ArticlePloS one2025
Association between sleep and physical activity data, and depressive symptoms in Thai elderly.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGeriatric depression often goes unnoticed due to recall bias and overlapping symptoms with normal aging by using questionnaire screening tool. Consequently, this study aims to explore the associations between passive sensing parameters, such as physical activity and sleep characteristics collected from smart devices, and depression screening scores, aiming to validate its efficacy as a detection tool for the Thai elderly.
methodsThe prospective cohort study was conducted from July to September 2023. One hundred and seventy-seven elderly individuals were purposefully selected from the main districts of each province across five regions in Thailand. Inclusion criteria required participants to be aged 60 years or older, socially active, and free from diagnoses of cognitive impairment and mental health disorders. Participants were required to wear an Actigraph wGT3X-BT collecting data on physical activity and sleep characteristics. The Patient Health Questionnaire (PHQ-9) was used to screen for depression every two weeks. Univariate and multivariate regression analyses were performed to identify association between passive sensing parameters and PHQ-9.
resultsThere is an association between physical activity parameters and depression score. A One- unit increase in Vector Magnitude in Counts per Minute (VM CPM) and step count, PHQ-9 score would be statistically significant reduced by 0.001 and 0.00007 to 0.00008 score over both two-week periods (p-value <0.1). Only Wakefulness After Sleep Onset (WASO) of all sleep variables showed an association with PHQ-9 in univariate analysis but it did not show further relationship after adjusting with baseline PHQ-9 score and other confounders. In addition, participants who lived outside Bangkok and being younger were more likely to have lower PHQ-9 score.
conclusionThere is a potential to apply passive sensing data in mental health issues in Thailand. However, more evidence is needed to ensure the accuracy of sensor detection and appropriate algorithm to predict depression. Moreover, the implication of passive sensing data from smart devices supporting public health policy should be explored.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.