study selection

A comprehensive search of peer-reviewed articles published between January 2000 and February 2014 (including online ahead of print publication) was conducted insix electronic databases (Pubmed, MEDLINE, PsycINFO,
the Cochrane Library, Web of Knowledge, and SPORTDiscus). The decision to restrict the selection to studies
published since 2000 is based on the fact that recent development in studies testing the effectiveness of lifestyleinterventions for obesity makes older studies less externally valid. For instance, in 1995, Friedman and Brownell[23] alerted for the need of a “third generation” of obesity treatment studies analyzing causal mechanisms between psychosocial variables and weight change. Despite
this, one decade later, it has been observed that very few
studies had investigated such mechanisms, and evenfewer looked into long-term changes [2].
Searches included various combinations of four sets ofterms: i) terms concerning the health condition or population of interest (overweight/obesity); ii) terms concerning
the intervention(s)/exposure(s) evaluated (for example,
behavior change/lifestyle obesity interventions); iii) terms

Page 3 of 16

respecting the outcomes of interest (weight change, physical activity, and dietary intake); iv) terms concerning
the predictors/mediators of interest (psychological, selfregulation); and v) terms concerning the type of analysesof relevance (for example, mediation, correlates, predictors). (See Additional file 1 for a search example; complete
search strategies can be obtained from the authors). Other
sources included manual cross-referencing of bibliographies cited in previous reviews [2,22,24-26] and included
studies, as well as manual searches of the content of key
scientific journals (Obesity Reviews; International Journalof Obesity; Obesity (Silver Spring); International Journal of
Behavioral Nutrition and Physical Activity; Journal of theAmerican Dietetic Association; Psychology of Sports andExercise; Health Psychology; Journal of Behavioral Medicine; Preventive Medicine).
Titles, abstracts, and references of potential articles werereviewed by two authors (EVC, MM) to identify studies
that met the eligibility criteria. Duplicate entries weremanually removed. Relevant articles were then retrieved
for a full read. The same two authors reviewed the full textof potential studies, and decisions to include or exclude
studies in the review were made by consensus.
Data coding and extraction

A data extraction form was developed, informed by the
PRISMA statement for reporting systematic reviews [21]and the Cochrane Collaboration’s tool for assessing riskof bias [27]. Data extraction included information about
study details (authors, year, country of publication, affiliations, and funding), participants (characteristics, recruitment, setting, attrition, compliance, and blinding),
study design and setting, outcomes of interest, mediators/
predictors (in/out list), intervention length and characteristics, psychosocial instruments, and statistical analysis, including mediation techniques (a complete coding form
can be obtained from the authors). Authors of included
studies were contacted when necessary to retrieve missing
data in published reports.
Considering that the main focus of this review was theidentification of mediators, data extraction was performed separately, starting with the studies formally testing mediation (see Additional file 2), followed by thosethat reported both the effect of the intervention on hypothesized mediators (path a) and the association of theputative mediator with the outcomes of interest (pathb), but did not test mediation (see Additional file 3). Regarding mediation and specifically in studies with formal
mediation tests, researchers could use Baron and Kenny’s approach [28] and check whether the main effects
were reduced in the presence of the mediator, or employ
more sophisticated techniques to directly test the significance of the indirect effect through the mediator (forinstance, by following MacKinnon’s approach [29], andTeixeira et al. BMC Medicine (2015) 13:84

using Preacher and Hayes mediation procedures or
structural equation modeling). Additional file 4 presents
a detailed description of the mediation analyses procedures and estimates for each study. In the latter (that is,predictor studies), we generally looked at a) whether significant intervention-control differences existed for agiven variable (or pre-post change in non-controlled designs); b) whether there was an association between
these changes (in intervention group only) and changes
in the outcome (weight/PA/diet) in this group. If bothwere present, results were deemed consistent withmediation.

Quality assessment

The quality of included studies was assessed using an
adapted version of the Quality Assessment Tool for
Quantitative Studies, developed by the Effective Public
Health Practice Project [30], and recommended for use
by the Cochrane Public Health Review Group [27]. The
current adaptation was based on recommendations fromseveral authors [31,32], and has been used in a previous
systematic review conducted as part of the SPOTLIGHT
project [33]. This tool was adapted to allow the evaluation of both experimental and observational studies
and contains 19 items, guiding the assessment of eight
key methodological domains – 1) study design, 2) blinding, 3) representativeness (selection bias), 4) representativeness (withdrawals/dropouts), 5) confounders, 6) datacollection, 7) data analysis, and 8) reporting. Each domain is classified as Strong (low risk of bias/high methodological quality), Moderate, or Low (high risk of bias/
low methodological quality) methodological quality. A
global rating is determined based on the scores of eachcomponent (see Additional file 5 for a full description of
the Assessment Tool components and scoring system).
Two researchers independently rated each of the eight
domains and overall quality (EVC, MM). Discrepancies
were resolved by consensus.
For studies employing formal tests of mediation, assessment of methodological quality was complemented with achecklist tool developed specifically for mediation analysis
by Lubans, Foster, and Biddle [34], and subsequently
adapted by Rhodes and Pfaeffli [35]. This tool includes 11
questions answered with a yes (1) or no (0) format, whosescores are added to generate a global score. High quality isrepresented by scores between 9 and 11, moderate quality
ranges between 5 and 8, and low quality is consideredwhen scores are below 5 (see Additional file 5 for a full description of the Checklist components and scoring system). Methodological quality of the mediation analyses
was also rated by two authors (EVC, MM), with conflicting judgments discussed to reach agreement. Inter-rater
agreement was good (Cohen’s kappa = 0.78).

Page 4 of 16

Data synthesis

This review analyzed psychological and self-regulation
mediators and predictors of change in body weight or
BMI (primary outcome), physical activity, and dietary intake, separately (Note: we will use the term predictors
when studies did not test for formal mediation, and mediators when they did). Intervention effects on the outcomes of interest were included in Additional files 2 and3. Results were divided according to the length of assessment of the outcomes, into short-term (<12 months
from the start of the intervention) and long-term (≥12
months), so that those variables mediating/predicting
more sustainable outcomes (the main focus of this review) could be more easily identified. Twelve months
has been indicated by an expert panel on obesity as an
appropriate threshold between weight loss and the maintenance of the weight lost [10]. In the synthesis of data
derived from studies formally testing mediation, only
controlled trials were included, to further strengthen inference regarding intervention effects on mediators andoutcomes. In the case of prediction studies (not formallytesting mediation), we included both controlled and uncontrolled trials, to capitalize on the (relatively) larger
number of studies available, which would otherwise be
excluded using the more stringent criteria. Table 1 describes the 35 included studies. In Tables 2, 3, 4, 5, 6,
and 7, mediation-specific results are discriminated fromthe general results, provided that the main goal of thisreview was the identification of self-regulation mediators
in behavior change obesity interventions. The overall results (considering multivariate, bivariate/correlational,
and mediation analyses) are also presented in each table(Tables 2, 3, 4, 5, 6, and 7). A total of 42 mediators/predictors were identified across outcomes. To facilitate
data interpretation, considering the very large number ofindividual variables, these were grouped together by similarity into categories. Categorization was done through theextraction of information from primary studies on the definition and operationalization of the constructs. The following 12 categories were formed: Self-regulatory skills use,
Processes of change, Coping mechanisms, Self-efficacy/barriers, Autonomous motivation, Controlled motivation, Decisional balance, Outcomes expectations/beliefs, Body
image/physical self-worth, Cognitive restraint, Eating disinhibition, Perceived hunger. Please refer to Additional file 6
for full details regarding the mediators/predictors identified per outcome.
Finally, Tables 2, 3, 4, 5, 6, and 7 show, separately foreach mediator/predictor, the number of studies thathave analyzed it, the number of times it was tested(some of them within the same study), and the numberof times a significant effect was found. Results are presented for mediation-specific results and for the overall
results.

Teixeira et al. BMC Medicine (2015) 13:84

Page 5 of 16

Table 1 Characteristics of intervention studies examining
potential mediators/predictors of weight control, physicalactivity, and food intake (n = 35)

Table 1 Characteristics of intervention studies examining
potential mediators/predictors of weight control, physicalactivity, and food intake (n = 35) (Continued)

Characteristics

Characteristics

Number ofstudies

Study design(n = 35)

Characteristics

Number ofstudiesIntervention(n = 35)

Trial

Aim

NCT

6

Weight loss

NRCT

1

Weight maintenance 6

RCT

28

Exercise adherence 3

1 arm

6

8

Other

2
b

2 arms18

Theoretical grounds

3 arms8SCT

23

4 or + arms

3

TTM

5

SDT

3

Sample size
<100

2

Other

8

100-199

12

N.R.

