Data Collection Mechanisms in Health and Wellness Apps: Review and Analysis.

BACKGROUND: There has been a steady rise in the availability of health wearables and built-in smartphone sensors that can be used to collect health data reliably and conveniently from end users. Given the feature overlaps and user tendency to use several apps, these are important factors impacting user experience. However, there is limited work on analyzing the data collection aspect of mobile health (mHealth) apps. OBJECTIVE: This study aims to analyze what data mHealth apps across different categories usually collect from end users and how these data are collected. This information is important to guide the development of a common data model from current widely adopted apps. This will also inform what built-in sensors and wearables, a comprehensive mHealth platform should support. METHODS: In our empirical investigation of mHealth apps, we identified app categories listed in a curated mHealth app library, which was then used to explore the Google Play Store for health and medical apps that were then filtered using our selection criteria. We downloaded these apps from a mirror site hosting Android apps and analyzed them using a script that we developed around the popular AndroGuard tool. We analyzed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects health data. RESULTS: We retrieved 3251 apps meeting our criteria, and our analysis showed that 10.74% (349/3251) of these apps requested Bluetooth access. We found that 50.9% (259/509) of the Bluetooth service universally unique identifiers to be known in these apps, with the remainder being vendor specific. The most common health-related Bluetooth Low Energy services using known universally unique identifiers were Heart Rate, Glucose, and Body Composition. App permissions showed the most used device module or sensor to be the camera (669/3251, 20.57%), closely followed by location (598/3251, 18.39%), with the highest occurrence in the staying healthy app category. CONCLUSIONS: We found that not many health apps used built-in sensors or peripherals for collecting health data. The small number of the apps using Bluetooth, with an even smaller number of apps using standard Bluetooth Low Energy services, indicates a wider use of proprietary algorithms and custom services, which restrict the device use. The use of standard profiles could open this ecosystem further and could provide end users more options for apps. The relatively small proportion of apps using built-in sensors along with a high reliance on manual data entry suggests the need for more research into using sensors for data collection in health and fitness apps, which may be more desirable and improve end user experience.

Access to exercise and its relation to cardiovascular health and gene expression in laboratory animals.

The interaction between genes and environment can influence cardiovascular disease (CVD). This 16 month study investigated if genes associated with cardiovascular (CV) regulation were expressed differently in animals having: 1) no access to physical activity or exercise (SED), 2) access to hour-long, twice weekly activity (PA), and 3) access every-other-day to a running wheel (EX). Out of 31,000 genes, a CV subset comprising 44 genes was investigated. Ten genes from this subset were expressed differently in EX compared with SED, and 34 genes were expressed differently in PA compared with SED (p<0.05). Total cholesterol (70+/-8 vs. 101+/-9 mg dl(-1)), triglycerides (104+/-8 vs. 127+/-4 mg dl(-1)), resting systolic blood pressure (130+/-3 vs. 141+/-3 mmHg), mean arterial pressure (110+/-2 vs. 120+/-2 mmHg) and heart rate (380+/-6 vs. 405+/-9 beats min(-1)) were lower in EX compared with SED (p<0.05), but intracellular adhesion molecule levels did not differ among groups. Mean gene expressions for Gja1, Fdft1, Edn1, Cd36, and Hmgb2 differed in animals according to access to physical activity. These genes play roles in heart rate, cholesterol biosynthesis, blood pressure, cell adhesion, and transcription and neurogenesis regulation, respectively. In conclusion, a total of 44 CV genes were expressed differently in SED compared to PA and EX; and SED showed more physiological evidence of CVD.

Exercise improves biomarkers of health and stress in animals fed ad libitum.

Voluntary and forced exercise decrease morbidity and mortality in laboratory animals. Caloric restriction has similar effects on health and unique benefits on life span. Nonetheless, in most experiments, animals do not have access to physical activity and are fed ad libitum (AL). We hypothesized that with regular access to either unlimited running wheel exercise (EX) or limited physical activity (PA), key biomarkers of health would be enhanced enough to counter some consequences of a sedentary AL lifestyle. This 16-month study compared body weight, tumor number and size, tissue lesions, oxidative stress, and reactive stress in (1) sedentary animals with no access to physical activity (SED); (2) animals with access to hour-long, twice weekly activity in a large box (PA); and (3) animals with access every other day to a running wheel (EX). At the end of the study, EX body weight was 8-9% lower than PA and SED. In addition, EX had no kidney lesions versus 50% in PA and SED, and had smaller tumor size (10+/-2 vs. 14+/-4 and 30+/-4 mm). Exhaustive exercise lowered glutathione/oxidized glutathione ratio in EX and PA, but in SED, the ratio was depressed even in resting animals. In all treatments, prolactin (PRL) levels were lower in resting animals than in acutely exercised animals. In conclusion, EX had the most favorable health biomarkers while SED had the least. PA did not confer gross health benefits different than the SED group, but was biochemically more similar to EX animals.