Challenges in Participant Engagement and Retention using Mobile Health Apps: A Literature Review (Preprint)

2021 ◽  
Author(s):  
Saki Amagai ◽  
Sarah Pila ◽  
Aaron J Kaat ◽  
Cindy J Nowinski ◽  
Richard C Gershon
Keyword(s):  
Regression Model ◽  
Mobile Health ◽  
Meta Analysis ◽  
Research Literature ◽  
Drop Out ◽  
Factors Affecting ◽  
Participant Engagement ◽  
Binomial Regression ◽  
Mobile Health Apps ◽  
Mhealth Apps

BACKGROUND Mobile health (mHealth) apps are revolutionizing the way clinicians and researchers monitor and manage the health of their participants. However, many studies using mHealth apps are hampered by substantial participant drop-out, or attrition, which may impact the representativeness of the sample and the effectiveness of the study. It is therefore imperative for researchers to understand what makes the participants stay with mHealth apps and/or studies using mHealth apps. OBJECTIVE This study aimed to review current peer-reviewed research literature in order to identify notable factors and strategies used in participant engagement and retention of adults. METHODS We conducted a systematic search of PubMed, MedLine, and PsycINFO databases for mHealth studies that evaluated and assessed issues and/or strategies to improve engagement and retention of adults from 2015 to 2020. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Notable themes were identified and narratively compared amongst different studies. A binomial regression model was generated to examine factors affecting retention. RESULTS Of the 389 identified studies, 62 were included in the review. Overall, the majority of studies were at least partially successful in maintaining participant engagement throughout. Factors related to particular elements of the app (e.g., feedback, appropriate reminders, and in-app support from peers or coaches) and strategies for research (e.g., compensation and niche samples) that promote retention were identified. Factors that obstruct retention were also identified (e.g., lack of support features, technical difficulties, and usefulness of app). The regression model results showed that a participant is more likely to not be retained than they would be retained. CONCLUSIONS Retaining participants is an omnipresent challenge for mHealth studies. The insights from this review should help inform future studies about factors and strategies to improve participant retention.

scholarly journals The Continued Use of Mobile Health Apps: Insights From a Longitudinal Study

10.2196/12983 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e12983 ◽  
Author(s):  
Isaac Vaghefi ◽  
Bengisu Tulu
Keyword(s):  
Longitudinal Study ◽  
Mobile Health ◽  
Interface Design ◽  
Future Research ◽  
Depth Of Knowledge ◽  
Health Goals ◽  
Mobile Health Apps ◽  
Potential Benefits ◽  
Continued Use ◽  
Mhealth Apps

Background Mobile health (mHealth) apps that support individuals pursuing health and wellness goals, such as weight management, stress management, smoking cessation, and self-management of chronic conditions have been on the rise. Despite their potential benefits, the use of these tools has been limited, as most users stop using them just after a few times of use. Under this circumstance, achieving the positive outcomes of mHealth apps is less likely. Objective The objective of this study was to understand continued use of mHealth apps and individuals’ decisions related to this behavior. Methods We conducted a qualitative longitudinal study on continued use of mHealth apps. We collected data through 34 pre- and postuse interviews and 193 diaries from 17 participants over two weeks. Results We identified 2 dimensions that help explain continued use decisions of users of mHealth apps: users’ assessment of mHealth app and its capabilities (user experience) and their persistence at their health goals (intent). We present the key factors that influence users’ assessment of an mHealth app (interface design, navigation, notifications, data collection methods and tools, goal management, depth of knowledge, system rules, actionable recommendations, and user system fit) and relate these factors to previous literature on behavior change technology design. Using these 2 dimensions, we developed a framework that illustrated 4 decisions users might make after initial interaction with mHealth apps (to abandon use, limit use, switch app, and continue use). We put forth propositions to be explored in future research on mHealth app use. Conclusions This study provides insight into the factors that shape users’ decisions to continue using mHealth apps, as well as other likely decision scenarios after the initial use experience. The findings contribute to extant knowledge of mHealth use and provide important implications for design of mHealth apps to increase long-term engagement of the users.

scholarly journals Privacy Assessment in Mobile Health Apps: Scoping Review (Preprint)