4

200-299

11

Intervention setting

≥300

10

University

15

Hospital/clinic

3

Participants(n = 35)

Exercise/fitness
club

12

GenderCommunity

3

Women only

13

Web

1

Both genders

22

Other

1

Mean age, years
23<6 months

6

45-64

11

6-12 months

16

N.R.

1

12-24 months

12≥24 months

BMI
25-29.9

2

1

Post-intervention follow-up (n = 11)

30-34.9

14<6 months

135-39.94

6-12 months

5

≥40

8

12-24 months

4

N.R.

7

≥24 months

1

Number ofstudies

Outcomes

Type of test

Weight change(n = 26)

Regression-based 2

Short-term(<12 months)

9

P and H/bootstrap 4

Long-term(≥12 months)

17

SEM/bootstrap

4

Mediation approach (and criteria)a

Physical activity(n = 19)

Baron and Kenny 3

Short-term(<12 months)

11

MacKinnon et al.

7Long-term(≥12 months)

8

Shrout and Bolger 2

Self-reported(MVPA)

15

MacArthurSelf-reported (LPA) 2

1

Quality assessment score

Self-reported(Energy Exp.)

1

Objectivec, d4

Quality score (EPHPP tool; n = 35)
Weak

7

Food/energy intake(n = 11)eModerate

15

Short-term (<12months)7Strong13

Long-term (≥12
months)4

Intervention length

25-44

Characteristics

Mediation analysis(n = 10)15

Health promotion

Arms

Number ofstudies

Quality score (Rhodes tool; n = 10)

Total energy intake 3

Low0

Fat/saturated fat

6

Moderate

10

Fruit/vegetable

7

High

0

Other (CH; protein) 1

Notes: NCT, non-controlled trial; NRCT, non-randomized controlled trial (forexample, a study comparing two interventions, but without a real control
group); RCT, randomized controlled trial (Note: two studies referred to post
hoc analyses of existing RCTs, for outcomes that were not planned originally);
N.R., not reported; P and H, Preacher and Hayes mediation procedures; SEM,
structural equation modeling; EPHPP, Effective Public Health Practice Project;
SCT, socio-cognitive theory; TTM, transtheoretical model; SDT, self-determination
theory; MVPA, moderate and vigorous physical activity; LPA, lifestyle physicalactivity; Energy Exp., energy expenditure. aTwo studies used more than one
mediation approach. bSix studies used more than one theoretical framework.
c
Objective assessments included accelerometry (n = 1), pedometry (n = 1), andactual attendance to a fitness club (n = 2). dTwo of the studies also assessedself-reported physical activity. eThree studies assessed more than one outcome.

Results
Study selection

The literature search yielded a total of 1,394 potentiallyrelevant records. Eight additional articles that were identified through manual searches and cross-referencing
were added, bringing the total number of potential

Teixeira et al. BMC Medicine (2015) 13:84

Page 6 of 16

Table 2 Mediators/predictors of short-term weight control (<12 months)
Putative mediators(categories)

Formal mediation analyses
Number of studies

All analyses

Times tested

Effect, %Number of studies

Times tested

Effect, %↑ Self-regulation skill use

6

12

92a

↑ Self-efficacy/barriers

6

9

67*b

↑ Body image/physical self-worth

2

6

67*

↓ Eating disinhibition

1

4

75*c

↑ Cognitive restraint

1

3

33*a

↑ Processes of change

1

2

50*

↑ Decisional balance (pros/cons)

1

1

100*

↓ Perceived hunger

1

1

100*c

↑ Positive outcome expectations

1

1

0

Notes: Blank spaces correspond to variables that were not tested using formal mediation analyses. Times tested refers to the number of times a variable wasanalyzed; Effect, % refers to the number of times an effect was found and is expressed as percentage. Since mediation analyses were not conducted, results areorganized according to the number of times each variable was tested in the overall analyses, depicted in the fifth column, in descent order. * ≥ 50% of theseeffects are based on correlational analyses. aTested twice in weak quality studies. bTested three times in weak quality studies. cTested in a weak quality study.
[↑] means that higher value of (variable) was associated with improved outcomes; [↓] means "lower value of (variable)" was associated with improved outcomes.

articles to 1,404. Of these, 770 abstracts were assessed
for eligibility (634 duplicates removed). After the initial
screening of titles and abstracts, 692 articles were excluded (Figure 1). Some articles were excluded for multiple reasons. Thirty-five articles describing 32 unique
lifestyle interventions met the eligibility criteria and were
therefore included [36-70]. Papers reporting on the sameintervention are identified in Additional files 2 and 3.
Study characteristics

The characteristics of included studies are summarized inTable 1 (for further details, see Additional files 2 and 3).
Most studies (n = 28) were randomized controlled trials,
mainly aiming at weight loss or weight loss maintenance(n = 21). Most interventions took place in university (n =15) or fitness club settings (n = 12), and most lasted morethan 6 months (n = 29). However, only 11 trials included afollow-up assessment period and, of these, more than half
were shorter than 12 months. Most studies were basedon, or at least informed by, one or more theories of behavior change; the most frequent being social cognitive theory(n = 23), the transtheoretical model (n = 5), and selfdetermination theory (n = 3). Eight interventions weregrounded in other theories, including group dynamics theory, problem solving model, theory of planned behavior,
health belief model, and self-regulation theory. Four studies
did not report using any theoretical framework. Samples
were mostly composed of obese individuals (n = 26), aged

Table 3 Mediators/predictors of medium/long-term weight control (≥12 months)
Putative mediators (categories)

Formal mediation analyses

All analyses

Number of studies

Times tested

Effect, %Number of studies

Times tested

Effect, %↓ Controlled motivation for PA

1

4

0

1

8

0

↑ Self-regulation skill use

3

3

75

6

6

83a

↑ Body image/self-worth

2

3

100

4

34

62*

↑ Self-efficacy/barriers

2

3

67

6

28

68*

↑ Autonomous motivation for PA

1

2

100

2

8

100*

↑ Flexible restraint

1

2

100

2

5

60

↑ Positive outcome expectations/beliefs

1

2

0

3

6

50

↓ Eating disinhibition

1

1

100

3

16

38*

↑ Cognitive restraint (total)

4

8

50*

↓ Perceived hunger

3

5

20*

↑ Coping mechanisms

1

2

0

Notes: Blank spaces correspond to variables that were not tested using formal mediation analyses. Times tested refers to the number of times a variable was analyzed;
Effect, % refers to the number of times an effect was found and is expressed as percentage. Results are organized according to the number of times each variable wastested in mediation analyses, depicted in the second column, in descent order. For variables not tested in mediation analyses, the fifth column (times tested in overall
analyses) should be taken as the reference. * ≥ 50% of these effects are based on correlational analyses. aTested once in a weak quality study. [↑] means that higher
value of (variable) was associated with improved outcomes; [↓] means "lower value of (variable)" was associated with improved outcomes.

Teixeira et al. BMC Medicine (2015) 13:84

Page 7 of 16

Table 4 Mediators/predictors of short-term physical activity (<12 months)
Putative mediators (categories)

Formal mediation analyses

All analyses

Number of studies

Times tested

Effect, %Number of studies

Times tested

Effect, %↑ Body image/physical self-worth

1

2

50

3

6

67*a

↑ Self-efficacy/barriers

1

1

0

10

15

67*

↑ Self-regulation skill usage

7

13

85*

↑ Motivational readiness

1

2

50

↑ Processes of change

1

2

50*

↑ Decisional balance (pros/cons)

1

1

100*

↑ Positive outcome expectations/beliefs

1

1

0

Notes: Blank spaces correspond to variables that were not tested in formal mediation analyses. Times tested refers to the number of times a variable was analyzed;
Effect, % refers to the number of times an effect was found and is expressed as percentage. Results are organized according to the number of times each variable
was tested in mediation analyses, depicted in the second column, in descent order. For variables not tested in mediation analyses, the fifth column (times tested
in overall analyses) should be taken as the reference. * ≥ 50% of these effects are based on correlational analyses. aTested once in a weak quality study. [↑] meansthat higher value of (variable) was associated with improved outcomes.

