Effective behavior change techniques to promote physical activity in adults with overweight or obesity: A systematic review and meta-analysis.
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SUBMITTER: Carraca E
PROVIDER: S-EPMC8365685 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Physical inactivity and sedentary behavior relate to poor health outcomes independently. Healthy inactive adults are a key target population for prevention. This systematic review and meta-analysis aimed to evaluate the effectiveness of physical activity and/or sedentary behavior interventions, measured postintervention (behavior change) and at follow-up (behavior change maintenance), to identify behavior change techniques (BCT) within, and report on fidelity. Included studies were randomized controlled trials, targeting healthy inactive adults, aiming to change physical activity and/or sedentary behavior, with a minimum postintervention follow-up of 6 months, using 16 databases from 1990. Two reviewers independently coded risk of bias, the "Template for Intervention Description and Replication" (TIDieR) checklist, and BCTs. Twenty-six studies were included; 16 pooled for meta-analysis. Physical activity interventions were effective at changing behavior (d = 0.32, 95% confidence intervals = 0.16-0.48, n = 2,346) and maintaining behavior change after 6 months or more (d = 0.21, 95% confidence intervals = 0.12-0.30, n = 2,190). Sedentary behavior interventions (n = 2) were not effective. At postintervention, physical activity intervention effectiveness was associated with the BCTs "Biofeedback," "Demonstration of the behavior," "Behavior practice/rehearsal," and "Graded tasks." At follow-up, effectiveness was associated with using "Action planning," "Instruction on how to perform the behavior," "Prompts/cues," "Behavior practice/rehearsal," "Graded tasks," and "Self-reward." Fidelity was only documented in one study. Good evidence was found for behavior change maintenance effects in healthy inactive adults, and underlying BCTs. This review provides translational evidence to improve research, intervention design, and service delivery in physical activity interventions, while highlighting the lack of fidelity measurement.
| S-EPMC6305562 | biostudies-literature
Project description:PurposeThis systematic review aims to explain the heterogeneity in results of interventions to promote physical activity and healthy eating for overweight and obese adults, by exploring the differential effects of behaviour change techniques (BCTs) and other intervention characteristics.MethodsThe inclusion criteria specified RCTs with ≥ 12 weeks' duration, from January 2007 to October 2014, for adults (mean age ≥ 40 years, mean BMI ≥ 30). Primary outcomes were measures of healthy diet or physical activity. Two reviewers rated study quality, coded the BCTs, and collected outcome results at short (≤6 months) and long term (≥12 months). Meta-analyses and meta-regressions were used to estimate effect sizes (ES), heterogeneity indices (I2) and regression coefficients.ResultsWe included 48 studies containing a total of 82 outcome reports. The 32 long term reports had an overall ES = 0.24 with 95% confidence interval (CI): 0.15 to 0.33 and I2 = 59.4%. The 50 short term reports had an ES = 0.37 with 95% CI: 0.26 to 0.48, and I2 = 71.3%. The number of BCTs unique to the intervention group, and the BCTs goal setting and self-monitoring of behaviour predicted the effect at short and long term. The total number of BCTs in both intervention arms and using the BCTs goal setting of outcome, feedback on outcome of behaviour, implementing graded tasks, and adding objects to the environment, e.g. using a step counter, significantly predicted the effect at long term. Setting a goal for change; and the presence of reporting bias independently explained 58.8% of inter-study variation at short term. Autonomy supportive and person-centred methods as in Motivational Interviewing, the BCTs goal setting of behaviour, and receiving feedback on the outcome of behaviour, explained all of the between study variations in effects at long term.ConclusionThere are similarities, but also differences in effective BCTs promoting change in healthy eating and physical activity and BCTs supporting maintenance of change. The results support the use of goal setting and self-monitoring of behaviour when counselling overweight and obese adults. Several other BCTs as well as the use of a person-centred and autonomy supportive counselling approach seem important in order to maintain behaviour over time.Trial registrationPROSPERO CRD42015020624.
| S-EPMC5370453 | biostudies-literature
Project description:BackgroundDecreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA.ObjectiveThe aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users' PA behavior.MethodsThe BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin Vívoactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature.ResultsThe most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin Vívoactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA.ConclusionsWearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.
