John McBeth

BACKGROUND Primary care electronic health records (EHRs) are increasingly used in observational and clinical research. However, EHR data are frequently affected by information gaps arising from missing values, incomplete coding, under-ascertainment of clinical conditions, information recorded only within unstructured text, and under-representation of population groups. These information gaps can compromise the validity, interpretability, and generalisability of research findings. OBJECTIVE To identify and describe approaches used in primary care EHR research to assess, characterise, and address missing and incomplete information. METHODS A rapid scoping review was conducted following the Arksey and O'Malley framework and PRISMA-ScR guidance. Systematic searches of MEDLINE, Embase, CINAHL, the Cochrane Database of Systematic Reviews, Web of Science, Scopus, and IEEE Xplore were undertaken from January 2000 to April 2026. Quantitative studies using primary care EHR data that applied statistical, computational, or algorithmic approaches to address missing or incomplete information were eligible. Data on study characteristics, information gaps addressed, analytical approaches, and reported impacts on study findings were extracted. Findings were synthesised narratively. RESULTS Seventy-five studies met the inclusion criteria. Studies addressed three broad types of information gaps: missing values in structured variables (n=40), incomplete coding or under-ascertainment (n=36), and under-representation of population groups (n=2). Missing values were primarily addressed using missingness-modelling approaches, whereas incomplete coding and under-ascertainment were predominantly addressed using information-recovery approaches such as natural language processing and classification algorithms. Only eight studies reported patterns of missingness. Methodological approaches also varied according to study purpose, with causal and epidemiological studies more commonly using missingness-modelling approaches and prediction-focused studies more frequently applying information-recovery approaches. CONCLUSIONS Different information gaps in primary care EHRs were addressed using different categories of methodological approaches. Method selection should therefore be informed by both the nature of the information gap and the purpose of the study. However, reporting of missingness patterns, assumptions regarding missingness mechanisms, and rationale for method selection remains limited. Greater methodological transparency may improve the robustness and interpretability of future primary care EHR research. CLINICALTRIAL N/A

Pain states fluctuate over time and across situations. Similarly, there is variation in risk and protective factors and how they impact on these pain-related transitions. We are interested in whether such variations are more than random, and whether they can be accounted for by observed variables. The availability of large longitudinal datasets, such as UK Biobank (https://www.ukbiobank.ac.uk/), offers a unique opportunity to study these variations at scale. However, such datasets bring a high risk of bias (eg, confounding) and danger of over-interpretation. It is therefore important to be transparent about our causal thinking. Directed acyclic graphs (DAGs) are graphical representations of the hypothesized causal relationships between variables. They are used to identify the smallest set of variables that need to be adjusted to remove confounding bias in estimating the causal effect of an exposure on an outcome. However, use of DAGs in pain research is not common, despite their potential to guide study design and data analysis. In this article, we present a workflow for building a DAG using domain knowledge from 3 different sources: researchers (theory-based), people with lived experience (person-based), and the literature (evidence-based). We created a DAG for the putative effect of executive function on the maintenance of chronic high-impact pain. The resulting DAG provides a valuable framework for guiding future research on the role of executive functioning in pain, and it underscores the broader potential of using DAGs to improve causal inference in pain research.

Prevalence estimates using primary care data health identify cases via code lists. Validation studies can discover and exclude false positives, but it is often difficult or impossible to find false negatives. This study aimed, using the example of psoriatic arthritis (PsA), to examine the extent of and adjust for misclassification by linking primary care records with text-mined outpatient letters from a Northwest regional hospital (2014-2019). Two hundred forty-five cases of PsA were identified among 188 286 adults registered with primary care, giving an observed prevalence of 0.13% [95% CI, 0.11%-0.15%]. Among a subgroup of 7532 primary care patients attending the hospital rheumatology clinic, 202 had a primary care PsA code: 188 were confirmed as true PsA, while 14 were false positives. Primary care codes failed to identify 196 hospital-diagnosed PsA cases, leading to a more than 2-fold underestimation. The adjusted prevalence, accounting for misclassification, was 0.25% [95% CI, 0.21%-0.28%]. Linking primary care with hospital records identified false positives and negatives, enabling correction of prevalence estimates. This highlights the value of text-mining hospital letters to replace the national absence of coded secondary care diagnosis data from outpatient departments, and the importance of considering the impact of false negatives.

Background: Mobile health (mHealth) technologies, such as smartphones and wearables, enable continuous assessments of individual health information. In chronic musculoskeletal conditions, pain flares, defined as periods of increased pain severity, often coincide with worsening disease activity and cause significant impacts on physical and emotional well-being. Using mHealth technologies can provide insights into individual pain patterns and associated factors. Objective: This study aims to characterize pain flares and identify associated factors in rheumatoid arthritis (RA) by (1) describing the frequency and duration of pain flares using progressively stringent definitions based on pain severity, and (2) exploring associations between pain flares and temporal changes in symptoms across emotional, cognitive, and behavioral domains. Methods: Our 30-day mHealth study collected daily pain severity and related symptoms (scores 1-5, higher are worse) via a smartphone app and passively recorded sleep and physical activity via a wrist-worn accelerometer. Pain flares were defined using a 5-point scale: (1) above average (AA): pain severity > personal median, (2) above threshold (AT): pain severity > 3, and (3) move above threshold (MAT): pain severity moves from 1, 2, 3 to 4 or 5. A case-crossover analysis compared within-person variations of daily symptoms across hazard (3 days before a pain flare) and control (3 days not preceding a pain flare) periods using mean and intraindividual standard deviation. Conditional logistic regression estimated the odds ratio (OR) for pain flare occurrence. Results: A total of 195 participants (160/195, 82.1% females; mean age 57.2 years; average years with RA: 11.3) contributed 5290 days of data. Of these, 88.7% (173/195) experienced at least 1 AA flare (median monthly rate 4, IQR 2.1-5). Nearly half experienced at least 1 AT or MAT flare (median monthly rate 2, IQR 1-4). These pain flares lasted 2 days (IQR 2-3) on average across definitions, with some extending up to 12 days. Worsening mood over 3 days was associated with a 2-fold increase in the likelihood of AT flares the following day (OR 2.04, IQR 1.06-3.94; P<.05). Greater variability in anxiety over 3 days increased the likelihood of both AT (OR 1.67, IQR 1.01-2.78; P<.05) and MAT flares (OR 1.82, IQR 1.08-3.07; P<.05). Similarly, greater variability in sleepiness (OR 1.89, IQR 1.03-3.47; P<.05) also increased the likelihood of AT flares. Sedentary time (%) consistently showed almost no influence across all definitions. Similarly, the simplest definition of AA demonstrated no significant associations across all symptoms. Conclusions: Pain flares were commonly observed in RA. Changes in sleep patterns and emotional distress were associated with pain flare occurrences. This analysis demonstrates the potential of daily mHealth data to identify pain flares, opening opportunities for timely monitoring and personalized management.

Purpose: To conduct a preliminary clustering analysis using the UK Biobank to (1) identify distinct chronic pain clusters based on age, sex, and number of pain sites; (2) assess the associations between chronic pain clusters and health-related outcomes; and (3) outline future directions for developing chronic pain personas to inform targeted digital health interventions. Methods: Participants were selected from a 2019 chronic pain survey. The domains included demographics, pain, daily functioning, and emotional health. The clustering analysis employed the k-prototype algorithm. Cluster characteristics were summarised and quantified using multinomial logistic regression. Preliminary data personas were described. Results: 89,853 people with chronic pain were analysed (60.4% female, mean age 66.5 years). Five clusters were identified: Fibromyalgia-like pain (FP, 11.2%), multisite pain (MP, 17.9%), younger with regional pain (21.9%), middle age with regional pain (MRP, 25.5%), and elderly with regional pain (ERP, 23.5%). FP was associated with more severe health-related outcomes, characterised by greater depression, fatigue, and difficulties with daily activities and social relationships. Sleep, mobility, and usual activities were commonly affected at mild and moderate levels across all clusters. Fatigue and depression varied, with FP and MP experiencing greater impacts. ERP and MRP were associated with a lower likelihood of adverse health-related outcomes. Conclusion: All chronic pain clusters identified from the UK Biobank showed common challenges in sleep, mobility and daily functioning; the impacts of fatigue and depression varied between clusters. The next step involves engaging key stakeholders to create, refine, and validate these personas to inform the development of targeted digital health interventions.

