scholarly journals The Dyadic Health Influence Model

2021 ◽  
pp. 108886832110548
Author(s):  
Chloe O. Huelsnitz ◽  
Rachael E. Jones ◽  
Jeffry A. Simpson ◽  
Keven Joyal-Desmarais ◽  
Erin C. Standen ◽  
...  
Keyword(s):  
Health Behavior ◽  
Health Behaviors ◽  
Health Outcomes ◽  
Health Beliefs ◽  
Health Behavior Change ◽  
Shared Environment ◽  
Influence Model ◽  
Relationship Partners ◽  
Initial Support ◽  
And Behavior

Relationship partners affect one another’s health outcomes through their health behaviors, yet how this occurs is not well understood. To fill this gap, we present the Dyadic Health Influence Model (DHIM). The DHIM identifies three routes through which a person (the agent) can impact the health beliefs and behavior of their partner (the target). An agent may (a) model health behaviors and shape the shared environment, (b) enact behaviors that promote their relationship, and/or (c) employ strategies to intentionally influence the target’s health behavior. A central premise of the DHIM is that agents act based on their beliefs about their partner’s health and their relationship. In turn, their actions have consequences not only for targets’ health behavior but also for their relationship. We review theoretical and empirical research that provides initial support for the routes and offer testable predictions at the intersection of health behavior change research and relationship science.

scholarly journals The Dyadic Health Influence Model

2020 ◽  
Author(s):  
Chloe O. Huelsnitz ◽  
Rachael E. Jones ◽  
Jeffry Simpson ◽  
Keven Joyal-Desmarais ◽  
Erin C. Standen ◽  
...  
Keyword(s):  
Health Behaviors ◽  
Health Outcomes ◽  
Health Beliefs ◽  
The Other ◽  
Influence Strategies ◽  
Influence Model ◽  
Relationship Partners ◽  
Close Relationship ◽  
Initial Support ◽  
Relational Behaviors

There is a need to understand how close relationship partners affect one another’s health outcomes through their health behaviors. To fill this gap, we present the Dyadic Health Influence Model (DHIM). The DHIM identifies three paths through which one relationship partner (the agent) can affect the health beliefs and/or behavior of the other partner (the target): (1) the agent’s health-relevant behaviors (health behavior transmission path), (2) the agent’s and target’s relationship-based beliefs and behaviors (relational behaviors path), and (3) the intentional influence the agent uses (influence strategies path). A central premise of the model is that agents’ behaviors result not only from their beliefs about targets’ health, but also from their beliefs about targets and their relationships. We incorporate theory and empirical research to provide initial support for the DHIM paths and identify novel hypotheses that can be derived from the model. Finally, we discuss important moderators of these paths.

Beliefs Are Not Behavior

2016 ◽  
Vol 24 (3) ◽  
pp. 119-129 ◽  
Author(s):  
Theda Radtke ◽  
Urte Scholz
Keyword(s):  
Alcohol Consumption ◽  
Health Behavior ◽  
Health Beliefs ◽  
Health Behavior Change ◽  
Cross Sectional ◽  
Confirmatory Factor Analyses ◽  
Unhealthy Behavior ◽  
Health Related ◽  
Sample Method ◽  
And Behavior

Abstract. Compensatory health beliefs (CHBs), defined as belief that an unhealthy behavior can be compensated by engaging in another healthy behavior, are negatively predictive of health-behavior change intentions and behavior. However, CHBs have to be distinguished from compensatory health behavior (CBs), which is defined as compensatory behavior that an individual engages in. As it has not been investigated to date, the aim of this study was to systematically examine the distinction between CHBs and CBs in the context of alcohol consumption. The baseline sample consisted of 898 participants (mainly students, mean age = 23.57 years). For running exploratory and confirmatory factor analyses on CHBs and CBs, the split-half sample method was used. Moreover, the relationships of CHBs and CBs with health-related variables were assessed by regression analyses. The cross-sectional analyses supported the distinction between CHBs and CBs. In contrast to the CHBs, CBs were positively predictive of the intention to drink less alcohol and alcohol consumption. The consideration of CBs when investigating health behavior is highly relevant.