between 25 to 44 years old (n = 23), and 13 studies targeted
women only.
Twenty-six studies evaluated mediators/predictors ofweight change, of which 17 reported medium/long-term
outcomes; 19 studies evaluated mediators/predictors ofphysical activity, with 8 of them reporting medium/longterm outcomes; finally, 11 studies investigated dietary intake as the outcome measure, 4 of them in the medium/long-term. Weight-related measurements were performed
with calibrated digital scales, and weight changes wereexpressed in weight change percent from baseline (n = 9),
in kilograms (n = 10), as residualized scores regressed on
the baseline scores (n = 6), or as BMI changes (n = 3).
Regarding physical activity, objective measures were
employed in 4 studies (for example, accelerometry, pedometry) and self-reported instruments in 17 studies; ofthese, 6 used the Seven-Day Physical Activity Recall [71]and 6 studies used the Godin Leisure-Time Exercise
Questionnaire [72]. Dietary and caloric intake, indirectly

assessed through the number of servings, was collectedwith the Food Intake Questionnaire in most studies (n =5), followed by the Three-Day Food Records in most of
the studies (n = 3), and the Block Food-Frequency Questionnaire (n = 1).
Quality assessment

The overall results of the quality assessment can befound in Table 1 and the total quality score for eachstudy in Additional files 2 and 3 (for a detailed classification of each item and study see Additional file 7). Regarding the overall methodological quality of the studies,
13 studies were rated as ”strong”, 15 were classified as”moderate”, and 7 were rated as ”weak”. All included
studies scored strong on the Study design, as they wereexperimental. Thirteen studies were rated as weak regarding Blinding of participants (during recruitment)
and outcome assessors, 13 were rated as moderate, 8 asstrong, and 1 did not receive a rating, as it was a non-Table 5 Mediators/predictors of medium/long-term physical activity (≥12 months)
Putative mediators (categories)

Formal mediation analyses

All analyses

Number of studies

Times tested

Effect, %Number of studies

Times tested

Effect, %↓ Controlled motivation for PA

2

8

0

2

12

25*

↑ Autonomous motivation for PA

2

6

83

2

14

93*

a

↑ Self-efficacy/barriers

4

6

67

6

12

75b

↑ Positive outcome expectations/beliefs

2

3

0

3

3

0

↑ Self-regulation skill usage

2

2

50

3

3

67b

↑ Coping mechanisms

1

2

0

1

2

0

a

↑ Decisional balance (pros/cons)

1

2

0

↑ Cognitive restraint

1

3

0

↑ Processes of change

1

2

0

Notes: Blank spaces correspond to variables that were not tested in formal mediation analyses. Times tested refers to the number of times a variable was analyzed;
Effect, % refers to the number of times an effect was found and is expressed as percentage. Results are organized according to the number of times each variable
was tested in mediation analyses, depicted in the second column, in descent order. For variables not tested in mediation analyses, the fifth column (times tested
in overall analyses) should be taken as the reference. * ≥ 50% of these effects are based on correlational analyses. aTested once in a weak quality study. bTested
twice in weak quality studies. [↑] means that higher value of (variable) was associated with improved outcomes; [↓] means "lower value of (variable)" wasassociated with improved outcomes.

Teixeira et al. BMC Medicine (2015) 13:84

Page 8 of 16

Table 6 Mediators/predictors of short-term dietary intake (<12 months)
Putative mediators(categories)

Formal mediation analyses
Number of studies

All analyses

Times tested

Effect, %↑ Self-efficacy/barriers

Number of studies

Times tested

Effect, %

6

12

75*

↑ Self-regulation skill usage

5

12

75*

↑ Motivational readiness

1

4

0

Notes: Blank spaces correspond to variables that were not tested in formal mediation analyses. Times tested refers to the number of times a variable was analyzed;
Effect, % refers to the number of times an effect was found and is expressed as percentage. Since mediation analyses were not conducted, results are organizedaccording to the number of times each variable was tested in the overall analyses, depicted in the fifth column, in descent order. * ≥ 50% of these effects are
based on correlational analyses. [↑] means that higher value of (variable) was associated with improved outcomes.

randomized trial. All studies except two (one scoredweak and the other scored strong) scored moderate regarding Representativeness (selection bias). Regarding
reporting of Withdrawals and dropouts, 5 studies wererated as weak, 16 as moderate, and 14 as strong. Fourstudies scored weak in the adjustment of analysis for Confounders, 10 scored moderate, and 21 strong. In terms of
Data collection tools, 4 studies were rated as weak as they
did not provide information on the validity or reliability ofthe measures used, 11 were classified as moderate, and 17
as strong. Three studies were not rated as they used a larger dataset for which information on psychometric properties of the measures is already provided. All studies
scored strong in the use of Appropriate statistical analyses. In terms of Reporting, 30 studies were rated asstrong, and 5 studies as moderate.
In addition, studies including formal tests of mediation(n = 10) were classified as of moderate (n = 10) quality
on the mediation analysis checklist. None of the studies
reported conducting pilot studies to test mediation, andin all except two studies, there was no specific information regarding the power of the analysis to detect mediation. In only six studies were the outcomes controlled
for baseline values.
Mediators/predictors tested in studies with weakmethodological quality are identified in Tables 2, 3, 4, 5,
6, and 7. Overall, there appeared to be no association

between the methodological quality of the studies andthe results of the mediation analyses. Only 2 out of the 7
studies with a global weak score reported significant results for all mediator/predictors.

Mediators/predictors of weight control

Of the total number of studies investigating mediators/
predictors of weight control (n = 26), 9 looked into shortterm outcomes (<12 months) [47-49,51,52,54,57,62,70].
Twenty-one variables, grouped into nine categories, were
tested as mediators/predictors of short-term weight control (Table 2). None of the studies performed formal testsof mediation. In the overall analyses (in this case, all were
multivariate), self-regulation skill use emerged as the mostconsistent predictor of short-term weight control (consistent with mediation in 92% of the times it was tested [12times in 6 studies]). Other variables that appear promising
as mediators of short-term weight control were higher
self-efficacy (and/or lower perceived barriers) and morepositive body image, both consistent with mediation in
67% of the times they were tested (a total of 9 and 6 times,
respectively). In the case of self-efficacy, 2 (out of 6) studies presented with low methodological quality. Although
lower eating disinhibition also appears to find empiricalsupport in non-formal mediation analyses, these results
come from a single, weak quality study, and are

Table 7 Mediators/predictors of medium/long-term dietary intake (≥12 months)
Putative mediators (categories)

Formal mediation analyses

All analyses

Number of studies

Times tested

Effect, %Number of studies

Times tested

Effect, %↑ Self-efficacy/barriers

1

2

50

3

8

25

↑ Coping mechanisms

1

2

0

2

4

0

↑ Positive outcome expectations/beliefs

1

1

0

2

2

0

↑ Processes of change

1

8

0

↑ Cognitive restraint

1

6

0

↑ Decisional balance (pros/cons)

1

4

0

↑ Self-regulation skill usage

2

2

100a

Notes: Blank spaces correspond to variables that were not tested in formal mediation analyses. Times tested refers to the number of times a variable was analyzed;
Effect, % refers to the number of times an effect was found and is expressed as percentage. Results are organized according to the number of times each variable
was tested in mediation analyses, depicted in the second column, in descent order. For variables not tested in mediation analyses, the fifth column (times tested
in overall analyses) should be taken as the reference. aTested twice in weak quality studies. [↑] means that higher value of (variable) was associated with improved
outcomes.

