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Objectives: To explore the relationship of serious infection risk with current and prior oral glucocorticoid (GC) therapy in elderly patients with rheumatoid arthritis (RA).Methods: A case-control analysis matched 1947 serious infection cases to five controls, selected from 16207 RA patients aged ≥ 65 between 1985-2003 in Quebec, Canada. Adjusted odds ratios for infection associated with different GC patterns were estimated using conventional models and a weighted cumulative dose (WCD) model.Results: The WCD model predicted risks better than conventional models. Current and recent GC doses had highest impact on current risk. Doses taken up to 2.5 years ago were also associated with increased risk, albeit to a lesser extent. A current user of 5mg prednisolone had a 30%, 46% or 100% increased risk of serious infection when used continuously for the last 3 months, 6 months or 3 years, respectively, compared to a non-user. The risk associated with 5mg prednisolone taken for the last 3 years was similar to that associated with 30 mg taken for the last month. Discontinuing a two-year course of 10mg prednisolone six months ago halved the risk compared to ongoing use.Conclusions: GC therapy is associated with infection risk in older patients with RA. The WCD model provided more accurate risk estimates than conventional models. Current and recent doses have greatest impact on infection risk, but the cumulative impact of doses taken in the last 2-3 years still affects risk. Knowing how risk depends on pattern of GC use will contribute to an improved benefit/harm assessment. [ABSTRACT FROM AUTHOR]

Background: Glucocorticoid therapy is strongly associated with an elevated risk of serious infections in patients with rheumatoid arthritis (RA). The association between glucocorticoids and common non-serious infections (NSI) is not well studied.Methods: A cohort of 16 207 patients with RA aged over 65 years was assembled using administrative data from Quebec. Glucocorticoid and disease-modifying antirheumatic drug (DMARD) therapy were identified from drug dispensing records. NSI cases were defined as first occurrence of a community physician billing code for infection or community-dispensed anti-infectives. A nested case-control analysis was performed considering drugs dispensed within 45 days of the index date, adjusting for age, sex, markers of disease severity, DMARD and comorbidity.Results: For 13 634 subjects, a NSI occurred during 28 695 person-years of follow-up, generating an incidence rate of 47.5/100 person-years. The crude rate of NSI in glucocorticoid-exposed and unexposed person time was 52.4 and 38.8/100 person-years, respectively. Glucocorticoid therapy was associated with an adjusted RR of 1.20 (95% CI 1.15 to 1.25). A dose response was seen, the adjusted RR increasing from 1.10 (<5 mg prednisolone/day) to 1.85 for doses greater than 20 mg/day. All glucocorticoid risk estimates (including <5 mg/day) were higher than that seen for methotrexate (adjusted RR 1.00; 0.95 to 1.04).Conclusion: Glucocorticoid therapy is associated with an increased risk of NSI. The magnitude of risk increases with dose, and is higher than that seen with methotrexate, although residual confounding may exist. While the RR is low at 1.20, the absolute risk is high with one additional infection seen for every 13 patients treated with glucocorticoids for 1 year. [ABSTRACT FROM AUTHOR]

Background: Anti-tumour necrosis factor (TNF)alpha treatments improve outcome in severe rheumatoid arthritis (RA) and are efficacious in psoriasis and psoriatic arthritis. However recent case reports describe psoriasis occurring as an adverse event in patients with RA receiving anti-TNFalpha therapy.Objectives: We aimed to determine whether the incidence rate of psoriasis was higher in patients with RA treated with anti-TNFalpha therapy compared to those treated with traditional disease-modifying antirheumatic drugs (DMARDs). We also compared the incidence rates of psoriasis between the three anti-TNFalpha drugs licensed for RA.Methods: We studied 9826 anti-TNF-treated and 2880 DMARD-treated patients with severe RA from The British Society for Rheumatology Biologics Register (BSRBR). All patients reported with new onset psoriasis as an adverse event were included in the analysis. Incidence rates of psoriasis were calculated as events/1000 person years and compared using incidence rate ratios (IRR).Results: In all, 25 incident cases of psoriasis in patients receiving anti-TNFalpha therapy and none in the comparison cohort were reported between January 2001 and July 2007. The absence of any cases in the comparison cohort precluded a direct comparison; however the crude incidence rate of psoriasis in those treated with anti-TNFalpha therapy was elevated at 1.04 (95% CI 0.67 to 1.54) per 1000 person years compared to the rate of 0 (upper 97.5% CI 0.71) per 1000 person years in the patients treated with DMARDs. Patients treated with adalimumab had a significantly higher rate of incident psoriasis compared to patients treated with etanercept (IRR 4.6, 95% CI 1.7 to 12.1) and infliximab (IRR 3.5, 95% CI 1.3 to 9.3).Conclusions: Results from this study suggest that the incidence of psoriasis is increased in patients treated with anti-TNFalpha therapy. Our findings also suggest that the incidence may be higher in patients treated with adalimumab. [ABSTRACT FROM AUTHOR]

