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Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.
Stamatis, Caitlin A; Meyerhoff, Jonah; Meng, Yixuan; Lin, Zhi Chong Chris; Cho, Young Min; Liu, Tony; Karr, Chris J; Liu, Tingting; Curtis, Brenda L; Ungar, Lyle H; Mohr, David C.
Afiliação
  • Stamatis CA; Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. caitlin.stamatis@northwestern.edu.
  • Meyerhoff J; Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Meng Y; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
  • Lin ZCC; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
  • Cho YM; Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA.
  • Liu T; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
  • Karr CJ; Roblox Corporation, San Mateo, CA, USA.
  • Liu T; Audacious Software, Chicago, IL, USA.
  • Curtis BL; Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA.
  • Ungar LH; Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Bethesda, MD, USA.
  • Mohr DC; Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Bethesda, MD, USA.
Npj Ment Health Res ; 3(1): 1, 2024 Jan 04.
Article em En | MEDLINE | ID: mdl-38609548
ABSTRACT
While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one's average was an early signal of PHQ-8 severity (distal ß = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (ß = 0.198, p = 0.022) and proximal (ß = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (ß = -0.131, p = 0.035) but did not predict (distal ß = 0.034, p = 0.577; medial ß = -0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article