Cloudy with a Chance of Pain: Engagement and Subsequent Attrition of Daily Data Entry in a Smartphone Pilot Study Tracking Weather, Disease Severity, and Physical Activity in Patients With Rheumatoid Arthritis

Abstract

BackgroundThe increasing ownership of smartphones provides major opportunities for epidemiological research through self-reported and passively collected data.ObjectiveThis pilot study aimed to codesign a smartphone app to assess associations between weather and joint pain in patients with rheumatoid arthritis (RA) and to study the success of daily self-reported data entry over a 60-day period and the enablers of and barriers to data collection.MethodsA patient and public involvement group (n=5) and 2 focus groups of patients with RA (n=9) supported the codesign of the app collecting self-reported symptoms. A separate “capture app” was designed to collect global positioning system (GPS) and continuous raw accelerometer data, with the GPS-linking providing local weather data. A total of 20 patients with RA were then recruited to collect daily data for 60 days, with entry and exit interviews. Of these, 17 were loaned an Android smartphone, whereas 3 used their own Android smartphones.ResultsOf the 20 patients, 6 (30%) withdrew from the study: 4 because of technical challenges and 2 for health reasons. The mean completion of daily entries was 68% over 2 months. Patients entered data at least five times per week 65% of the time. Reasons for successful engagement included a simple graphical user interface, automated reminders, visualization of data, and eagerness to contribute to this easily understood research question. The main barrier to continuing engagement was impaired battery life due to the accelerometer data capture app. For some, successful engagement required ongoing support in using the smartphones.ConclusionsThis successful pilot study has demonstrated that daily data collection using smartphones for health research is feasible and achievable with high levels of ongoing engagement over 2 months. This result opens important opportunities for large-scale longitudinal epidemiological research.

Factors that influence older people’s engagement with digital health technology

“…In a study that required older people with rheumatoid arthritis (RA) to use smartphones to monitor their symptoms, users were highly motivated to participate because they expressed a perception that they would personally benefit alongside providing researchers with clinically relevant information (Reade et al 2017). Middlemass et al (2017) identified that older people with chronic obstructive pulmonary disease perceived an advantage of engagement with DHT through the monitoring of their health trends which enabled detection of the early onset of infections.…”

Feel My Pain

“…Early examples of electronic pain diaries used web-based desktop interfaces [20] or personal digital assistants (PDAs) [24,41] to facilitate pain logging ( [1]) and there is now a plethora of commercial apps that allow people to monitor their pain [22,39]. Several research prototypes based on standardised scales have also been developed, with user feedback indicating positive acceptance of these applications [33]. However, most of these applications have received little or no formative evaluation in the field, with studies instead relying on preliminary user feedback and usability testing to justify their designs (e.g.…”

“…However, most of these applications have received little or no formative evaluation in the field, with studies instead relying on preliminary user feedback and usability testing to justify their designs (e.g. [20,21,33]).…”

“…The use of lowcost (yet robust) components limited the device's value and gave it the veneer of a 'prototype' rather than a high-value computing device. The prompting of pain scores is achieved through scheduled audio and visual reminders to help ensure compliance with pain logging [30,33]. The device's size (approximately 9.5 x 6.5 x 3cm) allows for it to be held in one hand, facilitating ease of input by older adults.…”

Pharmacoepidemiology in the Era of Real-World Evidence

“…Data-adaptive techniques (such as machine learning) combined with thoughtful human input are increasingly being used to mine electronic health record databases [34, 35] and improve analytic methods commonly used in pharmacoepidemiology [36, 37]. The ability to collect more data from mobile devices enables exploration of new issues, e.g., the relation between weather and joint pain in patients with rheumatoid arthritis [38]. Pushing the boundaries and leveraging progress made in other research areas will continue to help advance the field of pharmacoepidemiology.…”