OBJECTIVE: In a recent observational study, we found that the risk of serious infection following anti-tumor necrosis factor alpha (anti-TNFalpha) therapy in patients with rheumatoid arthritis (RA) was not importantly increased compared with the background risk in routinely treated RA patients with similar disease severity. Observational data sets are, however, subject to a number of important biases related to selection factors for the timing of starting and stopping therapy. Infection risk is also likely to vary with duration of therapy. This study was undertaken to examine the influences of these biases and of the method of analysis on the risk of infection. METHODS: We compared the risk of serious infection in 8,659 patients treated with anti-TNFalpha with that in 2,170 patients treated with traditional disease-modifying antirheumatic drugs (DMARDs) recruited to the British Society for Rheumatology Biologics Register. We applied a number of statistical models in which we varied the length of the followup period by using different definitions of the date of discontinuation of treatment and different lag periods of risk following drug cessation. RESULTS: When the at-risk period was defined as 'receiving treatment', the adjusted incidence rate ratio comparing patients receiving anti-TNFalpha therapy with patients receiving DMARD therapy was 1.22 (95% confidence interval [95% CI] 0.88-1.69). Limiting followup to the first 90 days, however, revealed an adjusted incidence rate ratio of 4.6 (95% CI 1.8-11.9). Rates of infection were increased in the 90 days immediately following drug discontinuation and beyond, explained by selection factors for drug discontinuation. CONCLUSION: These findings show that overall, the way in which UK rheumatologists select patients for starting and discontinuing anti-TNFalpha therapy explains our previous finding of no increase in risk. However, there may be important increases in true risk, notably early in the course of treatment, that would become more evident depending on the definition of at-risk period. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Rheumatoid arthritis (RA) is associated with an increased risk of coronary artery disease, possibly acting via shared mechanisms of inflammation. This study was undertaken to test the hypothesis that the powerful antiinflammatory effect of anti-tumor necrosis alpha (anti-TNFalpha) therapy might lead to a reduction in the incidence of myocardial infarction (MI) in patients with RA. METHODS: Using data from the British Society for Rheumatology Biologics Register, a national prospective observational study, we compared MI rates in 8,670 patients with RA treated with anti-TNFalpha and 2,170 patients with active RA treated with traditional disease-modifying antirheumatic drugs (DMARDs). RESULTS: Through July 2006, 63 MIs occurred in the anti-TNFalpha cohort during 13,233 person-years of followup and 17 MIs occurred in the DMARD cohort during 2,893 person-years of followup, equivalent to a rate of 4.8 events per 1,000 person-years and 5.9 events per 1,000 person-years, respectively. After adjustment for baseline risk factors, there was no reduction in the rate of MI in the anti-TNFalpha cohort compared with the DMARD cohort (incidence rate ratio 1.44 [95% confidence interval 0.56-3.67]). In an analysis of anti-TNFalpha-treated patients who responded to the treatment within 6 months versus those who did not, MI rates were found to be 3.5 events per 1,000 person-years in responders and 9.4 events per 1,000 person-years in nonresponders. The adjusted incidence rate ratio (95% confidence interval) for responders compared with nonresponders was 0.36 (0.19-0.69). CONCLUSION: These results indicate that RA patients treated with anti-TNFalpha do not have a lower incidence of MI compared with RA patients treated with traditional DMARDs. However, the risk of MI is markedly reduced in those who respond to anti-TNFalpha therapy by 6 months compared with nonresponders. This finding supports the notion that inflammation plays a pivotal role in MI. