Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression

Emily A. Kringle, Danielle Tucker, Yichao Wu, Nan Lv, Thomas Kannampallil, Amruta Barve, Sushanth Dosala, Nancy Wittels, Ruixuan Dai, Jun Ma

Research output: Contribution to journalArticlepeer-review


Purpose: To examine the relationship between features of daily measured step count trajectories and clinical outcomes among people with comorbid obesity and depression in the ENGAGE-2 Trial. Methods: This post hoc analysis used data from the ENGAGE-2 trial where adults (n = 106) with comorbid obesity (BMI ≥30.0 or 27.0 if Asian) and depressive symptoms (Patient Health Questionnaire-9 score ≥10) were randomized (2:1) to receive the experimental intervention or usual care. Daily step count trajectories over the first 60 days (Fitbit Alta HR) were characterized using functional principal component analyses. 7-day and 30-day trajectories were also explored. Functional principal component scores that described features of step count trajectories were entered into linear mixed models to predict weight (kg), depression (Symptom Checklist-20), and anxiety (Generalized Anxiety Disorder Questionnaire-7) at 2-months (2M) and 6-months (6M). Results: Features of 60-day step count trajectories were interpreted as overall sustained high, continuous decline, and disrupted decline. Overall sustained high step count was associated with low anxiety (2M, β = −0.78, p <.05; 6M, β = −0.80, p <.05) and low depressive symptoms (6M, β = −0.15, p <.05). Continuous decline in step count was associated with high weight (2M, β = 0.58, p <.05). Disrupted decline was not associated with clinical outcomes at 2M or 6M. Features of 30-day step count trajectories were also associated with weight (2M, 6M), depression (6M), and anxiety (2M, 6M); Features of 7-day step count trajectories were not associated with weight, depression, or anxiety at 2M or 6M. Conclusions: Features of step count trajectories identified using functional principal component analysis were associated with depression, anxiety, and weight outcomes among adults with comorbid obesity and depression. Functional principal component analysis may be a useful analytic method that leverages daily measured physical activity levels to allow for precise tailoring of future behavioral interventions.

Original languageEnglish (US)
Article number100512
JournalMental Health and Physical Activity
StatePublished - Mar 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd


  • Activity
  • Activity tracker
  • Anxiety
  • Body weight
  • Physical activity

PubMed: MeSH publication types

  • Journal Article


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