scholarly journals Key Elements of mHealth Interventions to Successfully Increase Physical Activity: Meta-Regression (Preprint)

2018 ◽  
Author(s):  
Lisa V. Eckerstorfer ◽  
Norbert K. Tanzer ◽  
Claudia Vogrincic-Haselbacher ◽  
Gayannee Kedia ◽  
Hilmar Brohmer ◽  
...  
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Meta Analysis ◽  
General Information ◽  
Group Differences ◽  
Behavior Change Techniques ◽  
Self Monitoring ◽  
Physical Activity Interventions ◽  
Baseline Group ◽  
Monitoring Information

BACKGROUND Mobile technology gives researchers unimagined opportunities to design new interventions to increase physical activity. Unfortunately, it is still unclear which elements are useful to initiate and maintain behavior change. OBJECTIVE In this meta-analysis, we investigated randomized controlled trials of physical activity interventions that were delivered via mobile phone. We analyzed which elements contributed to intervention success. METHODS After searching four databases and science networks for eligible studies, we entered 50 studies with N=5997 participants into a random-effects meta-analysis, controlling for baseline group differences. We also calculated meta-regressions with the most frequently used behavior change techniques (behavioral goals, general information, self-monitoring, information on where and when, and instructions on how to) as moderators. RESULTS We found a small overall effect of the Hedges g=0.29, (95% CI 0.20 to 0.37) which reduced to g=0.22 after correcting for publication bias. In the moderator analyses, behavioral goals and self-monitoring each led to more intervention success. Interventions that used neither behavioral goals nor self-monitoring had a negligible effect of g=0.01, whereas utilizing either technique increased effectiveness by Δg=0.31, but combining them did not provide additional benefits (Δg=0.36). CONCLUSIONS Overall, mHealth interventions to increase physical activity have a small to moderate effect. However, including behavioral goals or self-monitoring can lead to greater intervention success. More research is needed to look at more behavior change techniques and their interactions. Reporting interventions in trial registrations and articles need to be structured and thorough to gain accurate insights. This can be achieved by basing the design or reporting of interventions on taxonomies of behavior change.

Behavior Change Techniques in Physical Activity Interventions for Multiple Sclerosis

2021 ◽  
Author(s):  
Stephanie L. Silveira ◽  
Trinh Huynh ◽  
Ariel Kidwell ◽  
Dena Sadeghi-Bahmani ◽  
Robert W. Motl
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Behavior Change ◽  
Behavior Change Techniques ◽  
Physical Activity Interventions

Behavior Change Techniques Used in Theory of Planned Behavior Physical Activity Interventions Among Older Adults: A Systematic Review

2019 ◽  
Vol 27 (5) ◽  
pp. 746-754 ◽  
Author(s):  
Valerie Senkowski ◽  
Clara Gannon ◽  
Paul Branscum
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Older Adults ◽  
Theory Of Planned Behavior ◽  
Behavior Change ◽  
Planned Behavior ◽  
Controlled Trial ◽  
The United States ◽  
Behavior Change Techniques ◽  
Physical Activity Interventions

Physical activity interventions among older adults vary widely in the techniques used to elicit behavior change. The purpose of this systematic review was to determine what behavior change techniques (BCTs) are used in interventions to increase physical activity among older adults using the theory of planned behavior and to make suggestions for BCTs that appear to be more effective. A database search identified peer-reviewed articles documenting interventions based on the theory of planned behavior. Seven articles (three randomized controlled trial, three quasi-experimental, and onen-of-1) from four countries (the United States, the United Kingdom, Australia, and the Netherlands) were included for review. Researchers independently coded BCTs using a hierarchical taxonomy of 93 BCTs. The most frequently coded BCTs includedGoal Setting(n = 5 studies),Action Planning(n = 5 studies), andCredible Source(n = 5 studies). Of the 93 BCTs in the taxonomy, only 26 were used, indicating potential opportunities to implement and evaluate less commonly used techniques in future studies.

scholarly journals Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis (Preprint)

2020 ◽  
Author(s):  
Peter Düking ◽  
Marie Tafler ◽  
Birgit Wallmann-Sperlich ◽  
Billy Sperlich ◽  
Sonja Kleih
Keyword(s):  
Physical Activity ◽  
Content Analysis ◽  
Behavior Change ◽  
Goal Setting ◽  
Wearable Sensors ◽  
Action Planning ◽  
Noncommunicable Diseases ◽  
Behavior Change Techniques ◽  
Self Monitoring ◽  
Current Behavior

BACKGROUND Decreasing 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. OBJECTIVE The 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. METHODS The 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. RESULTS The 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. CONCLUSIONS Wearables 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.

scholarly journals Behavior change techniques in mobile applications for sedentary behavior

2018 ◽  
Vol 4 ◽  
pp. 205520761878579 ◽  
Author(s):  
Emily E Dunn ◽  
Heather L Gainforth ◽  
Jennifer E Robertson-Wilson
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Sedentary Behavior ◽  
Mobile Applications ◽  
Health Behavior Change ◽  
Behavior Change Techniques ◽  
Self Monitoring ◽  
Behavior Change Interventions ◽  
And Behavior ◽  
Keyword Searches

