Electronic Patient-Generated Health Data to Facilitate Disease Prevention and Health Promotion: Scoping Review

Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review

BACKGROUND

Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.

OBJECTIVE

This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.

METHODS

A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.

RESULTS

The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.

CONCLUSIONS

PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/39389.

Barriers to Postdischarge Smartphone App Use Among Patients With Traumatic Rib Fractures

Rib fractures commonly result from traumatic injury and often require hospitalization for pain control and supportive pulmonary care. Although the use of mobile health technology to share patient-generated health data has increased, it remains limited in patients with traumatic injuries. We sought to assess the feasibility of mobile health tracking in patients with rib fractures by using a smartphone app to monitor postdischarge recovery. We encountered patient, institutional, and process-related obstacles that limited app use. The success of future work requires the acknowledgment of these limitations and the use of an implementation science framework to effectively integrate technological tools for personalized trauma care.

Patient and Provider Perspectives About the Use of Patient-Generated Health Data During Pregnancy: Qualitative Exploratory Study

BACKGROUND

There is increasing interest in using patient-generated health data (PGHD) to improve patient-centered care during pregnancy. However, little research has examined the perspectives of patients and providers as they report, collect, and use PGHD to inform obstetric care.

OBJECTIVE

This study aims to explore the perspectives of patients and providers about the use of PGHD during pregnancy, including the benefits and challenges of reporting, collecting, and using these data, as well as considerations for expanding the use of PGHD to improve obstetric care.

METHODS

We conducted one-on-one interviews with 30 pregnant or postpartum patients and 14 health care providers from 2 obstetrics clinics associated with an academic medical center. Semistructured interview guides included questions for patients about their experience and preferences for sharing PGHD and questions for providers about current processes for collecting PGHD, opportunities to improve or expand the collection of PGHD, and challenges faced when collecting and using this information. Interviews were conducted by phone or videoconference and were audio recorded, transcribed verbatim, and deidentified. Interview transcripts were analyzed deductively and inductively to characterize and explore themes in the data.

RESULTS

Patients and providers described how PGHD, including physiologic measurements and experience of symptoms, were currently collected during and between in-person clinic visits for obstetric care. Both patients and providers reported positive perceptions about the collection and use of PGHD during pregnancy. Reported benefits of collecting PGHD included the potential to use data to directly inform patient care (eg, identify issues and adjust medication) and to encourage ongoing patient involvement in their care (eg, increase patient attention to their health). Patients and providers had suggestions for expanding the collection and use of PGHD during pregnancy, and providers also shared considerations about strategies that could be used to expand PGHD collection and use. These strategies included considering the roles of both patients and providers in reporting and interpreting PGHD. Providers also noted the need to consider the unintended consequences of using PGHD that should be anticipated and addressed.

CONCLUSIONS

Acknowledging the challenges, suggestions, and considerations voiced by patients and providers can inform the development and implementation of strategies to effectively collect and use PGHD to support patient-centered care during pregnancy.

Willingness to pay for health apps, its sociodemographic correlates, and reasons for being unwilling to pay

Background

Knowledge about whether, how much, and why individuals are willing to pay for health apps is limited.

Objectives

This study aimed to examine (1) the proportion of individuals willing to pay for health apps, (2) their willingness to pay (WTP; i.e. the maximum price the individual is willing to pay) for health apps, (3) the sociodemographic correlates determining whether individuals are willing to pay for these apps, (4) the sociodemographic correlates of their WTP, and (5) reasons for being unwilling to pay.

Methods

Six hundred adults were invited to participate in a questionnaire survey examining their sociodemographic characteristics, WTP for health apps, and reasons for being unwilling to pay. Sociodemographic characteristics and WTP for health apps were analyzed using descriptive statistics. Logistic regression was applied to examine the sociodemographic variables correlated with whether individuals were willing to pay for health apps. Among those who were willing to pay, log-linear regression was conducted to examine the sociodemographic correlates of their WTP. The reasons for unwillingness to pay were descriptively analyzed.

Results

A total of 577 individuals completed the questionnaire. Of them, 58.9% were willing to pay for health apps. Their median WTP was HK$50 (HK$1 ≈ US$0.13). Participants with a bachelor's degree or higher and those who had previously installed health apps were more inclined to pay for health apps. WTP was positively associated with the maximum price previously paid for a health app. The most frequently cited reasons for being unwilling to pay were the belief that the government should provide free health apps, distrust in health apps, and a lack of awareness of health apps and their benefits.

Conclusions

This study provides insights that can inform strategies to enhance the marketability, affordability, and accessibility of health apps.

Simultaneous Comparisons of 25 Acute Migraine Medications Based on 10 Million Users' Self-Reported Records From a Smartphone Application

BACKGROUND AND OBJECTIVES

Many acute treatment options exist for migraine. However, large-scale, head-to-head comparisons of treatment effectiveness from real-world patient experience reports are lacking.

METHODS

This is a retrospective analysis of 10,842,795 migraine attack records extracted from an e-diary smartphone application between June 30, 2014, and July 2, 2020. We analyzed 25 acute medications among 7 classes-acetaminophen, nonsteroid anti-inflammatory drugs (NSAIDs), triptans, combination analgesics, ergots, antiemetics, and opioids. Gepants and ditan were not included in this analysis. Different doses and formulations of each medication, according to the generic names, were combined in this analysis. We used a 2-level nested logistic regression model to analyze the odds ratio (OR) of treatment effectiveness of each medication by adjusting concurrent medications and the covariance within the same user. Subgroup analyses were conducted for users in the United States, the United Kingdom, and Canada.

