No abstract

Smartphone apps and wearable devices for tracking physical activity and other health behaviors have become popular in recent years and provide a largely untapped source of data about health behaviors in the free-living environment. The data are large in scale, collected at low cost in the “wild”, and often recorded in an automatic fashion, providing a powerful complement to traditional surveillance studies and controlled trials. These data are helping to reveal, for example, new insights about environmental and social influences on physical activity. The observational nature of the datasets and collection via commercial devices and apps pose challenges, however, including the potential for measurement, population, and/or selection bias, as well as missing data. In this article, we review insights gleaned from these datasets and propose best practices for addressing the limitations of large-scale data from apps and wearables. Our goal is to enable researchers to effectively harness the data from smartphone apps and wearable devices to better understand what drives physical activity and other health behaviors.

Background The 6-minute walk test (6MWT) independently predicts congestive heart failure (CHF) severity, death and heart failure hospitalizations, but must be administered in clinic by qualified staff on a pre-measured course. As part of the Health eHeart Study we sought to develop and validate a self-administered 6MWT mobile application (SA-6MWTapp) for independent use at home by patients. Methods and Results We performed a validation study of a SA-6MWTapp in 103-participants. In phase one (n=52), we developed a distance estimation algorithm for the SA-6MWTapp by comparing step counts from an Actigraph and measured distance on a pre-measured 6MWT course to step counts and estimated distance obtained simultaneously from our SA-6MWTapp (best estimation algorithm, r = 0.89 [95% CI 0.78 – 0.99]). In phase two, 32 participants (including those with CHF and pHTN) used the SA-6MWTapp independently in clinic and the distance estimated by the SA-6MWTapp was compared to the measured distance (r = 0.83 [95% CI 0.79-0.92]). In phase three, 19 patients with CHF and pHTN consecutively enrolled from clinic, performed 3.2 ±1 SA-6MWTapp tests per week at home over 2 weeks. Distances estimated from the SA-6MWTapp during home 6MWTs were highly repeatable (coefficient of variation = 4.6%) and correlated with in-clinic measured distance (r = 0.88 [95% CI 0.87-0.89]). Usability surveys performed during the second (in-clinic) and third (at-home) phases demonstrated that the SA-6MWTapp was simple and easy to use independently. Conclusions A self-administered 6MWTapp is easy to use and yields accurate repeatable measurements in the clinic and at home.

Emerging technology allows patients to measure and record their heart rate (HR) remotely by photoplethysmography (PPG) using smart devices like smartphones. However, the validity and expected distribution of such measurements are unclear, making it difficult for physicians to help patients interpret real-world, remote and on-demand HR measurements. Our goal was to validate HR-PPG, measured using a smartphone app, against HR-electrocardiogram (ECG) measurements and describe out-of-clinic, real-world, HR-PPG values according to age, demographics, body mass index, physical activity level, and disease. To validate the measurements, we obtained simultaneous HR-PPG and HR-ECG in 50 consecutive patients at our cardiology clinic. We then used data from participants enrolled in the Health eHeart cohort between 1 April 2014 and 30 April 2018 to derive real-world norms of HR-PPG according to demographics and medical conditions. HR-PPG and HR-ECG were highly correlated (Intraclass correlation = 0.90). A total of 66,788 Health eHeart Study participants contributed 3,144,332 HR-PPG measurements. The mean real-world HR was 79.1 bpm ± 14.5. The 95th percentile of real-world HR was ≤110 in individuals aged 18–45, ≤100 in those aged 45–60 and ≤95 bpm in individuals older than 60 years old. In multivariable linear regression, the number of medical conditions, female gender, increasing body mass index, and being Hispanic was associated with an increased HR, whereas increasing age was associated with a reduced HR. Our study provides the largest real-world norms for remotely obtained, real-world HR according to various strata and they may help physicians interpret and engage with patients presenting such data.