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To determine whether the rate of serious infection is higher in anti-tumor necrosis factor (anti-TNF)-treated rheumatoid arthritis (RA) patients compared with RA patients treated with traditional disease-modifying antirheumatic drugs (DMARDs). METHODS: This was a national prospective observational study of 7,664 anti-TNF-treated and 1,354 DMARD-treated patients with severe RA from the British Society for Rheumatology Biologics Register. All serious infections, stratified by site and organism, were included in the analysis. RESULTS: Between December 2001 and September 2005, there were 525 serious infections in the anti-TNF-treated cohort and 56 in the comparison cohort (9,868 and 1,352 person-years of followup, respectively). The incidence rate ratio (IRR), adjusted for baseline risk, for the anti-TNF-treated cohort compared with the comparison cohort was 1.03 (95% confidence interval 0.68-1.57). However, the frequency of serious skin and soft tissue infections was increased in anti-TNF-treated patients, with an adjusted IRR of 4.28 (95% confidence interval 1.06-17.17). There was no difference in infection risk between the 3 main anti-TNF drugs. Nineteen serious bacterial intracellular infections occurred, exclusively in patients in the anti-TNF-treated cohort. CONCLUSION: In patients with active RA, anti-TNF therapy was not associated with increased risk of overall serious infection compared with DMARD treatment, after adjustment for baseline risk. In contrast, the rate of serious skin and soft tissue infections was increased, suggesting an important physiologic role of TNF in host defense in the skin and soft tissues beyond that in other tissues. [ABSTRACT FROM AUTHOR]

Background: There are avoidable differences (i.e., inequities) in the prevalence and distribution of chronic pain across diverse populations, as well as in access to and outcomes of pain management services. Digital pain self-reporting tools have the potential to reduce or exacerbate these inequities. This study aimed to better understand how to optimise the health equity impact of digital pain self-reporting tools on people who are experiencing (or are at risk of) digital pain inequities., Methods: This was a qualitative study, guided by the Health Equity Impact Assessment tool-digital health supplement (HEIA-DH). We conducted three scoping focus groups with multiple stakeholders to identify the potential impacts of digital pain self-reporting tools and strategies to manage these impacts. Each group focused on one priority group experiencing digital pain inequities, including older adults, ethnic minorities, and people living in socio-economically deprived areas. A fourth consensus focus group was organised to discuss and select impact management strategies. Focus groups were audio-recorded, transcribed verbatim, and analysed using a framework approach. We derived codes, grouped them under four pre-defined categories from the HEIA-DH, and illustrated them with participants' quotes., Results: A total of fifteen people living with musculoskeletal pain conditions and thirteen professionals took part. Participants described how digital pain self-reports can have a positive health equity impact by better capturing pain fluctuations and enriching patient-provider communication, which in turn can enhance clinical decisions and self-management practices. Conversely, participants identified that incorrect interpretation of pain reports, lack of knowledge of pain terminologies, and digital (e.g., no access to technology) and social (e.g., gender stereotyping) exclusions may negatively impact on people's health equity. The participants identified 32 strategies, of which 20 were selected as being likely to mitigate these negative health equity impacts. Example strategies included, e.g., option to customise self-reporting tools in line with users' personal preferences, or resources to better explain how self-reported pain data will be used to build trust., Conclusion: Linked to people's personal and social characteristics, there are equity-based considerations for developing accessible digital pain self-reporting tools, as well as resources and skills to enable the adoption and use of these tools among priority groups. Future research should focus on implementing these equity-based considerations or strategies identified by our study and monitoring their impact on the health equity of people living with chronic pain., Competing Interests: Declarations Ethics approval and consent to participate The study received an ethical approval from the University of Manchester’s Research Ethics Committee (ref # 2022–14094-23756). Informed consent was obtained from all study participants. The study was conducted in accordance with the Declaration of Helsinki and its later amendments. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Abstract: Although opioids continue to be used internationally for noncancer pain, evidence to date on the comparative safety of different opioids is sparse and conflicting. The aim of this study was to examine the comparative risk of all-cause mortality in patients newly initiated on opioids for noncancer pain, across 3 jurisdictions in the United Kingdom (UK), United States, and Canada. A multicentre retrospective, population-based cohort study was conducted. Data sources included UK national primary care electronic health records (Clinical Practice Research Datalink), The Partners HealthCare Research Patient Data in Boston (US), and The Montreal Population Health Record data (Canada). New users of opioids aged ≥18 years without cancer were included. Patients with a diagnosis of a pain condition and with known back pain were analysed separately. Fully adjusted hazard ratios (HRs) were calculated using Cox-proportional models and adjusted for confounders. In total, 1,066,216 patients were included (UK: n = 993,294; Boston, US: n = 43,243; Montreal, Canada: n = 26,116). Compared with codeine, patients using morphine had a significantly higher adjusted risk in the UK {HR: 12.58 [95% confidence interval (CI), 11.87-13.32]}, US (HR: 8.62 [95% CI, 3.34-22.27]), and Canadian cohorts (HR: 6.69; [95% CI, 1.35-32.22]). In addition, other factors associated with higher mortality were being on combination opioids, fentanyl, buprenorphine, and oxycodone. Compared with those on <50 morphine milligram equivalents/day, patients on higher-doses experience an incremental increase in risk. In new users of opioids, compared with codeine, strong opioids, including morphine, fentanyl, buprenorphine, oxycodone, and combination opioids, and those on ≥50 morphine milligram equivalent/day were associated with a higher subsequent risk of all-cause mortality., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association for the Study of Pain.)

Background: Mobile health devices are increasingly available, presenting exciting opportunities to remotely collect high-frequency, electronic patient-generated health data (ePGHD). This novel data type may provide detailed insights into disease activity outside usual clinical settings. Assessing treatment responses, which can be hampered by the infrequency of appointments and recall bias, is a promising, novel application of ePGHD. Drugs with short treatment effects, such as intramuscular steroid injections, illustrate the challenge, as patients are unlikely to accurately recall treatment responses at follow-ups, which often occur several months later. Retrospective assessment means that responses may be over- or underestimated. High-frequency ePGHD, such as daily, app-collected, patient-reported symptoms between clinic appointments, may bridge this gap. However, the potential of ePGHD remains untapped due to the absence of established definitions for treatment response using ePGHD or established methodological approaches for analyzing this type of data., Objective: This study aims to explore the feasibility of evaluating treatment responses to intramuscular steroid therapy in a case series of patients with rheumatoid arthritis tracking daily symptoms using a smartphone app., Methods: We report a case series of patients who collected ePGHD through the REmote Monitoring Of Rheumatoid Arthritis (REMORA) smartphone app for daily remote symptom tracking. Symptoms were tracked on a 0-10 scale. We described the patients' longitudinal pain scores before and after intramuscular steroid injections. The baseline pain score was calculated as the mean pain score in the 10 days prior to the injection. This was compared to the pain scores in the days following the injection. "Response" was defined as any improvement from the baseline score on the first day following the injection. The response end time was defined as the first date when the pain score exceeded the pre-steroid baseline., Results: We included 6 patients who, between them, received 9 steroid injections. Average pre-injection pain scores ranged from 3.3 to 9.3. Using our definitions, 7 injections demonstrated a response. Among the responders, the duration of response ranged from 1 to 54 days (median 9, IQR 7-41), average pain score improvement ranged from 0.1 to 5.3 (median 3.3, IQR 2.2-4.0), and maximum pain score improvement ranged from 0.1 to 7.0 (median 4.3, IQR 1.7 to 6.0)., Conclusions: This case series demonstrates the feasibility of using ePGHD to evaluate treatment response and is an important exploratory step toward developing more robust methodological approaches for analysis of this novel data type. Issues highlighted by our analysis include the importance of accounting for one-off data points, varying response start times, and confounders such as other medications. Future analysis of ePGHD across a larger population is required to address issues highlighted by our analysis and to develop meaningful consensus definitions for treatment response in time-series data., (©Mariam Al-Attar, Kesmanee Assawamartbunlue, Julie Gandrup, Sabine N van der Veer, William G Dixon. Originally published in JMIR Formative Research (https://formative.jmir.org), 28.10.2024.)

