Abstract Details

Bladder dysfunction (BD) affects many PwMS and other neurological conditions. Sequelae include inherent bother, life-threatening infections, and decreased quality of life. Remote monitoring of BD through wearables could reduce substantial existing accessibility barriers to monitoring BD progression and instituting timely treatment.

Report findings from a pilot study of bladder wearables in people with multiple sclerosis (PwMS)

Phase I, a “Wearables for the Bladder” kit was selected by stakeholders using a human-centered design. The highest scoring tool for initial evaluation was DFree (a small non-invasive ultrasound patch measuring bladder urine volume). Phase II (feasibility study), women with MS (progressive or relapsing) and BD were enrolled (target N=20). Activities included a clinical post-void residual (PVR), wearing DFree for at least 3-5 days/month over 12-weeks, and a 3-day bladder diary. From raw minute-level device data, two measures were generated: home PVR (dPVR) and urinary frequency (dFrequency). Participant qualitative feedback on daily use was captured.

Among 21 participants enrolled (13 completers, 6 on-study, 2 dropouts), mean age was 50 (SD8.0), median EDSS was 4.0. Device utilization was on-target: mean use was 14.1 days.  Bland-Altman agreement was good between PVR and dPVR, with no systematic bias. Similar agreement was noted using all available dPVR data vs. just first 7 days; but d-PVR further visualized day-to-day variability not appreciated using single clinic snapshots. The correlation between dFrequency (mean 7.4, SD 2.9) and diary-reported frequency was 0.81. Participant feedback showed excellent usability, usefulness and ease of use and highlighted utility of DFree data for BD self-management: planning activities outside the home, reducing leakage frequency, and motivating to seek further treatment.

This pilot study provides initial insights into feasibility, validity and clinical utility of wearables to assess, monitor and treat BD. Knowledge gained includes optimization of device usage, data cleaning into novel outcomes, and targets for behavioral change.