Facilitating the Adoption of Digital Health Technologies by Older Adults to Support Their Health

2021 ◽  
pp. 164-179
Author(s):  
Maurita T. Harris ◽  
Wendy A. Rogers
Keyword(s):  
Older Adults ◽  
Health Behavior ◽  
Behavior Change ◽  
Technology Acceptance ◽  
Digital Health ◽  
Theoretical Models ◽  
Health Behavior Change ◽  
The United States ◽  
Positive Health ◽  
Health Technologies

With over 50% of older adults in the United States managing at least one chronic condition, it is crucial to understand how to promote their self-management of positive health behaviors. Health interventions through digital health technologies are becoming more commonplace. Theoretical models related to health behavior change and technology acceptance can guide the design of these healthcare tools and lead to adoption by older adults to support their health. This chapter provides an overview of health behavior change and technology acceptance models to inform the development of digital health technology for older adults. This chapter illustrates the application of these models by describing two design personas that represent human factors designers. This chapter discusses the lack of inclusion of technology adoption and other long-term concepts and the need for further exploration that could inform understanding of technology integration into everyday health activities.

scholarly journals Digital and Mobile Technologies to Promote Physical Health Behavior Change and Provide Psychological Support for Patients Undergoing Elective Surgery: Meta-Ethnography and Systematic Review

10.2196/19237 ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. e19237
Author(s):  
Anna Robinson ◽  
Umay Oksuz ◽  
Robert Slight ◽  
Sarah Slight ◽  
Andrew Husband
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Elective Surgery ◽  
Health Behavior Change ◽  
Psychological Support ◽  
Behavior Changes ◽  
Health Technologies ◽  
Digital Interventions ◽  
Surgical Pathway ◽  
Health Behavior Changes

Background Digital technology has influenced many aspects of modern living, including health care. In the context of elective surgeries, there is a strong association between preoperative physical and psychological preparedness, and improved postoperative outcomes. Health behavior changes made in the pre- and postoperative periods can be fundamental in determining the outcomes and success of elective surgeries. Understanding the potential unmet needs of patients undergoing elective surgery is central to motivating health behavior change. Integrating digital and mobile health technologies within the elective surgical pathway could be a strategy to remotely deliver this support to patients. Objective This meta-ethnographic systematic review explores digital interventions supporting patients undergoing elective surgery with health behavior changes, specifically physical activity, weight loss, dietary intake, and psychological support. Methods A literature search was conducted in October 2019 across 6 electronic databases (International Prospective Register of Systematic Reviews [PROSPERO]: CRD42020157813). Qualitative studies were included if they evaluated the use of digital technologies supporting behavior change in adult patients undergoing elective surgery during the pre- or postoperative period. Study quality was assessed using the Critical Appraisal Skills Programme tool. A meta-ethnographic approach was used to synthesize existing qualitative data, using the 7 phases of meta-ethnography by Noblit and Hare. Using this approach, along with reciprocal translation, enabled the development of 4 themes from the data. Results A total of 18 studies were included covering bariatric (n=2, 11%), cancer (n=13, 72%), and orthopedic (n=3, 17%) surgeries. The 4 overarching themes appear to be key in understanding and determining the effectiveness of digital and mobile interventions to support surgical patients. To successfully motivate health behavior change, technologies should provide motivation and support, enable patient engagement, facilitate peer networking, and meet individualized patient needs. Self-regulatory features such as goal setting heightened patient motivation. The personalization of difficulty levels in virtual reality–based rehabilitation was positively received. Internet-based cognitive behavioral therapy reduced depression and distress in patients undergoing cancer surgery. Peer networking provided emotional support beyond that of patient-provider relationships, improving quality of life and care satisfaction. Patients expressed the desire for digital interventions to be individually tailored according to their physical and psychological needs, before and after surgery. Conclusions These findings have the potential to influence the future design of patient-centered digital and mobile health technologies and demonstrate a multipurpose role for digital technologies in the elective surgical pathway by motivating health behavior change and offering psychological support. Through the synthesis of patient suggestions, we highlight areas for digital technology optimization and emphasize the importance of content tailored to suit individual patients and surgical procedures. There is a significant rationale for involving patients in the cocreation of digital health technologies to enhance engagement, better support behavior change, and improve surgical outcomes.

scholarly journals Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study (Preprint)

2020 ◽  
Author(s):  
Samuel Tomczyk ◽  
Simon Barth ◽  
Silke Schmidt ◽  
Holger Muehlan
Keyword(s):  
General Population ◽  
Health Behavior ◽  
Behavior Change ◽  
Social Norms ◽  
Technology Acceptance ◽  
Health Behavior Change ◽  
Contact Tracing ◽  
Cross Sectional ◽  
Privacy Concerns ◽  
Technology Acceptance Models

