ArticleJMIR mHealth and uHealth2017
Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study.
Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Engagement and attrition with eHealth tools for remote monitoring in chronic arthritis: a systematic review and meta-analysis.RMD open · 2022Pooled it
- A systematic review of methods used to conduct decentralised clinical trials.British journal of clinical pharmacology · 2022Pooled it
- Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2020Pooled it
- Smartphone based behavioral therapy for pain in multiple sclerosis (MS) patients: A feasibility acceptability randomized controlled study for the treatment of comorbid migraine and ms pain.Multiple sclerosis and related disorders · 2020Trial
- Smartphone-Delivered Progressive Muscle Relaxation for the Treatment of Migraine in Primary Care: A Randomized Controlled Trial.Headache · 2020Trial
- Heartrate variability biofeedback for migraine using a smartphone application and sensor: A randomized controlled trial.General hospital psychiatryTrial
- Patterns and predictors of variability in patient-generated daily pain severity collected via a mobile health smartphone app.PloS one · 2026Article
- Describing variability of intensively collected longitudinal ordinal data with latent spline models.Scientific reports · 2025Article
- Collecting patient-reported outcomes via the COTIDIANA smartphone app: a prospective validity study.RMD open · 2025Article
- Longitudinal and Combined Smartwatch and Ecological Momentary Assessment in Racially Diverse Older Adults: Feasibility, Adherence, and Acceptability Study.JMIR human factors · 2025Article
- Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies.Journal of medical Internet research · 2024Article
- Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis.JMIR mHealth and uHealth · 2024Article
- Feasibility and user acceptability of BreezingSmart health (Amsterdam, Netherlands) · 2023Article
- Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study.PLOS digital health · 2023Article
- Health disparities in systemic lupus erythematosus-a narrative review.Clinical rheumatology · 2022Review
- Enrollment and Retention of Participants in Remote Digital Health Studies: Scoping Review and Framework Proposal.Journal of medical Internet research · 2022Article
- Remote sampling of biomarkers of inflammation with linked patient generated health data in patients with rheumatic and musculoskeletal diseases: an Ecological Momentary Assessment feasibility study.BMC musculoskeletal disorders · 2022Article
- Smartphones for musculoskeletal research - hype or hope? Lessons from a decennium of mHealth studies.BMC musculoskeletal disorders · 2022Review
- Converting from face-to-face to postal follow-up and its effects on participant retention, response rates and errors: lessons from the EQUAL study in the UK.BMC medical research methodology · 2022Article
- Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs).Therapeutic advances in musculoskeletal disease · 2022Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundThe huge increase in smartphone use heralds an enormous opportunity for epidemiology research, but there is limited evidence regarding long-term engagement and attrition in mobile health (mHealth) studies.
objectiveThe objective of this study was to examine how representative the Cloudy with a Chance of Pain study population is of wider chronic-pain populations and to explore patterns of engagement among participants during the first 6 months of the study.
methodsParticipants in the United Kingdom who had chronic pain (≥3 months) and enrolled between January 20, 2016 and January 29, 2016 were eligible if they were aged ≥17 years and used the study app to report any of 10 pain-related symptoms during the study period. Participant characteristics were compared with data from the Health Survey for England (HSE) 2011. Distinct clusters of engagement over time were determined using first-order hidden Markov models, and participant characteristics were compared between the clusters.
resultsCompared with the data from the HSE, our sample comprised a higher proportion of women (80.51%, 5129/6370 vs 55.61%, 4782/8599) and fewer persons at the extremes of age (16-34 and 75+). Four clusters of engagement were identified: high (13.60%, 865/6370), moderate (21.76%, 1384/6370), low (39.35%, 2503/6370), and tourists (25.44%, 1618/6370), between which median days of data entry ranged from 1 (interquartile range; IQR: 1-1; tourist) to 149 (124-163; high). Those in the high-engagement cluster were typically older, whereas those in the tourist cluster were mostly male. Few other differences distinguished the clusters.
conclusionsCloudy with a Chance of Pain demonstrates a rapid and successful recruitment of a large, representative, and engaged sample of people with chronic pain and provides strong evidence to suggest that smartphones could provide a viable alternative to traditional data collection methods.
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