Application of Theory-Based Health Behavior Change Techniques to the Prevention of Obesity in Children

2006 ◽  
Vol 21 (4) ◽  
pp. 266-275 ◽  
Author(s):  
Heidi Beckman ◽  
Suzanne Hawley ◽  
Thomas Bishop
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Health Behavior Change ◽  
Behavior Change Techniques ◽  
Obesity In Children

scholarly journals Behavior Change Techniques Included in Reports of Social Media Interventions for Promoting Health Behaviors in Adults: Content Analysis Within a Systematic Review

10.2196/16002 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e16002 ◽  
Author(s):  
Rosiane Simeon ◽  
Omar Dewidar ◽  
Jessica Trawin ◽  
Stephanie Duench ◽  
Heather Manson ◽  
...  
Keyword(s):  
Social Support ◽  
Social Media ◽  
Health Promotion ◽  
Health Behavior ◽  
Behavior Change ◽  
Health Behavior Change ◽  
Behavior Change Techniques ◽  
Coding Manual ◽  
Credible Source ◽  
Media Interventions

Background Social media are an increasingly commonly used platform for delivering health promotion interventions. Although recent research has focused on the effectiveness of social media interventions for health promotion, very little is known about the optimal content within such interventions, and the active ingredients to promote health behavior change using social media are not clear. Identifying which behavior change techniques (BCTs) are reported may help to clarify the content of interventions using a generalizable terminology that may facilitate future intervention development. Objective This study aimed to identify which BCTs are reported in social media interventions for promoting health behavior change in adults. Methods We included 71 studies conducted with adult participants (aged ≥18 years) and for which social media intervention was considered interactive in a Cochrane review of the effectiveness of such interventions. We developed a coding manual informed by the Behavior Change Technique Taxonomy version 1 (BCTTv1) to identify BCTs in the included studies. We identified BCTs in all study arms (including control) and described BCTs in the group and self-directed components of studies. We characterized the dose of delivery for each BCT by low and high intensity. We used descriptive analyses to characterize the reported BCTs. Results Our data consisted of 71 studies published from 2001 to 2017, mainly conducted in high-income countries (n=65). Most studies (n=31) used tailored, interactive websites to deliver the intervention; Facebook was the most used mainstream platform. In developing our coding manual, we adapted some BCTTv1 instructions to better capture unique nuances of how BCTs were operationalized in social media with respect to likes, retweets, smiles, congratulations, and badges. Social support (unspecified), instruction on how to perform the behavior, and credible source were most frequently identified BCTs in intervention arms of studies and group-delivery settings, whereas instruction on how to perform the behavior was most commonly applied in self-directed components of studies, control arms, and individual participant settings. Instruction on how to perform the behavior was also the most frequently reported BCT in both intervention and control arms simultaneously. Instruction on how to perform the behavior, social support (unspecified), self-monitoring of behavior, information about health consequences, and credible source were identified in the top 5 BCTs delivered with the highest intensity. Conclusions This study within a review provides a detailed description of the BCTs and their dose to promote behavior change in web-based, interactive social media interventions. Clarifying active ingredients in social media interventions and the intensity of their delivery may help to develop future interventions that can more clearly build upon the existing evidence.

scholarly journals Digital Health Behavior Change Technology: Bibliometric and Scoping Review of Two Decades of Research

10.2196/13311 ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. e13311 ◽  
Author(s):  
Fawad Taj ◽  
Michel C A Klein ◽  
Aart van Halteren
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Behavior Change ◽  
Scoping Review ◽  
Behavioral Change ◽  
Digital Technologies ◽  
Health Behavior Change ◽  
The United States ◽  
Behavior Change Techniques ◽  
Research Areas