RESULTS

Our final analysis included 4,777,524 medication-outcome pairs from 3,119,517 migraine attacks among 278,006 users. Triptans (mean OR 4.8), ergots (mean OR 3.02), and antiemetics (mean OR 2.67) were the top 3 classes of medications with the highest effectiveness, followed by opioids (mean OR 2.49), NSAIDs (other than ibuprofen, mean OR 1.94), combination analgesics (acetaminophen/acetylsalicylic acid/caffeine) (OR 1.69, 95% CI 1.67-1.71), others (OR 1.49, 95% CI 1.47-1.50), and acetaminophen (OR 0.83, 95% CI 0.83-0.84), using ibuprofen as the reference. Individual medications with the highest ORs were eletriptan (OR 6.1, 95% CI 6.0-6.3), zolmitriptan (OR 5.7, 95% CI 5.6-5.8), and sumatriptan (OR 5.2, 95% CI 5.2-5.3). The ORs of acetaminophen, NSAIDS, combination analgesics, and opioids were mostly around or less than 1, suggesting similar or lower reported effectiveness compared with ibuprofen. The ORs for 24 medications, except that of acetylsalicylic acid, achieved statistical significance with p < 0.0001, and our nested logistic regression model achieved an area under the curve (AUC) of 0.849. Country-specific subgroup analyses revealed similar ORs of each medication and AUC (United States 0.849, United Kingdom 0.864, and Canada 0.842), demonstrating the robustness of our analysis.

DISCUSSION

Using a big data approach, we analyzed patient-generated real-time records of 10 million migraine attacks and conducted simultaneous head-to-head comparisons of 25 acute migraine medications. Our findings that triptans, ergots, and antiemetics are the most effective classes of medications align with the guideline recommendations and offer generalizable insights to complement clinical practice.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that for patients with migraine, selected acute medications (e.g., triptans, ergots, antiemetics) are associated with higher odds of user-rated positive response than ibuprofen.

Virtual social networks and health technologies in the daily life of clients and households: care and health promotion

This study aimed to understand the use of technosociality in the daily lives of the Family Health Strategy clients during the COVID-19 pandemic for care and health promotion. This holistic, qualitative, multiple case study based on the Comprehensive Everyday Life Sociology was conducted with 61 clients from three Brazilian municipalities, two in Minas Gerais and one in Santa Catarina. The sources of evidence were the open-ended individual interview and field notes. We adopted thematic content analysis to analyze data. The use of virtual social networks and health technologies for care, monitoring, prevention of risks and conditions, health promotion, and access to information is found in clients' daily lives. We highlight the importance of support and solidarity networks. The infodemic and misinformation about COVID-19 denote uncertainty about the veracity of information and concern about mental health. We should pay close attention to using technologies and social networks for health promotion, enabling strategies to enhance their use and minimize the indicated harms.

Catalyzing dementia care through the learning health system and consumer health information technology

To advance care for persons with Alzheimer's disease and related dementias (ADRD), real-world health system effectiveness research must actively engage those affected to understand what works, for whom, in what setting, and for how long-an agenda central to learning health system (LHS) principles. This perspective discusses how emerging payment models, quality improvement initiatives, and population health strategies present opportunities to embed best practice principles of ADRD care within the LHS. We discuss how stakeholder engagement in an ADRD LHS when embedding, adapting, and refining prototypes can ensure that products are viable when implemented. Finally, we highlight the promise of consumer-oriented health information technologies in supporting persons living with ADRD and their care partners and delivering embedded ADRD interventions at scale. We aim to stimulate progress toward sustainable infrastructure paired with person- and family-facing innovations that catalyze broader transformation of ADRD care.

Digital Transformation in Healthcare: Technology Acceptance and Its Applications

Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.

Digital health for chronic disease management: An exploratory method to investigating technology adoption potential

INTRODUCTION

The availability of consumer-facing health technologies for chronic disease management is skyrocketing, yet most are limited by low adoption rates. Improving adoption requires a better understanding of a target population's previous exposure to technology. We propose a low-resource approach of capturing and clustering technology exposure, as a mean to better understand patients and target health technologies.

METHODS

Using Multiple Sclerosis (MS) as a case study, we applied exploratory multivariate factorial analyses to survey data from the Swiss MS Registry. We calculated individual-level factor scorings, aiming to investigate possible technology adoption clusters with similar digital behavior patterns. The resulting clusters were transformed using radar and then compared across sociodemographic and health status characteristics.

RESULTS

Our analysis included data from 990 respondents, resulting in three clusters, which we defined as the (1) average users, (2) health-interested users, and (3) low frequency users. The average user uses consumer-facing technology regularly, mainly for daily, regular activities and less so for health-related purposes. The health-interested user also uses technology regularly, for daily activities as well as health-related purposes. The low-frequency user uses technology infrequently.

CONCLUSIONS

Only about 10% of our sample has been regularly using (adopting) consumer-facing technology for MS and health-related purposes. That might indicate that many of the current consumer-facing technologies for MS are only attractive to a small proportion of patients. The relatively low-resource exploratory analyses proposed here may allow for a better characterization of prospective user populations and ultimately, future patient-facing technologies that will be targeted to a broader audience.

