JMIR Publications

ABSTRACT

Background:

Newer mobile technologies have expanded the scope of mHealth to include consumer-grade devices. mHealth involves using mobile technologies for wellness, disease management, and diagnosis. Although mHealth technologies used in the clinical setting have typically been medical-grade devices, passive and active sensing available in consumer-grade devices have the potential to bridge information gaps about patients’ behaviours, environment, lifestyle, and other data generated ubiquitously. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PHGD), including self-reported outcomes and biometric sensor data. Healthcare professionals (HCPs) can integrate PGHD from consumer devices into care for multiple purposes. However, there is limited research on real-life experiences of HPCs using non-medical mHealth solutions and their PGHD in clinical tasks.

Objective:

This systematic review aims to analyse the existing literature to determine how HCPS are utilizing PGHD from consumer mobile devices in clinical tasks. The objectives are to identify the types of PGHD that are useful for HCPs, how they have been using it and in which cases, to understand the reasons behind their acceptance to review of such data.

Methods:

Established guidelines for conducting Systematics Literature Reviews in software engineering and medicine were followed. Eligible studies were identified through comprehensive searches in health, biomedicine and computer science databases, and a complimentary 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 two stages, and data extraction was performed using a predefined form.

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 mHealth solutions portals and patients’ mobile devices. Behavioural types of PGHD seemed particularly useful for HCPs. Our findings suggest that PGHD is more commonly used by HCPs to treat patients with conditions related to lifestyles, such as diabetes and obesity. Physicians were the most represented type of HCPs, participating in over 80% of the studies.

Conclusions:

PGHD collection through mHealth solutions has proven beneficial for patients, and it can also support HCPs in patient-centred assessment, treatment discussions, and patient empowerment. PGHD can be particularly useful to treat conditions related to lifestyle, like diabetes, cardiovascular diseases, and obesity, or in domains with a high level of uncertainty about the factors affecting a patient’s health, such as in the case of infertility. Integrating PGHD into clinical care poses challenges related to stakeholder needs, privacy, and accessibility; however, some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, its perceived clinical value outweighs the alternative of having no data.