Background: Although many people are supportive of their deidentified health care data being used for research, concerns about privacy, safety, and security of health care data remain. There is low awareness about how data are used for research and related governance. Transparency about how health data are used for research is crucial for building public trust. One proposed solution is to ensure that affected communities are notified, particularly marginalized communities where there has previously been a lack of engagement and mistrust., Objective: This study aims to explore patient and public perspectives on the use of deidentified data from electronic health records for musculoskeletal research and to explore ways to build and sustain public trust in health data sharing for a research program (known as "the Data Jigsaw") piloting new ways of using and analyzing electronic health data. Views and perspectives about how best to engage with local communities informed the development of a public notification campaign about the research., Methods: Qualitative methods data were generated from 20 semistructured interviews and 8 focus groups, comprising 48 participants in total with musculoskeletal conditions or symptoms, including 3 carers. A presentation about the use of health data for research and examples from the specific research projects within the program were used to trigger discussion. We worked in partnership with a patient and public involvement group throughout the research and cofacilitated wider community engagement., Results: Respondents were supportive of their health care data being shared for research purposes, but there was low awareness about how electronic health records are used for research. Security and governance concerns about data sharing were noted, including collaborations with external companies and accessing social care records. Project examples from the Data Jigsaw program were viewed positively after respondents knew more about how their data were being used to improve patient care. A range of different methods to build and sustain trust were deemed necessary by participants. Information was requested about: data management; individuals with access to the data (including any collaboration with external companies); the National Health Service's national data opt-out; and research outcomes. It was considered important to enable in-person dialogue with affected communities in addition to other forms of information., Conclusions: The findings have emphasized the need for transparency and awareness about health data sharing for research, and the value of tailoring this to reflect current and local research where residents might feel more invested in the focus of research and the use of local records. Thus, the provision for targeted information within affected communities with accessible messages and community-based dialogue could help to build and sustain public trust. These findings can also be extrapolated to other conditions beyond musculoskeletal conditions, making the findings relevant to a much wider community., (©Zainab K Yusuf, William G Dixon, Charlotte Sharp, Louise Cook, Søren Holm, Caroline Sanders. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.10.2024.)