Teixeira et al. BMC Medicine (2015) 13:84

Page 9 of 16

Additional records identified throughother sources (n =8)
Journal of Consulting and Clinical Psychology(n=1); Health Education Research (n=1);
International Journal of Obesity (n=1); Journalof Obesity (n=1); Journal of Behavioral
Medicine (n=1); International Journal of
Behavioral Nutrition and Physical Activity(n=1); The Permanente Journal (n=2)

Figure 1 Flow diagram of studies.

correlational in nature. There were no other consistent
mediators/predictors of short-term weight control.
Seventeen studies investigated potential mediators/predictors of long-term (≥12 months) weight outcomes, themain focus of the review [36,39,40,43-45,55,56,58,59,63-66].
Of these, six were RCTs that included formal tests of mediation [36,39,40,43-45]. Thirty variables, grouped in 12 categories, were tested as potential mediators/predictors(Table 3). The variables with stronger empirical support
in formal mediation studies were body image, which
was significant in all the times it was tested (3 times),and self-regulation skills, which was identified as a mediator in 67% of the times it was tested (2 times out of
3 studies). Self-efficacy was a significant mediator in 2of the 3 times it was tested. For autonomous motivationand flexible eating restraint, results appear promising
but derive from a single study in each case. Results observed in non-mediation analyses were consistent withthe most stringent analyses, especially those concerning
self-regulation skill use, autonomous motivation, andself-efficacy. For self-regulation skill use, significant effects were found in 83% of the 6 times it was tested,and every time in multivariate analyses. For autonomous motivation, results were consistent with mediationin all cases, but they originate from only two studies. On
the other hand, empirical support from non-mediation
analyses for other variables like body image and self-

efficacy appears comparatively weaker and correlationbased; yet, the number of times each of these variables
was tested is substantially higher (34 and 28 times, respectively). Eating disinhibition, which appeared to be an
additional predictor in the short-term, does not seem tobe consistent in the long-term provided that it was significant only in 38% of the 16 times it was tested. There wereno other consistent mediators/predictors of long-term
weight control.
Mediators/predictors of physical activity

Of the total number of studies investigating mediators/
predictors of physical activity (n = 19), 11 looked into
short-term outcomes (less than 6 months beyond the startof the intervention) [37,46,51-53,60,61,67-70]. Of these,
only one formally tested mediation [37]. Fourteen variables, grouped in seven categories, were tested as mediators/predictors of short-term weight control (Table 4).
Regarding mediation-specific results, body image emerged
as a significant mediator only in one of the two times it
was tested. In non-mediation studies, stronger empiricalsupport was found for self-regulation skill use, which wassignificant in 11 of the 13 times it was tested (corresponding to 7 different studies). Body image and self-efficacy appear to be promising as mediators of short-term physicalactivity, showing significant results in 4 (out of 6) and 10(out of 15) times they were tested, respectively. No otherTeixeira et al. BMC Medicine (2015) 13:84

consistent mediators/predictors of short-term physical activity were identified.
Eight studies analyzed mediators/predictors of long-term
physical activity [36,38,41-43,50,64,65], of which five used
formal tests of mediation [36,38,41-43]. Twenty-three variables, grouped in nine categories, were tested as predictors (Table 5). The main predictors of long-term physicalactivity were autonomous motivation and self-efficacy,
considering both mediation-specific analyses and the overall analyses. For autonomous motivation, results from twostudies showed that mediation analyses were significant in
83% of the times and overall analyses showed consistency
with mediation in 93% of the times (out of 14). For
self-efficacy, results originated from 6 different studies.
Mediation analyses were significant in 67% of the times
self-efficacy was tested (6 times); and in the overall analyses, results were consistent with mediation in 75% of
the times (out of 12). Controlled motivation was also
consistently unrelated with physical activity outcomes,
independent of the type of analyses performed. Finally,
self-regulation skill use appears to mediate long-term
physical activity in one out of two (formal mediation)and two out of the three (all analyses) times tested, butthese results derive from two studies with low methodological quality.
Mediators/predictors of dietary intake

Of the total number of studies (n = 11) investigating mediators/predictors of dietary intake, seven looked into
short-term outcomes [46,53,61,67-70] and four into
long-term outcomes [41,50,64,65]. Only one study formally tested mediation [41]. Seven variables (grouped inthree categories) were tested as mediators/predictors ofshort-term dietary intake, while 12 variables (grouped inseven categories) were tested in the long-term (Tables 6
and 7). Self-efficacy/barriers and self-regulation skill use
appear promising as mediators of dietary intake in the
short-term, both showing results consistent with mediation in 75% of the times they were tested (12 times outof 6 studies for self-efficacy, and 12 times out of 5 studies for self-regulation skills). No consistent mediators/
predictors were identified in the longer time frame. Yet,
self-efficacy was consistently unrelated with long-term
dietary intake, looking less promising as a mediator (results were consistent with mediation only in 2 of the 8times it was tested).

Discussion
This review sought to identify the most consistentindividual-level mediators of weight change, physical activity, and obesity-related dietary variables, in the context
of lifestyle obesity interventions aimed at overweight andobese adults. These mediators or predictors of intervention effects were assessed by self-report, and are thought

Page 10 of 16

to represent psychological mechanisms or processes bywhich interventions affect body weight, through changes
in energy-balance related behaviors. Note that this review
did not focus on the efficacy of the interventions’ main effects per se. However, mediation mechanisms can be evaluated even in the absence of main significant effects ofinterventions, particularly in controlled trials [20].
Special emphasis was given to variables tested as formal
mediators of changes in the outcomes of interest, as thisprovides the best possible inferences regarding causal determinants of behavior change [73]; to the extent a consistent mediator is identified, it can more confidently betargeted in future interventions of comparable characteristics. Moreover, because it is unlikely that any single factor(self-regulatory or otherwise) by itself will explain a large
share of variance of change in complex behaviors suchas physical activity and diet (as a result of an intervention), the identification of groups of significant predictors, which can be then discussed in the context ofcurrent theories of behavior change, can additionally contribute to understanding the role of theory in health behavior change [74,75].
As in many systematic reviews of behavior change interventions, the diversity of studies available - reflected on asimilarly diverse set of independent (and dependent) variables, study designs, measurement methods, populations
represented, and so forth - is a substantial limitation. In
the present review, the large number of predictors per
study, combined with substantial heterogeneity in study
length, type and format of interventions (for example,
web-based, face to face, group-based), and assessments
employed for each variable made the task at hand especially difficult. In this scenario, the fact that several variables were identified as predictors or, in some cases,
actual mediators of intervention-related change in weightcontrol and physical activity is encouraging. Specifically,
the present review shows that positive changes in body
image, in autonomous motivation for physical activity, in
self-efficacy (and fewer perceived barriers), and in the useof self-regulation skills (such as self-monitoring) arepromising aspects that may explain the variability of results in current lifestyle obesity treatment interventions.
Increases in flexible restraint could also be in this groupwith respect to weight outcomes, but with lower inference.
Therefore, these are currently the best evidence-based
candidates to target in future individual-level, real-worldinterventions in this domain.
Some qualifications to the previous conclusions are ofnote. First, for short-term results, formal tests of mediation were only reported for one of the outcomes ofinterest (physical activity) and taking into account only
two mediators (body image/self-worth and self-efficacy/barriers). Second, there are currently too few studies using
dietary variables as outcomes to allow us to draw

Teixeira et al. BMC Medicine (2015) 13:84

meaningful conclusions, and only one study tested formal
mediation for both time frames. Third, the external validity of some of the reported findings, such as regarding
self-regulation skills and autonomous motivation, may belimited, because these findings were derived from few
studies conducted by a small number of research groups,
using similar study designs.
Body image appeared important as a mechanism leading to change in body weight in several studies. Body
image is a multidimensional concept [76] that depicts attitudes, perceptions, and in some cases behaviors associated with mental representations of one’s body (or someof its parts) [77,78]. Poor body image often reflects ahigh level of concern with body weight or shape, what is
known as dysfunctional investment in body image, whenbody esteem occupies an excessive role as a determinantof overall self-esteem [79]. Previous reviews [2,22] haveidentified poor body image as a predictor of less successat body weight loss (or, conversely, better body image asa positive factor in obesity treatment interventions). Potential reasons for this association range from excessive
psychological pressure leading to more rigid and inconsistent eating regulation [80-82] - poor body image being
associated with a history of failed attempts and thus being a marker for other physiological, psychological, or
socio-environmental risk factors for weight gain/regain[83,84] - to motivational factors in which external pressures and goals predominate but tend not to produce
behavior change in consistent or healthy ways (for example, wanting to be thin for reasons related to social
comparison and perceived desirability) [85-87].
Autonomous motivation, a concept derived from selfdetermination theory (SDT, [88]), generally indicates thedegree to which individuals self-endorse, feel that they
have a choice about, and attribute deeply reflected value
to a certain behavior. In contrast with the most common
quantitative view of motivation (how much?), the levelof autonomy represents a qualitative analysis of people’s
psychological energy to act, which is perceived as internal(reflecting a sense of “ownership” over the behavior). Autonomous motivation is often associated with goals suchas pursuing positive personal challenges, attaining/preserving health and well-being, social affiliation, personal
development, and self-expression [89]. Additionally, because self-determined, well-internalized behaviors are associated with the satisfaction of the needs for autonomy,competence, and relatedness - and with the feelings of internal coherence and well-being that are thought toemerge from those experiences - this provides an explanation for the behavior to be pursued consistently [89]. A
recent meta-analysis [90] and other reviews provide empirical support for both the SDT motivation model andthe association of autonomous motivation with health behavior change in different areas [91].