2020 ◽  
Author(s):  
Jaime Benjumea ◽  
Jorge Ropero ◽  
Octavio Rivera-Romero ◽  
Enrique Dorronzoro-Zubiete ◽  
Alejandro Carrasco
Keyword(s):  
Mobile Health ◽  
Scoping Review ◽  
Evaluation Criteria ◽  
Large Set ◽  
Scoring Method ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Mobile Health Apps ◽  
Legal Frameworks ◽  
Mhealth Apps

BACKGROUND Privacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments. OBJECTIVE This scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed. METHODS We followed the methodology defined by Arksey and O’Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy. RESULTS The database searches retrieved a total of 710 citations—24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps. CONCLUSIONS The privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.

scholarly journals Privacy Assessment in Mobile Health Apps: Scoping Review

10.2196/18868 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e18868 ◽  
Author(s):  
Jaime Benjumea ◽  
Jorge Ropero ◽  
Octavio Rivera-Romero ◽  
Enrique Dorronzoro-Zubiete ◽  
Alejandro Carrasco
Keyword(s):  
Mobile Health ◽  
Scoping Review ◽  
Evaluation Criteria ◽  
Large Set ◽  
Scoring Method ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Mobile Health Apps ◽  
Legal Frameworks ◽  
Mhealth Apps

Background Privacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments. Objective This scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed. Methods We followed the methodology defined by Arksey and O’Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy. Results The database searches retrieved a total of 710 citations—24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps. Conclusions The privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.

scholarly journals The Federal Trade Commission and Consumer Protections for Mobile Health Apps

2020 ◽  
Vol 48 (S1) ◽  
pp. 103-114 ◽  
Author(s):  
Jennifer K. Wagner
Keyword(s):  
Consumer Protection ◽  
Mobile Health ◽  
Federal Trade Commission ◽  
Health Apps ◽  
Mobile Health Apps ◽  
Mhealth Apps

The Federal Trade Commission (FTC) has an important role to play in the governmental oversight of mobile health apps, ensuring consumer protections from unfair and deceptive trade practices and curtailing anti-competitive methods. The FTC’s consumer protection structure and authority is outlined before reviewing the recent FTC enforcement activities taken on behalf of consumers and against developers of mhealth apps. The article concludes with identification of some challenges for the FTC and modest recommendations for strengthening the consumer protections it provides.

scholarly journals Multimodal mental models: Understanding users’ design expectations for mHealth apps

2019 ◽  
Vol 26 (3) ◽  
pp. 1493-1506
Author(s):  
J Scott Brennen ◽  
Allison J Lazard ◽  
Elizabeth Troutman Adams
Keyword(s):  
Mental Models ◽  
Mobile Health ◽  
Shared Mental Models ◽  
Health Interventions ◽  
Structured Interviews ◽  
Health Apps ◽  
Mobile Health Apps ◽  
Salient Features ◽  
Mhealth Apps

Employing qualitative structured interviews with mobile health app users, this research describes shared mental models for mHealth and reveals their complexity. The findings uncover prototypical design components common to mental models beyond health apps and suggest that users’ mental models are multimodal, containing distinct and often contradictory dimensions for evaluations of aesthetics and for craftsmanship. The findings also indicate that users’ mental models are informed by experiences with apps from across the mobile landscape. This research suggests that designers of consumer mobile health apps and mobile health interventions should incorporate prototypical or salient features. In doing so, they should index designs to trends across the larger app landscape and innovate the means to balance between multidimensional and conflicting mental models.

scholarly journals The Needle in the Haystack: Identifying Credible Mobile Health Apps for Pediatric Populations during a Pandemic and beyond

2020 ◽  
Vol 45 (10) ◽  
pp. 1106-1113
Author(s):  
Alexandra M Psihogios ◽  
Colleen Stiles-Shields ◽  
Martha Neary
Keyword(s):  
Mobile Health ◽  
Digital Health ◽  
Health Behavior Change ◽  
Clinical Expertise ◽  
The Public ◽  
Health Apps ◽  
Theoretical Support ◽  
Mobile Health Apps ◽  
Pediatric Populations ◽  
Mhealth Apps