Patient Portals to Support Care Partner Engagement in Adolescent and Adult Populations: A Scoping Review

IMPORTANCE

Family and other unpaid care partners may bridge accessibility challenges in interacting with the patient portal, but the extent and nature of this involvement is not well understood.

OBJECTIVE

To inform an emerging research agenda directed at more purposeful inclusion of care partners within the context of digital health equity by (1) quantifying care partners' uptake and use of the patient portal in adolescent and adult patients, (2) identifying factors involving care partners' portal use across domains of the System Engineering Initiative for Patient Safety model, and (3) assessing evidence of perceived or actual outcomes of care partners' portal use.

EVIDENCE REVIEW

Following Arksey and O'Malley's methodologic framework, a scoping review of manuscripts published February 1 and March 22, 2022, was conducted by hand and a systematic search of PubMed, PsycInfo, Embase, and Web of Science. The search yielded 278 articles; 125 were selected for full-text review and 41 were included.

FINDINGS

Few adult patient portal accounts had 1 or more formally registered care partners (<3% in 7 of 7 articles), but care partners commonly used the portal (8 of 13 contributing articles reported >30% use). Care partners less often authored portal messages with their own identity credentials (<3% of portal messages in 3 of 3 articles) than with patient credentials (20%-60% of portal messages in 3 of 5 articles). Facilitators of care partner portal use included markers of patient vulnerability (13 articles), care partner characteristics (15 articles; being female, family, and competent in health system navigation), and task-based factors pertaining to ease of information access and care coordination. Environmental (26 articles) and process factors (19 articles, eg, organizational portal registration procedures, protection of privacy, and functionality) were identified as influential to care partner portal use, but findings were nuanced and precluded reporting on effects. Care partner portal use was identified as contributing to both patient and care partner insight into patient health (9 articles), activation (7 articles), continuity of care (8 articles), and convenience (6 articles).

CONCLUSIONS AND RELEVANCE

In this scoping review, care partners were found to be infrequently registered for the patient portal and more often engaged in portal use with patient identity credentials. Formally registering care partners for the portal was identified as conferring potential benefits for patients, care partners, and care quality.

Assessing Engagement With Patient-Generated Health Data Recording and Its Impact on Health Behavior Changes in Multicomponent Interventions: Supplementary Analysis

Background

The use and sharing of patient-generated health data (PGHD) by clinicians or researchers is expected to enhance the remote monitoring of specific behaviors that affect patient health. In addition, PGHD use could support patients’ decision-making on preventive care management, resulting in reduced medical expenses. However, sufficient evidence on the use and sharing of PGHD is lacking, and the impact of PGHD recording on patients’ health behavior changes remains unclear.

Objective

This study aimed to assess patients’ engagement with PGHD recording and to examine the impact of PGHD recording on their health behavior changes.

Methods

This supplementary analysis used the data of 47 postpartum women who had been assigned to the intervention group of our previous study for managing urinary incontinence. To assess the patients’ engagement with PGHD recording during the intervention period (8 weeks), the fluctuation in the number of patients who record their PGHD (ie, PGHD recorders) was evaluated by an approximate curve. In addition, to assess adherence to the pelvic floor muscle training (PFMT), the weekly mean number of pelvic floor muscle contractions performed per day among 17 PGHD recorders was examined by latent class growth modeling (LCGM).

Results

The fluctuation in the number of PGHD recorders was evaluated using the sigmoid curve formula (R²=0.91). During the first week of the intervention, the percentage of PGHD recorders was around 64% (30/47) and then decreased rapidly from the second to the third week. After the fourth week, the percentage of PGHD recorders was 36% (17/47), which remained constant until the end of the intervention. When analyzing the data of these 17 PGHD recorders, PFMT adherence was categorized into 3 classes by LCGM: high (7/17, 41%), moderate (3/17, 18%), and low (7/17, 41%).

Conclusions

The number of PGHD recorders declined over time in a sigmoid curve. A small number of users recorded PGHD continuously; therefore, patients’ engagement with PGHD recording was low. In addition, more than half of the PGHD recorders (moderate- and low-level classes combined: 10/17, 59%) had poor PFMT adherence. These results suggest that PGHD recording does not always promote health behavior changes.

Patient-Generated Health Photos and Videos Across Health and Well-being Contexts: Scoping Review

Background

Patient-generated health data are increasingly used to record health and well-being concerns and engage patients in clinical care. Patient-generated photographs and videos are accessible and meaningful to patients, making them especially relevant during the current COVID-19 pandemic. However, a systematic review of photos and videos used by patients across different areas of health and well-being is lacking.

Objective

This review aims to synthesize the existing literature on the health and well-being contexts in which patient-generated photos and videos are used, the value gained by patients and health professionals, and the challenges experienced.

Methods

Guided by a framework for scoping reviews, we searched eight health databases (CINAHL, Cochrane Library, Embase, PsycINFO, PubMed, MEDLINE, Scopus, and Web of Science) and one computing database (ACM), returning a total of 28,567 studies. After removing duplicates and screening based on the predefined inclusion criteria, we identified 110 relevant articles. Data were charted and articles were analyzed following an iterative thematic approach with the assistance of NVivo software (version 12; QSR International).

