Willingness to Share Data From Wearable Health and Activity Trackers: Analysis of the 2019 Health Information National Trends Survey Data

Veteran Preferences and Willingness to Share Patient-Generated Health Data

BACKGROUND

Technologies, including mobile health applications (apps) and wearables, offer new potential for gathering patient-generated health data (PGHD) from patients; however, little is known about patient preferences for and willingness to collect and share PGHD with their providers and healthcare systems.

OBJECTIVE

Describe how patients use their PGHD and factors important to patients when deciding whether to share PGHD with a healthcare system.

DESIGN

Cross-sectional mailed longitudinal survey supplemented with administrative data within the Veterans Health Administration (VHA).

SUBJECTS

National sample of Veterans who use VHA healthcare.

MAIN MEASURES

Survey questions asked about demographics, willingness to use different devices to collect and share PGHD, what Veterans do with their PGHD, and factors important to Veterans when deciding whether to share PGHD with VHA. Administrative data provided information on Veteran health conditions. Multiple logistic regression models assessed factors associated with sharing PGHD with VHA.

KEY RESULTS

Overall, 47% of our analytic cohort (n = 383/807) indicated that they share PGHD collected through apps or digital health devices with VHA. In adjusted logistic regression models, Veterans who believed the following factors were Very Important (versus Somewhat/Not At All Important) had higher odds of sharing PGHD with VHA: if their doctor (OR = 1.4; 95%CI, 1.0-2.0) or other healthcare team members (OR = 1.4; 95%CI, 1.0-1.9) recommended they do so; and knowing that their healthcare team would look at the data (OR = 1.4; 95%CI, 1.0-2.0) or use the information to inform their healthcare (OR = 1.5; 95%CI, 1.1-2.1).

CONCLUSIONS

Our data suggest that healthcare team members can influence patient sharing of PGHD, as can a patient's knowledge that PGHD will be used in clinical practice. Efforts to increase the number of patients who share PGHD with a healthcare system may benefit from buy-in among healthcare team members, who appear to play an influential role in patient decisions to share data.

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial

BACKGROUND

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design.

OBJECTIVE

The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems.

METHODS

The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership.

RESULTS

The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership.

CONCLUSIONS

Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines.

CLINICALTRIAL

ClinicalTrials.gov-Identifier: NCT04359238.

Health data sharing attitudes towards primary and secondary use of data: a systematic review

Background

To receive the best care, people share their health data (HD) with their health practitioners (known as sharing HD for primary purposes). However, during the past two decades, sharing for other (i.e., secondary) purposes has become of great importance in numerous fields, including public health, personalized medicine, research, and development. We aimed to conduct the first comprehensive overview of all studies that investigated people's HD sharing attitudes-along with associated barriers/motivators and significant influencing factors-for all data types and across both primary and secondary uses.

Methods

We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for relevant studies published in English between database inception and February 28, 2023, using a predefined set of keywords. Studies were included, regardless of their design, if they reported outcomes related to attitudes towards sharing HD. We extracted key data from the included studies, including the type of HD involved and findings related to: HD sharing attitudes (either in general or depending on type of data/user); barriers/motivators/benefits/concerns of the study participants; and sociodemographic and other variables that could impact HD sharing behaviour. The qualitative synthesis was conducted by dividing the studies according to the data type (resulting in five subgroups) as well as the purpose the data sharing was focused on (primary, secondary or both). The Newcastle-Ottawa Scale (NOS) was used to assess the quality of non-randomised studies. This work was registered with PROSPERO, CRD42023413822.

Findings

Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n = 17 studies and 10,771 participants), personal HD in general (n = 69 studies and 117,054 participants), Biobank data (n = 7 studies and 27,073 participants), genomic data (n = 13 studies and 54,716 participants), and miscellaneous data (n = 10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies.

Interpretation

Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people's concerns, and focus on spreading awareness about the data sharing process and benefits.

Funding

None.

Assessment of ownership of smart devices and the acceptability of digital health data sharing

Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18-25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies.

Use Patterns of Smartphone Apps and Wearable Devices Supporting Physical Activity and Exercise: Large-Scale Cross-Sectional Survey

Background

Physical inactivity is a global health issue, and mobile health (mHealth) apps are expected to play an important role in promoting physical activity. Empirical studies have demonstrated the efficacy and efficiency of app-based interventions, and an increasing number of apps with more functions and richer content have been released. Regardless of the success of mHealth apps, there are important evidence gaps in the literature; that is, it is largely unknown who uses what app functions and which functions are associated with physical activity.

Objective

This study aims to investigate the use patterns of apps and wearables supporting physical activity and exercise in a Japanese-speaking community sample.