Background Research on digital technology to change health behavior has increased enormously in recent decades. Due to the interdisciplinary nature of this topic, knowledge and technologies from different research areas are required. Up to now, it is not clear how the knowledge from those fields is combined in actual applications. A comprehensive analysis that systematically maps and explores the use of knowledge within this emerging interdisciplinary field is required. Objective This study aims to provide an overview of the research area around the design and development of digital technologies for health behavior change and to explore trends and patterns. Methods A bibliometric analysis is used to provide an overview of the field, and a scoping review is presented to identify the trends and possible gaps. The study is based on the publications related to persuasive technologies and health behavior change in the last 18 years, as indexed by the Web of Science and Scopus (317 and 314 articles, respectively). In the first part, regional and time-based publishing trends; research fields and keyword co-occurrence networks; influential journals; and collaboration network between influential authors, countries, and institutions are examined. In the second part, the behavioral domains, technological means and theoretical foundations are investigated via a scoping review. Results The literature reviewed shows a clear and emerging trend after 2001 in technology-based behavior change, which grew exponentially after the introduction of the smartphone around 2009. Authors from the United States, Europe, and Australia have the highest number of publications in the field. The three most active research areas are computer science, public and occupational health, and psychology. The keyword “mhealth” was the dominant term and predominantly used together with the term “physical activity” and “ehealth”. A total of three strong clusters of coauthors have been found. Nearly half of the total reported papers were published in three journals. The United States, the United Kingdom, and the Netherlands have the highest degree of author collaboration and a strong institutional network. Mobile phones were most often used as a technology platform, regardless of the targeted behavioral domain. Physical activity and healthy eating were the most frequently targeted behavioral domains. Most articles did not report about the behavior change techniques that were applied. Among the reported behavior change techniques, goal setting and self-management were the most frequently reported. Conclusions Closer cooperation and interaction between behavioral sciences and technological areas is needed, so that theoretical knowledge and new technological advancements are better connected in actual applications. Eventually, this could result in a larger societal impact, an increase of the effectiveness of digital technologies for health behavioral change, and more insight in the relationship between behavioral change strategies and persuasive technologies' effectiveness.

scholarly journals Behavior Change Techniques Included in Reports of Social Media Interventions for Promoting Health Behaviors in Adults: Content Analysis Within a Systematic Review (Preprint)

2019 ◽  
Author(s):  
Rosiane Simeon ◽  
Omar Dewidar ◽  
Jessica Trawin ◽  
Stephanie Duench ◽  
Heather Manson ◽  
...  
Keyword(s):  
Social Support ◽  
Social Media ◽  
Health Promotion ◽  
Health Behavior ◽  
Behavior Change ◽  
Health Behavior Change ◽  
Behavior Change Techniques ◽  
Coding Manual ◽  
Credible Source ◽  
Media Interventions

BACKGROUND Social media are an increasingly commonly used platform for delivering health promotion interventions. Although recent research has focused on the effectiveness of social media interventions for health promotion, very little is known about the optimal content within such interventions, and the active ingredients to promote health behavior change using social media are not clear. Identifying which behavior change techniques (BCTs) are reported may help to clarify the content of interventions using a generalizable terminology that may facilitate future intervention development. OBJECTIVE This study aimed to identify which BCTs are reported in social media interventions for promoting health behavior change in adults. METHODS We included 71 studies conducted with adult participants (aged ≥18 years) and for which social media intervention was considered interactive in a Cochrane review of the effectiveness of such interventions. We developed a coding manual informed by the Behavior Change Technique Taxonomy version 1 (BCTTv1) to identify BCTs in the included studies. We identified BCTs in all study arms (including control) and described BCTs in the group and self-directed components of studies. We characterized the dose of delivery for each BCT by low and high intensity. We used descriptive analyses to characterize the reported BCTs. RESULTS Our data consisted of 71 studies published from 2001 to 2017, mainly conducted in high-income countries (n=65). Most studies (n=31) used tailored, interactive websites to deliver the intervention; Facebook was the most used mainstream platform. In developing our coding manual, we adapted some BCTTv1 instructions to better capture unique nuances of how BCTs were operationalized in social media with respect to likes, retweets, smiles, congratulations, and badges. Social support (unspecified), instruction on how to perform the behavior, and credible source were most frequently identified BCTs in intervention arms of studies and group-delivery settings, whereas instruction on how to perform the behavior was most commonly applied in self-directed components of studies, control arms, and individual participant settings. Instruction on how to perform the behavior was also the most frequently reported BCT in both intervention and control arms simultaneously. Instruction on how to perform the behavior, social support (unspecified), self-monitoring of behavior, information about health consequences, and credible source were identified in the top 5 BCTs delivered with the highest intensity. CONCLUSIONS This study within a review provides a detailed description of the BCTs and their dose to promote behavior change in web-based, interactive social media interventions. Clarifying active ingredients in social media interventions and the intensity of their delivery may help to develop future interventions that can more clearly build upon the existing evidence.