Results

Patient-generated photos and videos are used across a wide range of health care services (39/110, 35.5% articles), for example, to diagnose skin lesions, assess dietary intake, and reflect on personal experiences during therapy. In addition, patients use them to self-manage health and well-being concerns (33/110, 30%) and to share personal health experiences via social media (36/110, 32.7%). Photos and videos create significant value for health care (59/110, 53.6%), where images support diagnosis, explanation, and treatment (functional value). They also provide value directly to patients through enhanced self-determination (39/110, 35.4%), social (33/110, 30%), and emotional support (21/110, 19.1%). However, several challenges emerge when patients create, share, and examine photos and videos, such as limited accessibility (16/110, 14.5%), incomplete image sets (23/110, 20.9%), and misinformation through photos and videos shared on social media (17/110, 15.5%).

Conclusions

This review shows that photos and videos engage patients in meaningful ways across different health care activities (eg, diagnosis, treatment, and self-care) for various health conditions. Although photos and videos require effort to capture and involve challenges when patients want to use them in health care, they also engage and empower patients, generating unique value. This review highlights areas for future research and strategies for addressing these challenges.

Digital Health Promotion and Prevention in Settings: Scoping Review

BACKGROUND

Digital technologies are increasingly integrating into people's daily living environments such as schools, sport clubs, and health care facilities. These settings play a crucial role for health promotion and prevention because they affect the health of their members, as the World Health Organization has declared. Implementing digital health promotion and prevention in settings offers the opportunity to reach specific target groups, lower the costs of implementation, and improve the health of the population. Currently, there is a lack of scientific evidence that reviews the research on digital health promotion and prevention in settings.

OBJECTIVE

This scoping review aims to provide an overview of research targeting digital health promotion and primary prevention in settings. It assesses the range of scientific literature regarding outcomes such as applied technology, targeted setting, and area of health promotion or prevention, as well as identifies research gaps.

METHODS

The scoping review was conducted following the Levac, Colquhoun, and O'Brien framework. We searched scientific databases and gray literature for articles on digital setting-based health promotion and prevention published from 2010 to January 2020. We included empirical and nonempirical publications in English or German and excluded secondary or tertiary prevention and health promotion at the workplace.

RESULTS

From 8888 records, the search resulted in 200 (2.25%) included publications. We identified a huge diversity of literature regarding digital setting-based health promotion and prevention. The variety of technology types extends from computer- and web-based programs to mobile devices (eg, smartphone apps) and telemonitoring devices (sensors). We found analog, digital, and blended settings in which digital health promotion and prevention takes place. The most frequent analog settings were schools (39/200, 19.5%) and neighborhoods or communities (24/200, 12%). Social media apps were also included because in some studies they were defined as a (digital) setting. They accounted for 31.5% (63/200) of the identified settings. The most commonly focused areas of health promotion and prevention were physical activity (81/200, 40.5%), nutrition (45/200, 22.5%), and sexual health (34/200, 17%). Most of the interventions combined several health promotion or prevention methods, including environmental change; providing information, social support, training, or incentives; and monitoring. Finally, we found that the articles mostly reported on behavioral rather than structural health promotion and prevention.

CONCLUSIONS

The research field of digital health promotion and prevention in settings is heterogeneous. At the same time, we identified research gaps regarding the absence of valid definitions of relevant terms (eg, digital settings) and the lack of literature on structural health promotion and prevention in settings. Therefore, it remains unclear how digital technologies can contribute to structural (or organizational) changes in settings. More research is needed to successfully implement digital technologies to achieve health promotion and prevention in settings.

Developing a Dietary Lifestyle Ontology (DILON) to Improve the Interoperability of Dietary Data: A Proof-of-Concept Study (Preprint)

Background

Dietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts.

Objective

The main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON.

Methods

By analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy.

Results

DILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results.

Conclusions

Ensuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.

Clinicians and Older Adults' Perceptions of the Utility of Patient-Generated Health Data in Caring for Older Adults: Exploratory Mixed Methods Study

Background

Many people are motivated to self-track their health and optimize their well-being through mobile health apps and wearable devices. The diversity and complexity of these systems have evolved over time, resulting in a large amount of data referred to as patient-generated health data (PGHD), which has recently emerged as a useful set of data elements in health care systems around the world. Despite the increased interest in PGHD, clinicians and older adults’ perceptions of PGHD are poorly understood. In particular, although some clinician barriers to using PGHD have been identified, such as concerns about data quality, ease of use, reliability, privacy, and regulatory issues, little is known from the perspectives of older adults.

Objective

This study aims to explore the similarities and differences in the perceptions of older adults and clinicians with regard to how various types of PGHD can be used to care for older adults.

Methods

A mixed methods study was conducted to explore clinicians and older adults’ perceptions of PGHD. Focus groups were conducted with older adults and health care providers from the Greater Toronto area and the Kitchener-Waterloo region. The participants were asked to discuss their perceptions of PGHD, including facilitators and barriers. A questionnaire aimed at exploring the perceived usefulness of a range of different PGHD was also embedded in the study design. Focus group interviews were transcribed for thematic analysis, whereas the questionnaire results were analyzed using descriptive statistics.