Page 11 of 16

Self-efficacy and perceived barriers are common variables in several theoretical frameworks concerned withhealth behaviors [92,93]. Self-efficacy measures one’s confidence to successfully implement a course of action by
successfully organizing internal and external resources[94]. Although efficacy can be assessed towards other aspects of behavior regulation, it is commonly conceptualized and assessed as “barriers efficacy” or confidence toovercome internal or external obstacles that may stand in
the way of one’s actions. Indeed, the correlation between
self-efficacy and perceived barriers is usually high [56](which explains our decision to group these variables inthe same category). Although conceptual differences exist,
self-efficacy is often equated to the concept of perceived
behavior control (from the theory of planned behavior) or
perceived competence (as used in self-determination theory). In practical and simple terms, enhancing confidence
and competence about a given health behavior appears tobe helpful in overcoming barriers - namely in initial stagesof adoption - and is often a first step to increase and improve motivation for change.
Flexible eating restraint involves regulating one’s foodintake so that no particular behavior is forbidden andthus subject to rigid control and scrutiny [95]. Flexible
restraint is generally associated with less internal pressure to diet and a more gradual understanding of the diet’s impact on energy balance. It stands in opposition torigid restraint [96]. Although, in the past, cognitive restraint was measured as a unified concept, its separation
into flexible and rigid dimensions is increasingly frequent in obesity studies and has proven useful in understanding diet and weight regulation, particularly in thelong-term. For example, we found that flexible, but not
rigid or total restraint, mediated 24-month weight loss inoverweight women [39] and, in the present review, results
for the total restraint scale and the flexible scale also differed, as in other studies [97]. More broadly, psychologicalflexibility appears to predict health and psychologicalwell-being [97], is thought to reflect more committed,
values-based goal pursuit [98,99] and is considered a hallmark of self-determination [89], factors which may help
explain successful health behavior self-regulation.
Finally, the use of certain self-regulation skills, for instance, monitoring weight, diet, and activity, as well as
employing goal setting and planning techniques, was also
identified as a relatively consistent predictor of successful
outcomes, most especially in the shorter-term analyses. In
brief, some of these skills may be important for people toultimately act on their positive intentions. Sometimes
associated with self-regulation theories (cf. [100]) thesevariables are more skill-based (in some cases, they are
discrete behaviors in themselves) and somewhat different than the previous set of predictors, more intrapsychic. Notably, recent behavior change models focusing

Teixeira et al. BMC Medicine (2015) 13:84

on the “intention-behavior gap” (see, for example,[7,101,102]) make the distinction between motivational
and implementation phases (sometimes referred to as“volitional” or “post-motivational”), with self-regulation
skills reviewed in the present study falling in the latter
phase [103]. Results from the present review suggestthat some combination of motivational and implementation factors is important. Although this needs confirmation, there is some indication that the latter may beespecially useful in early stages of behavioral adoption,
whereas motivational factors may be operative along theentire continuum from adoption to maintenance, as
highlighted recently in a separate study [104].
In looking at the collective findings from the present
analysis, the temptation to interpret them in an integrative
way is unavoidable. In principle, there should be “a logic”
as to why this set of predictors emerged and not a different one, even considering the intrinsic limitations of theavailable data (see below). In this exercise, we are informed by our own research, for instance, linking improved eating regulation, including flexible eating, withimproved body image [105] and with exercise autonomous motivation [18] and also by other studies. For example, recently, in a large dataset of women in New
Zealand, autonomous motivation for eating was associatedwith less binge eating and slower speed of eating (and amuch healthier diet), indicative of improved eating selfregulation [106]. The literature looking at relations between body image and eating behavior is also fertile insuggesting a close association between improved body
image, improved eating regulation, and better weight control (see, for example, [55,87]). In this respect, an attempt
was recently made to provide an integrative view of eating
regulation and weight maintenance, which also includes
an explanation for the etiology and role of body image
concerns and disordered eating, while considering motivational and self-regulatory aspects [80]. It links goals (suchas appearance versus health focus) and the predominant
approach to eating regulation (such as rigid versus flexible
restraint; focus on quality versus quantity) with the satisfaction of the needs for competence, autonomy, and relatedness, resulting in more or less adaptive diet andweight regulation (see Figures one and two in [80] formore details). The evidence from other recent systematic
reviews and meta-analyses, showing that more autonomous forms of health behavior regulation, in physical activity [91], weight control [2], and in health more generally[90] are predictive of better adherence and improved outcomes, is also consistent with the relationships found in
the present study.
While some limitations of the present work have beenpresented above, others need to be considered wheninterpreting the findings of this review. The large heterogeneity in the study-specific mediation methods andPage 12 of 16

estimates reported in the primary studies prevented us
from deriving a single comparable estimate for each variable and reporting on the pooled magnitude of mediation
effects. This variability, as well as the limited number ofstudies for each mediator, did not recommend the use ofmeta-analytical techniques to pool data across studies. In
this review, we used a narrative synthesis approach including vote counting of the number of significant mediation
effects for a given variable in relation to all tests of mediation available for that variable. Although this method
is not as robust as other quantitative approaches tosynthesize data, it provides a reasonably good indicationof whether that variable can be identified as a formal mediator (or a variable consistent with mediation) of eachintervention, for each specific outcome. It should also be
considered that in the primary studies included in this review, statistical significance of the mediation effects was
typically the parameter used to infer that a given variable
mediated the intervention effect.
Some studies were characterized by poor methodological quality, and none of the studies employing formal
mediation analysis presented strong methodological quality. Nonetheless, for most mediators we did not find an association between the methodological quality of thestudies and the direction/strength of the effects reported.
As exceptions, we did find that in the analyses in which
eating disinhibition had consistent significant effects, thiswas tested mostly in studies of poor quality. A similar result was found for self-regulation skills for the long-term
effects in physical activity and dietary intake. Future reviews would benefit from sensitivity analyses. The diversity of outcome measures, especially for physical activity,
is also a limitation, as different types of physical activity
may be predicted by different factors. The fact that thecoding of study characteristics was based on the description provided in the articles is also limiting, given that inmany cases these descriptions did not provide enough information regarding mediation analysis, which measures
were used, or the content of the interventions. Futurestudies should provide more detail on the content of theinterventions and self-regulation factors addressed to facilitate data interpretation and inference. The choice ofthe year 2000 to start our search was largely arbitrary andcould be seen as a limitation. Finally, the inclusion of noncontrolled trials in some of the analyses could be viewed
as a limiting factor; on balance, we found this an acceptable compromise (for non-mediation studies only) against
the prospect of altogether excluding several studies fromthis review.
These limitations notwithstanding, this study identified
a small number of intervention-related aspects with supporting evidence for an important role played in the difficult path of successful weight control. Since all evidence
was derived from intervention studies and independentTeixeira et al. BMC Medicine (2015) 13:84

variables were analyzed as to their mediating position in
the behavioral causal chain (although with variable levels
of inference), we believe this is a first step leading to theirformal inclusion in recommendations for lifestyle programs aiming at weight control. In practical terms, this
could mean that strategies or “behavior change techniques” [107] identified as the most effective to specificallychange these variables (for example, self-efficacy [108] orautonomous motivation [109]) would be integrated into
future interventions in a widespread fashion, and that
health professionals would be appropriately trained on
how to target them regularly in their practices. It could
also mean that bedside instruments (such as brief questionnaires or interview items) would be made available forprofessionals to quickly assess their patients for these variables (for example, to assess their body image or level ofself-regulation skill use [1,110]) and tailor interventions to
the most relevant targets for each person. In the area ofmotivation enhancement, the techniques and instruments
used in motivational interviewing [111,112] are a good
example of such potential application in medicine andhealth care.