BACKGROUND To combat the global COVID-19 pandemic, contact tracing apps have been discussed as digital health solutions to track infection chains and provide appropriate information. However, observational studies point to low acceptance in most countries, and few studies have yet examined theory-based predictors of app use in the general population to guide health communication efforts. OBJECTIVE This study utilizes established health behavior change and technology acceptance models to predict adoption intentions and frequency of current app use. METHODS We conducted a cross-sectional online survey between May and July 2020 in a German convenience sample (N=349; mean age 35.62 years; n=226, 65.3% female). To inspect the incremental validity of model constructs as well as additional variables (privacy concerns, personalization), hierarchical regression models were applied, controlling for covariates. RESULTS The theory of planned behavior and the unified theory of acceptance and use of technology predicted adoption intentions (R<sup>2</sup>=56%-63%) and frequency of current app use (R<sup>2</sup>=33%-37%). A combined model only marginally increased the predictive value by about 5%, but lower privacy concerns and higher threat appraisals (ie, anticipatory anxiety) significantly predicted app use when included as additional variables. Moreover, the impact of perceived usefulness was positive for adoption intentions but negative for frequency of current app use. CONCLUSIONS This study identified several theory-based predictors of contact tracing app use. However, few constructs, such as social norms, have a consistent positive effect across models and outcomes. Further research is required to replicate these observations, and to examine the interconnectedness of these constructs and their impact throughout the pandemic. Nevertheless, the findings suggest that promulgating affirmative social norms and positive emotional effects of app use, as well as addressing health concerns, might be promising strategies to foster adoption intentions and app use in the general population. CLINICALTRIAL

scholarly journals Factors Associated with Initiation and Sustenance of Stress Management Behaviors in Veterinary Students: Testing of Multi-Theory Model (MTM)

2020 ◽  
Vol 17 (2) ◽  
pp. 631 ◽  
Author(s):  
Vinayak K. Nahar ◽  
Julia K. Wells ◽  
Robert E. Davis ◽  
Elizabeth C. Johnson ◽  
Jason W. Johnson ◽  
...  
Keyword(s):  
United States ◽  
Health Behavior ◽  
Behavior Change ◽  
Stress Management ◽  
Health Behavior Change ◽  
The United States ◽  
Theory Model ◽  
Veterinary Students ◽  
Cross Sectional ◽  
Management Behaviors

Veterinary students across the United States face the challenge of stress during school every day. When managed improperly, stress can become chronic and manifest in physical and emotional consequences. The purpose of this study was to examine the utility of the multi-theory model (MTM) of health behavior change in predicting the initiation and sustenance of stress management behaviors among veterinary students. A cross-sectional design was used to study the efficacy of the MTM in predicting initiation and sustenance of stress management behaviors among veterinary students at a private College of Veterinary Medicine in the Southeast United States. Researchers collected data using a 54-item valid and reliable survey. Only students who did not already engage in daily stress management behaviors were included in the study. After recruitment and exclusion, a total of 140 students remained and participated in the study. Hierarchical multiple regression revealed that, for initiation of stress management behaviors, 49.5% of the variance was explained by depression, academic classification, and behavioral confidence. Regarding sustenance of stress management behaviors, 50.4% of the variance was explained by perceived stress, depression, academic classification, and emotional transformation. MTM serves as a promising framework for predicting initiation and sustenance of health behavior change. Based on the results of this study, interventions aimed to promote stress management behaviors in veterinary students should focus on the MTM constructs of behavioral confidence and emotional transformation.

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

Innovative methods for predicting and changing complex health behaviors: Four propositions

2020 ◽  
Author(s):  
Guillaume Chevance ◽  
Olga Perski ◽  
Eric B. Hekler
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Health Behaviors ◽  
Computational Models ◽  
Theoretical Models ◽  
Health Behavior Change ◽  
Traditional Group ◽  
Individual Level ◽  
Innovative Methods ◽  
Behavioral Assessments

Background and purpose: Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. Methods: Narrative review of the methods used to observe and change complex health behaviors. Results: Based on available literature, we argue that health behavior change researchers should progressively transition from (i) low- to high-resolution behavioral assessments, (ii) group only to group- and individual-level statistical inference, (iii) narrative theoretical models to dynamic computational models, and (iv) static to adaptive and continuous tuning interventions. Rather than providing an exhaustive and technical presentation of each method and approach, this article articulates why and how researchers interested in health behavior change can apply these innovative methods. Practical examples contributing to these efforts are presented. Conclusion: If successfully adopted and implemented, the four propositions made in this article have the potential to greatly improve our public health and behavior change practices in the near future.

scholarly journals Innovative methods for observing and changing complex health behaviors: four propositions

2020 ◽  
Author(s):  
Guillaume Chevance ◽  
Olga Perski ◽  
Eric B Hekler
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Health Behaviors ◽  
Computational Models ◽  
Theoretical Models ◽  
Health Behavior Change ◽  
Traditional Group ◽  
Individual Level ◽  
Innovative Methods ◽  
Behavioral Assessments

Abstract Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized, and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. This article is a narrative review of the methods used to observe and change complex health behaviors. On the basis of the available literature, we argue that health behavior change researchers should progressively transition from (i) low- to high-resolution behavioral assessments, (ii) group-only to group- and individual-level statistical inference, (iii) narrative theoretical models to dynamic computational models, and (iv) static to adaptive and continuous tuning interventions. Rather than providing an exhaustive and technical presentation of each method and approach, this article articulates why and how researchers interested in health behavior change can apply these innovative methods. Practical examples contributing to these efforts are presented. If successfully adopted and implemented, the four propositions in this article have the potential to greatly improve our public health and behavior change practices in the near future.

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