Results

Of the 9 participants, 4 (44%) were clinicians (average age 38.3 years, SD 7 years), and 5 (56%) were older adults (average age 81.0 years, SD 9.1 years). Four main themes were identified from the focus group interviews: influence of PGHD on patient-provider trust, reliability of PGHD, meaningful use of PGHD and PGHD-based decision support systems, and perceived clinical benefits and intrusiveness of PGHD. The questionnaire results were significantly correlated with the frequency of PGHD mentioned in the focus group interviews (r=0.42; P=.03) and demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing health and delivering high-quality care.

Conclusions

This embedded mixed methods study generated several important findings about older adults and clinicians’ perceptions and perceived usefulness of a range of PGHD. Owing to the exploratory nature of this study, further research is needed to understand the concerns about data privacy, potential negative impact on the trust between older adults and clinicians, data quality and quantity, and usability of PGHD-related technologies for older adults.

Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions

Various digital healthcare devices and apps, such as blood glucose meters, blood pressure monitors, and step-trackers are commonly used by patients; however, digital healthcare devices have not been widely accepted in the medical market as of yet. Despite the various legal and privacy issues involved in their use, the main reason for its poor acceptance is that users do not use such devices voluntarily and continuously. Digital healthcare devices generally do not provide valuable information to users except for tracking self-checked glucose or walking. To increase the use of these devices, users must first understand the health data produced in the context of their personal health, and the devices must be easy to use and integrated into everyday life. Thus, users need to know how to manage their own data. Medical staff must teach and encourage users to analyze and manage their patient-generated healthcare data, and users should be able to find medical values from these digital devices. Eventually, a single customized service that can comprehensively analyze various medical data to provide valuable customized services to users, and which can be linked to various heterogeneous digital healthcare devices based on the integration of various health data should be developed. Digital healthcare professionals should have detailed knowledge about a variety of digital healthcare devices and fully understand the advantages and disadvantages of digital healthcare to help patients understand and embrace the use of such devices.

Provider Preferences for Patient-Generated Health Data Displays in Pediatric Asthma: A Participatory Design Approach

Objective  There is a lack of evidence on how to best integrate patient-generated health data (PGHD) into electronic health record (EHR) systems in a way that supports provider needs, preferences, and workflows. The purpose of this study was to investigate provider preferences for the graphical display of pediatric asthma PGHD to support decisions and information needs in the outpatient setting.

Methods  In December 2019, we conducted a formative evaluation of information display prototypes using an iterative, participatory design process. Using multiple types of PGHD, we created two case-based vignettes for pediatric asthma and designed accompanying displays to support treatment decisions. Semi-structured interviews and questionnaires with six participants were used to evaluate the display usability and determine provider preferences.

Results  We identified provider preferences for display features, such as the use of color to indicate different levels of abnormality, the use of patterns to trend PGHD over time, and the display of environmental data. Preferences for display content included the amount of information and the relationship between data elements.

Conclusion  Overall, provider preferences for PGHD include a desire for greater detail, additional sources, and visual integration with relevant EHR data. In the design of PGHD displays, it appears that the visual synthesis of multiple PGHD elements facilitates the interpretation of the PGHD. Clinicians likely need more information to make treatment decisions when PGHD displays are introduced into practice. Future work should include the development of interactive interface displays with full integration of PGHD into EHR systems.

The Development of a Digital Patient-Reported Outcome Measurement for Adults With Chronic Disease (The Parsley Symptom Index): Prospective Cohort Study

Background

The monitoring and management of chronic illness has always been a challenge. Patient-reported outcome measures (PROMs) can be powerful tools for monitoring symptoms and guiding treatment of chronic diseases, but the available PROM tools are either too broad or too disease specific for the needs of a primary care practice focused on longitudinal care.

Objective

In this study we describe the development and preliminary validation of the Parsley Symptom Index (PSI).

Methods

This prospective cohort study took place from January 5, 2018, to June 05, 2020, among a sample of 4621 adult patients at Parsley Health. After a review of literature, followed by binning and winnowing of potential items, a 45-item PROM that also served as a review of systems (ROS) was developed. The PSI was deployed and completed by patients via an online portal. Construct and face validity was performed by clinicians, tested on patients, and feasibility was measured by response rate, completion rate, and percentage of missing data.

Results

The response rate for 12,175 collected PSIs was 93.72% (4331/4621) with a 100% item completion rate. A confirmatory factor analysis confirmed the model structure was satisfactory by a Comparative Fit Index of 0.943, Tucker–Lewis index of 0.938, and root mean square error of approximation of 0.028.

Conclusions

A 45-item ROS-style PROM designed to capture chronic disease symptoms was developed, and preliminary validation suggests that the PSI can be deployed, completed, and helpful to both patients and clinicians.

Remote patient monitoring sustains reductions of hemoglobin A1c in underserved patients to 12 months

AIMS

We sought to determine whether underserved patients enrolled in a statewide remote patient monitoring (RPM) program for diabetes achieve sustained improvements in hemoglobin A_1c at 6 and 12 months and whether those improvements are affected by demographic and clinical variables.

METHODS

Demographic and clinical variables were obtained at baseline, 6 months and 12 months. Baseline HbA1c values were compared with those obtained at 6 and 12 months via paired t-tests. A multivariable regression model was developed to identify patient-level variables associated with HbA_1c change at 12 months.