| S-EPMC7714647 | biostudies-literature
Project description:BackgroundInterventions delivered through new device technology, including mobile phone apps, appear to be an effective method to reach young adults. Previous research indicates that self-efficacy and social support for physical activity and self-regulation behavior change techniques (BCT), such as goal setting, feedback, and self-monitoring, are important for promoting physical activity; however, little is known about evaluations by the target population of BCTs applied to physical activity apps and whether these preferences are associated with individual personality characteristics.ObjectiveThis study aimed to explore young adults' opinions regarding BCTs (including self-regulation techniques) applied in mobile phone physical activity apps, and to examine associations between personality characteristics and ratings of BCTs applied in physical activity apps.MethodsWe conducted a cross-sectional online survey among healthy 18 to 30-year-old adults (N=179). Data on participants' gender, age, height, weight, current education level, living situation, mobile phone use, personality traits, exercise self-efficacy, exercise self-identity, total physical activity level, and whether participants met Dutch physical activity guidelines were collected. Items for rating BCTs applied in physical activity apps were selected from a hierarchical taxonomy for BCTs, and were clustered into three BCT categories according to factor analysis: "goal setting and goal reviewing," "feedback and self-monitoring," and "social support and social comparison."ResultsMost participants were female (n=146), highly educated (n=169), physically active, and had high levels of self-efficacy. In general, we observed high ratings of BCTs aimed to increase "goal setting and goal reviewing" and "feedback and self-monitoring," but not for BCTs addressing "social support and social comparison." Only 3 (out of 16 tested) significant associations between personality characteristics and BCTs were observed: "agreeableness" was related to more positive ratings of BCTs addressing "goal setting and goal reviewing" (OR 1.61, 95% CI 1.06-2.41), "neuroticism" was related to BCTs addressing "feedback and self-monitoring" (OR 0.76, 95% CI 0.58-1.00), and "exercise self-efficacy" was related to a high rating of BCTs addressing "feedback and self-monitoring" (OR 1.06, 95% CI 1.02-1.11). No associations were observed between personality characteristics (ie, personality, exercise self-efficacy, exercise self-identity) and participants' ratings of BCTs addressing "social support and social comparison."ConclusionsYoung Dutch physically active adults rate self-regulation techniques as most positive and techniques addressing social support as less positive among mobile phone apps that aim to promote physical activity. Such ratings of BCTs differ according to personality traits and exercise self-efficacy. Future research should focus on which behavior change techniques in app-based interventions are most effective to increase physical activity.
| S-EPMC4704888 | biostudies-literature
Project description:Women in midlife experience health risks that could be mitigated by regular physical activity and reduced sedentary time, but this population rarely achieves physical activity levels that would protect their health. As a result, many behavioral interventions are designed to promote physical activity in this population, which are purportedly guided by theoretical models of health behavior (change) and activate an associated set of behavior change techniques (BCTs). The efficacy and effectiveness of these interventions appear to be limited, however, raising questions about their design and adaptation for women in midlife. Several aspects of these interventions are currently unclear. Specifically, which women they target (i.e., how "midlife" and "sedentary" or "inactive" are defined), which theoretical models or behavior BCTs are used, and how BCTs are activated in such interventions. A synthesis of this information would be useful as an initial step toward improving physical activity interventions for this at-risk group, and thus, represented the goal of the present scoping review. Eligibility required publication in a peer-reviewed journal in English between 2000 and 2021, inclusion of only women in midlife who did not have any medical or other restrictions on their physical activity (e.g., cancer diagnosis), and free-living physical activity or sedentary behavior as the target outcome (with associated assessment). Of the 4,410 initial results, 51 articles met inclusion criteria, and these described 36 unique interventions. More than half of the articles (59%) named an underlying theoretical model and interventions included an average of 3.76 identifiable BCTs (range 1-11). However, descriptions of many interventions were limited and did not provide enough detail to determine whether or how specific BCTs were activated. Interventions also used a wide range of inclusion criteria for age range and starting activity level, which has implications for targeting/tailoring and effectiveness, and many interventions focused on marginalized populations (e.g., women from racial/ethnic minority backgrounds, those un- or under-insured). The present review identifies some strengths and highlights important limitations of existing literature, as well as key opportunities for advancing the design and potential utility of physical activity interventions for women in midlife.Systematic review registrationhttps://osf.io/g8tuc.
| S-EPMC9534296 | biostudies-literature
Project description:(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately.
| S-EPMC11355742 | biostudies-literature
Project description:BackgroundE- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it's not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs.MethodsIn a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention 'MyPlan2.0' for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB.ResultsFirst, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = - 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590, p = 0.014; x2 = 17.722, p < 0.001; x2 = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389, p = 0.031) and self-monitoring alone (x2 = 8.858, p = 003), respectively.ConclusionsThis study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future.Trial registrationThis study was preregistered as a clinical trial (ID number: NCT03274271 ). Release date: 20 October 2017.
