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

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.

Download Full-text

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

10.2196/preprints.19237 ◽

2020 ◽

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.

Download Full-text

Mapping Canadian Men’s Recent and Intended Health Behavior Changes Through the Don’t Change Much Electronic Health Program

Journal of Medical Internet Research ◽

10.2196/16174 ◽

2020 ◽

Vol 22 (5) ◽

pp. e16174

Author(s):

John L Oliffe ◽

Nick Black ◽

Jeffrey Yiu ◽

Ryan K Flannigan ◽

Donald R McCreary ◽

...

Keyword(s):

Health Behavior ◽

Behavior Change ◽

Effect Size ◽

Reference Group ◽

Health Behavior Change ◽

Behavior Changes ◽

High Exposure ◽

Exposure Levels ◽

Electronic Health ◽

Health Behavior Changes

Background Although evaluation studies confirm the strong potential of men’s electronic health (eHealth) programs, there have been calls to more fully understand acceptability, engagement, and behavior change to guide future work. Relatedly, mapping of behavior changes using health promotion theories including the transtheoretical model (or stages of change) has been recommended to build a translatable empirical base to advance design and evaluation considerations for men’s eHealth programs. Objective This study aimed to use a benchmark sample as a reference group to map the recent and intended health behavior changes in Canadian men who use the Don’t Change Much (DCM) eHealth program. The hypothesis being tested was that increased exposure to DCM would be positively associated with men’s recent and intended health behavior changes. Methods DCM users (n=863) were sampled for demographic data and self-reported recent and intended health behavior changes. Respondents also reported their usage (frequency and duration) for each of the 3 DCM components (web, newsletter, and social media) and were allocated to limited exposure (257/863, 29.8%), low exposure (431/863, 49.9%), and high exposure (175/863, 20.3%) subgroups. A benchmark sample (n=2000), comprising respondents who had not accessed DCM provided a reference group. Bivariate analysis of recent and intended health behavior changes and DCM exposure levels were used to compute the strength of association between the independent variables (exposure levels) and the 10 categorical dependent variables (recent and intended health behavior changes). Binary logistic regression models were computed for each of the 10 recent and intended health behavior changes. Linear regression was used to model the association between the number of recent and intended changes and the level of exposure to DCM. Results Compared with the benchmark reference group, DCM high-exposure respondents had significantly increased odds for 9 of the 10 health behavior changes, with the largest effect size observed for Changed diet or Improved eating habits (odds ratio [OR] 5.628, 95% CI 3.932-8.055). High-exposure respondents also had significantly increased odds for 9 intended health changes, with the largest effect sizes observed for Reduce stress level (OR 4.282, 95% CI 3.086-5.941). Moderate effect size (goodness of fit) was observed for increased total number of recent (F12,2850=25.52; P.001; adjusted R2=.093) and intended health behavior changes (F12,2850=36.30; P.001; adjusted R2=.129) among high-exposure respondents. Conclusions DCM respondents contrasted the predominately precontemplative benchmark sample mapping across the contemplative, preparation, and action stages of the transtheoretical health behavior change model. Almost 10% of variation in the recent and 13% of variation in the intended health behavior changes can be explained by DCM exposure and demographic factors, indicating the acceptability of this men’s eHealth resource.

Download Full-text

Mapping Canadian Men’s Recent and Intended Health Behavior Changes Through the Don’t Change Much Electronic Health Program (Preprint)

10.2196/preprints.16174 ◽

2019 ◽

Author(s):

John L Oliffe ◽

Nick Black ◽

Jeffrey Yiu ◽

Ryan K Flannigan ◽

Donald R McCreary ◽

...

Keyword(s):