Methods

We recruited 20,573 web-based panelists who completed questionnaires concerning demographics, regular physical activity levels, and use of apps and wearables supporting physical activity. Participants who indicated that they were using a physical activity app or wearable were presented with a list of app functions (eg, sensor information, goal setting, journaling, and reward), among which they selected any functions they used.

Results

Approximately one-quarter (n=4465) of the sample was identified as app users and showed similar demographic characteristics to samples documented in the literature; that is, compared with app nonusers, app users were younger (odds ratio [OR] 0.57, 95% CI 0.50-0.65), were more likely to be men (OR 0.83, 95% CI 0.77-0.90), had higher BMI scores (OR 1.02, 95% CI 1.01-1.03), had higher levels of education (university or above; OR 1.528, 95% CI 1.19-1.99), were more likely to have a child (OR 1.16, 95% CI 1.05-1.28) and job (OR 1.28, 95% CI 1.17-1.40), and had a higher household income (OR 1.40, 95% CI 1.21-1.62). Our results revealed unique associations between demographic variables and specific app functions. For example, sensor information, journaling, and GPS were more frequently used by men than women (ORs <0.84). Another important finding is that people used a median of 2 (IQR 1-4) different functions within an app, and the most common pattern was to use sensor information (ie, self-monitoring) and one other function such as goal setting or reminders.

Conclusions

Regardless of the current trend in app development toward multifunctionality, our findings highlight the importance of app simplicity. A set of two functions (more precisely, self-monitoring and one other function) might be the minimum that can be accepted by most users. In addition, the identified individual differences will help developers and stakeholders pave the way for the personalization of app functions.

Implementing Remote Patient Monitoring of Physical Activity in Clinical Practice

PURPOSE

Remote patient monitoring (RPM) is a tool for patients to share data collected outside of office visits. RPM uses technology and the digital transmission of data to inform clinician decision-making in patient care. Using RPM to track routine physical activity is feasible to operationalize, given contemporary consumer-grade devices that can sync to the electronic health record. Objective monitoring through RPM can be more reliable than patient self-reporting for physical activity.

DESIGN AND METHODS

This article reports on four pilot studies that highlight the utility and practicality of RPM for physical activity monitoring in outpatient clinical care. Settings include endocrinology, cardiology, neurology, and pulmonology settings.

RESULTS

The four pilot use cases discussed demonstrate how RPM is utilized to monitor physical activity, a shift that has broad implications for prediction, prevention, diagnosis, and management of chronic disease and rehabilitation progress.

CLINICAL RELEVANCE

If RPM for physical activity is to be expanded, it will be important to consider that certain populations may face challenges when accessing digital health services.

CONCLUSION

RPM technology provides an opportunity for clinicians to obtain objective feedback for monitoring progress of patients in rehabilitation settings. Nurses working in rehabilitation settings may need to provide additional patient education and support to improve uptake.

Determinants of American Adults' Use of Digital Health and Willingness to Share Health Data to Providers, Family, and Social Media: A Cross-sectional Study

With the global pandemic driving the adoption of digital health, understanding the predictors or determinants of digital health usage and information sharing gives an opportunity to advocate for broader adoption. We examined the prevalence and predictors of digital health usage and information-sharing behaviors among American adults. Data were from the Health Information National Trends Survey 5 Cycle 4. More than two-thirds used a digital resource for health-related activities (eg, to check test results). About 81% were willing to share their digital data with their provider, 75% with family, and 58% with friends. Only 14% shared health information on social media. Gender, education, device types, and performance expectancy of digital health were common factors associated with both digital health usage and information-sharing behaviors. Other predictors included rurality, patient portal access, income, and having a chronic disease. Of note, we found that Asian American Pacific Islanders, compared with Whites, were less likely to share information with providers. Performance expectancy was a significant determinant of information sharing. Those diagnosed with diabetes were 4% less likely to share information with their providers. With the growing digital divide, there is a need to advocate for more usable and accessible digital health to assist with person-centered care.

Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020

Importance

Wearable devices may be able to improve cardiovascular health, but the current adoption of these devices could be skewed in ways that could exacerbate disparities.

Objective

To assess sociodemographic patterns of use of wearable devices among adults with or at risk for cardiovascular disease (CVD) in the US population in 2019 to 2020.

Design, Setting, and Participants

This population-based cross-sectional study included a nationally representative sample of the US adults from the Health Information National Trends Survey (HINTS). Data were analyzed from June 1 to November 15, 2022.

Exposures

Self-reported CVD (history of heart attack, angina, or congestive heart failure) and CVD risk factors (≥1 risk factor among hypertension, diabetes, obesity, or cigarette smoking).

Main Outcomes and Measures

Self-reported access to wearable devices, frequency of use, and willingness to share health data with clinicians (referred to as health care providers in the survey).

