scholarly journals Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review

2021 ◽  
Vol 3 ◽  
Author(s):  
Jaime Martín-Martín ◽  
Cristina Roldán-Jiménez ◽  
Irene De-Torres ◽  
Antonio Muro-Culebras ◽  
Adrian Escriche-Escuder ◽  
...  
Keyword(s):  
Social Support ◽  
Systematic Review ◽  
Behavior Change ◽  
Sedentary Behavior ◽  
Randomized Clinical Trials ◽  
Digital Health ◽  
Clinical Population ◽  
Outcome Variable ◽  
Behavior Change Techniques ◽  
Behavior Change Interventions

Background: Sedentary behavior (SB) negatively impact health and is highly prevalent in the population. Digital behavior change interventions (DBCIs) have been developed to modify behaviors such as SB by technologies. However, it is unknown which behavior change techniques (BCTs) are most frequently employed in SB as well as the effect associated with DBCIs in this field. The aim of this systematic review was: (a) to evaluate the BCT most frequently employed in digital health including all technologies available and interventions aimed at increasing physical activity (PA), reducing sedentary time, and improving adherence to exercise in the clinical population, and (b) to review the effect associated with DBCIs in this field.Methods: The database used was Medline, as well as Scopus, Scielo, and Google Scholar. For the search strategy, we considered versions of behavior/behavioral, mHealth/eHealth/telemedicine/serious game/gamification. The terms related to PA and SB were included, the criteria for inclusion were randomized clinical trials (RCTs), adults, intervention based on digital media, and outcome variable lifestyle modification; a last 5 years filter was included. Michie's Taxonomy was used to identify BCTs. The study was registered under the number PROSPERO CRD42019138681.Results: Eighteen RCTs were included in the present systematic review, 5 of them healthy adults, and 13 of them with some illness. Studies included 2298 sedentary individuals who were followed up for 5 weeks−3 years. The most used BCTs were goal setting, problem solving, review outcomes/goals, feedback on behavior and outcomes of behavior, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal. The effect associated with DBCIs showed improvements, among several related to PA and physiologic self-reported and anthropometric outcomes.Conclusion: The BCTs most used in digital health to change outcomes related to SB were goals and planning, feedback and monitoring, social support, natural consequences, repetition, and substitution. Besides these findings, DBCIs are influenced by several factors like the type of intervention, patients' preferences and values, or the number of BCTs employed. More research is needed to determine with precision which DBCIs or BCTs are the most effective to reduce SB in the clinical population.

scholarly journals Systematic review of context-aware digital behavior change interventions to improve health

2020 ◽  
Vol 11 (5) ◽  
pp. 1037-1048
Author(s):  
Kelly J Thomas Craig ◽  
Laura C Morgan ◽  
Ching-Hua Chen ◽  
Susan Michie ◽  
Nicole Fusco ◽  
...  
Keyword(s):  
Systematic Review ◽  
Behavior Change ◽  
Health Behaviors ◽  
Digital Health ◽  
Health Improvement ◽  
Context Aware ◽  
Behavior Change Techniques ◽  
Health Technologies ◽  
Behavior Change Interventions ◽  
Improve Health

Abstract Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013–2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users’ health behaviors.

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.

Patient-facing Mobile Applications to Support Physiotherapy Care: Protocol for a Systematic Review of Apps (Preprint)

2021 ◽  
Author(s):  
Mark Merolli ◽  
Jillian Francis ◽  
Patrick Vallance ◽  
Kim Bennell ◽  
Peter Malliaras ◽  
...  
Keyword(s):  
Systematic Review ◽  
Behavior Change ◽  
Behaviour Change ◽  
Mobile Applications ◽  
Rating Scale ◽  
Mobile Apps ◽  
Digital Health ◽  
Behaviour Change Techniques ◽  
Behavior Change Techniques ◽  
Care Protocol

BACKGROUND Care delivered by physiotherapists aims to facilitate positive health behaviors by patients (e.g. adherence to exercise). However, research suggests that behavioral interventions are frequently omitted from care. Hence, better understanding of strategies that can be used by physiotherapists to support patients to engage in positive behaviors are important and likely to optimise outcomes. Digital health interventions delivered via mobile applications (apps) are garnering attention for their ability to support behavior change. They have the potential to incorporate numerous behavior change techniques to support goals of physiotherapy care; including (but not limited to): self-monitoring, goal setting, and prompts/alerts. Despite their potential to support physiotherapy care, much is still unknown about what apps are available, the behavior change techniques they use, their quality, and their potential to change behaviors. OBJECTIVE The primary aim of this systematic review is to describe what mobile apps intended for use by patients are available to support physiotherapy care, including the behavior change techniques within these apps. The secondary aims are to evaluate the quality and behavior change potential of these apps. METHODS A systematic review of apps in app stores will be undertaken. This will follow recommendations for reviews in line with the PRISMA statement, which has been adapted to suit our app store search. Apple Store and Google Play will be searched with a two-step search strategy, using terms relevant to physiotherapy, physiotherapists, and common physiotherapy care. Key eligibility will be that apps are intended for use by patients, and are self-contained or, stand-alone without the need of additional wearable devices or other add-ons. Included apps will be coded for behaviour change techniques (BCTs) and rated for quality using the Mobile Application Rating Scale (MARS) and potential to change behavior using the App Behavior Change Scale (ABACUS). RESULTS The protocol is registered to PROSPERO. App screening and inclusion has started, and data extraction is expected to commence by March, 2021. CONCLUSIONS Knowledge gained from this review will support clinical practice, as well as informing research, by providing a greater understanding about the quality of currently available mobile apps and their potential to support patient behaviour change goals of physiotherapy care.

Behavior change interventions in patients with type 2 diabetes: a systematic review of the effects on self-management and A1c

2021 ◽  
Author(s):  
Ana Paula Delgado Bomtempo Batalha ◽  
Isabela Coelho Ponciano ◽  
Gabriela Chaves ◽  
Diogo Carvalho Felício ◽  
Raquel Rodrigues Britto ◽  
...  
Keyword(s):  
Systematic Review ◽  
Type 2 Diabetes ◽  
Behavior Change ◽  
Self Management ◽  
Behavior Change Interventions

scholarly journals Diet Behavior Change Techniques in Type 2 Diabetes: A Systematic Review and Meta-analysis

Diabetes Care ◽  
2017 ◽  
Vol 40 (12) ◽  
pp. 1800-1810 ◽  
Author(s):  
Kevin A. Cradock ◽  
Gearóid ÓLaighin ◽  
Francis M. Finucane ◽  
Rhyann McKay ◽  
Leo R. Quinlan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Type 2 Diabetes ◽  
Behavior Change ◽  
Meta Analysis ◽  
Behavior Change Techniques ◽  
Diet Behavior

scholarly journals Key facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects – An umbrella review

2020 ◽  
Author(s):  
Janis Fiedler ◽  
Tobias Eckert ◽  
Kathrin Wunsch ◽  
Alexander Woll
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Sedentary Behavior ◽  
Healthy Eating ◽  
Social Contexts ◽  
Just In Time ◽  
Umbrella Review ◽  
Behavior Change Techniques ◽  
Adaptive Interventions ◽  
The Impact

Abstract Background: Electronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology.Methods: PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized.Results: The systematic search reveled eleven systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions.Conclusion: Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions.Trial registration: The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902.

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