JMIR Publications

ABSTRACT

Background:

Background:

The growing field of personal sensing harnesses sensor data collected from an individual’s smartphone to understand their behaviors and experiences. Such data could be a powerful tool within mental health care. However, it is important to note that the nature of this data differs from the information usually available to, or discussed with, health care professionals. To design digital mental health tools that are acceptable to users, understanding how personal sensing data can be used and shared is critical.

Objective:

This study investigates individuals’ perspectives about sharing of different types of sensed research data beyond the research context, specifically with doctors, electronic health records (EHR), and family members.

Methods:

Questions regarding sensed data privacy explored participants’ comfort in sharing six types of sensed data: physical activity, mood, sleep, communication logs, location, and social activity. Participants were asked their comfort with sharing this data with three different recipients: doctors, electronic health record (EHR), and family members. A series of principal component analyses (PCA, one for each data recipient) were performed to identify clusters of sensor data types according to participants’ comfort sharing them. Relationships between recipients and sensor clusters were then explored using generalized estimating equation logistic regression models.

Results:

Two-hundred and eleven participants completed the privacy survey. The majority were female (81.0% 171/211) and the mean age was 38 years. PCA consistently identified two clusters of sensors across the three data recipients: “health information” – sleep, mood, and physical activity; and “personal data” – communication logs, location, and social activity. Overall, participants were significantly more comfortable sharing any type of sensed data with their doctor than the EHR, or family members (P < .001) and more comfortable sharing “health information” than “personal data” (P < .001). Participant characteristics such as age or mental health status did not influence sensed data sharing comfort.

Conclusions:

Results align with the theory of contextual integrity, verifying the frameworks’ utility in guiding the use of sensed data in mental health. Given the identified differences in sensed data sharing comfort, contextual factors of data type and data recipient appear to be critically important as we design systems that harness sensor data for mental health treatment and support.