Objective Mobile applications (apps) are increasingly being utilized in health behavior change interventions. To determine the presence of underlying behavior change mechanisms, apps for physical activity have been coded for behavior change techniques (BCTs). However, apps for sedentary behavior have yet to be assessed for BCTs. Thus, the purpose of the present study was to review apps designed to decrease sedentary time and determine the presence of BCTs. Methods Systematic searches of the iTunes App and Google Play stores were completed using keyword searches. Two reviewers independently coded free ( n = 36) and paid ( n = 14) app descriptions using a taxonomy of 93 BCTs (December 2016–January 2017). A subsample ( n = 4) of free apps were trialed for one week by the reviewers and coded for the presence of BCTs (February 2017). Results In the free and paid app descriptions, only 10 of 93 BCTs were present with a mean of 2.42 BCTs (range 0–6) per app. The BCTs coded most frequently were “prompts/cues” ( n = 43), “information about health consequences” ( n = 31), and “self-monitoring of behavior” ( n = 17). For the four free apps that were trialed, three additional BCTs were coded that were not coded in the descriptions: “graded tasks,” “focus on past successes,” and “behavior substitution.” Conclusions These sedentary behavior apps have fewer BCTs compared with physical activity apps and traditional (i.e., non-app) physical activity and healthy eating interventions. The present study sheds light on the behavior change potential of sedentary behavior apps and provides practical insight about coding for BCTs in apps.

scholarly journals Benefits of physical activity interventions combining self-monitoring with other components versus self-monitoring alone: a systematic review and meta-analysis

The Lancet ◽  
2021 ◽  
Vol 398 ◽  
pp. S87
Author(s):  
Tomas Vetrovsky ◽  
Charlotte Wahlich ◽  
Agnieszka Borowiec ◽  
Roman Jurik ◽  
Witold Smigielski ◽  
...  
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Meta Analysis ◽  
Self Monitoring ◽  
Physical Activity Interventions

scholarly journals How can interventions increase motivation for physical activity? A systematic review and meta-analysis

2018 ◽  
Author(s):  
Keegan Phillip Knittle ◽  
Johanna Nurmi ◽  
Rik Crutzen ◽  
Nelli Hankonen ◽  
Marguerite Beattie ◽  
...  
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Behavior Change ◽  
Meta Analysis ◽  
Self Regulation ◽  
Stage Of Change ◽  
Autonomous Motivation ◽  
Self Monitoring ◽  
Motivational Constructs ◽  
Behavior Change Interventions

Motivation is a proximal determinant of behavior in many psychological theories, and increasing motivation is central to most behavior change interventions. This systematic review and meta-analysis sought to fill a gap in the literature by identifying features of behavior change interventions associated with favorable changes in three prominent motivational constructs: intention, stage of change and autonomous motivation. A systematic literature search identified 88 intervention studies (N = 18,804) which assessed changes in at least one of these motivational constructs for physical activity (PA). Intervention descriptions were coded for potential moderators, including behavior change techniques (BCTs), modes of delivery and theory use. Random effects comparative subgroup analyses identified 19 BCTs and 12 modes of delivery associated with changes in at least one motivational outcome. Interventions which were delivered face-to-face or in gym settings, or which included the BCTs problem solving, self-monitoring of behavior or behavioral practice/rehearsal, or which included the combination of self-monitoring of behavior with any other BCTs derived from control theory, were all associated with beneficial changes in multiple motivational constructs. Meta-regression analyses indicated that increases in intention and stage of change, but not autonomous motivation, were related to increases in PA. The intervention characteristics identified here as effective in changing motivation seemed to form clusters related to behavioral experience and self-regulation, which have previously been linked to changes in behavior as well. These BCTs and modes of delivery merit further systematic study, and could be used as a foundation for improving interventions targeting increases in motivation for PA.

Behavior Change Techniques Associated With Changes in Postintervention and Maintained Changes in Self-Efficacy For Physical Activity: A Systematic Review With Meta-analysis

2018 ◽  
Vol 53 (9) ◽  
pp. 801-815 ◽  
Author(s):  
Mei Yee Tang ◽  
Debbie M Smith ◽  
Jennifer Mc Sharry ◽  
Mark Hann ◽  
David P French
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Behavior Change ◽  
Self Efficacy ◽  
Meta Analysis ◽  
Hierarchical Cluster ◽  
Effect Sizes ◽  
Behavior Change Techniques ◽  
Social Environmental ◽  
Meta Analyses

Abstract Background Self-efficacy is an important determinant of physical activity but it is unclear how best to increase self-efficacy for physical activity and to maintain these changes. Purpose This systematic review aimed to identify which specific behavior change techniques (BCTs), BCT clusters, and number of BCTs were associated with changes in postintervention and maintained changes in self-efficacy for physical activity across all adult populations. Methods A systematic search yielded 180 randomized trials (204 comparisons) which reported changes in self-efficacy. BCTs were coded using the BCT Taxonomy v1. Hierarchical cluster analysis explored the clustering of BCTs. Meta-analyses and moderator analyses examined whether the presence and absence of individual BCTs in interventions were associated with effect-size changes for self-efficacy. Results Small intervention effects were found for postintervention self-efficacy for physical activity (d = 0.26; 95% CI: [0.21, 0.31]; I2 = 75.8 per cent). “Information about social, environmental, and emotional consequences” was associated with higher effect sizes, whereas “social support (practical)” was associated with lower effect sizes. Small and nonsignificant effects were found for maintained changes in self-efficacy for physical activity (d = 0.08; CI: [−0.05, 0.21]; I2 = 83.8 per cent). Lack of meaningful clustering of BCTs was found. A significant positive relationship was found between number of BCTs and effect sizes for maintained changes in self-efficacy for physical activity. Conclusions There does not appear to be a single effective approach to change self-efficacy for physical activity in all adults: different approaches are required for different populations. Interventions with more BCTs seem more effective at maintaining changes in self-efficacy for physical activity.

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