Dynamic Association Between Passive Sensing Activity Levels and Behavioral Activation in Late-Life Depression (Preprint)
Year: 2026
Type: preprint
Abstract: BACKGROUND A key symptom of depression is reduced behavioral activation, namely, low activity levels and meaningful engagement with the external environment. Thus, objective and timely measures of activity levels are useful tools to precisely track individuals’ activity levels during treatment. Prior adult depression studies have shown that activity levels measured using passive sensing (e.g., step count, time spent away from home) predict depression relapse, persistence and poor response to psychosocial interventions. Yet there is scarce research on how passive sensing measures relate to behavioral activation, and especially in late-life depression. OBJECTIVE We examined the association between passive sensing activity levels and self-reported behavioral activation during psychosocial interventions in community-dwelling older adults with depression. METHODS Sample was comprised of depressed older adults from three clinical trials at the Weill Cornell ALACRITY Center (N=75; Mean Age: 71 (IQR: [65.0, 77.5]); 91% Female; 61% White). Participants were randomized to 9 weeks of either behavioral interventions or comparison conditions. Activity levels were measured by smartphone-recorded daily step count and time away from home. Self-reported behavioral activation was measured using the BADS. We applied a functional regression (scalar-on-function) to test the association between activity levels and pre-post intervention changes in behavioral activation. RESULTS In the behavioral intervention group, higher daily activity – particularly step count – was initially associated with lower behavioral activation but became positively associated later in treatment (β(43) = 0.54, 95% CI: [0.05, 1.03]). In contrast, in the comparison group, greater time spent away from home showed a consistent negative association with behavioral activation throughout the intervention (minimum: β(32) = -0.80, 95% CI: [-1.11, -0.48]; maximum: β(2) = -0.04, 95% CI: [-0.04, -0.04]). CONCLUSIONS Our results suggest that passive sensing in older adults can measure changes in activity levels during interventions for late-life depression. It is a promising alternative to self-reports that can guide future development and personalization of interventions for older adults with depression.
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W7125899010
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