Conclusion
In conclusion, based on the scientific literature to date, autonomous motivation, self-efficacy, and self-regulation
skill use emerge as the most promising individual-level
mediators of positive weight outcomes and increased
physical activity. For long-term weight control, promoting
a positive body image and flexible eating control may also
be important. These aspects represent potential entry
points for future lifestyle obesity interventions in adults.
Additional files
Additional file 1: An Example of the Conducted Search (Medline).
Additional file 2: Characteristics of Included Studies With Formal
Mediation Analyses.
Additional file 3: Characteristics of Included Studies Without
Formal Mediation Analyses.
Additional file 4: Indirect effects’ estimates in studies with formal
mediation analysis.
Additional file 5: EPHPP Quality Assessment Tool (adapted by the
SPOTLIGHT Consortium).
Additional file 6: Complete results for weight change, physicalactivity and dietary behaviors.
Additional file 7: Consensus Ratings of Methodological Study
Quality.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
PJT, JB, JL, JMO, HR, and IB conceived the study, and PJT and EVC developeda systematic review protocol. EVC and MM conducted the literature searchand selected the studies based on the title and the abstract. EVC and MM
extracted and coded the data from all studies. Study outcomes were

Page 13 of 16

summarized by PJT, EVC, and MM. They wrote the initial draft of themanuscript, and JB, JL, JMO, HR, and IB made significant revisions andcontributions. All authors read and approved the final manuscript.
Acknowledgements
This work is part of the SPOTLIGHT project and is funded by the Seventh
Framework Program (CORDIS FP7) of the European Commission, HEALTH(FP7-HEALTH-2011-two-stage), Grant agreement no. 278186. The content of
this article reflects only the authors’ views, and the European Commission is
not liable for any use that may be made of the information contained
therein.
Other information
This systematic review is reported in accordance with the PRISMA statement
for reporting systematic reviews [21].
Author details
Interdisciplinary Centre for the Study of Human Performance (CIPER), Facultyof Human Kinetics, University of Lisbon, Lisbon, Portugal. 2European Centre
on Health of Societies in Transition, London School of Hygiene and Tropical
Medicine, London, UK. 3Department of Nutrition Pitié-Salpetrière (AP-HP),
Université Pierre et Marie Curie-Paris 6, Paris, France. 4UREN (Nutritional
Epidemiology Research Unit), Université Paris 13, Sorbonne Paris Cité, Inserm(U557), Inra (U1125), Cnam, F-93017, Bobigny, France. 5Department ofMovement and Sport Sciences, Ghent University, Ghent, Belgium.
6
Department of General Practice and Elderly Care, The EMGO Institute for
Health and Care Research, VU University Medical Center, Amsterdam, theNetherlands. 7Department of Epidemiology and Biostatistics, The EMGO
Institute for Health and Care Research, VU University Medical Center,
Amsterdam, the Netherlands.
1

Received: 12 November 2014 Accepted: 11 March 2015

References
1. Sniehotta FF, Scholz U, Schwarzer R. Bridging the intention-behaviour gap:
planning, self-efficacy, and action control in the adoption and maintenanceof physical exercise. Psychol Health. 2005;20:143–60.
2. Teixeira PJ, Going SB, Sardinha LB, Lohman TG. A review of psychosocial
pre-treatment predictors of weight control. Obes Rev. 2005;6:43–65.
3. Maes S, Karoly P. Self-regulation assessment and intervention in physicalhealth and illness: a review. Appl Psychol Int Rev. 2005;54:267–99.
4. Bandura A. The primacy of self-regulation in health promotion. Appl Psychol
Health Well Being. 2005;54:245–54.
5. Ajzen I. From intentions to actions: a theory of planned behavior. In:
Kuhl J, Beckmann J, editors. Action control. Berlin Heidelberg: Springer;
1985. p. 11–39.
6. Deci EL, Ryan RM. Intrinsic motivation and self-determination in humanbehavior. New York: Plenum; 1985. p. 113–48.
7. Heckhausen H, Gollwitzer P. Thought contents and cognitive functioning inmotivational versus volitional states of mind. Motiv Emot. 1987;11:101–20.
8. Schwarzer R. Modeling health behavior change: how to predict and modify
the adoption and maintenance of health behaviors. Appl Psychol HealthWell Being. 2008;57:1–29.
9. Carver CS, Scheier MF. On the self-regulation of behavior. New York:
University Press; 1998.
10. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, et al.
2013 AHA/ACC/TOS guideline for the management of overweight andobesity in adults: a report of the American College of Cardiology/American
Heart Association Task Force on Practice Guidelines and The Obesity
Society. Circulation. 2014;129:S139–40.
11. Ackermann RT, Finch EA, Brizendine E, Zhou H, Marrero DG. Translating thediabetes prevention program into the community. The DEPLOY Pilot StudyAm J Prev Med. 2008;35:357–63.
12. Eriksson MK, Franks PW, Eliasson M. A 3-year randomized trial of lifestyleintervention for cardiovascular risk reduction in the primary care setting:
the Swedish Björknäs study. PLoS One. 2009;4:e5195.
13. Kinmonth AL, Wareham NJ, Hardeman W, Sutton S, Prevost AT, Fanshawe T,
et al. Efficacy of a theory-based behavioural intervention to increase physicalactivity in an at-risk group in primary care (ProActive UK): a randomised trial.
Lancet. 2008;371:41–8.

Teixeira et al. BMC Medicine (2015) 13:84

14. van Sluijs EMF, van Poppel MNM, van Mechelen W. Stage-based lifestyleinterventions in primary care: are they effective? Am J Prev Med.
2004;26:330–43.
15. Lakerveld J, Bot SD, Chinapaw MJ, van Tulder MW, Kostense PJ, Dekker JM,
et al. Motivational interviewing and problem solving treatment to reducetype 2 diabetes and cardiovascular disease risk in real life: a randomized
controlled trial. Int J Behav Nutr Phys Act. 2013;10:47.
16. Wing RR, Hill JO. Successful weight loss maintenance. Annu Rev Nutr.
2001;21:323–41.
17. Rossner S, Hammarstrand M, Hemmingsson E, Neovius M, Johansson K.
Long-term weight loss and weight-loss maintenance strategies. Obes Rev.
2008;9:624–30.
18. Mata J, Silva MN, Vieira PN, Carraça EV, Andrade AM, Coutinho SR, et al.
Motivational “spill-over” during weight control: increased self-determinationand exercise intrinsic motivation predict eating self-regulation. Health
Psychol. 2009;28:709–16.
19. Lakerveld J, Brug J, Bot S, Teixeira P, Rutter H, Woodward E, et al.
Sustainable prevention of obesity through integrated strategies: the
SPOTLIGHT project’s conceptual framework and design. BMC Public Health.
2012;12:1471–2458.
20. Kraemer HC, Wilson GT, Fairburn CG, Agras WS. Mediators and moderatorsof treatment effects in randomized clinical trials. Arch Gen Psychiatry.
2002;59:877–83.
21. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al.
The PRISMA statement for reporting systematic reviews and meta-analysesof studies that evaluate health care interventions: explanation and elaboration.
PLoS Med. 2009;6:e1000100.
22. Elfhag K, Rossner S. Who succeeds in maintaining weight loss? A conceptual
review of factors associated with weight loss maintenance and weight regain.
Obes Rev. 2005;6:67–85.
23. Friedman MA, Brownell KD. Psychological correlates of obesity: moving to
the next research generation. Psychol Bull. 1995;117:3–20.
24. Barte J, ter Bogt NC, Bogers RP, Teixeira PJ, Blissmer B, Mori TA, et al.
Maintenance of weight loss after lifestyle interventions for overweight andobesity, a systematic review. Obes Rev. 2010;11:899–906.
25. Franz MJ, Van Wormer JJ, Crain AL, Boucher JL, Histon T, Caplan W,
et al. Weight loss outcomes: a systematic review and meta-analysis ofweight loss clinical trials with a minimum 1-year follow-up. J Am Diet
Assoc. 2007;107:1755–67.
26. Curioni CC, Lourenco PM. Long-term weight loss after diet and exercise: asystematic review. Int J Obes (Lond). 2005;29:1168–74.
27. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, et al. The
Cochrane Collaboration’s tool for assessing risk of bias in randomised trials.
Br Med J. 2011;343:d5928.
28. Baron RM, Kenny DA. The moderator-mediator variable distinction in social
psychological research: conceptual, strategic, and statistical considerations.
J Pers Soc Psychol. 1986;51:1173–82.
29. MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol.
2007;58:593–614.
30. Thomas BH, Ciliska D, Dobbins M, Micucci S. A process for systematicallyreviewing the literature: providing the research evidence for public healthnursing interventions. Worldviews Evid Based Nurs. 2004;1:176–84.
31. Deeks JJ, Dinnes J, D’Amico R, Sowden AJ, Sakarovitch C, Song F, et al.
Evaluating non-randomised intervention studies. Health Technol Assess.
2003;7:iii–x.1–173.
32. Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, Stroup DF. Improving thequality of reports of meta-analyses of randomised controlled trials: theQUOROM statement. Lancet. 1999;354:1896–900.
33. Mackenbach JD, Rutter H, Compernolle S, Glonti K, Oppert JM, Charreire H,
et al. Obesogenic environments: a systematic review of the association
between the physical environment and adult weight status, the SPOTLIGHT
project. BMC Public Health. 2014;14:233.
34. Lubans DR, Foster C, Biddle S. A review of mediators of behavior ininterventions to promote physical activity among children and adolescents.
Prev Med. 2008;47:463–70.
35. Rhodes RE, Pfaeffli LA. Mediators of physical activity behaviour change
among adult non-clinical populations: a review update. Int J Behav Nutr
Phys Act. 2010;7:37–48.
36. Silva MN, Markland D, Carraça EV, Vieira PN, Coutinho SR, Minderico CS,
et al. Exercise autonomous motivation predicts 3-year weight loss inwomen. Med Sci Sports Exerc. 2011;43:728–37.

