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AB1138 Engagement in a uk smartphone study examining the assocation between weather and pain: preliminary results from cloudy with a chance of pain
  1. KL Druce1,
  2. J McBeth1,2,
  3. SN van der Veer1,
  4. DA Selby3,
  5. B Vidgen4,
  6. K Georgatzis5,
  7. AM Chowdry1,
  8. R Lakshminarayana6,
  9. DM Schultz7,
  10. C Sanders8,
  11. JC Sergeant1,2,
  12. WG Dixon1,2
  1. 1Arthritis Research UK Centre for Epidemiology, University of Manchester
  2. 2NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester
  3. 3Department of Statistics, University of Warwick, Coventry
  4. 4Oxford Internet Institute, University of Oxford, Oxford
  5. 5School of Informatics, University of Edinburgh, Edinburgh
  6. 6uMotif, London
  7. 7Centre for Atmospheric Science, School of Earth and Environmental Sciences
  8. 8Medical Sociology, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom

Abstract

Background Smartphones can facilitate collection of temporally-rich self-reported data, and have proven to enable large recruitment. However, their viability to support epidemiological research is uncertain due to concerns about selection bias and unsustained engagement.

Objectives To examine the characteristics and engagement of participants in the first six months of Cloudy with a Chance of Pain, a UK smartphone-based study investigating the link between the weather and chronic pain.

Methods Between 20th of January and 29th of February 2016, we recruited UK residents 17 years or older with chronic pain (≥3 months) who owned a smartphone. Participants received prompts from an app developed by uMotif, which they used to daily report the severity of ten pain-related symptoms. Of those who enrolled, those eligible for analysis provided sufficient baseline data to confirm they were ≥17 years old, and at least one symptom. The characteristics of those who were eligible were examined. Engagement per day was defined based on whether participants had completed any of the ten symptoms. Participants were then clustered by their engagement over time using a first-order hidden Markov models. Participant characteristics were then compared between the clusters.

Results Of 7972 people who registered to participate, 6370 were eligible. 81% of participants were female, with a mean age of 49 years (SD 12.9). The most common diagnosis was arthritis (40% type unspecified, 19% rheumatoid arthritis), followed by fibromyalgia/chronic widespread pain (24%) and “other pain diagnosis” (23%). We identified four clusters of engagement: high (14%), moderate (22%), low (39%) and tourists (25%). Median days of data entry ranged from 1 (1–1) to 175 (IQR: 152–177) for the tourist and high engagement clusters respectively. Those in the high and moderate clusters (n=2249, 35%) engaged on at least 50% of days in the study (high: 79%; moderate: 50%). Highly engaged participants were older (median 56 (47–63)) when compared to those who were low engagers (47 (39–57)) or tourists (49 (40–58)). A lower proportion of tourists were women (76% (95% CI: 74–78), than in any other cluster (high: 82% (80–85), moderate: 84% (82–86), low: 81% (79–82)).

Conclusions Cloudy with a Chance of Pain recruited a large sample of people with chronic pain, of whom over one in three participants engaged in smartphone-based symptom reporting for at least 50% of days in the first six months. Smartphone studies enable quick mass participation with sustained daily data entry, providing unprecedented volumes of daily data. While there may be selection bias towards older females in our study, younger men are also less likely to participate in studies using traditional data collection methods. Our study suggests that smartphones could provide a viable alternative to traditional data collection methods.

Disclosure of Interest None declared

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