Digital Health Behavior Change Technology: Bibliometric and Scoping Review of Two Decades of Research (Preprint)

2019 ◽  
Author(s):  
Fawad Taj ◽  
Michel C A Klein ◽  
Aart van Halteren
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Behavior Change ◽  
Scoping Review ◽  
Behavioral Change ◽  
Digital Technologies ◽  
Health Behavior Change ◽  
The United States ◽  
Behavior Change Techniques ◽  
Research Areas

BACKGROUND Research on digital technology to change health behavior has increased enormously in recent decades. Due to the interdisciplinary nature of this topic, knowledge and technologies from different research areas are required. Up to now, it is not clear how the knowledge from those fields is combined in actual applications. A comprehensive analysis that systematically maps and explores the use of knowledge within this emerging interdisciplinary field is required. OBJECTIVE This study aims to provide an overview of the research area around the design and development of digital technologies for health behavior change and to explore trends and patterns. METHODS A bibliometric analysis is used to provide an overview of the field, and a scoping review is presented to identify the trends and possible gaps. The study is based on the publications related to persuasive technologies and health behavior change in the last 18 years, as indexed by the Web of Science and Scopus (317 and 314 articles, respectively). In the first part, regional and time-based publishing trends; research fields and keyword co-occurrence networks; influential journals; and collaboration network between influential authors, countries, and institutions are examined. In the second part, the behavioral domains, technological means and theoretical foundations are investigated via a scoping review. RESULTS The literature reviewed shows a clear and emerging trend after 2001 in technology-based behavior change, which grew exponentially after the introduction of the smartphone around 2009. Authors from the United States, Europe, and Australia have the highest number of publications in the field. The three most active research areas are computer science, public and occupational health, and psychology. The keyword “mhealth” was the dominant term and predominantly used together with the term “physical activity” and “ehealth”. A total of three strong clusters of coauthors have been found. Nearly half of the total reported papers were published in three journals. The United States, the United Kingdom, and the Netherlands have the highest degree of author collaboration and a strong institutional network. Mobile phones were most often used as a technology platform, regardless of the targeted behavioral domain. Physical activity and healthy eating were the most frequently targeted behavioral domains. Most articles did not report about the behavior change techniques that were applied. Among the reported behavior change techniques, goal setting and self-management were the most frequently reported. CONCLUSIONS Closer cooperation and interaction between behavioral sciences and technological areas is needed, so that theoretical knowledge and new technological advancements are better connected in actual applications. Eventually, this could result in a larger societal impact, an increase of the effectiveness of digital technologies for health behavioral change, and more insight in the relationship between behavioral change strategies and persuasive technologies' effectiveness.

Predictors of Health Behavior Change in School-Based Obesity Prevention

2008 ◽  
Author(s):  
Kara Harrington ◽  
Maureen E. Kenny ◽  
Deirdre Brogan ◽  
Lynn Y. Walsh
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Obesity Prevention ◽  
Health Behavior Change ◽  
School Based

A review of mHealth depression apps: Assessing app functions for the Self-Determination Theory (Preprint)

2020 ◽  
Author(s):  
Luke Brownlow
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Basic Needs ◽  
Health Behavior Change ◽  
Self Determination Theory ◽  
Healthcare Research ◽  
The Self ◽  
Self Determination ◽  
Ideal Tool ◽  
Google Play

BACKGROUND Smartphone applications (apps) are an ideal tool that is highly accessible to people who wish to begin self-treatment for depression. While many studies have performed content analyses on healthcare apps, few studies have reviewed these apps for adherence to behavior theory. Furthermore, apps for depression management are underrepresented in healthcare research. OBJECTIVE The objective of this study is to assess mHealth depression apps using SDT as a theoretical framework for meeting needs of autonomy, competence and, relatedness METHODS All depression healthcare apps available in Australia from the iTunes and Google Play app stores that met the inclusion criteria were analyzed. Each app was reviewed based on price options, store availability, download rates, and how in-app functions met the three basic needs for motivation towards health behavior change outlined in the Self-Determination Theory (SDT). RESULTS The analysis of the apps showed that most apps were free to download (69.9%) and addressed at least one of the three needs (81.4%) of SDT. However, few apps addressed all three of the basic needs through their functions (7.7%), and no apps hosted all functions expected to stimulate motivation for health behavior change. Furthermore, neither store availability, price option nor download rate were accurate predictors that apps hosted in-app functions expected to meet the basic needs. CONCLUSIONS The results suggest that some depression healthcare apps that meet the basic needs would effectively stimulate motivation (i.e., autonomy, competence, and relatedness). However, each individual app is limited in its number of functions that meet the basic needs. People who want access to more functions would need to download a suite of apps.

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