Page 14 of 16

37. Annesi JJ, Unruh JL, Marti CN, Gorjala S, Tennant G. Effects of the coachapproach intervention on adherence to exercise in obese women:
assessing mediation of social cognitive theory factors. Res Q Exerc Sport.
2011;82:99–108.
38. Roesch SC, Norman GJ, Villodas F, Sallis JF, Patrick K. Intervention-mediatedeffects for adult physical activity: a latent growth curve analysis. Soc Sci
Med. 2010;71:494–501.
39. Teixeira PJ, Silva MN, Coutinho SR, Palmeira AL, Mata J, Vieira PN, et al.
Mediators of weight loss and weight loss maintenance in middle-aged
women. Obesity (Silver Spring). 2010;18:725–35.
40. Perri MG, Limacher MC, Durning PE, Janicke DM, Lutes LD, Bobroff LB, et al.
Extended-care programs for weight management in rural communities: thetreatment of obesity in underserved rural settings (TOURS) randomized trial.
Arch Intern Med. 2008;168:2347–54.
41. Burke V, Beilin LB, Cutt HE, Mansour J, Mori TA. Moderators and mediatorsof behaviour change in a lifestyle program for treated hypertensives: arandomized controlled trial (ADAPT). Health Educ Res. 2008;23:583–91.
42. Silva MN, Markland DM, Vieira PN, Coutinho SR, Carraça EV, Palmeira AL,
et al. Helping overweight women become more active: need support andmotivational regulations for different forms of physical activity. PsycholSports Exerc. 2010;11:591–601.
43. Anderson-Bill ES, Winett RA, Wojcik JR, Williams DM. Aging and the social
cognitive determinants of physical activity behavior and behavior change:
evidence from the Guide to Health trial. J Aging Res. 2011. doi:10.4061/
2011/505928.
44. Palmeira AL, Markland DA, Silva MN, Branco TL, Martins SC, Minderico CS,
et al. Reciprocal effects among changes in weight, body image, and otherpsychological factors during behavioral obesity treatment: a mediation
analysis. Int J Behav Nutr Phys Act. 2009;6:9.
45. Coughlin JW, Gullion CM, Brantley PJ, Stevens VJ, Bauck A, Champagne CM,
et al. Behavioral mediators of treatment effects in the weight loss
maintenance trial. Ann Behav Med. 2013;46:369–81.
46. Lee RE, O’Connor DP, Smith-Ray R, Mama SK, Medina AV, Reese-Smith JY, et al.
Mediating effects of group cohesion on physical activity and diet in women ofcolor: health is power. Am J Health Promot. 2012;26:e116–25.
47. Annesi JJ, Vaughn LL. Relationship of exercise volume with change indepression and its association with self-efficacy to control emotional eating
in severely obese women. Adv Prev Med. 2011. doi:104061/2011/514271.
48. Annesi JJ, Gorjala S. Relationship of exercise program participation withweight loss in adults with severe obesity: assessing psychologically based
mediators. South Med J. 2010;103:1119–23.
49. Linde JA, Rothman AJ, Baldwin AS, Jeffery RW. The impact of self-efficacy on
behavior change and weight change among overweight participants in aweight loss trial. Health Psychol. 2006;25:282–91.
50. Riebe D, Blissmer B, Greene G, Caldwell M, Ruggiero L, Stillwell KM, et al.
Long-term maintenance of exercise and healthy eating behaviors inoverweight adults. Prev Med. 2005;40:769–78.
51. Gallagher KI, Jakicic JM, Napolitano MA, Marcus BH. Psychosocial factors
related to physical activity and weight loss in overweight women. Med Sci
Sports Exerc. 2006;38:971–80.
52. Annesi JJ, Marti CN. Path analysis of exercise treatment-induced changes in
psychological factors leading to weight loss. Psychol Health. 2011;26:1081–98.
53. Annesi JJ. Self-regulatory skills usage strengthens the relations of self-efficacy
for improved eating, exercise, and weight in the severely obese: toward
an explanatory model. Behav Med. 2011;37:71–6.
54. Annesi JJ. Behaviorally supported exercise predicts weight loss in obese
adults through improvements in mood, self-efficacy, and self-regulation,rather than by caloric expenditure. Pers J. 2011;15:23–7.
55. Palmeira AL, Branco TL, Martins SC, Minderico CS, Silva MN, Vieira PN, et al.
Change in body image and psychological well-being during behavioral
obesity treatment: Associations with weight loss and maintenance. Body
Image. 2010;7:187–93.
56. Teixeira PJ, Going SB, Houtkooper LB, Cussler EC, Metcalfe LL, Blew RM, et al.
Exercise motivation, eating, and body image variables as predictors ofweight control. Med Sci Sports Exerc. 2006;38:179–88.
57. Annesi JJ. Relations of changes in physical self-appraisal and perceived energy
with weight change in obese women beginning a supported exercise andnutrition information program. Soc Behav Personal Int J. 2007;35:295–300.
58. Moore SD, King AC, Kiernan M, Gardner CD. Outcome expectations andrealizations as predictors of weight regain among dieters. Eat Behav.
2011;12:60–3.