RESULTS

HbA_1c values were obtained for 302 participants at 6 months and 125 participants at 12 months. Compared to baseline, HbA_1c values were 1.8% (19 mmol/mol) lower at 6 months (p < 0.01) and 1.3% (14 mmol/mol) lower at 12 months (p < 0.01). Reductions at 12 months were consistent across clinical settings. A regression model for change in HbA_1c showed no statistically significant difference for patient age, sex, race, household income, insurance, or clinic type.

CONCLUSIONS

Patients enrolled in RPM had improved diabetes control at 6 and 12 months. Neither clinic type nor sociodemographic variables significantly altered the likelihood that patients would benefit from this type of technology. These results suggest the promise of RPM for delivering care to underserved populations.

Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study

BACKGROUND

The use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes.

OBJECTIVE

This study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels.

METHODS

This retrospective real-world analysis followed 998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases. Subjects included "nontaggers" (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and "taggers" (users who used these features, n=413) representing increased digital engagement. Within- and between-person variabilities in tagging behavior were disaggregated to reveal the association between tagging behavior and blood glucose levels. The associations between an individual's tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout.

RESULTS

Analysis revealed significant improvement in the monthly average blood glucose level during the first 6 months (t=-10.01, P<.001), which was maintained during the following 6 months (t=-1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to nontaggers (t=2.15, P=.03). Additional findings included a within-user quasicausal nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=-11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20).

CONCLUSIONS

This study sheds light on the source of the association between user engagement with a diabetes tracking app and the clinical condition, highlighting the importance of within-person changes versus between-person differences. Our findings underscore the need for and provide a basis for a personalized approach to digital health.

Toward a Working Definition of eCohort Studies in Health Research: Narrative Literature Review

Background

The wide availability of internet-connected devices and new sensor technologies increasingly infuse longitudinal observational study designs and cohort studies. Simultaneously, the costly and time-consuming nature of traditional cohorts has given rise to alternative, technology-driven designs such as eCohorts, which remain inadequately described in the scientific literature.

Objective

The aim of this study was to outline and discuss what may constitute an eCohort, as well as to formulate a first working definition for health researchers based on a review of the relevant literature.

Methods

A two-staged review and synthesis process was performed comparing 10 traditional cohorts and 10 eCohorts across the six core steps in the life cycle of cohort designs.

Results

eCohorts are a novel type of technology-driven cohort study that are not physically linked to a clinical setting, follow more relaxed and not necessarily random sampling procedures, are primarily based on self-reported and digitally collected data, and systematically aim to leverage the internet and digitalization to achieve flexibility, interactivity, patient-centeredness, and scalability. This approach comes with some hurdles such as data quality, generalizability, and privacy concerns.

Conclusions

eCohorts have similarities to their traditional counterparts; however, they are sufficiently distinct to be treated as a separate type of cohort design. The novelty of eCohorts is associated with a range of strengths and weaknesses that require further exploration.

Preferred Functions of Personal Health Records in Rural Primary Health Clinics in Canada: Health Care Team Perspectives

BACKGROUND

Personal health records (PHR) provide opportunities for improved patient engagement, collection of patient-generated data, and overcome health-system inefficiencies. While PHR use is increasing, uptake in rural populations is lower than in urban areas.

OBJECTIVES

The study aimed to identify priorities for PHR functionality and gain insights into meaning, value, and use of patient-generated data for rural primary care providers.

METHODS

We performed PHR preimplementation focus groups with rural providers and their health care teams from five primary care clinics in a sparsely populated mountainous region of British Columbia, Canada to obtain their understanding of PHR functionality, needs, and perceived challenges.

RESULTS

Eight general practitioners (GP), five medical office assistants, two nurse practitioners (NP), and two registered nurses (14 females and 3 males) participated in focus groups held at their respective clinics. Providers (GPs, NPs, and RNs) had been practicing for a median of 9.5 (range = 1-38) years and had used an electronic medical record for 7.0 (1-20) years. Participants expressed interest in incorporating functionality around two-way communication and appointment scheduling, previsit data gathering, patient and provider data sharing, virtual care including visits using videoconferencing tools, and postvisit sharing of educational materials. Three further themes emerged from the focus groups: (1) the context in which the providers' practice matters, (2) the need for providing patients and providers with choice (e.g., which data to share, who gets to initiate/respond in communications, and processes around virtual care visits), and (3) perceived risks of system use (e.g., increased complexity for older patients and workload barriers for the health care team).

CONCLUSION

Rural primary care teams perceived PHR opportunities for increased patient engagement and access to patient-generated data, while worries about changes in workflow were the biggest perceived risk. Recommendations for PHR adoption in a rural primary health network include setting provider-patient expectations about response times, ability to share notes selectively, and automatically augmented note-taking from virtual-care visits.

Interrelationships Between Patients' Data Tracking Practices, Data Sharing Practices, and Health Literacy: Onsite Survey Study

BACKGROUND

Although the use of patient-generated data (PGD) in the optimization of patient care shows great promise, little is known about whether patients who track their PGD necessarily share the data with their clinicians. Meanwhile, health literacy-an important construct that captures an individual's ability to manage their health and to engage with their health care providers-has often been neglected in prior studies focused on PGD tracking and sharing. To leverage the full potential of PGD, it is necessary to bridge the gap between patients' data tracking and data sharing practices by first understanding the interrelationships between these practices and the factors contributing to these practices.