Purpose: Examine the development, implementation and evaluation of just‐in‐time adaptive interventions (JITAIs) in behaviour change and evaluate the quality of intervention reporting. Methods: A scoping review of JITAIs incorporating mobile health (mHealth) technologies to improve health‐related behaviours in adults. We searched MEDLINE, Embase and PsycINFO using terms related to JITAIs, mHealth, behaviour change and intervention methodology. Narrative analysis assessed theoretical foundations, real‐time data capturing and processing methods, outcome evaluation and summarized JITAI efficacy. Quality of intervention reporting was assessed using the template for intervention description and replication (TIDieR) checklist. Results: Sixty‐two JITAIs across physical activity, sedentary behaviour, dietary behaviour, substance use, sexual behaviour, fluid intake, treatment adherence, social skills, gambling behaviour and self‐management skills were included. The majority (71%) aimed to evaluate feasibility, acceptability and/or usability. Supporting evidence for JITAI development was identified in 46 studies, with 67% applying this to develop tailored intervention content. Over half (55%) relied solely on self‐reported data for tailoring, and 13 studies used only passive monitoring data. While data processing methods were commonly reported, 44% did not specify their techniques. 89% of JITAI designs achieved full marks on the TIDieR checklist and provided sufficient details on JITAI components. Overall, JITAIs proved to be feasible, acceptable and user‐friendly across behaviours and settings. Randomized trials showed tailored interventions were efficacious, though outcomes varied by behaviour. Conclusions: JITAIs offer a promising approach to developing personalized interventions, with their potential effects continuously growing. The recommended checklist emphasizes the importance of reporting transparency in establishing robust intervention designs.

Pain states fluctuate over time, and across situations. Similarly, there is variation in risk and protective factors and how they impact on these pain-related transitions. We are interested in whether such variations are more than random, and whether they can be accounted for by observed variables. The availability of large longitudinal datasets, such as UK Biobank (https://www.ukbiobank.ac.uk/), offers a unique opportunity to study these variations at scale. However, such datasets bring a high risk of bias (e.g. confounding) and danger of over-interpretation. It is therefore important to be transparent about our causal thinking. Directed Acyclic Graphs (DAGs) are graphical representations of the hypothesized causal relationships between variables. They are used to identify the smallest set of variables that need to be adjusted for to remove confounding bias in estimating the causal effect of an exposure on an outcome. However, use of DAGs in pain research is not common, despite their potential to guide study design and data-analysis. In this paper we present a workflow for building a DAG using domain knowledge from three different sources: researchers, people with lived experience, and the literature. We created a DAG for the putative effect of executive function on the maintenance of chronic high impact pain. The resulting DAG provides a valuable framework for guiding future research on the role of executive functioning in pain and it underscores the broader potential of using DAGs to improve causal inference in pain research.

Objectives: Pain, fatigue and sleep disturbances are common symptoms in patients with rheumatic and musculoskeletal diseases (RMDs) that may prompt the use of over-the-counter (OTC) supplements, sleep aids and analgesics as self-management strategies. This study evaluated the prevalence of OTC supplements, sleep aids and pain relievers and the financial burden associated with their use in rheumatology. Methods: A web-based survey developed with patients was administered in rheumatology clinics in an English hospital. Participants shared demographic information and detailed their use of OTC supplements, sleep aids and pain relief in the past week. The data were analysed using descriptive statistics and logistic regression models to identify influencing factors. Results: A total of 876 people consented to participate in the survey. More than half of patients (54.5%) reported daily supplement intake, typically spending £10/month (interquartile range 5–20), ranging up to £200/month. The most commonly administered supplements were vitamin D, multivitamins, vitamin C, vitamin B/B complex and omega-3/-6 supplements, with multiple overlaps. Prescription, OTC or non-prescription pain relief use was reported by 82% of respondents, with sleep aids being used by 13%. Of the 327 patients who took NSAIDs, 165 (50.4%) also reported taking OTC supplements, while among the 131 patients using opioids (20.5%), 66 (50.3%) reported supplement use, some of which have documented interactions. Conclusion: The use of OTC supplements, pain relief and sleep aids is common in patients with RMDs. Healthcare professionals should be encouraged to proactively ask about these during consultations, especially from a drug safety perspective, but also to provide timely, reliable advice about such strategies that may be sought by patients.