Teixeira et al. BMC Medicine (2015) 13:84

59. Haapala I, Barengo NC, Biggs S, Surakka L, Manninen P. Weight loss by mobile
phone: a 1 year effectiveness study. Public Health Nutr. 2009;12:2382–91.
60. Annesi JJ, Whitaker AC. Weight loss and psychologic gain in obese women participants in a supported exercise intervention. The Permanente Journal.
2008;12:36–45.
61. Annesi JJ. Supported exercise improves controlled eating and weight
through its effects on psychosocial factors: extending a systematic research
program toward treatment development. Pers J. 2012;16:7–18.
62. Karhunen L, Lyly M, Lapveteläinen A, Kolehmainen M, Laaksonen DE,
Lähteenmäki L, Poutanen K. Psychobehavioural factors are more strongly
associated with successful weight management than predetermined satiety
effect or other characteristics of diet. J Obes. 2012. doi:10.1155/2012/274068.
63. Wing RR, Papandonatos G, Fava JL, Gorin AA, Phelan S, McCaffery J, et al.
Maintaining large weight losses: the role of behavioral and psychologicalfactors. J Consult Clin Psychol. 2008;76:1015–21.
64. McGuire MT, Jeffery RW, French SA, Hannan PJ. The relationship between
restraint and weight and weight-related behaviors among individuals in acommunity weight gain prevention trial. Int J Obes Relat Metab Disord.
2001;25:574–80.
65. Burke V, Mansour J, Mori TA, Beilin LB, Cutt HE, Wilson A. Changes incognitive measures associated with a lifestyle program for treatedhypertensives: a randomized controlled trial (ADAPT). Health Educ Res.
2008;23:202–17.
66. Warziski MT, Sereika SM, Styn MA, Music E, Burke LE. Changes in self-efficacy
and dietary adherence: the impact on weight loss in the PREFER study.
J Behav Med. 2008;31:81–92.
67. Annesi JJ, Mareno N. Temporal aspects of psychosocial predictors of increased
fruit and vegetable intake in adults with severe obesity: mediation by physicalactivity. J Community Health. 2014;39:454–63.
68. Annesi J. Effects of treatment differences on psychosocial predictors of
exercise and improved eating in obese, middle-age adults. J Phys Act
Health. 2013;10:1024–31.
69. Annesi JJ, Porter KJ. Reciprocal effects of treatment-induced increases in
exercise and improved eating, and their psychosocial correlates, in obese
adults seeking weight loss: a field-based trial. Int J Behav Nutr Phys Act.
2013;10:133.
70. Fitzpatrick SL, Bandeen-Roche K, Stevens VJ, Coughlin JW, Rubin RR,
Brantley PJ, et al. Examining the behavioral processes through which
lifestyle interventions promote weight loss: results from the PREMIER Trial.
Obesity (Silver Spring). 2014;22:1002–7.
71. Blair SN, Haskell WL, Ho P, Paffenbager Jr RS, Vranizan KM, Farquhar JW,
et al. Assessment of habitual physical activity by a seven-day recall in acommunity survey and controlled experiments. Am J Epidemiol.
1985;122:794–804.
72. Godin G, Shephard RJ. Godin leisure-time exercise questionnaire. Med Sci
Sports Exerc. 1997;29:S36–8.
73. Hayes AF. Introduction to mediation, moderation, and conditional process
analysis: a regression-based approach. NY: Guilford Press; 2013.
74. Hagger MS. Theoretical integration in health psychology: unifying ideas andcomplementary explanations. Br J Health Psychol. 2009;14:189–94.
75. Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP.
A refined taxonomy of behaviour change techniques to help peoplechange their physical activity and healthy eating behaviours: the CALO-RE
taxonomy. Psychol Health. 2011;26:1479–98.
76. Cash TF. Cognitive-behavioral perspectives on body image. In: Cash TF,
Pruzinsky T, editors. Body image: a handbook of theory, research, andclinical practice. New York: Guilford Press; 2002. p. 38–46.
77. Cash TF. A ‘negative body image’. In: Cash TF, Pruzinsky T, editors. Body
image: a handbook of theory, research, and clinical practice. New York:
Guilford Press; 2002. p. 269–76.
78. Avalos L, Tylka TL, Wood-Barcalow N. The body appreciation scale: development
and psychometric evaluation. Body Image. 2005;2:285–97.
79. Cash TF, Melnyk SE, Hrabosky JI. The assessment of body image investment:
an extensive revision of the appearance schemas inventory. Int J Eat Disord.
2004;35:305–16.
80. Verstuyf J, Patrick H, Vansteenkiste M, Teixeira PJ. Motivational dynamics ofeating regulation: a self-determination theory perspective. Int J Behav Nutr
Phys Act. 2012. 9:21. doi:10.1186/1479-5868-9-21.
81. Pelletier LG, Dion SC. An examination of general and specific motivational
mechanisms for the relations between body dissatisfaction and eating
behaviors. J Soc Clin Psychol. 2007;26:303–33.

Page 15 of 16

82. Boone L, Soenens B, Braet C. Perfectionism, body dissatisfaction, andbulimic symptoms: the intervening role of perceived pressure to be thinand thin ideal internalization. J Soc Clin Psychol. 2011;30:1043–68.
83. Fabricatore A, Wadden T. Psychological functioning of obese individuals.
Diabetes Spectrum. 2003;16:245–52.
84. Schwartz MB, Brownell KD. Obesity and body image. Body Image. 2004;1:43–56.
85. Verstuyf J, Vansteenkiste M, Soenens B. Eating regulation and bulimic
symptoms: the differential correlates of health-focused and appearancefocused eating regulation. Body Image. 2012;9:108–17.
86. Thogersen-Ntoumani C, Ntoumanis N, Nikitaras N. Unhealthy weight control
behaviours in adolescent girls: a process model based on self-determination
theory. Psychol Health. 2010;25:535–50.
87. Pelletier LG, Dion SC, D’Angelo MS, Reid RD. Why do you regulate what you
eat? Relationships between forms of regulation, eating behaviors, sustained
dietary behavior change, and psychological adjustment. Motiv Emot.
2004;28:245–77.
88. Ryan RM, Deci EL. Self-regulation and the problem of human autonomy:
does psychology need choice, self-determination, and will? J Pers.
2006;74:1557–85.
89. Deci EL, Ryan RM. The ‘what’ and ‘why’ of goal pursuits: human needs andthe self-determination of behavior. Psychoanal Inq. 2000;11:227–68.
90. Ng JYY, Ntoumanis N, Thøgersen-Ntoumani C, Deci EL, Ryan RM, Duda JL,
et al. Self-determination theory applied to health contexts: a meta-analysis.
Perspect Psychol Sci. 2012;7:325–40.
91. Teixeira PJ, Carraça EV, Markland D, Silva MN, Ryan RM. Exercise, physicalactivity, and self-determination theory: a systematic review. Int J Behav Nutr
Phys Act. 2012;9:78.
92. Armitage CJ, Conner M. Social cognition models and health behaviour: astructured review. Psychol Health. 2000;15:173–89.
93. Williams SL, French DP. What are the most effective intervention techniques
for changing physical activity self-efficacy and physical activity behaviour and are they the same? Health Educ Res. 2011;26:308–22.
94. Bandura A. Self-efficacy: the exercise of control. New York: W.H. Freeman
and Company; 1997.
95. Westenhoefer J. Dietary restraint and disinhibition: is restraint a homogeneous
construct? Appetite. 1991;16:45–55.
96. Bryant EJ, King NA, Blundell JE. Disinhibition: its effects on appetite andweight regulation. Obes Rev. 2008;9:409–19.
97. Kashdan TB, Rottenberg J. Psychological flexibility as a fundamental aspectof health. Clin Psychol Rev. 2010;30:865–78.
98. McCracken LM. Committed action: an application of the psychologicalflexibility model to activity patterns in chronic pain. J Pain. 2013;14:828–35.
99. Hayes SC, Luoma JB, Bond FW, Masuda A, Lillis J. Acceptance andcommitment therapy: model, processes and outcomes. Behav Res Ther.
2006;44:1–25.
100. Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniquesin healthy eating and physical activity interventions: a meta-regression. Health
Psychol. 2009;28:690–701.
101. Hagger MS, Luszczynska A. Implementation intention and action planninginterventions in health contexts: state of the research and proposals for theway forward. Appl Psychol Health Well Being. 2014;6:1–47.
102. Sniehotta FF. Towards a theory of intentional behaviour change: plans,planning, and self-regulation. Br J Health Psychol. 2009;14:261–73.
103. Hagger M, Chatzisarantis N. An integrated behavior change model forphysical activity. Exerc Sport Sci Rev. 2014;42:62–9.
104. D’Angelo ME, Pelletier LG, Reid RD, Huta V. The roles of self-efficacy andmotivation in the prediction of short- and long-term adherence to exercise
among patients with coronary heart disease. Health Psychol. 2014;33:1344–53.
105. Carraça EV, Silva MN, Markland D, Vieira PN, Minderico CS, Sardinha LB, et al.
Body image change and improved eating self-regulation in a weight management
intervention in women. Int J Behav Nutr Phys Act. 2011;8:75–85.
106. Leong SL, Madden C, Gray A, Horwath C. Self-determined, autonomous
regulation of eating behavior is related to lower body mass index in anationwide survey of middle-aged women. J Acad Nutr Diet.
2012;112:1337–46.
107. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W,
et al. The behavior change technique taxonomy (v1) of 93 hierarchicallyclustered techniques: building an international consensus for the reportingof behavior change interventions. Ann Behav Med. 2013;46:81–95.
108. Olander EK, Fletcher H, Williams S, Lou A, Turner A, French DP. What are themost effective techniques in changing obese individuals’ physical activity

Teixeira et al. BMC Medicine (2015) 13:84

Page 16 of 16

self-efficacy and behaviour: a systematic review and meta-analysis. Int J
Behav Nutr Phys Act. 2013;10:29.
109. Su Y-L, Reeve J. A meta-analysis of the effectiveness of intervention programs
designed to support autonomy. Educ Psychol Rev. 2011;23:159–88.
110. Janssen V, Gucht V, Exel H, Maes S. A self-regulation lifestyle programfor post-cardiac rehabilitation patients has long-term effects on exercise
adherence. J Behav Med. 2014;37:308–21.
111. Miller W, Rollnick S. Motivational interviewing: helping people change. 3rd
ed. NY: The Guilford Press; 2012.
112. Moyers TB, Martin T, Manuel JK, Hendrickson SM, Miller WR. Assessing
competence in the use of motivational interviewing. J Subst Abuse Treat.
2005;28:19–26.

Submit your next manuscript to BioMed Central
and take full advantage of:• Convenient online submission• Thorough peer review• No space constraints or color ﬁgure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit