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.

Introduction People living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices. Aim The aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M. Methods ‘Watch Your Steps’ was a prospective observational study, administering multiple questions and active tasks over 90 days. Adults with more than one clinician-diagnosed long-term conditions were loaned Fossil® Sport smartwatches, pre-loaded with the study app. Around 20 questions were prompted per day. Daily completion rates were calculated to describe engagement patterns over time, and to explore how these varied by patient characteristics and question type. Results Fifty three people with MLTC-M took part in the study. Around half were male ( = 26; 49%) and the majority had a white ethnic background ( n = 45; 85%). About a third of participants engaged with the smartwatch app nearly every day. The overall completion rate of symptom questions was 45% inter-quartile range (IQR 23–67%) across all study participants. Older patients and those with greater MLTC-M were more engaged, although engagement was not significantly different between genders. Conclusion It was feasible for people living with MLTC-M to report multiple symptoms per day over 3 months. User engagement appeared as good as other mobile health studies that recruited people with single health conditions, despite the higher daily data entry burden.

Objective To explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with rheumatoid arthritis (RA). Methods We used data from the Remote Monitoring of Rheumatoid Arthritis (REMORA) study, where patients tracked their daily symptoms and weekly flares on an app. We summarised the number of self-reported flare weeks. For each week preceding a flare question, we calculated three summary features for daily symptoms: mean, variability and slope. Mixed effects logistic regression models quantified associations between flare weeks and symptom summary features. Pain was used as an example symptom for multivariate modelling. Results Twenty patients tracked their symptoms for a median of 81 days (interquartile range 80, 82). 15/20 participants reported at least one flare week, adding up to 54 flare weeks out of 198 participant weeks in total. Univariate mixed effects models showed that higher mean and steeper upward slopes in symptom scores in the week preceding the flare increased the likelihood of flare occurrence, but the association with variability was less strong. Multivariate modelling showed that for pain, mean scores and variability were associated with higher odds of flare, with odds ratios 1.83 (95% confidence interval, 1.15–2.97) and 3.12 (95% confidence interval, 1.07–9.13), respectively. Conclusion Our study suggests that patient-reported flares are common and are associated with higher daily RA symptom scores in the preceding week. Enabling patients to collect daily symptom data on their smartphones may ultimately facilitate prediction and more timely management of imminent flares.

Immunoglobulin (Ig) is used to treat chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) and multifocal motor neuropathy with conduction block (MMNCB). Regular infusions may be used for symptom control. Disease activity is monitored with clinical outcome measurements. We examined outcome measure variation during clinically stable periods in Ig‐treated CIDP and MMNCB patients. We explored utility of serial outcome measurement in long‐term outcome prediction. Retrospective longitudinal analysis of a single neuroscience centre's Ig‐treated CIDP and MMNCB patients, 2009‐2020, was performed. Mean and percentage change for grip strength, Rasch‐built overall disability scales (RODS) and MRC sum scores (MRC‐SS) during periods of clinical stability were compared to score‐specific minimal clinically important differences (MCID). Latent class mixed modelling (LCMM) was used to identify longitudinal trends and factors influencing long‐term outcome. We identified 85 CIDP and 23 MMNCB patients (1423 datapoints; 5635 treatment‐months). Group‐averaged outcome measures varied little over time. Intra‐individual variation exceeded MCID for RODS in 44.2% CIDP and 16.7% MMNCB datapoints, grip strength in 10.6% (CIDP) and 8.8%/27.2% (MMNCB right/left hand) and MRC‐SS in 43.5% (CIDP) and 20% (MMNCB). Multivariate LCMM identified subclinical trends towards improvement (32 patients) and deterioration (73 patients) in both cohorts. At baseline, CIDP ‘deteriorators’ were older than ‘improvers’ (66.2 vs 57 years, P = .025). No other individual factors predicted categorisation. The best model for ‘deteriorator’ identification was contiguous sub‐MCID decline in more than one outcome measure (CIDP: sensitivity 74%, specificity 59%; MMNCB: sensitivity 73%, specificity 88%). Outcome measure interpretation determines therapeutic decision‐making in Ig‐dependent neuropathy patients, but intra‐individual variation is common, often exceeding MCID. Here we show sub‐MCID contiguous changes in more than one outcome measurement are a better predictor of long‐term outcome.

Background As management of chronic pain continues to be suboptimal, there is a need for tools that support frequent, longitudinal pain self-reporting to improve our understanding of pain. This study aimed to assess the feasibility and acceptability of daily pain self-reporting using a smartphone-based pain manikin. Methods For this prospective feasibility study, we recruited adults with lived experience of painful musculoskeletal condition. They were asked to complete daily pain self-reports via an app for 30 days. We assessed feasibility by calculating pain report completion levels, and investigated differences in completion levels between subgroups. We assessed acceptability via an end-of-study questionnaire, which we analysed descriptively. Results Of the 104 participants, the majority were female ( n = 87; 84%), aged 45-64 ( n = 59; 57%), and of white ethnic background ( n = 89; 86%). The mean completion levels was 21 (± 7.7) pain self-reports. People who were not working (odds ratio (OR) = 1.84; 95% confidence interval (CI), 1.52-2.23) were more likely, and people living in less deprived areas (OR = 0.77; 95% CI, 0.62-0.97) and of non-white ethnicity (OR = 0.45; 95% CI, 0.36-0.57) were less likely to complete pain self-reports than their employed, more deprived and white counterparts, respectively. Of the 96 participants completing the end-of-study questionnaire, almost all participants agreed that it was easy to complete a pain drawing ( n = 89; 93%). Conclusion It is feasible and acceptable to self–report pain using a smartphone–based manikin over a month. For its wider adoption for pain self–reporting, the feasibility and acceptability should be further explored among people with diverse socio–economic and ethnic backgrounds.

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Purpose: Routinely collected prescription data provides drug exposure information for pharmacoepidemiology, informing start/stop dates and dosage. Prescribing information includes structured data and unstructured free-text instructions, which can include inherent variability, such as "one to two tablets up to four times a day".Preparing drug exposure data from raw prescriptions to a research ready dataset is rarely fully reported, yet assumptions have considerable implications for pharmacoepidemiology. This may have bigger consequences for "pro re nata" (PRN) drugs. Our aim was, using a worked example of opioids and fracture risk, to examine the impact of incorporating narrative prescribing instructions and subsequent drug preparation assumptions on adverse event rates.Methods: R-packages for extracting free-text medication prescription instructions in a structured form (doseminer) and an algorithm for transparently processing drug exposure information (drugprepr) were developed. Clinical Practice Research Datalink GOLD was used to define a cohort of adult new opioid users without prior cancer. A retrospective cohort study was performed using data between January 1, 2017 and July 31, 2018. We tested the impact of varying drug preparation assumptions by estimating the risk of opioids on fracture risk using Cox proportional hazards models.Results: During the study window, 60 394 patients were identified with 190 754 opioid prescriptions. Free-text prescribing instruction variability, where there was flexibility in the number of tablets to be administered, was present in 42% prescriptions.Variations in the decisions made during preparing raw data for analysis led to marked differences impacting the event number (n = 303-415) and person years of drug

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