scholarly journals Designing Behavioral Feedback Visualizations to Support Health Behavior Change

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 124-125
Author(s):  
Qiong Nie ◽  
Daniel Morrow ◽  
Maurita Harris ◽  
Wendy Rogers
Keyword(s):  
Behavior Change ◽  
Visual Feedback ◽  
Change Process ◽  
Health Behavior Change ◽  
Smartphone Application ◽  
Health Technology ◽  
Limited Success ◽  
Action Process ◽  
Visualization Techniques ◽  
Behavioral Feedback

Abstract Health technology has the potential to support behavior change by measuring performance and providing users with visualizations of this performance as feedback. Such visual feedback has had limited success in changing health behaviors, but it is not clear why. We conducted a systematic review of the visual feedback literature to develop an organizational framework representing the visual feedback-action process. We identified the components that have been investigated in the context of visual feedback. These components are classified into four categories: visualization types (e.g., bar graph) and variables (e.g., color); feedback characteristics (e.g., social comparison); psychological processes (e.g., motivation) and action (e.g., exercise). The insights will inform the design of feedback visualizations in a smartphone application to support medication adherence for older adults. More broadly, this integrative perspective will yield principles of feedback visualization techniques and components that influence the behavior change process and develop a roadmap to facilitate the design.

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.

Health action process approach

2008 ◽  
Vol 16 (3) ◽  
pp. 157-160 ◽  
Author(s):  
Ralf Schwarzer ◽  
Sonia Lippke ◽  
Jochen P. Ziegelmann
Keyword(s):  
Health Psychology ◽  
Health Behavior ◽  
Behavior Change ◽  
Old Age ◽  
Experimental Studies ◽  
Health Behavior Change ◽  
Process Approach ◽  
Health Action Process Approach ◽  
Health Action ◽  
Action Process

Abstract. Health Psychology at the Freie Universität Berlin is devoted to research and teaching in the entire field of health psychology, including stress, coping, social support, self-efficacy, personality, quality of life, and health behavior change. In this article, we briefly describe one theory that represents our line of thinking (the Health Action Process Approach), followed by examples of longitudinal and experimental studies on health behavior change. A major finding is that interventions to improve physical activity, healthy nutrition, and dental hygiene are most effective when matched to three stages of change. Moreover, we address the field of health self-regulation across the life span: We are involved in the consortium Autonomy Despite Multimorbidity in Old Age (AMA), co-investigating the project Health Behaviors and Multiple Illnesses in Old Age (PREFER), and we are the home institution of the project Fostering Lifelong Autonomy and Resources in Europe: Behaviour and Successful Aging (FLARE-BSA).

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.

Sex differential mediation effects of planning within the health behavior change process

2018 ◽  
Vol 211 ◽  
pp. 137-146 ◽  
Author(s):  
Daniela Lange ◽  
Milena Barz ◽  
Linda Baldensperger ◽  
Sonia Lippke ◽  
Nina Knoll ◽  
...  
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Change Process ◽  
Health Behavior Change ◽  
Mediation Effects

Health Behavior Change in Older Adults: Testing the Health Action Process Approach at the Inter- and Intraindividual Level

2017 ◽  
Vol 9 (3) ◽  
pp. 324-348 ◽  
Author(s):  
Walter Bierbauer ◽  
Jennifer Inauen ◽  
Sabine Schaefer ◽  
Maike Margarethe Kleemeyer ◽  
Janina Lüscher ◽  
...  
Keyword(s):  
Older Adults ◽  
Health Behavior ◽  
Behavior Change ◽  
Health Behavior Change ◽  
Process Approach ◽  
Health Action Process Approach ◽  
Health Action ◽  
Action Process

scholarly journals Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change

2017 ◽  
Vol 45 (3) ◽  
pp. 331-348 ◽  
Author(s):  
Artur Direito ◽  
Deirdre Walsh ◽  
Moohamad Hinbarji ◽  
Rami Albatal ◽  
Mark Tooley ◽  
...  
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Behavioral Intervention ◽  
Intervention Mapping ◽  
Health Behavior Change ◽  
Smartphone Application ◽  
Sedentary Behaviors ◽  
Mhealth Intervention ◽  
Optimal Behavior ◽  
The Individual

Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals’ behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs.

Mobile Applications for Behavioral Change

2021 ◽  
pp. 130-154
Author(s):  
Martinson Q. Ofori ◽  
Omar F. El-Gayar
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Mobile Phones ◽  
Mobile Apps ◽  
Change Theory ◽  
Health Behavior Change ◽  
Health Technology ◽  
Behavior Change Theory ◽  
Evidence Review ◽  
Mhealth Apps

Access to the internet and the proliferation of mobile phones has resulted in a rising trend of mobile apps developed for disease self-management. This use of mobile health technology (mHealth) is viewed as an effective way to induce health behavior change. The authors conducted an evidence review of articles published in PubMed/Medline, Web of Science, and ACM Digital Library between January 2015 and January 2020 that developed and evaluated mHealth apps informed by behavior change theory. A total of 31 studies reviewed developed apps to encourage physical activity, dietary changes, diabetes, Alzheimer's disease, and others. The prevalent way of applying behavior theory to apps was through behavior change techniques (BCT) applied in 45% of the selected studies. Over 54% of the selected studies reported positive outcomes in inducing health behavior change. The results indicate that the use of behavior change theory to inform application design will result in statistically significant effects in improving health outcomes of a condition.

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