In contrast to the use of traditional unidimensional paper-based scales, a mobile health (mHealth) assessment of pain in children and young people (CYP) with juvenile idiopathic arthritis (JIA) enables comprehensive and complex multidimensional pain data to be captured remotely by individuals. However, how professionals use multidimensional pain data to interpret and synthesize pain reports gathered using mHealth tools is not yet known.
The aim of this study was to explore the salience and prioritization of different mHealth pain features as interpreted by key stakeholders involved in research and management of pain in CYP with JIA.
Pain and rheumatology specialists were purposively recruited via professional organizations. Face-to-face focus groups were conducted for each specialist group. Participants were asked to rank order 9 static vignette scenarios created from real patient mHealth multidimensional pain data. These data had been collected by a researcher in a separate study using My Pain Tracker, a valid and acceptable mHealth iPad pain communication tool that collects information about intensity, severity, location, emotion, and pictorial pain qualities. In the focus groups, specialists discussed their decision-making processes behind each rank order in the focus groups. The total group rank ordering of vignette scenarios was calculated. Qualitative data from discussions were analyzed using latent thematic analysis.
A total of 9 pain specialists took part in 1 focus group and 10 rheumatology specialists in another. In pain specialists, the consensus for the highest pain experience (44%) was poorer than their ranking of the lowest pain experiences (55%). Conversely, in rheumatology specialists, the consensus for the highest pain experience (70%) was stronger than their ranking of the lowest pain experience (50%). Pain intensity was a high priority for pain specialists, but rheumatology specialists gave high priority to intensity and severity taken together. Pain spread was highly prioritized, with the number of pain locations (particular areas or joints) being a high priority for both groups; radiating pain was a high priority for pain specialists only. Pain emotion was challenging for both groups and was only perceived to be a high priority when specialists had additional confirmatory evidence (such as information about pain interference or clinical observations) to validate the pain emotion report. Pain qualities such as particular word descriptors, use of the color red, and fire symbols were seen to be high priority by both groups in interpretation of CYP pain reports.
Pain interpretation is complex. Findings from this study of specialists' decision-making processes indicate which aspects of pain are prioritized and weighted more heavily than others by those interpreting mHealth data. Findings are useful for developing electronic graphical summaries which assist specialists in interpreting patient-reported mHealth pain data more efficiently in clinical and research settings.