Evaluable data were collected from 48 participants with advanced cancer and their family caregivers.
Caregiver smartphone data explained small-to-medium variance in 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 large variance for mental health (0.36) and burden (0.50).
For patient outcomes, caregiver smartphone data accounted for small variance in anxiety symptoms (0.07), while patient data predicted large variance in anxiety symptoms (0.24).
Combined data explained medium variance in patient depressive symptoms (0.18).
Simplified
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 , 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 (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-Ras the measure of effect size (small = 0.02, medium = 0.13, large = 0.26). 2
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.
Key numbers
0.15
Variance explained in caregiver depression
Adjusted R for caregiver depressive symptoms predicted by caregiver smartphone data.
0.50
Variance explained in caregiver burden
Adjusted R for caregiver burden predicted by combined caregiver and patient smartphone data.
0.36
Variance explained in PROMIS-mental health
Adjusted R for mental health outcomes predicted by combined smartphone data.
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Declarations. Ethics approval and consent to participate: This study was approved by the University of Alabama at Birmingham Institutional Review Board (IRB-300006575). All caregiver and patient participants completed informed consent. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
