{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:00:14Z","timestamp":1790791214966,"version":"4.1.0"},"reference-count":43,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n A key symptom of depression is reduced behavioral activation, namely, low activity levels and reduced meaningful engagement with the external environment. Thus, objective and timely measures of activity levels are useful tools to precisely track individuals\u2019 activity levels during treatment. Prior adult depression studies have shown that activity levels measured using passive sensing (eg, step counts and time spent away from home) predict depression relapse, persistence, and poor response to psychosocial interventions. However, there is scarce research on how passive sensing measures relate to behavioral activation, especially in late-life depression.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to examine the association between passive sensing activity levels and self-reported behavioral activation during psychosocial interventions in community-dwelling older adults with depression.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n The sample comprised depressed older adults from 3 clinical trials at the Weill Cornell ALACRITY Center (N=75; mean age 70.7, SD 8.34 y; n=68, 90.7% female; n=46, 61.3% 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 (Behavioral Activation for Depression Scale). We applied a functional regression model (scalar-on-function) to test the association between activity levels and pre-post intervention changes in behavioral activation.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In the psychotherapy group, higher step count showed a positive pointwise association with greater improvement in behavioral activation during the final phase of treatment (days 46\u201063). At the start of this interval, the estimated coefficient was \u03b2(46)=1.51 (95% CI 0.07-2.94), increasing to \u03b2(63)=6.61 (95% CI 0.80-12.41). In the active control group, greater time away from home showed a negative pointwise association with behavioral activation from day 2 to day 49. The estimate was \u03b2(2)=\u22120.04 (95% CI \u22120.07 to \u22120.001) and \u03b2(49)=\u22120.50 (95% CI \u22120.98 to \u22120.03) at day 49, with the largest negative estimate at day 32 (\u03b2[32]=\u22120.75, 95% CI \u22121.37 to \u22120.13). These pointwise intervals are descriptive and not multiplicity-adjusted.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n These preliminary, hypothesis-generating findings suggest that passive sensing in older adults can measure changes in activity levels during interventions for late-life depression. Because clinical change was modest and between-group functional differences were not formally tested, passive sensing should be interpreted as a complementary, low-burden measure rather than a replacement for validated clinical assessments.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/92348","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T16:50:09Z","timestamp":1786985409000},"page":"e92348-e92348","source":"Crossref","is-referenced-by-count":0,"title":["Smartphone-Based Passive Sensing of Activity Levels and Behavioral Activation During Psychosocial Interventions for Older Adults With Depression: Longitudinal Observational 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