Health Behavior ◽

Behavior Change ◽

Effect Size ◽

Reference Group ◽

Health Behavior Change ◽

Behavior Changes ◽

High Exposure ◽

Exposure Levels ◽

Electronic Health ◽

Health Behavior Changes

BACKGROUND Although evaluation studies confirm the strong potential of men’s electronic health (eHealth) programs, there have been calls to more fully understand acceptability, engagement, and behavior change to guide future work. Relatedly, mapping of behavior changes using health promotion theories including the transtheoretical model (or stages of change) has been recommended to build a translatable empirical base to advance design and evaluation considerations for men’s eHealth programs. OBJECTIVE This study aimed to use a benchmark sample as a reference group to map the recent and intended health behavior changes in Canadian men who use the Don’t Change Much (DCM) eHealth program. The hypothesis being tested was that increased exposure to DCM would be positively associated with men’s recent and intended health behavior changes. METHODS DCM users (n=863) were sampled for demographic data and self-reported recent and intended health behavior changes. Respondents also reported their usage (frequency and duration) for each of the 3 DCM components (web, newsletter, and social media) and were allocated to limited exposure (257/863, 29.8%), low exposure (431/863, 49.9%), and high exposure (175/863, 20.3%) subgroups. A benchmark sample (n=2000), comprising respondents who had not accessed DCM provided a reference group. Bivariate analysis of recent and intended health behavior changes and DCM exposure levels were used to compute the strength of association between the independent variables (exposure levels) and the 10 categorical dependent variables (recent and intended health behavior changes). Binary logistic regression models were computed for each of the 10 recent and intended health behavior changes. Linear regression was used to model the association between the number of recent and intended changes and the level of exposure to DCM. RESULTS Compared with the benchmark reference group, DCM high-exposure respondents had significantly increased odds for 9 of the 10 health behavior changes, with the largest effect size observed for Changed diet or Improved eating habits (odds ratio [OR] 5.628, 95% CI 3.932-8.055). High-exposure respondents also had significantly increased odds for 9 intended health changes, with the largest effect sizes observed for Reduce stress level (OR 4.282, 95% CI 3.086-5.941). Moderate effect size (goodness of fit) was observed for increased total number of recent (F12,2850=25.52; P.001; adjusted R2=.093) and intended health behavior changes (F12,2850=36.30; P.001; adjusted R2=.129) among high-exposure respondents. CONCLUSIONS DCM respondents contrasted the predominately precontemplative benchmark sample mapping across the contemplative, preparation, and action stages of the transtheoretical health behavior change model. Almost 10% of variation in the recent and 13% of variation in the intended health behavior changes can be explained by DCM exposure and demographic factors, indicating the acceptability of this men’s eHealth resource.

Download Full-text

Developing Educational Materials to Support Older Adults With Hypertension Management

Innovation in Aging ◽

10.1093/geroni/igab046.882 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 230-230

Author(s):

Qiong Nie ◽

Maurita Harris ◽

Stacy Al-Saleh ◽

Ysabel Beatrice Floresca ◽

Wendy Rogers

Keyword(s):

Health Behavior ◽

Behavior Change ◽

Information Seeking ◽

Controlled Trial ◽

Management Strategies ◽

Health Behavior Change ◽

Primary Objective ◽

Hypertension Management ◽

Educational Materials ◽

Health Behavior Changes

Abstract A comprehensive approach to hypertension management requires medication adherence as well as more general health behavior changes. Our primary objective is to provide evidence-based and tailored education about hypertension, medications, and health self-management strategies with consideration for different stages of behavior change, health literacy, education, disease knowledge, and experience. To facilitate health behavior change, enable information seeking, and increase engagement, the educational materials provide different layers of information, including tips and information in the MEDSReM app, as well as more detailed educational content on the web portal. We will present examples of the materials in different formats to show how they are tailored to ease comprehension, support adherence, and influence behavior change. These educational materials will have broad utility outside of the MEDSReM system, and will also serve as the education-only comparison condition for the randomized controlled trial.

Download Full-text

A Procedural Model for Planning and Evaluating Behavioral Interventions

Methods of Information in Medicine ◽

10.1055/s-0038-1633967 ◽

2005 ◽

Vol 44 (02) ◽

pp. 299-302

Author(s):

G. C. Hyner

Keyword(s):

Health Behavior ◽

Behavior Change ◽

Behavioral Interventions ◽

Health Behavior Change ◽

Behavior Changes ◽

Change Strategies ◽

Innovative Strategies ◽

Proposed Model ◽

Influence Behavior

Summary Objective: A model for planning, implementing and evaluating health behavior change strategies is proposed. Variables are presented which can be used in the model or serve as examples for how the model is utilized once a theory of health behavior is adopted. Results: Examples of three innovative strategies designed to influence behavior change are presented so that the proposed model can be modified for use following comprehensive screening and baseline measurements. Three measurement priorities: clients, methods and agency are subjected to three phases of assessment: goals, implementation and effects. Conclusion: Lifestyles account for the majority of variability in quality-of-life and premature morbidity and mortality. Interventions designed to influence healthy behavior changes must be driven by theory and carefully planned and evaluated. The proposed model is offered as a useful tool for the behavior change strategist.