Background: People with chronic pain experience variability in their trajectories of pain severity. Previous studies have explored pain trajectories by clustering sparse data; however, to understand daily pain variability, there is a need to identify clusters of weekly trajectories using daily pain data. Between-week variability can be explored by quantifying the week-to-week movement between these clusters. We propose that future work can use clusters of pain severity in a forecasting model for short-term (eg, daily fluctuations) and longer-term (eg, weekly patterns) variability. Specifically, future work can use clusters of weekly trajectories to predict between-cluster movement and within-cluster variability in pain severity. Objective: This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model. Methods: Data from a population-based mobile health study were used to compile weekly pain trajectories (n=21,919) that were then clustered using a k-medoids algorithm. Sensitivity analyses tested the impact of assumptions related to the ordinal and longitudinal structure of the data. The characteristics of people within clusters were examined, and a transition analysis was conducted to understand the movement of people between consecutive weekly clusters. Results: Four clusters were identified representing trajectories of no or low pain (1714/21,919, 7.82%), mild pain (8246/21,919, 37.62%), moderate pain (8376/21,919, 38.21%), and severe pain (3583/21,919, 16.35%). Sensitivity analyses confirmed the 4-cluster solution, and the resulting clusters were similar to those in the main analysis, with at least 85% of the trajectories belonging to the same cluster as in the main analysis. Male participants spent longer (participant mean 7.9, 95% bootstrap CI 6%-9.9%) in the no or low pain cluster than female participants (participant mean 6.5, 95% bootstrap CI 5.7%-7.3%). Younger people (aged 17-24 y) spent longer (participant mean 28.3, 95% bootstrap CI 19.3%-38.5%) in the severe pain cluster than older people (aged 65-86 y; participant mean 9.8, 95% bootstrap CI 7.7%-12.3%). People with fibromyalgia (participant mean 31.5, 95% bootstrap CI 28.5%-34.4%) and neuropathic pain (participant mean 31.1, 95% bootstrap CI 27.3%-34.9%) spent longer in the severe pain cluster than those with other conditions, and people with rheumatoid arthritis spent longer (participant mean 7.8, 95% bootstrap CI 6.1%-9.6%) in the no or low pain cluster than those with other conditions. There were 12,267 pairs of consecutive weeks that contributed to the transition analysis. The empirical percentage remaining in the same cluster across consecutive weeks was 65.96% (8091/12,267). When movement between clusters occurred, the highest percentage of movement was to an adjacent cluster. Conclusions: The clusters of pain severity identified in this study provide a parsimonious description of the weekly experiences of people with chronic pain. These clusters could be used for future study of between-cluster movement and within-cluster variability to develop accurate and stakeholder-informed pain-forecasting tools.

Introduction: Long-term conditions are a major burden on health systems. One way to facilitate more research and better clinical care among patients with long-term conditions is to collect accurate data on their daily symptoms (patient-generated health data) using wearable technologies. Whilst evidence is growing for the use of wearable technologies in single conditions, there is less evidence of the utility of frequent symptom tracking in those who have more than one condition. Aims: To explore patient views of the acceptability of collecting daily patient-generated health data for three months using a smartwatch app. Methods: Watch Your Steps was a longitudinal study which recruited 53 patients to track over 20 symptoms per day for a 90-day period using a study app on smartwatches. Semi-structured interviews were conducted with a sub-sample of 20 participants to explore their experience of engaging with the app. Results: In a population of older people with multimorbidity, patients were willing and able to engage with a patient-generated health data app on a smartwatch. It was suggested that to maintain engagement over a longer period, more ‘real-time’ feedback from the app should be available. Participants did not seem to consider the management of more than one condition to be a factor in either engagement or use of the app, but the presence of severe or chronic pain was at times a barrier. Conclusion: This study has provided preliminary evidence that multimorbidity was not a major barrier to engagement with patient-generated health data via a smartwatch symptom tracking app.