Abstract Background The COVID-19 pandemic has ignited wider clinical adoption of digital health tools, including mobile health apps (mHealth apps), to address mental and behavioral health concerns at a distance. While mHealth apps offer many compelling benefits, identifying effective apps in the crowded and largely unregulated marketplace is laborious. Consumer demand and industry productivity are increasing, although research is slower, making it challenging for providers to determine the most credible and safe apps for patients in need. Objectives/Methods This commentary offers a practical, empirically guided framework and associated resources for selecting appropriate mHealth apps for pediatric populations during the pandemic and beyond. Results In the first stage, Narrow the target problem, end user, and contender apps. Beginning the search with continuously updated websites that contain expert app ratings can help expedite this process (e.g., Psyberguide). Second, Explore each contender app’s: (a) scientific and theoretical support (e.g., are app components consistent with health behavior change theories?), (b) privacy policies, and (c) user experience (e.g., through crowdsourcing feedback about app usability and appeal via social media). Third, use clinical expertise and stakeholder feedback to Contextualize whether the selected app is a good fit for a particular patient and/or caregiver (e.g., by considering age, race/ethnicity, ability, gender, sexual orientation, technology access), including conducting a brief self-pilot of the app. Conclusion Youth are increasingly turning to technology for support, especially during the pandemic, and pediatric psychologists must be primed to recommend the most credible tools. We offer additional recommendations for rapidly disseminating evidence-based apps to the public.

scholarly journals The Continued Use of Mobile Health Apps: Insights From a Longitudinal Study (Preprint)

2018 ◽  
Author(s):  
Isaac Vaghefi ◽  
Bengisu Tulu
Keyword(s):  
Longitudinal Study ◽  
Mobile Health ◽  
Interface Design ◽  
Future Research ◽  
Depth Of Knowledge ◽  
Health Goals ◽  
Mobile Health Apps ◽  
Potential Benefits ◽  
Continued Use ◽  
Mhealth Apps

BACKGROUND Mobile health (mHealth) apps that support individuals pursuing health and wellness goals, such as weight management, stress management, smoking cessation, and self-management of chronic conditions have been on the rise. Despite their potential benefits, the use of these tools has been limited, as most users stop using them just after a few times of use. Under this circumstance, achieving the positive outcomes of mHealth apps is less likely. OBJECTIVE The objective of this study was to understand continued use of mHealth apps and individuals’ decisions related to this behavior. METHODS We conducted a qualitative longitudinal study on continued use of mHealth apps. We collected data through 34 pre- and postuse interviews and 193 diaries from 17 participants over two weeks. RESULTS We identified 2 dimensions that help explain continued use decisions of users of mHealth apps: users’ assessment of mHealth app and its capabilities (user experience) and their persistence at their health goals (intent). We present the key factors that influence users’ assessment of an mHealth app (interface design, navigation, notifications, data collection methods and tools, goal management, depth of knowledge, system rules, actionable recommendations, and user system fit) and relate these factors to previous literature on behavior change technology design. Using these 2 dimensions, we developed a framework that illustrated 4 decisions users might make after initial interaction with mHealth apps (to abandon use, limit use, switch app, and continue use). We put forth propositions to be explored in future research on mHealth app use. CONCLUSIONS This study provides insight into the factors that shape users’ decisions to continue using mHealth apps, as well as other likely decision scenarios after the initial use experience. The findings contribute to extant knowledge of mHealth use and provide important implications for design of mHealth apps to increase long-term engagement of the users.

scholarly journals Effects of Social Media and Mobile Health Apps on Pregnancy Care: Meta-Analysis (Preprint)

2018 ◽  
Author(s):  
Ko Ling Chan ◽  
Mengtong Chen
Keyword(s):  
Social Media ◽  
Health Outcomes ◽  
Mobile Health ◽  
Effect Size ◽  
Program Effectiveness ◽  
Maternal Mental Health ◽  
Meta Analysis ◽  
Well Being ◽  
Pregnancy Care ◽  
Mhealth Apps