| S-EPMC7539442 | biostudies-literature
Project description:BackgroundBehavioral interventions to prevent pediatric obesity have shown inconsistent results across the field. Studying what happens within the "black box" of these interventions and how differences in implementation lead to different outcomes will help researchers develop more effective interventions.AimTo compare the implementation of three features of a phone-based intervention for parents (time spent discussing weight-related behaviors, behavior change techniques used in sessions, and intervention activities implemented by parents between sessions) with study outcomes.MethodsA random selection of 100 parent-child dyads in the intervention arm of a phone-based obesity prevention trial was included in this analysis. Sessions were coded for overall session length, length of time spent discussing specific weight-related behaviors, number of behavior change techniques used during the sessions, and number of intervention-recommended activities implemented by the parents between sessions (e.g., parent-reported implementation of behavioral practice/rehearsal between sessions). The primary study outcome, prevention of unhealthy increase in child body mass index (BMI) percentile, was measured at baseline and 12 months.ResultsOverall session length was associated with decreases in child BMI percentile ( b = -0.02, p = .01). There was no association between the number of behavior change techniques used in the sessions and decreases in child BMI percentile ( b = -0.29, p = .27). The number of activities the parents reported implementing between sessions was associated with decreases in child BMI percentile ( b = -1.25, p = .02).DiscussionTo improve future interventions, greater attention should be paid to the intended and delivered session length, and efforts should be made to facilitate parents' implementation of intervention-recommended activities between sessions (ClinicalTrials.gov, No. NCT01084590).
| S-EPMC6417983 | biostudies-literature
Project description:PurposeHealthy eating, physical activity and smoking interventions for low-income groups may have small, positive effects. Identifying effective intervention components could guide intervention development. This study investigated which content and delivery components of interventions were associated with increased healthy behavior in randomised controlled trials (RCTs) for low-income adults.MethodData from a review showing intervention effects in 35 RCTs containing 45 interventions with 17,000 participants were analysed to assess associations with behavior change techniques (BCTs) and delivery/context components from the template for intervention description and replication (TIDieR) checklist. The associations of 46 BCTs and 14 delivery/context components with behavior change (measures of healthy eating, physical activity and smoking cessation) were examined using random effects subgroup meta-analyses. Synergistic effects of components were examined using classification and regression trees (meta-CART) analyses based on both fixed and random effects assumptions.ResultsFor healthy eating, self-monitoring, delivery through personal contact, and targeting multiple behaviors were associated with increased effectiveness. Providing feedback, information about emotional consequences, or using prompts and cues were associated with reduced effectiveness. In synergistic analyses, interventions were most effective without feedback, or with self-monitoring excluding feedback. More effective physical activity interventions included behavioral practice/rehearsal or instruction, focussed solely on physical activity or took place in home/community settings. Information about antecedents was associated with reduced effectiveness. In synergistic analyses, interventions were most effective in home/community settings with instruction. No associations were identified for smoking.ConclusionThis study identified BCTs and delivery/context components, individually and synergistically, linked to increased and reduced effectiveness of healthy eating and physical activity interventions. The identified components should be subject to further experimental study to help inform the development effective behavior change interventions for low-income groups to reduce health inequalities.
| S-EPMC6244564 | biostudies-literature
Project description:ObjectiveEvidence on the effectiveness of walking and cycling interventions is mixed. This may be partly attributable to differences in intervention content, such as the cognitive and behavioral techniques (BCTs) used. Adopting a taxonomy of BCTs, this systematic review addressed two questions: (a) What are the behavior change techniques used in walking and cycling interventions targeted at adults? (b) What characterizes interventions that appear to be associated with changes in walking and cycling in adults?MethodPrevious systematic reviews and updated database searches were used to identify controlled studies of individual-level walking and cycling interventions involving adults. Characteristics of intervention design, context, and methods were extracted in addition to outcomes. Intervention content was independently coded according to a 26-item taxonomy of BCTs.ResultsStudies of 46 interventions met the inclusion criteria. Twenty-one reported a statistically significant effect on walking and cycling outcomes. Analysis revealed substantial heterogeneity in the vocabulary used to describe intervention content and the number of BCTs coded. "Prompt self-monitoring of behavior" and "prompt intention formation" were the most frequently coded BCTs.ConclusionFuture walking and cycling intervention studies should ensure that all aspects of the intervention are reported in detail. The findings lend support to the inclusion of self-monitoring and intention formation techniques in future walking and cycling intervention design, although further exploration of these and other BCTs is required. Further investigation of the interaction between BCTs and study design characteristics would also be desirable.
| S-EPMC3727344 | biostudies-literature