Individual Study / Ben-Zeev et al.; Psychiatr Rehabil J. 2015

Next-generation Psychiatric Assessment: Using Smartphone Sensors to Monitor Behavior and Mental Health

Next-generation Psychiatric Assessment: Using Smartphone Sensors to Monitor Behavior and Mental Health

Initiatives -
The objective of this study was to examine whether the information captured with multimodal smartphone sensors can serve as behavioral markers for one's mental health. It was hypothesized that (a) unobtrusively collected smartphone sensor data would be associated with individuals' daily levels of stress, and (b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time. Note: All published information has been collected from the article referenced in the Marker Paper box below. Therefore, there may be variations with more advanced versions of the study.
Start Year
2013
Funding
R01 MH103148/MH/NIMH NIH HHS/United States

Design

Study design
Population cohort

Marker Paper

Ben-Zeev D, Scherer EA, Wang R, Xie H, Campbell AT. Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health [published correction appears in Psychiatr Rehabil J. 2015 Dec;38(4):313]. Psychiatr Rehabil J. 2015;38(3):218‐226. doi:10.1037/prj0000130

PUBMED 25844912

Number of participants

Number of participants
47
Number of participants with biosamples

Access

Availability of data and biosamples

Data
Biosamples
Other

Timeline

Population

A total of 47 young adults (age range: 19-30 years) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period.
Selection Criteria
Minimum age
19
Maximum age
30
Newborns
Twins
Countries
  • United States of America
Ethnic Origin
Health Status

Recruitment

Sources of recruitment
  • General population

Number of participants

Number of participants
47
Number of participants with biosamples
Data Collection Event
Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980).
Start Date
2013
Data sources
  • Mobile data collection
    • Smartphone apps
    • Smartwatch and wearables
  • Geospatial technology
    • Global navigation satellite systems (GNSS) (e.g. GPS, GLONASS, Galileo, etc.)