Creating a digitally enhanced interactive and individualized experience to record of pain history and deliver pain education across different age groups: chatbot development, pilot testing and acceptability of the Dolores mobile app (Preprint)

BACKGROUND The delivery of education about pain neuroscience and the evidence for different treatment approaches has become a key component of contemporary persistent pain management. Chatbots, or more formally conversation agents, are increasingly being used in health care settings due to their versatility to provide interactive and individualized approaches to both capture and deliver information. Research focused on the acceptability of diverse chatbot formats can assist in developing a better understanding of the education needs of target populations and maximize the impact of these software applications within the health field. OBJECTIVE This study aimed to 1) detail the development and initial pilot testing of a multi-modality pain education chatbot (Dolores) that can be utilized across different age groups, and 2) investigate if acceptability and feedback was comparable across age groups following pilot testing. METHODS Following an initial design phase involving software engineers (n=2) and expert clinicians (n=6), 60 individuals with chronic pain who attended an outpatient clinic at one of two pain centers in Australia were recruited for pilot testing. Twenty adolescents (10-18 years), 20 young adults (19-35 years), and 20 adults (over 35 years of age) with persistent pain were recruited. Participations were given an iPad with the Dolores app installed and spent 20-30 minutes completing interactive chatbot activities to: i) gather a pain history and ii) provide education about pain and pain treatments. After the chatbot activities, participants completed a custom-made feedback questionnaire measuring acceptability constructs pertaining to health education chatbots. To determine the effect of age group on acceptability ratings and feedback provided, a series of binomial logistic regression models and cumulative odds ordinal logistic regression models with proportional odds were then generated. RESULTS Overall, acceptability was high for the following constructs: engagement, perceived value, usability, accuracy, responsiveness, adoption intention, aesthetics and overall quality. The effect of age group on all acceptability ratings was small and not statistically significant. Analysis of open-ended question responses revealed that major frustrations with the app were related to Dolores’ speech which was explored further through a comparative analysis. For providing negative feedback about Dolores’ speech, a logistic regression model showed that the effect of age group was statistically significant (χ2(2) = 11.68, P = .003) and explained 27.1% of the variance (Nagelkerke R2). Adults and young adults were less likely to comment on Dolores’ speech compared to adolescent participants (OR=0.20, 95% CI [0.05, 0.84]; OR=0.05, 95% CI [0.01, 0.43] respectively). Comments related to both speech rate (too slow) and quality (unpleasant and robotic). CONCLUSIONS This study provided support for the acceptability of pain history and education chatbots across different age groups. Chatbot acceptability for adolescent cohorts may be improved by enabling self-selection of speech characteristics such as rate and personable tone.