Download Full-text

Educational and Gender Differences in Health Behavior Changes After a Gateway Diagnosis

Journal of Aging and Health ◽

10.1177/0898264316678756 ◽

2016 ◽

Vol 30 (3) ◽

pp. 342-364 ◽

Cited By ~ 19

Author(s):

Elaine M. Hernandez ◽

Rachel Margolis ◽

Robert A. Hummer

Keyword(s):

Gender Differences ◽

Health Behavior ◽

Health Behaviors ◽

Health Behavior Change ◽

The United States ◽

Behavior Changes ◽

Socioeconomic Gradient ◽

Educational Differences ◽

Hypertension Diagnosis ◽

Health Behavior Changes

Objective: Hypertension represents a gateway diagnosis to more serious health problems that occur as people age. We examine educational differences in three health behavior changes people often make after receiving this diagnosis in middle or older age, and test whether these educational differences depend on (a) the complexity of the health behavior change and (b) gender. Method: We use data from the Health and Retirement Study and conduct logistic regression analysis to examine the likelihood of modifying health behaviors post diagnosis. Results: We find educational differences in three behavior changes—antihypertensive medication use, smoking cessation, and physical activity initiation—after a hypertension diagnosis. These educational differences in health behaviors were stronger among women compared with men. Discussion: Upon receiving a hypertension diagnosis, education is a more important predictor of behavior changes for women compared with men, which may help explain gender differences in the socioeconomic gradient in health in the United States.

Download Full-text

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

Advances in Human and Social Aspects of Technology - Human Factors Issues and the Impact of Technology on Society ◽

10.4018/978-1-7998-6453-0.ch007 ◽

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.

Download Full-text

Toward a Digital Platform for the Self-Management of Noncommunicable Disease: Systematic Review of Platform-Like Interventions

Journal of Medical Internet Research ◽

10.2196/16774 ◽

2020 ◽

Vol 22 (10) ◽

pp. e16774

Author(s):

Sarah A Tighe ◽

Kylie Ball ◽

Finn Kensing ◽

Lars Kayser ◽

Jonathan C Rawstorn ◽

...

Keyword(s):

Health Behavior ◽

Behavior Change ◽

Health Behavior Change ◽

Self Management ◽

Evidence Based ◽

Digital Platform ◽

Digital Interventions ◽

Disease Specific ◽

Specific Health

Background Digital interventions are effective for health behavior change, as they enable the self-management of chronic, noncommunicable diseases (NCDs). However, they often fail to facilitate the specific or current needs and preferences of the individual. A proposed alternative is a digital platform that hosts a suite of discrete, already existing digital health interventions. A platform architecture would allow users to explore a range of evidence-based solutions over time to optimize their self-management and health behavior change. Objective This review aims to identify digital platform-like interventions and examine their potential for supporting self-management of NCDs and health behavior change. Methods A literature search was conducted in January 2020 using EBSCOhost, PubMed, Scopus, and EMBASE. No digital platforms were identified, so criteria were broadened to include digital platform-like interventions. Eligible platform-like interventions offered a suite of discrete, evidence-based health behavior change features to optimize self-management of NCDs in an adult population and provided digitally supported guidance for the user toward the features best suited to their needs and preferences. Data collected on interventions were guided by the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist, including evaluation data on effectiveness and process outcomes. The quality of the included literature was assessed using the Mixed Methods Appraisal Tool. Results A total of 7 studies were included for review. Targeted NCDs included cardiovascular diseases (CVD; n=3), diabetes (n=3), and chronic obstructive pulmonary disease (n=1). The mean adherence (based on the number of follow-up responders) was 69% (SD 20%). Of the 7 studies, 4 with the highest adherence rates (80%) were also guided by behavior change theories and took an iterative, user-centered approach to development, optimizing intervention relevance. All 7 interventions presented algorithm-supported user guidance tools, including electronic decision support, smart features that interact with patterns of use, and behavior change stage-matching tools. Of the 7 studies, 6 assessed changes in behavior. Significant effects in moderate-to-vigorous physical activity were reported, but for no other specific health behaviors. However, positive behavior change was observed in studies that focused on comprehensive behavior change measures, such as self-care and self-management, each of which addresses several key lifestyle risk factors (eg, medication adherence). No significant difference was found for psychosocial outcomes (eg, quality of life). Significant changes in clinical outcomes were predominately related to disease-specific, multifaceted measures such as clinical disease control and cardiovascular risk score. Conclusions Iterative, user-centered development of digital platform structures could optimize user engagement with self-management support through existing, evidence-based digital interventions. Offering a palette of interventions with an appropriate degree of guidance has the potential to facilitate disease-specific health behavior change and effective self-management among a myriad of users, conditions, or stages of care.