Heat-Health Behavior Change During Summer 2020 in African American Alabama Residents

2021 ◽  
Vol 111 (8) ◽  
pp. 1443-1447
Author(s):  
Suwei Wang ◽  
Ethel Johnson ◽  
Sheila Tyson ◽  
Julia M. Gohlke
Keyword(s):  
African American ◽  
Health Behavior ◽  
Behavior Change ◽  
Health Behaviors ◽  
Health Behavior Change ◽  
Public Spaces ◽  
Police Brutality ◽  
Telephone Surveys ◽  
Safety Concerns

To investigate how heat-health behaviors changed in summer 2020 compared with previous summers, our community–academic partnership conducted telephone surveys to collect data on cooling behaviors, safety concerns, and preferences for cooling alternatives for 101 participants living in Alabama. Participants indicating they would visit cooling centers declined from 23% in previous summers to 10% in summer 2020. The use of cooling centers and other public spaces may be less effective in reducing heat-related illness because of safety concerns amid the COVID-19 pandemic and police brutality.

scholarly journals Triggers and Their Influence on Health Behaviors

2017 ◽  
Vol 23 (2) ◽  
pp. 82-87 ◽  
Author(s):  
Christine S. Gipson
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Health Behaviors ◽  
Health Behavior Change ◽  
Concept Analysis ◽  
Healthy Behaviors ◽  
Conceptual Definition ◽  
Optimal Health ◽  
Definition Of ◽  
Significant Health

This article provides a conceptual definition of the concept trigger within the context of health behaviors and applies it to the highly significant health issue of obesity. Healthy behaviors are essential to life and happiness, but they do not just happen. They are triggered, and an inner drive keeps them alive. To help patients gain and retain optimal health, nurses must understand the triggers of healthy behaviors. Walker and Avant’s (2011) method of concept analysis is used as the basis for defining the concept of trigger. The antecedents, defining attributes, and consequences of trigger are identified. Findings suggest that nurses can play a role in triggering health behavior change through simple motivational efforts.

scholarly journals Interdisciplinary Perspectives on Maintenance of Health Behavior Change

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 817-817
Author(s):  
Jaime Hughes ◽  
Janet Bettger ◽  
Susan Hughes ◽  
Mina Raj
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Health Behaviors ◽  
Chronic Conditions ◽  
Intervention Development ◽  
Health Behavior Change ◽  
Cognitive Capacity ◽  
Multiple Chronic Conditions ◽  
Intervention Science ◽  
Behavior Maintenance

Abstract Modifying health behaviors can be difficult, especially for older adults who are challenged by multiple chronic conditions, reduced functional and/or cognitive capacity, and limited social support. Although much attention has been given to the theories, skills, and resources behind initiating and achieving behavior change, less work has focused on maintenance of health behaviors over time. This presentation will showcase pilot research inspired by RCCN’s first workshop, Achieving and Sustaining Behavior Change. Specifically, this pilot brings together an interdisciplinary team of behavioral scientists and health services researchers working at the intersection of intervention science and implementation science to better understand the construct of maintenance and discuss emerging methods for intervention development and evaluation. The presentation will utilize physical activity as an example behavior to demonstrate the value of interdisciplinary research, including recommendations on how some of the six NIA research centers can make unique contributions to understanding health behavior maintenance.

scholarly journals Fear of Cancer Recurrence: A Model Examination of Physical Symptoms, Emotional Distress, and Health Behavior Change

2019 ◽  
Vol 15 (9) ◽  
pp. e787-e797 ◽  
Author(s):  
Daniel L. Hall ◽  
Rachel B. Jimenez ◽  
Giselle K. Perez ◽  
Julia Rabin ◽  
Katharine Quain ◽  
...  
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Behavior Change ◽  
Health Behaviors ◽  
Emotional Distress ◽  
Cancer Recurrence ◽  
Health Behavior Change ◽  
Physical Symptoms ◽  
Fear Of Cancer Recurrence ◽  
Fear Of Cancer