BACKGROUND The use of social media and mobile health (mHealth) apps has been increasing in pregnancy care. However, the effectiveness of these interventions is still unclear. OBJECTIVES We conducted a meta-analysis to examine the effectiveness of these interventions with regard to different health outcomes in pregnant and postpartum women and investigate the characteristics and components of interventions that may affect program effectiveness. METHOD We performed a comprehensive literature search of major electronic databases and reference sections of related reviews and eligible studies. A random effects model was used to calculate the effect size. RESULTS Fifteen randomized controlled trial studies published in and before June 2018 that met the inclusion criteria were included in the meta-analysis. The interventions were effective in promoting maternal physical health including weight management, gestational diabetes mellitus control, and asthma control with a moderate to large effect size (d=0.72). Large effect sizes were also found for improving maternal mental health (d=0.84) and knowledge about pregnancy (d=0.80). Weight control interventions using wearable devices were more effective. CONCLUSION Social media and mHealth apps have the potential to be widely used in improving maternal well-being. More large-scale clinical trials focusing on different health outcomes are suggested for future studies.

scholarly journals Assessment of the Fairness of Privacy Policies of Mobile Health Apps: Scale Development and Evaluation in Cancer Apps (Preprint)

2019 ◽  
Author(s):  
Jaime Benjumea ◽  
Jorge Ropero ◽  
Octavio Rivera-Romero ◽  
Enrique Dorronzoro-Zubiete ◽  
Alejandro Carrasco
Keyword(s):  
Mobile Health ◽  
Mobile Apps ◽  
Privacy Policies ◽  
Sensitive Data ◽  
General Data Protection Regulation ◽  
Android Apps ◽  
Mobile Health Apps ◽  
General Data ◽  
Adjusted Scores ◽  
Mhealth Apps

BACKGROUND Cancer patients are increasingly using mobile health (mHealth) apps to take control of their health. Many studies have explored their efficiency, content, usability, and adherence; however, these apps have created a new set of privacy challenges, as they store personal and sensitive data. OBJECTIVE The purpose of this study was to refine and evaluate a scale based on the General Data Protection Regulation and assess the fairness of privacy policies of mHealth apps. METHODS Based on the experience gained from our previous work, we redefined some of the items and scores of our privacy scale. Using the new version of our scale, we conducted a case study in which we analyzed the privacy policies of cancer Android apps. A systematic search of cancer mobile apps was performed in the Spanish version of the Google Play website. RESULTS The redefinition of certain items reduced discrepancies between reviewers. Thus, use of the scale was made easier, not only for the reviewers but also for any other potential users of our scale. Assessment of the privacy policies revealed that 29% (9/31) of the apps included in the study did not have a privacy policy, 32% (10/31) had a score over 50 out of a maximum of 100 points, and 39% (12/31) scored fewer than 50 points. CONCLUSIONS In this paper, we present a scale for the assessment of mHealth apps that is an improved version of our previous scale with adjusted scores. The results showed a lack of fairness in the mHealth app privacy policies that we examined, and the scale provides developers with a tool to evaluate their privacy policies.

scholarly journals Comparison between Efficiency of Poisson Regression Model and Negative Binomial Regression in the Analysis of Factors Affecting Mortality from Cardiovascular Diseases in Yazd Province in 2017

2021 ◽  
Author(s):  
Mohammad Mirjani Arjenan ◽  
Mohsen Askarshahi ◽  
Mahmud Vakili
Keyword(s):  
Cardiovascular Diseases ◽  
Regression Model ◽  
Poisson Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Poisson Regression Model ◽  
Registration System ◽  
Factors Affecting ◽  
Binomial Regression

Introduction: Despite the advances in cardiovascular diseases, death caused by these diseases is still considered as the leading cause of mortality. In this study, some of the effective factors on the deaths caused by cardiovascular diseases were investigated Methods: This cross-sectional analytical study investigated the efficacy of Poisson regression models and negative binomial regression models on factors affecting mortality from cardiovascular diseases. The death data were extracted from the death registration system for Yazd province in 2017.Gender, age, education, occupation, location, and city of death were also extracted for each deceased. The two regression models were then fitted to the data Results:  A total of 5,015 deaths were recorded, of which 1,642 were due to cardiovascular diseases. Cardiovascular disease mortality rates were significant using negative binomial regression in terms of the educational variables, place of residence, type of residence, and age. Death rates caused by cardiovascular diseases were not significant for age and occupational, educational, and residential variables. Conclusion: If the time of death is considered as an offset variable, the regression model of two negative sentences is more effective in showing the factors affecting death due to cardiovascular diseases according to AIC and BIC criteria. In the case that the total number of deaths is considered as the offset variable, the Poisson regression model is more efficient.

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