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. 2021 Aug 27:7:20552076211037227.
doi: 10.1177/20552076211037227. eCollection 2021 Jan-Dec.

Using mobile sensing data to assess stress: Associations with perceived and lifetime stress, mental health, sleep, and inflammation

Michelle L Byrne  1   2 , Monika N Lind  1 , Sarah R Horn  1 , Kathryn L Mills  1 , Benjamin W Nelson  3 , Melissa L Barnes  1 , George M Slavich  4 , Nicholas B Allen  1   5
Affiliations

Using mobile sensing data to assess stress: Associations with perceived and lifetime stress, mental health, sleep, and inflammation

Michelle L Byrne et al. Digit Health. .

Abstract

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 estimate exposure to acute and chronic stressors that have health-damaging effects. This initial validation study validated a mobile-sensing collection tool against assessments of perceived and lifetime stress, mental health, sleep duration, and inflammation.

Methods: Participants were 25 well-characterized healthy young adults (M age = 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 duration, and basal inflammatory activity (i.e. salivary C-reactive protein and interleukin-1β).

Results: Three measures of affective language (i.e. total positive words, total negative words, and total affective words) were strongly associated with lifetime stress exposure, and total negative words typed was related to fewer hours slept (all large effect sizes: r = 0.50 - 0.78). Total positive words, total negative words, and total affective words typed were also associated with higher perceived stress and lower salivary C-reactive protein levels (medium effect sizes; r = 0.22 - 0.32).

Conclusions: Data from this initial 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.

Keywords: Mobile sensing; affective language; inflammation; mental health; stress; text.

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Conflict of interest statement

Declaration of conflicting interests: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Using mobile sensing data to assess stress: Associations with perceived and lifetime stress, mental health, sleep, and inflammation. Nicholas Allen and Michelle Byrne both hold equity interests in Ksana Health Inc., a company that has the sole commercial license for certain versions of the Effortless Assessment of Risk States (EARS) mobile phone application and some related EARS tools. The other authors have nothing else to disclose.

Figures

Figure 1.
Figure 1.
Effect sizes (Pearson’s correlations) between all variables.
Figure 2.
Figure 2.
Medium and large effect sizes for mobile sensing variables.
See this image and copyright information in PMC

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