JMIR Publications

ABSTRACT

Background:

The increasing availability of mobile health devices presents exciting opportunities for remote collection of high frequency electronic patient-generated health data (ePGHD). This novel type of data has the potential to provide detailed insights into disease activity between clinical reviews and treatment response to clinical interventions, but its potential remains untapped due to the absence of established methodological approaches.

Objective:

This study aims to explore the feasibility of evaluating treatment response to intramuscular steroid therapy in rheumatoid arthritis using ePGHD collected using a smartphone app.

Methods:

We report a case-series of patients who collected ePGHD through use of a smartphone app for daily remote symptom tracking. We described their longitudinal pain-scores before and after intramuscular steroid injections. A baseline pain-score was calculated as a mean pain-score in the 10 days prior to the injection. This was compared to the pain-scores in the days following the injection. Response was defined as any improvement in the baseline score on the first day following the injection.

Results:

We included six patients who, between them, received nine steroid injections. Average pre-injection pain-scores ranged 3.3-9.3. Using our definitions, seven injections demonstrated response. Of the responders, duration of response ranged from 1-54 days (median 9); average pain-score improvement ranged from 0.1-5.3 (median 3.3); maximum pain-score improvement ranged from 0.1-7.0 (median 4.3).

Conclusions:

This case-series demonstrates the feasibility of using ePGHD to evaluate treatment response and is an important exploratory step towards developing more robust methodological approaches for analysis of this novel data type. Future analysis of ePGHD across a larger population is required to address issues highlighted by our analysis and to develop meaningful consensus definitions for treatment response in time-series data.