OBJECTIVE

This study aims to systematically examine the interrelationships between PGD tracking practices, data sharing practices, and health literacy among individual patients.

METHODS

We surveyed 109 patients at the time they met with a clinician at a university health center, unlike prior research that often examined patients' retrospective experience after some time had passed since their clinic visit. The survey consisted of 39 questions asking patients about their PGD tracking and sharing practices based on their current clinical encounter. The survey also contained questions related to the participants' health literacy. All the participants completed the survey on a tablet device. The onsite survey study enabled us to collect ecologically valid data based on patients' immediate experiences situated within their clinic visit.

RESULTS

We found no evidence that tracking PGD was related to self-reports of having sufficient information to manage one's health; however, the number of data types participants tracked positively related to their self-assessed ability to actively engage with health care providers. Participants' data tracking practices and their health literacy did not relate to their data sharing practices; however, their ability to engage with health care providers positively related to their willingness to share their data with clinicians in the future. Participants reported several benefits of, and barriers to, sharing their PGD with clinicians.

CONCLUSIONS

Although tracking PGD could help patients better engage with health care providers, it may not provide patients with sufficient information to manage their health. The gaps between tracking and sharing PGD with health care providers call for efforts to inform patients of how their data relate to their health and to facilitate efficient clinician-patient communication. To realize the full potential of PGD and to promote individuals' health literacy, empowering patients to effectively track and share their PGD is important-both technologies and health care providers can play important roles.

Patient-generated health data and electronic health record integration: a scoping review

Objectives

Patient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs).

Methods

In August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed.

Results

A total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review.

Discussion

PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.

Self-Monitoring App Preferences for Sun Protection: Discrete Choice Experiment Survey Analysis

Background

The availability and use of health apps continues to increase, revolutionizing the way mobile health interventions are delivered. Apps are increasingly used to prevent disease, improve well-being, and promote healthy behavior. On a similar rise is the incidence of skin cancers. Much of the underlying risk can be prevented through behavior change and adequate sun protection. Self-monitoring apps have the potential to facilitate prevention by measuring risk (eg, sun intensity) and encouraging protective behavior (eg, seeking shade).

Objective

Our aim was to assess health care consumer preferences for sun protection with a self-monitoring app that tracks the duration and intensity of sun exposure and provides feedback on when and how to protect the skin.

Methods

We conducted an unlabeled discrete choice experiment with 8 unique choice tasks, in which participants chose among 2 app alternatives, consisting of 5 preidentified 2-level attributes (self-monitoring method, privacy control, data sharing with health care provides, reminder customizability, and costs) that were the result of a multistep and multistakeholder qualitative approach. Participant preferences, and thus, the relative importance of attributes and their levels were estimated using conditional logit modeling. Analyses consisted of 200 usable surveys, yielding 3196 observations.

Results

Our respondents strongly preferred automatic over manually operated self-monitoring (odds ratio [OR] 2.37, 95% CI 2.06-2.72) and no cost over a single payment of 3 Swiss francs (OR 1.72, 95% CI 1.49-1.99). They also preferred having over not having the option of sharing their data with a health care provider of their choice (OR 1.66, 95% CI 1.40-1.97), repeated over single user consents, whenever app data are shared with commercial thirds (OR 1.57, 95% CI 1.31-1.88), and customizable over noncustomizable reminders (OR 1.30, 95% CI 1.09-1.54). While most participants favored thorough privacy infrastructures, the attribute of privacy control was a relatively weak driver of app choice. The attribute of self-monitoring method significantly interacted with gender and perceived personal usefulness of health apps, suggesting that female gender and lower perceived usefulness are associated with relatively weaker preferences for automatic self-monitoring.

Conclusions

Based on the preferences of our respondents, we found that the utility of a self-monitoring sun protection app can be increased if the app is simple and adjustable; requires minimal effort, time, or expense; and has an interoperable design and thorough privacy infrastructure. Similar features might be desirable for preventive health apps in other areas, paving the way for future discrete choice experiments. Nonetheless, to fully understand these preference dynamics, further qualitative or mixed method research on mobile self-monitoring-based sun protection and broader preventive mobile self-monitoring is required.

International Registered Report Identifier (IRRID)

RR2-10.2196/16087

Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study

Background

Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests.

Objective

The aim of this study was to develop a machine learning–based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app.

Methods

We trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient’s fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user’s age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set.

Results

We achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09).

Conclusions

These findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions.

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning

In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.

Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare

We live in a digital world where a variety of wearable medical devices are available. These technologies enable us to measure our health in our daily lives. It is increasingly possible to manage our own health directly through data gathered from these wearable devices. Likewise, healthcare professionals have also been able to indirectly monitor patients' health. Healthcare professionals have accepted that digital technologies will play an increasingly important role in healthcare. Wearable technologies allow better collection of personal medical data, which healthcare professionals can use to improve the quality of healthcare provided to the public. The use of continuous glucose monitoring systems (CGMS) is the most representative and desirable case in the adoption of digital technology in healthcare. Using the case of CGMS and examining its use from the perspective of healthcare professionals, this paper discusses the necessary adjustments required in clinical practices. There is a need for various stakeholders, such as medical staff, patients, industry partners, and policy-makers, to utilize and harness the potential of digital technology.

Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study

Background

Patient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients’ illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health–related PGHD is unknown.

Objective

A mixed methods study was conducted to understand clinicians’ perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians’ perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD?

Methods

The study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews.

Results

The results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing.

Conclusions

This study investigates clinicians’ perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes.

Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial

Background

Though maintaining physical conditioning and a healthy weight are requirements of active military duty, many US veterans lose conditioning and rapidly gain weight after discharge from active duty service. Mobile health (mHealth) interventions using wearable devices are appealing to users and can be effective especially with personalized coaching support. We developed Stay Strong, a mobile app tailored to US veterans, to promote physical activity using a wrist-worn physical activity tracker, a Bluetooth-enabled scale, and an app-based dashboard. We tested whether adding personalized coaching components (Stay Strong+Coaching) would improve physical activity compared to Stay Strong alone.

Objective

The goal of this study is to compare 12-month outcomes from Stay Strong alone versus Stay Strong+Coaching.

Methods

Participants (n=357) were recruited from a national random sample of US veterans of recent wars and randomly assigned to the Stay Strong app alone (n=179) or Stay Strong+Coaching (n=178); both programs lasted 12 months. Personalized coaching components for Stay Strong+Coaching comprised of automated in-app motivational messages (3 per week), telephone-based human health coaching (up to 3 calls), and personalized weekly goal setting. All aspects of the enrollment process and program delivery were accomplished virtually for both groups, except for the telephone-based coaching. The primary outcome was change in physical activity at 12 months postbaseline, measured by average weekly Active Minutes, captured by the Fitbit Charge 2 device. Secondary outcomes included changes in step counts, weight, and patient activation.

Results

The average age of participants was 39.8 (SD 8.7) years, and 25.2% (90/357) were female. Active Minutes decreased from baseline to 12 months for both groups (P<.001) with no between-group differences at 6 months (P=.82) or 12 months (P=.98). However, at 12 months, many participants in both groups did not record Active Minutes, leading to missing data in 67.0% (120/179) for Stay Strong and 61.8% (110/178) for Stay Strong+Coaching. Average baseline weight for participants in Stay Strong and Stay Strong+Coaching was 214 lbs and 198 lbs, respectively, with no difference at baseline (P=.54) or at 6 months (P=.28) or 12 months (P=.18) postbaseline based on administrative weights, which had lower rates of missing data. Changes in the number of steps recorded and patient activation also did not differ by arm.

Conclusions

Adding personalized health coaching comprised of in-app automated messages, up to 3 coaching calls, plus automated weekly personalized goals, did not improve levels of physical activity compared to using a smartphone app alone. Physical activity in both groups decreased over time. Sustaining long-term adherence and engagement in this mHealth intervention proved difficult; approximately two-thirds of the trial’s 357 participants failed to sync their Fitbit device at 12 months and, thus, were lost to follow-up.

Trial Registration

ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293

International Registered Report Identifier (IRRID)

RR2-10.2196/12526

Preferences for Sun Protection With a Self-Monitoring App: Protocol of a Discrete Choice Experiment Study

BACKGROUND

The incidence of sun-exposure-related skin conditions, such as melanoma, is a gradually increasing and largely preventable public health problem. Simultaneously, the availability of mobile apps that enable the self-monitoring of health behavior and outcomes is ever increasing. Inevitably, recent years have seen an emerging volume of electronic patient-generated health data (PGHD), as well as their targeted application across primary prevention areas, including sun protection and skin health. Despite their preventive potential, the actual impact of these apps relies on the engagement of health care consumers, who are primarily responsible for recording, sharing, and using their PGHD. Exploring preferences is a key step toward facilitating consumer engagement and ultimately realizing their potential.

OBJECTIVE

This paper describes an ongoing research project that aims to elicit the preferences of health care consumers for sun protection via app-based self-monitoring.

METHODS

A discrete choice experiment (DCE) will be conducted to explore how healthy consumers choose between two alternative preventive self-monitoring apps. DCE development and attribute selection were built on extensive qualitative work, consisting of the secondary use of a previously conducted scoping review, a rapid review of reviews, 13 expert interviews, and 12 health care consumer interviews, the results of which are reported in this paper. Following D-optimality criteria, a fractional factorial survey design was generated. The final DCE will be administered in the waiting room of a travel clinic, targeting a sample of 200 participants. Choice data will be analyzed with conditional logit and multinomial logit models, accounting for individual participant characteristics.

RESULTS

An ethics approval was waived by the Ethics Committee Zurich. The study started in September 2019 and estimated data collection and completion is set for January 2020. Five two-level attributes have been selected for inclusion in the DCE, addressing (1) data generation methods, (2) privacy control, (3) data sharing with general practitioner, (4) reminder timing, and (5) costs. Data synthesis, analysis, and reporting are planned for January and February 2020. Results are expected to be submitted for publication by February 2020.

CONCLUSIONS

Our results will target technology developers, health care providers, and policy makers, potentially offering some guidance on how to design or use sun-protection-focused self-monitoring apps in ways that are responsive to consumer preferences. Preferences are ultimately linked to engagement and motivation, which are key elements for the uptake and success of digital health. Our findings will inform the design of person-centered apps, while also inspiring future preference-eliciting research in the field of emerging and complex eHealth services.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/16087.