PURPOSE: Fear of cancer recurrence is highly prevalent among adult survivors of cancer. The role of fear of recurrence in the emotional distress of survivors of cancer, as well as health behaviors that may directly affect their health, remains unclear. To advance oncology practice, this study sought to examine the extent to which fear of recurrence stemming from physical symptoms accounts for emotional distress in a large sample of adult survivors of cancer and to extend the model to explain postdiagnosis self-reported health behavior change. METHODS: In 2016, 258 survivors of cancer at an academic hospital completed a survey of psychosocial needs. Items assessed physical symptoms (checklist), fear of cancer recurrence (Assessment of Survivor Concerns), emotional distress (anxiety and depressed mood), and health behaviors (current alcohol use, physical activity, diet, and sunscreen use, as well as changes after cancer diagnosis) informed by National Comprehensive Cancer Network survivorship guidelines. Indirect effects regression models accounting for relevant covariates (age and treatment history) used 5,000-iteration bootstrapping. RESULTS: Higher fear of cancer recurrence was associated with greater number of physical symptoms ( P < .001), greater emotional distress ( P < .05), lower moderate or vigorous physical activity ( P < .05), higher sunscreen use ( P < .05), and postdiagnosis increases in alcohol use ( P < .01) and reductions in physical activity ( P < .01). Fear of cancer recurrence models accounted for almost half of the variance in distress of survivors of cancer ( R2 = 0.44, P < .001) and, to a lesser yet significant extent, changes in alcohol consumption ( R2 = 0.09, P < .001) and physical activity ( R2 = 0.06, P = .003). CONCLUSION: Fear of cancer recurrence plays a central role in the emotional distress and key health behaviors of survivors of cancer. These findings support fear of cancer recurrence as a potential target for emotional health and health behavior change interventions.

scholarly journals Archetypes of Gamification: Analysis of mHealth Apps

10.2196/19280 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e19280
Author(s):  
Manuel Schmidt-Kraepelin ◽  
Philipp A Toussaint ◽  
Scott Thiebes ◽  
Juho Hamari ◽  
Ali Sunyaev
Keyword(s):  
Best Practices ◽  
Health Behavior ◽  
Health Behaviors ◽  
Mobile Health ◽  
Positive Reinforcement ◽  
Mobile Apps ◽  
Health Behavior Change ◽  
Future Research ◽  
Research Approach ◽  
Health Related

Background Nowadays, numerous health-related mobile apps implement gamification in an attempt to draw on the motivational potential of video games and thereby increase user engagement or foster certain health behaviors. However, research on effective gamification is still in its infancy and researchers increasingly recognize methodological shortcomings of existing studies. What we actually know about the phenomenon today stems from fragmented pieces of knowledge, and a variety of different perspectives. Existing research primarily draws on conceptual knowledge that is gained from research prototypes, and isolated from industry best practices. We still lack knowledge on how gamification has been successfully designed and implemented within the industry and whether certain gamification approaches have shown to be particularly suitable for certain health behaviors. Objective We address this lack of knowledge concerning best practices in the design and implementation of gamification for health-related mobile apps by identifying archetypes of gamification approaches that have emerged in pertinent health-related mobile apps and analyzing to what extent those gamification approaches are influenced by the underlying desired health-related outcomes. Methods A 3-step research approach is employed. As a first step, a database of 143 pertinent gamified health-related mobile apps from the Apple App Store and Google Play Store is set up. Second, the gamification approach of each app within the database is classified based on an established taxonomy for gamification in health-related apps. Finally, a 2-step cluster analysis is conducted in order to identify archetypes of the most dominant gamification approaches in pertinent gamified health-related mobile apps. Results Eight archetypes of gamification emerged from the analysis of health-related mobile apps: (1) competition and collaboration, (2) pursuing self-set goals without rewards, (3) episodical compliance tracking, (4) inherent gamification for external goals, (5) internal rewards for self-set goals, (6) continuous assistance through positive reinforcement, (7) positive and negative reinforcement without rewards, and (8) progressive gamification for health professionals. The results indicate a close relationship between the identified archetypes and the actual health behavior that is being targeted. Conclusions By unveiling salient best practices and discussing their relationship to targeted health behaviors, this study contributes to a more profound understanding of gamification in mobile health. The results can serve as a foundation for future research that advances the knowledge on how gamification may positively influence health behavior change and guide practitioners in the design and development of highly motivating and effective health-related mobile health apps.