Background: Management of rheumatoid arthritis (RA) relies on symptoms reported by patients during infrequent outpatient clinic visits. These reports are often incomplete and inaccurate due to poor recall, leading to suboptimal treatment decisions and outcomes. Asking people to track symptoms in-between visits and integrating the data into clinical pathways may improve this. However, knowledge on how to implement this into practice and its impact on services and outcomes remains scarce in RA. Therefore, we evaluate the comparative effectiveness and cost-effectiveness of integrated symptom tracking in people with RA over and above usual care, while generating insights on factors for successful implementation., Methods: In this superiority stepped wedge cluster-randomized controlled trial with continuous recruitment short exposure design, 16 rheumatology outpatient departments (clusters) recruit a total of 732 people with active RA. They initially offer clinic visits according to standard of care before switching in pairs to visits with integrated symptom tracking. Clusters switch in randomized order every 3 weeks. Integrated symptom tracking consists of (1) a mobile app for patients to track their symptoms daily and other RA aspects weekly/monthly, and (2) an interactive dashboard visualizing the app data, which healthcare professionals access from their electronic health record system. Clinic visits happen according to usual practice, with tracked symptom data only reviewed during visits. Our primary outcome is a difference in marginal mean disease activity score at 12 ± 3 months between standard of care and integrated symptom tracking, after accounting for baseline values, cluster, and other covariates. Secondary outcomes include patient-reported disease activity, quality of life and quality-adjusted life-years, medication/resource use, consultation and decision-making experience, self-management, and illness perception. We also conduct interviews and observations as part of a parallel process evaluation to gather information on implementation., Discussion: Our trial will generate high-quality evidence of comparative and cost-effectiveness of integrated symptom tracking compared to standard of care in people with RA, with our process evaluation delivering knowledge on successful implementation. This optimizes the chances of integrated symptom tracking being adopted more widely if we find it is (cost-) effective., Trial Registration: Registered 4-Jun-2024 on https://www.isrctn.com/ , ISRCTN51539448. TRIAL OPEN SCIENCE FRAMEWORK REPOSITORY: https://osf.io/sj9ha/ ., (© 2024. The Author(s).)

Rationale: Patients with rheumatoid arthritis (RA) experience a range of symptoms including joint pain and inflammation, stiffness, fatigue, anxiety, and low mood. Similar to patients with other long-term conditions, they may have periods of time when their disease is under control, and times when their condition is less stable, requiring treatment adjustments. The REMORA2 feasibility study explored the implementation of an integrated symptom-tracking system using a smartphone application (app), enabling patients to track day-to-day symptoms. The data was available in the electronic health record to be viewed at subsequent consultations., Aims and Objectives: This paper explores patients' comments on living with RA, and how patient-reported symptom data supports informed interactions as patients and clinicians work together to coproduce meaning from the data., Method: Individual semi-structured interviews were conducted with 21 patients and 7 clinicians, supplemented by nonparticipant observations of 5 clinical appointments. Thematic analysis was used to analyse data from the interviews, with an ethnographic approach used to analyse the observational data., Results: Both clinicians and patients reported the benefits of reviewing the data in the clinic together. This helped inform decisions about pain management and identified patients who might otherwise have dismissed symptoms such as pain, because of their natural inclination to be stoical., Conclusion: Improved insights on the care of RA were generated as patients and clinicians discuss symptom tracking data. This can assist the patient-clinician dyad in the process of two-way learning and shared decision-making on the management of a long-term condition., (© 2024 The Author(s). Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Background: Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people's chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person's health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration. A multi-stakeholder Knowledge Exchange event was organised to facilitate conversations across silos within this research ecosystem., Methods: A survey was sent to an initial list of stakeholders to identify potential discussion topics. Additional stakeholders were identified through iterative discussions on perspectives that needed representation. Co-design meetings with attendees were held to discuss the scope, format and ethos of the event. The event itself featured a cross-disciplinary selection of talks, a panel discussion, small-group discussions facilitated via a rolling seating plan and audience participation via Slido. A transcript was generated from the day, which, together with the output from Slido, provided a record of the day's discussions. Finally, meetings were held following the event to identify the key challenges for digital endpoints which emerged and reflections and recommendations for dissemination., Results: Several challenges for digital endpoints were identified in the following areas: patient adherence and acceptability; algorithms and software for devices; design, analysis and conduct of clinical trials with digital endpoints; the environmental impact of digital endpoints; and the need for ongoing ethical support. Learnings taken for next generation events include the need to include additional stakeholder perspectives, such as those of funders and regulators, and the need for additional resources and facilitation to allow patient and public contributors to engage meaningfully during the event., Conclusions: The event emphasised the importance of consortium building and highlighted the critical role that collaborative, multi-disciplinary, and cross-sector efforts play in driving innovation in research design and strategic partnership building moving forward. This necessitates enhanced recognition by funders to support multi-stakeholder projects with patient involvement, standardised terminology, and the utilisation of open-source software., (© 2024. The Author(s).)

