Factors Influencing the Use of an Artificial Intelligence-Based App (SELFBACK) for Tailored Self-Management Support Among Adults with Neck and/or Low Back Pain
Objective: Tailored self-management support is recommended as first-line treatment for neck and low back pain. Using mHealth applications has been put forward as a promising way to provide such support. However, there is limited knowledge about factors influencing the engagement with app-delivered self-management interventions. The aim of this study was to assess barriers and facilitators for engaging with a self-management mHealth app among adults suffering from neck and/or low back pain. Methods: We carried out an observational cohort study among adults with neck and/or low back pain. The artificial intelligence-based SELFBACK app supports tailored self-management of neck and low back pain and was used by the participants for six weeks. After six weeks of use, participants were interviewed by phone. A deductive approach was used for analysing the barriers and facilitators. Results: Thirty-two adults (17 males) with neck and/or low back pain participated in this study (mean age=54.9 (SD=15.8)). Our results show that the mode of delivery (i.e. the way the intervention is delivered to the users (via smartphone)) and the novelty of the SELFBACK app were perceived most often as a barrier to use the app, and the action plans (i.e. weekly self-management recommendations) of the app and health-related factors (e.g. pain relief) were perceived most often as facilitating factors to use the app. Conclusions: This study provides strategies on how to improve an eHealth service, by tackling the barriers and incorporating the facilitators. Furthermore, our study shows that these adults are willing and ready to receive blended treatment.