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 Information Architecture of Web-Based Interventions to Improve Health Outcomes: Systematic Review (Preprint)

2017 ◽  
Author(s):  
Jillian Pugatch ◽  
Emily Grenen ◽  
Stacy Surla ◽  
Mary Schwarz ◽  
Heather Cole-Lewis
Keyword(s):  
Health Behavior ◽  
Health Outcomes ◽  
Digital Health ◽  
Behavioral Outcomes ◽  
Evidence Base ◽  
Information Architecture ◽  
Site Condition ◽  
Behavior Outcomes ◽  
And Behavior ◽  
The Impact

BACKGROUND The rise in usage of and access to new technologies in recent years has led to a growth in digital health behavior change interventions. As the shift to digital platforms continues to grow, it is increasingly important to consider how the field of information architecture (IA) can inform the development of digital health interventions. IA is the way in which digital content is organized and displayed, which strongly impacts users’ ability to find and use content. While many information architecture best practices exist, there is a lack of empirical evidence on the role it plays in influencing behavior change and health outcomes. OBJECTIVE Our aim was to conduct a systematic review synthesizing the existing literature on website information architecture and its effect on health outcomes, behavioral outcomes, and website engagement. METHODS To identify all existing information architecture and health behavior literature, we searched articles published in English in the following databases (no date restrictions imposed): ACM Digital Library, CINAHL, Cochrane Library, Google Scholar, Ebsco, and PubMed. The search terms used included information terms (eg, information architecture, interaction design, persuasive design), behavior terms (eg, health behavior, behavioral intervention, ehealth), and health terms (eg, smoking, physical activity, diabetes). The search results were reviewed to determine if they met the inclusion and exclusion criteria created to identify empirical research that studied the effect of IA on health outcomes, behavioral outcomes, or website engagement. Articles that met inclusion criteria were assessed for study quality. Then, data from the articles were extracted using a priori categories established by 3 reviewers. However, the limited health outcome data gathered from the studies precluded a meta-analysis. RESULTS The initial literature search yielded 685 results, which was narrowed down to three publications that examined the effect of information architecture on health outcomes, behavioral outcomes, or website engagement. One publication studied the isolated impact of information architecture on outcomes of interest (ie, website use and engagement; health-related knowledge, attitudes, and beliefs; and health behaviors), while the other two publications studied the impact of information architecture, website features (eg, interactivity, email prompts, and forums), and tailored content on these outcomes. The paper that investigated IA exclusively found that a tunnel IA improved site engagement and behavior knowledge, but it decreased users’ perceived efficiency. The first study that did not isolate IA found that the enhanced site condition improved site usage but not the amount of content viewed. The second study that did not isolate IA found that a tailored site condition improved site usage, behavior knowledge, and some behavior outcomes. CONCLUSIONS No clear conclusion can be made about the relationship between IA and health outcomes, given limited evidence in the peer-reviewed literature connecting IA to behavioral outcomes and website engagement. Only one study reviewed solely manipulated IA, and we therefore recommend improving the scientific evidence base such that additional empirical studies investigate the impact of IA in isolation. Moreover, information from the gray literature and expert opinion might be identified and added to the evidence base, in order to lay the groundwork for hypothesis generation to improve empirical evidence on information architecture and health and behavior outcomes.

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