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.

scholarly journals Impact of the Method of Delivering Electronic Health Behavior Change Interventions in Survivors of Cancer on Engagement, Health Behaviors, and Health Outcomes: Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Kate Furness ◽  
Mitchell N Sarkies ◽  
Catherine E Huggins ◽  
Daniel Croagh ◽  
Terry P Haines
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Health Behavior ◽  
Behavior Change ◽  
Health Outcomes ◽  
Meta Analysis ◽  
Health Behavior Change ◽  
Patients With Cancer ◽  
Behavior Change Interventions

BACKGROUND Increased accessibility to the internet and mobile devices has seen a rapid expansion in electronic health (eHealth) behavior change interventions delivered to patients with cancer and survivors using synchronous, asynchronous, and combined delivery methods. Characterizing effective delivery methods of eHealth interventions is required to enable improved design and implementation of evidence-based health behavior change interventions. OBJECTIVE This study aims to systematically review the literature and synthesize evidence on the success of eHealth behavior change interventions in patients with cancer and survivors delivered by synchronous, asynchronous, or combined methods compared with a control group. Engagement with the intervention, behavior change, and health outcomes, including quality of life, fatigue, depression, and anxiety, were examined. METHODS A search of Scopus, Ovid MEDLINE, Excerpta Medica dataBASE, Cumulative Index to Nursing and Allied Health Literature Plus, PsycINFO, Cochrane CENTRAL, and PubMed was conducted for studies published between March 2007 and March 2019. We looked for randomized controlled trials (RCTs) examining interventions delivered to adult cancer survivors via eHealth methods with a measure of health behavior change. Random-effects meta-analysis was performed to examine whether the method of eHealth delivery impacted the level of engagement, behavior change, and health outcomes. RESULTS A total of 24 RCTs were included predominantly examining dietary and physical activity behavior change interventions. There were 11 studies that used a synchronous approach and 11 studies that used an asynchronous approach, whereas 2 studies used a combined delivery method. Use of eHealth interventions improved exercise behavior (standardized mean difference [SMD] 0.34, 95% CI 0.21-0.48), diet behavior (SMD 0.44, 95% CI 0.18-0.70), fatigue (SMD 0.21, 95% CI −0.08 to 0.50; SMD change 0.22, 95% CI 0.09-0.35), anxiety (SMD 1.21, 95% CI: 0.36-2.07; SMD change 0.15, 95% CI −0.09 to 0.40), depression (SMD 0.15, 95% CI 0.00-0.30), and quality of life (SMD 0.12, 95% CI −0.10 to 0.34; SMD change 0.14, 95% CI 0.04-0.24). The mode of delivery did not influence the amount of dietary and physical activity behavior change observed. CONCLUSIONS Physical activity and dietary behavior change eHealth interventions delivered to patients with cancer or survivors have a small to moderate impact on behavior change and a small to very small benefit to quality of life, fatigue, depression, and anxiety. There is insufficient evidence to determine whether asynchronous or synchronous delivery modes yield superior results. Three-arm RCTs comparing delivery modes with a control with robust engagement reporting are required to determine the most successful delivery method for promoting behavior change and ultimately favorable health outcomes.

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 Relations Between Autonomous Motivation and Leisure-Time Physical Activity Participation: The Mediating Role of Self-Regulation Techniques

2016 ◽  
Vol 38 (2) ◽  
pp. 128-137 ◽  
Author(s):  
Johanna Nurmi ◽  
Martin S. Hagger ◽  
Ari Haukkala ◽  
Vera Araújo-Soares ◽  
Nelli Hankonen
Keyword(s):  
Physical Activity ◽  
Process Model ◽  
Leisure Time Physical Activity ◽  
Self Regulation ◽  
Autonomous Motivation ◽  
Self Report ◽  
Physical Activity Participation ◽  
Activity Participation ◽  
Self Monitoring ◽  
Coping Planning

This study tested the predictive validity of a multitheory process model in which the effect of autonomous motivation from self-determination theory on physical activity participation is mediated by the adoption of self-regulatory techniques based on control theory. Finnish adolescents (N = 411, aged 17–19) completed a prospective survey including validated measures of the predictors and physical activity, at baseline and after one month (N = 177). A subsample used an accelerometer to objectively measure physical activity and further validate the physical activity self-report assessment tool (n = 44). Autonomous motivation statistically significantly predicted action planning, coping planning, and self-monitoring. Coping planning and self-monitoringmediated the effect of autonomous motivation on physical activity, although self-monitoring was the most prominent. Controlled motivation had no effect on self-regulation techniques or physical activity. Developing interventions that support autonomous motivation for physical activity may foster increased engagement in self-regulation techniques and positively affect physical activity behavior.

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.

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