Download Full-text

The Impact of Racial and Non-racial Discrimination on Health Behavior Change Among Visible Minority Adults During the COVID-19 Pandemic

Journal of Racial and Ethnic Health Disparities ◽

10.1007/s40615-021-01189-z ◽

2021 ◽

Author(s):

Cheryl L. Currie ◽

Erin K. Higa

Keyword(s):

Health Behavior ◽

Behavior Change ◽

Racial Discrimination ◽

Health Behavior Change ◽

Short Version ◽

Visible Minority ◽

Logistic Regression Models ◽

Stress Eating ◽

Health Behavior Changes ◽

The Impact

Abstract Introduction Pre-pandemic health behavior has been put forward as a reason for excess COVID-19 infection and death in some racialized groups. At the same time, scholars have labeled racism the other pandemic and argued for its role in the adverse COVID-19 outcomes observed. The purpose of this study was to examine the impact of discrimination on health behavior change among racialized adults in the early stages of the pandemic. Methods Data were collected from 210 adults who identified as a visible minority in Alberta, Canada, in June 2020. The Everyday Discrimination Scale (Short Version) was adapted to examine past-month experiences. Four questions asked if alcohol/cannabis use and stress eating had significantly increased, and if sleep and exercise had significantly decreased in the past month. Logistic regression models examined associations between discrimination attributed to racial and non-racial causes and health behavior change adjusted for covariates. Results The majority of adults (56.2%) reported past-month discrimination including 26.7% who attributed it to their race. Asian adults reported more racial discrimination and discrimination due to people believing they had COVID-19 than other visible minorities. Racial discrimination during the pandemic was strongly associated with increased substance use (OR: 4.0, 95% CI 1.2, 13.4) and decreased sleep (OR: 7.0, 95% CI 2.7, 18.4), and weakly associated with decreased exercise (OR: 2.2, 95% CI 1.1, 4.5). Non-racial discrimination was strongly associated with decreased sleep (OR: 4.8, 95% CI 1.8, 12.5). Conclusion Racial discrimination may have a particularly important effect on intensifying adverse health behavior changes among racialized adults during a time of global crisis.

Download Full-text

Toward a Digital Platform for the Self-Management of Noncommunicable Disease: Systematic Review of Platform-Like Interventions (Preprint)

10.2196/preprints.16774 ◽

2019 ◽

Author(s):

Sarah A Tighe ◽

Kylie Ball ◽

Finn Kensing ◽

Lars Kayser ◽

Jonathan C Rawstorn ◽

...

Keyword(s):

Health Behavior ◽

Behavior Change ◽

Health Behavior Change ◽

Self Management ◽

Evidence Based ◽

Digital Platform ◽

Digital Interventions ◽

Disease Specific ◽

Specific Health

BACKGROUND Digital interventions are effective for health behavior change, as they enable the self-management of chronic, noncommunicable diseases (NCDs). However, they often fail to facilitate the specific or current needs and preferences of the individual. A proposed alternative is a digital platform that hosts a suite of discrete, already existing digital health interventions. A platform architecture would allow users to explore a range of evidence-based solutions over time to optimize their self-management and health behavior change. OBJECTIVE This review aims to identify digital platform-like interventions and examine their potential for supporting self-management of NCDs and health behavior change. METHODS A literature search was conducted in January 2020 using EBSCOhost, PubMed, Scopus, and EMBASE. No digital platforms were identified, so criteria were broadened to include digital platform-like interventions. Eligible platform-like interventions offered a suite of discrete, evidence-based health behavior change features to optimize self-management of NCDs in an adult population and provided digitally supported guidance for the user toward the features best suited to their needs and preferences. Data collected on interventions were guided by the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist, including evaluation data on effectiveness and process outcomes. The quality of the included literature was assessed using the Mixed Methods Appraisal Tool. RESULTS A total of 7 studies were included for review. Targeted NCDs included cardiovascular diseases (CVD; n=3), diabetes (n=3), and chronic obstructive pulmonary disease (n=1). The mean adherence (based on the number of follow-up responders) was 69% (SD 20%). Of the 7 studies, 4 with the highest adherence rates (80%) were also guided by behavior change theories and took an iterative, user-centered approach to development, optimizing intervention relevance. All 7 interventions presented algorithm-supported user guidance tools, including electronic decision support, smart features that interact with patterns of use, and behavior change stage-matching tools. Of the 7 studies, 6 assessed changes in behavior. Significant effects in moderate-to-vigorous physical activity were reported, but for no other specific health behaviors. However, positive behavior change was observed in studies that focused on comprehensive behavior change measures, such as self-care and self-management, each of which addresses several key lifestyle risk factors (eg, medication adherence). No significant difference was found for psychosocial outcomes (eg, quality of life). Significant changes in clinical outcomes were predominately related to disease-specific, multifaceted measures such as clinical disease control and cardiovascular risk score. CONCLUSIONS Iterative, user-centered development of digital platform structures could optimize user engagement with self-management support through existing, evidence-based digital interventions. Offering a palette of interventions with an appropriate degree of guidance has the potential to facilitate disease-specific health behavior change and effective self-management among a myriad of users, conditions, or stages of care.

Download Full-text