Antifragile Behavior Change through Digital Behavior Change Interventions (Preprint)

2021 ◽  
Author(s):  
Benjamin T Kaveladze ◽  
Sean D Young ◽  
Stephen M Schueller
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Behavior Change ◽  
Health Outcomes ◽  
Real World ◽  
Change Process ◽  
Digital Health ◽  
Health Behavior Change ◽  
Behavior Change Interventions ◽  
Future Work

UNSTRUCTURED Digital health behavior change interventions (DHBCIs) are popular and widely-accessible tools for helping people to pursue behavior change goals. However, their effectiveness tends to be low in real-world settings. Drawing from Nassim Nicholas Taleb’s concept of antifragility, we introduce antifragile behavior change, a strategy that leverages user-specific characteristics to make the behavior change process more efficient. Next, we propose two principles for designing DHBCIs to support antifragile behavior change: first, DHBCIs should provide personalized guidance that accounts for user-specific circumstances and goals; second, DHBCIs should prioritize user agency by refraining from using nudges that might manipulate user decision-making. We hope this paper will encourage researchers and product developers to reconsider DHBCI design through the lens of antifragility. Future work can examine if DHBCIs that are consistent with our principles of designing for antifragile behavior change lead to better mental health outcomes than other DHBCIs.

scholarly journals Peer Support: a Human Factor to Enhance Engagement in Digital Health Behavior Change Interventions

2019 ◽  
Vol 4 (2) ◽  
pp. 152-161 ◽  
Author(s):  
Karen L. Fortuna ◽  
Jessica M. Brooks ◽  
Emre Umucu ◽  
Robert Walker ◽  
Phillip I. Chow
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Peer Support ◽  
Human Factor ◽  
Digital Health ◽  
Health Behavior Change ◽  
Behavior Change Interventions

scholarly journals Do Interventions Based on Behavioral Theory Work in the Real World?

2019 ◽  
Author(s):  
Martin S Hagger ◽  
Mike Weed
Keyword(s):  
Behavior Change ◽  
Health Outcomes ◽  
Real World ◽  
Behavioral Interventions ◽  
Population Level ◽  
Health Behavior Change ◽  
Individual Behavior ◽  
Behavioral Theory ◽  
The Real ◽  
Effectiveness Trials

BackgroundBehavioral scientists suggest that for behavior change interventions to work effectively, and deliver population-level health outcomes, they must be underpinned by behavioral theory. However, despite implementation of such interventions, population levels of both health outcomes and linked behaviors have remained relatively static. We debate the extent to which interventions based on behavioral theory work in the real world to address population health outcomes.DiscussionHagger argues there is substantive evidence supporting the efficacy and effectiveness of interventions based on behavioral theory in promoting population-level health behavior change in the ‘real world’. However, large-scale effectiveness trials within existing networks are relatively scarce, and more are needed leveraging insights from implementation science. Importantly, sustained investment in effective behavioral interventions is needed, and behavioral scientists should engage in greater advocacy to persuade gatekeepers to invest in behavioral interventions.Weed argues there is no evidence to demonstrate behavioral theory interventions are genuinely effective in real world settings in populations that are offered them: they are merely efficacious for those that receive them. Despite behavioral volatility that is a normal part of maintaining steady-state population behavior levels creating the illusion of effectiveness, interventions fail in shifting the curve of population behaviors because they focus on individuals rather than populations.Hagger responds that behavioral interventions work in the ‘real world’ in spite of, not because of, flux in health behaviors, and that the contention that behavioral theory focuses solely on individual behavior change is inaccurate.Weed responds that the focus on extending the controls of efficacy trials into implementation is impractical, uneconomic and futile, and this has squandered opportunities to conduct genuine effectiveness trials in naturalistic settings.SummaryHagger contends that interventions based on behavioral theory are effective in changing population-level behavior in ‘real world’ contexts, but more evidence on how best to implement them and how to engage policymakers and practitioners to provide sustained funding is needed. Weed argues for a paradigm shift, away from aggregative attempts to effect individual behavior change towards a focus on disrupting social practices, underpinned by understanding social and economic causation of the distribution and acceptance of behaviors in a population.

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 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

10.2196/16112 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e16112
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.

The Impact of Mobile Application Systems Use on Mental Health Outcomes: A Secondary Analysis of the Effect of Systems Usage on Anxiety, Worry, & Emotional Reactivity Among Participants in the Unwinding Anxiety Program (Preprint)

2021 ◽  
Author(s):  
William Nardi ◽  
Alexandra Roy ◽  
Shira Dunsiger ◽  
Judson Brewer
Keyword(s):  
Mental Health ◽  
Health Outcomes ◽  
Emotional Regulation ◽  
Mobile Application ◽  
Secondary Analysis ◽  
Health Behavior Change ◽  
Mental Health Outcomes ◽  
Interoceptive Awareness ◽  
Point Increase ◽  
The Impact

