Introduction
Patient-reported outcome measures (PROMs) serve multiple purposes, including shared decision-making and patient communication, treatment monitoring and health-technology assessment. Patient monitoring using PROMs is constrained by recall and non-response bias, respondent burden and missing data. We evaluated the potential of behavioural digital biomarkers obtained from a wearable accelerometer to achieve personalised predictions of PROMs

Methods
Data from the multicenter, prospective SafeHeart study conducted at Amsterdam University Medical Center in the Netherlands and Copenhagen University Hospital, Rigshospitalet in Copenhagen, Denmark, was used. The study enrolled patients with an implantable cardioverter defibrillator (ICD) between May 2021 and September 2022 who then wore wearable devices with raw acceleration output to capture digital biomarkers reflecting physical behaviour. To collect PROMs, patients received the KCCQ and EQ5D-5 L questionnaire at two instances; baseline and after 6 months. Multivariable Tobit regression models were used to explore associations between digital biomarkers and PROMs, specifically whether digital biomarkers could enable PROM prediction.

Results
The study population consisted of 303 patients (mean age 62.9 ± 10.9 years, 81.2% male). Digital biomarkers showed significant correlations to patient-reported physical and social limitations, severity and frequency of symptoms and quality of life. Prospective validation of the Tobit models indicated moderate correlations between the observed and predicted scores for KCCQ (concordance correlation coefficient (CCC) = 0.49, mean difference: 1.07 points) and EQ5D-5 L (CCC = 0.38, mean difference 0.02 points).

Conclusion
Wearable digital biomarkers correlate with PROMs, and may be leveraged for real-time prediction. These findings hold promise for monitoring of PROMs through wearable accelerometers.