Results

Of the overall 9303 HINTS participants representing 247.3 million US adults (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) representing 20.3 million US adults had CVD (mean [SD] age, 62.2 [17.0] years; 43% [95% CI, 37%-49%] women), and 5185 (55.7%) representing 134.9 million US adults were at risk for CVD (mean [SD] age, 51.4 [16.9] years; 43% [95% CI, 37%-49%] women). In nationally weighted assessments, an estimated 3.6 million US adults with CVD (18% [95% CI, 14%-23%]) and 34.5 million at risk for CVD (26% [95% CI, 24%-28%]) used wearable devices compared with an estimated 29% (95% CI, 27%-30%) of the overall US adult population. After accounting for differences in demographic characteristics, cardiovascular risk factor profile, and socioeconomic features, older age (odds ratio [OR], 0.35 [95% CI, 0.26-0.48]), lower educational attainment (OR, 0.35 [95% CI, 0.24-0.52]), and lower household income (OR, 0.42 [95% CI, 0.29-0.60]) were independently associated with lower use of wearable devices in US adults at risk for CVD. Among wearable device users, a smaller proportion of adults with CVD reported using wearable devices every day (38% [95% CI, 26%-50%]) compared with overall (49% [95% CI, 45%-53%]) and at-risk (48% [95% CI, 43%-53%]) populations. Among wearable device users, an estimated 83% (95% CI, 70%-92%) of US adults with CVD and 81% (95% CI, 76%-85%) at risk for CVD favored sharing wearable device data with their clinicians to improve care.

Conclusions and Relevance

Among individuals with or at risk for CVD, fewer than 1 in 4 use wearable devices, with only half of those reporting consistent daily use. As wearable devices emerge as tools that can improve cardiovascular health, the current use patterns could exacerbate disparities unless there are strategies to ensure equitable adoption.

Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults

The use of wearable technology, which is often acquired to support well-being and a healthy lifestyle, has become popular in Western countries. At the same time, healthcare is gradually taking the first steps to introduce wearable technology into patient care, even though on a large scale the evidence of its' effectiveness is still lacking. The objective of this study was to identify the factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the Finnish adult population (20-99) and among older adults (65-99). The study utilized a cross-sectional population survey of Finnish adults aged 20 and older (n = 6,034) to analyse non-causal relationships between wearable technology use and the users' characteristics. Logistic regression models of wearable technology use were constructed using statistically significant sociodemographic, well-being, health, benefit, and lifestyle variables. Both in the general adult population and among older adults, wearable technology use was associated with getting aerobic physical activity weekly according to national guidelines and with marital status. In the general adult population, wearable technology use was also associated with not sleeping enough and agreeing with the statement that social welfare and healthcare e-services help in taking an active role in looking after one's own health and well-being. Younger age was associated with wearable technology use in the general adult population but for older adults age was not a statistically significant factor. Among older adults, non-use of wearable technology went hand in hand with needing guidance in e-service use, using a proxy, or not using e-services at all. The results support exploration of the effects of wearable technology use on maintaining an active lifestyle among adults of all ages.

From intention to action: The factors affecting health data sharing intention and action

INTRODUCTION

Effective prevention and treatment of diseases requires utilization of health-related lifestyle data, which has thus become increasingly important. According to some studies, participants were willing to share their health data for use in medical care and research. Although intention does not always accurately reflect action, few studies have examined the question of whether data-sharing intention leads to data-sharing action.

OBJECTIVE

The aim of this study was to examine the extent of actualizing data-sharing intention to data-sharing action and to identify the factors that influence data-sharing intention and action.

METHODS

A web-based survey of members of a university examined the data-sharing intention and issues of concern when making decisions on data sharing. The participants were asked to deposit their armband data for use in research at the end of the survey. A comparison of data-sharing intention and action in relation to the participants' characteristics was performed. Factors having a significant effect on data-sharing intention and action were identified using logistic regressions.

RESULTS

Of 386 participants, 294 expressed willingness to share health data. However, only 73 participants deposited their armband data. The primary reason for refusal to deposit armband data was the inconvenience of the data transfer process (56.3%). Appropriate compensation had a significant effect on data-sharing intention (OR: 3.3, CI: 1.86-5.75) and action (OR: 2.8, CI: 1.14-8.21). The compensation for data sharing (OR:2.8, CI:1.14-8.21) and familiarity with data (OR:3.1, CI:1.36-8.21) were significant predictors of data sharing action, however, data-sharing intention was not (OR: 1.5, CI:0.65-3.72).

CONCLUSION

Despite expressing willingness to share their health data, the participants' intention was not actualized to data-sharing behavior for depositing armband data. Implementation of a streamlined data transfer process and providing appropriate compensation might facilitate data-sharing. These findings could be useful in development of strategies to facilitate sharing and reuse of health data.

