Logo image
Back
Associations between smartphone GPS data and changes in psychological health and burden outcomes among family caregivers and patients with advanced cancer: an exploratory longitudinal cohort study
Journal article   Open access   Peer reviewed

Associations between smartphone GPS data and changes in psychological health and burden outcomes among family caregivers and patients with advanced cancer: an exploratory longitudinal cohort study

J. Nicholas Odom, Kyungmi Lee, Erin R. Harrell, Kristen Allen Watts, Avery C. Bechthold, Sally Engler, Frank Puga, Natashia Bibriescas, Arif H. Kamal, Christine S. Ritchie, …
BMC cancer, Vol.25(1), 614
04/04/2025
DOI: 10.1186/s12885-025-14009-y
PMID: 40186196
url
https://doi.org/10.1186/s12885-025-14009-yView
Published (Version of record) Open Access

Abstract

Background Managing advanced cancer can be psychologically distressing and burdensome for family caregivers and their care recipients. Innovations in the collection and modelling of passive data from personally-owned smartphones (e.g., GPS), called digital phenotyping, may afford the possibility of remotely monitoring and detecting distress and burden. We explored the potential of using passively-collected GPS data from smartphones to assess and predict caregiver and patient distress and burden. Methods This exploratory longitudinal cohort study enrolled smartphone-owning family caregiver and patient participants with advanced cancer (August 2021-July 2023) recruited via an oncology clinic or self-referral through Facebook. Participants downloaded a digital phenotyping research app, called Beiwe, that passively collected GPS data for 24 weeks. Participants completed self-report measures (PROs) of anxiety and depressive symptoms (Hospital Anxiety and Depression Scale [HADS]), mental health (PROMIS Mental Health), and caregiver burden (Montgomery-Borgatta Caregiver Burden scale) at baseline and every 6 weeks for 24 weeks. After pre-processing raw GPS data into daily GPS features (e.g., time spent at home, distance traveled/day), computing biweekly moving averages and standard deviations, and conducting a principal components analysis (PCA) of the resulting variables, within-person regression models were used to assess associations between changes in PRO measures and changes in PCA scores, with adjusted-R2 as the measure of effect size (small = 0.02, medium = 0.13, large = 0.26). Results Evaluable data were collected from 48 participants (family caregivers = 32; patients = 16). Caregiver smartphone data explained small-to-medium variance in caregiver anxiety (0.06), depression (0.15), and mental health (0.07). Patient smartphone data predicted small to medium variance in caregiver depressive symptoms (0.12) and burden (0.05). Combined caregiver and patient smartphone data explained small variance in caregiver depressive (0.02) and anxiety symptoms (0.10) and large variance for PROMIS-mental health (0.36) and burden (0.50). For patient outcomes, caregiver smartphone data accounted for small variance in anxiety symptoms (0.07); patient smartphone data predicted large variance in anxiety symptoms (0.24). Combined data explained medium variance in patient depressive symptoms (0.18). Conclusions The exploratory study demonstrates the potential predictive utility of using passive smartphone data to detect changes in caregiver and patient psychological distress and burden. A larger study is needed to validate these findings and further explore the clinical application of digital phenotyping in cancer.
 Advanced cancer Digital phenotyping Family caregivers Remote monitoring

Details

Metrics

1 Record Views
2 Times Cited - Web of Science
Logo image