Using Mobile Sensing Data to Assess Stress: Associations with Perceived and Lifetime Stress, Mental Health, Sleep, and Inflammation

Background: Although stress is a risk factor for mental and physical health problems, it can be difficult to assess, especially on a continual, non-invasive basis. Mobile sensing data, which are continuously collected from naturalistic smartphone use, may address this issue by estimating individuals’ exposure to acute and chronic stressors that have health-damaging effects. We conducted an initial validation study where we validated a mobile sensing collection tool against assessments of perceived and lifetime stress, mental health, sleep, and inflammation. Method: Participants were 25 well-characterized healthy young adults (Mage = 20.64 years, SD = 2.74; 13 men, 12 women). We collected affective text language use with a custom smartphone keyboard. We assessed participants’ perceived and lifetime stress, depression and anxiety levels, sleep, and inflammatory activity (i.e., salivary C-reactive protein, interleukin-1β). Results: Three measures of affective language (total positive words, total negative words, and total affective words) were strongly associated with lifetime stress exposure, and total negative words were related to fewer hours slept (all large effect sizes; r = .50 to .78). Total positive words, total negative words, and total affective words were also associated with higher perceived stress and lower salivary C-reactive protein (medium effect sizes; r = .22 to .32). Conclusions: Data from this longitudinal validation study suggest that total and affective text use may be useful mobile sensing measures insofar as they are associated with several other stress, mental health, behavioral, and biological outcomes. This tool may thus help identify individuals at increased risk for stress-related health problems.