Grains of Sand to Clinical Pearls: Realizing the Potential of Wearable Data

Despite the rapid growth of wearables as a consumer technology sector and a growing evidence base supporting their use, they have been slow to be adopted by the health system into clinical care. As regulatory, reimbursement, and technical barriers recede, a persistent challenge remains how to make wearable data actionable for clinicians-transforming disconnected grains of wearable data into meaningful clinical "pearls". In order to bridge this adoption gap, wearable data must become visible, interpretable, and actionable for the clinician. We showcase emerging trends and best practices that illustrate these 3 pillars, and offer some recommendations on how the ecosystem can move forward.

Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults

This scoping review identified observational studies of adults that utilized accelerometry to assess physical activity and sedentary behavior. Key elements on accelerometry data collection were abstracted to describe current practices and completeness of reporting. We searched three databases (PubMed, Web of Science, and SPORTDiscus) on June 1, 2021 for articles published up to that date. We included studies of non-institutionalized adults with an analytic sample size of at least 500. The search returned 5686 unique records. After reviewing 1027 full-text publications, we identified and abstracted accelerometry characteristics on 155 unique observational studies (154 cross-sectional/cohort studies and 1 case control study). The countries with the highest number of studies included the United States, the United Kingdom, and Japan. Fewer studies were identified from the continent of Africa. Five of these studies were distributed donor studies, where participants connected their devices to an application and voluntarily shared data with researchers. Data collection occurred between 1999 to 2019. Most studies used one accelerometer (94.2%), but 8 studies (5.2%) used 2 accelerometers and 1 study (0.6%) used 4 accelerometers. Accelerometers were more commonly worn on the hip (48.4%) as compared to the wrist (22.3%), thigh (5.4%), other locations (14.9%), or not reported (9.0%). Overall, 12.7% of the accelerometers collected raw accelerations and 44.6% were worn for 24 hours/day throughout the collection period. The review identified 155 observational studies of adults that collected accelerometry, utilizing a wide range of accelerometer data processing methods. Researchers inconsistently reported key aspects of the process from collection to analysis, which needs addressing to support accurate comparisons across studies.

Stepping up with GGIR: Validity of step cadence derived from wrist-worn research-grade accelerometers using the verisense step count algorithm

The Verisense Step Count Algorithm facilitates generation of steps from wrist-worn accelerometers. Based on preliminary evidence suggesting a proportional bias with overestimation at low steps/day, but underestimation at high steps/day, the algorithm parameters have been revised. We aimed to establish validity of the original and revised algorithms relative to waist-worn ActiGraph step cadence. We also assessed whether step cadence was similar across accelerometer brand and wrist. Ninety-eight participants (age: 58.6±11.1 y) undertook six walks (~500 m hard path) at different speeds (cadence: 92.9±9.5-127.9±8.7 steps/min) while wearing three accelerometers on each wrist (Axivity, GENEActiv, ActiGraph) and an ActiGraph on the waist. Of these, 24 participants also undertook one run (~1000 m). Mean bias for the original algorithm was -21 to -26.1 steps/min (95% limits of agreement (LoA) ~±65 steps/min) and mean absolute percentage error (MAPE) 17-22%. This was unevenly distributed with increasing error as speed increased. Mean bias and 95%LoA were halved with the revised algorithm parameters (~-10 to -12 steps/min, 95%LoA ~30 steps/min, MAPE ~10-12%). Performance was similar across brand and wrist. The revised step algorithm provides a more valid measure of step cadence than the original, with MAPE similar to recently reported wrist-wear summary MAPE (7-11%).

The future of telemedicine and wearable technology in IBD

PURPOSE OF REVIEW

Telemedicine has quickly become an essential part of modern healthcare, particularly in the management of chronic conditions like inflammatory bowel disease. The purpose of this review is to describe the current use of telehealth, mobile applications and wearable devices in inflammatory bowel disease and potential future applications.

RECENT FINDINGS

Telemedicine was increasingly used during the Coronavirus Disease 2019 pandemic. Virtual consultations allowed clinical care to continue despite pandemic-related restrictions without compromising the quality of care for patients with inflammatory bowel disease (IBD). It also benefits patients who would not have access to care due to financial or geographical barriers. Mobile applications allow patients with IBD to record disease activity among other metrics, allowing for earlier healthcare provider intervention. Wearable devices are increasingly being explored to monitor physiological indicators of disease activity and flare.

SUMMARY

Telehealth and remote patient monitoring has been successfully integrated into the care of IBD patients. The advantages of these modalities include better access to specialist care and remote noninvasive disease monitoring. Careful consideration must be given to patient privacy, data protection and equitable access. These modalities have enormous potential to improve patient care through accurate consistent data collection and even the prediction of disease activity.