BACKGROUND Mobile health applications provide a promising avenue to help mitigate the burden on mental health services by complimenting therapist-led treatments for anxiety. However, it remains unclear how specific systems' use of application components (i.e., tools) may be associated with changes in clinical symptomatology (i.e., anxiety, worry). OBJECTIVE This study was a secondary analysis of systems usage data from the Stage I randomized controlled trial testing the impact of the Unwinding Anxiety mobile application among adults with GAD. This secondary analysis was conducted to assess how using specific application tools may be associated with improvements in anxiety, worry, emotional regulation, and interoceptive awareness. METHODS We present analyses of the intervention group (i.e., those who received the Unwinding Anxiety program) during the Stage 1 trial. Total use of specific mobile application tools (i.e., ecological tools, meditation practices, educational modules) as well use specific to each tool (e.g., stress meter, lovingkindness meditation practice) were calculated. We utilized multivariate linear models to investigate the effect of total use of these tools on anxiety, worry, interoceptive awareness, emotional regulation at 2-months post-program initiation controlling for baseline scores, age, and education level. In addition, associations between systems usage metrics and baseline participant characteristics were assessed for differences in usage groupings. RESULTS The sample was primarily female (n=25; 92.6%) and the average age was 42.9 years old (SD=15.6) and educational module completion, the central intervention component, averaged 20.2 + 11.4 modules out of XXX for the total sample. Multivariate models revealed that completing >75% of the program was associated with an average 22.6-point increase in interoceptive awareness (SE=8.32, p=0.013) and an 11.6-point decrease in worry (SE=4.12, p=0.009). In addition, a single log unit change in total number of meditations was associated with a 0.95-point reduction in GAD-7 scores (SE=0.27, p=0.005) while a single log unit use of the stress meter was associated with an average of a 0.5-point increase in emotional regulation scores (FFMQ) (SE=0.21, p=0.027). CONCLUSIONS The work presented offers a clearer understanding of the impact of specific mobile app systems use on mental health outcomes. In addition, this research lays the groundwork for future comprehensive investigations of systems usage in dosing studies for health behavior change. CLINICALTRIAL Developing a Novel Digital Therapeutic for the Treatment of Generalized Anxiety Disorder (NCT03683472).

Beliefs as Motivational Influences at Stages in Behavior Change

1992 ◽  
Vol 13 (1) ◽  
pp. 3-29 ◽  
Author(s):  
Tom Baranowski
Keyword(s):  
Health Education ◽  
Health Behavior ◽  
Behavior Change ◽  
Change Process ◽  
Diffusion Of Innovations ◽  
Reasoned Action ◽  
Health Belief ◽  
Cost Benefit ◽  
Health Behavior Change ◽  
Education Practice

A problem for health education practice is how to interest people in making a health behavior change and maintain that interest throughout the behavior change process. Beliefs can provide motivational force for people to perform health behaviors. Five theories: 1) Diffusion of Innovations (DIT); 2) Health Belief Model (HBM); 3) Reasoned Action (TRA); 4) Locus of Control (LOC); and 5) Social Learning (SLT), are reviewed for motivational factors in promoting health behavior changes at each of six stages in the behavior change process: precontemplation, decision, training, initiation, and maintenance. A degree of overlap and complementariness are identified among the theories resulting in a syntheoretical model of beliefs as motivators in the behavior change process. The common emphasis among the theories on expectancies or cost-benefit calculations is highlighted, suggesting several strategies for employing these considerations in health education campaigns. The paucity of motivational ideas for promoting change among the externally controlled—late majority is noted. Further research must be conducted before these ideas should be generally implemented in practice.

scholarly journals Preventive digital mental health interventions for children and young people: a review of the design and reporting of research

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Aislinn D. Bergin ◽  
Elvira Perez Vallejos ◽  
E. Bethan Davies ◽  
David Daley ◽  
Tamsin Ford ◽  
...  
Keyword(s):  
Mental Health ◽  
Young People ◽  
Real World ◽  
Mental Health Problems ◽  
Digital Health ◽  
Health Problems ◽  
Poor Mental Health ◽  
Research Literature ◽  
Health Interventions ◽  
Children And Young People

Abstract Digital health interventions (DHIs) have frequently been highlighted as one way to respond to increasing levels of mental health problems in children and young people. Whilst many are developed to address existing mental health problems, there is also potential for DHIs to address prevention and early intervention. However, there are currently limitations in the design and reporting of the development, evaluation and implementation of preventive DHIs that can limit their adoption into real-world practice. This scoping review aimed to examine existing evidence-based DHI interventions and review how well the research literature described factors that researchers need to include in their study designs and reports to support real-world implementation. A search was conducted for relevant publications published from 2013 onwards. Twenty-one different interventions were identified from 30 publications, which took a universal (n = 12), selective (n = 3) and indicative (n = 15) approach to preventing poor mental health. Most interventions targeted adolescents, with only two studies including children aged ≤10 years. There was limited reporting of user co-design involvement in intervention development. Barriers and facilitators to implementation varied across the delivery settings, and only a minority reported financial costs involved in delivering the intervention. This review found that while there are continued attempts to design and evaluate DHIs for children and young people, there are several points of concern. More research is needed with younger children and those from poorer and underserved backgrounds. Co-design processes with children and young people should be recognised and reported as a necessary component within DHI research as they are an important factor in the design and development of interventions, and underpin successful adoption and implementation. Reporting the type and level of human support provided as part of the intervention is also important in enabling the sustained use and implementation of DHIs.

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