Common to all inflammatory arthritides, namely rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, and juvenile idiopathic arthritis, is a potential for reduced mobility that manifests through joint pain, swelling, stiffness, and ultimately joint damage. Across these conditions, consensus has been reached on the need to capture outcomes related to mobility, such as functional capacity and physical activity, as core domains in randomised controlled trials. Existing endpoints within these core domains rely wholly on self-reported questionnaires that capture patients' perceptions of their symptoms and activities. These questionnaires are subjective, inherently vulnerable to recall bias, and do not capture the granularity of fluctuations over time. Several early adopters have integrated sensor-based digital health technology (DHT)-derived endpoints to measure physical function and activity in randomised controlled trials for conditions including Parkinson's disease, Duchenne's muscular dystrophy, chronic obstructive pulmonary disease, and heart failure. Despite these applications, there have been no sensor-based DHT-derived endpoints in clinical trials recruiting patients with inflammatory arthritis. Borrowing from case studies across medicine, we outline the opportunities and challenges in developing novel sensor-based DHT-derived endpoints that capture the symptoms and disease manifestations most relevant to patients with inflammatory arthritis., Competing Interests: Declaration of interests HY reports receiving a personal payment via the University of Oxford for developed software licenses. APC reports being employed by the University of Oxford at the time of submission and was partially funded by GSK, at the time of publication they are employed by Sanome and no company mentioned is involved or affiliated with this Review. JG reports being an unpaid volunteer member on the Board of Directors for the Digital Medicine Society and Coalition for Health AI. WGD reports receiving consultancy fees from Google unrelated to this work. AD is supported by the Wellcome Trust [223100/Z/21/Z], Novo Nordisk, Swiss Re, the British Heart Foundation Centre of Research Excellence (grant number RE/18/3/34214), and Health Data Research UK; AD's research group reports receiving funding from GSK; reports receiving personal payments via the University of Oxford for consulting services provided to Harvard University and the University of Wisconsin; reports receiving a personal payment via the University of Oxford for developed software licenses. LCC reports receiving research grants from AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB; reports receiving consulting fees from AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Moonlake, Novartis, Pfizer, and UCB; reports receiving payment for lectures and presentations from AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, GSK, Janssen, Medac, Novartis, Pfizer, and UCB. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

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., (©Claire L Little, David M Schultz, Thomas House, William G Dixon, John McBeth. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 19.07.2024.)

Introduction: At the beginning of the COVID-19 pandemic, the UK's Scientific Committee issued extreme social distancing measures, termed 'shielding', aimed at a subpopulation deemed extremely clinically vulnerable to infection. National guidance for risk stratification was based on patients' age, comorbidities and immunosuppressive therapies, including biologics that are not captured in primary care records. This process required considerable clinician time to manually review outpatient letters. Our aim was to develop and evaluate an automated shielding algorithm by text-mining outpatient letter diagnoses and medications, reducing the need for future manual review., Methods: Rheumatology outpatient letters from a large UK foundation trust were retrieved. Free-text diagnoses were processed using Intelligent Medical Objects software (Concept Tagger), which used interface terminology for each condition mapped to Systematized Medical Nomenclature for Medicine-Clinical Terminology (SNOMED-CT) codes. We developed the Medication Concept Recognition tool (Named Entity Recognition) to retrieve medications' type, dose, duration and status (active/past) at the time of the letter. Age, diagnosis and medication variables were then combined to calculate a shielding score based on the most recent letter. The algorithm's performance was evaluated using clinical review as the gold standard. The time taken to deploy the developed algorithm on a larger patient subset was measured., Results: In total, 5942 free-text diagnoses were extracted and mapped to SNOMED-CT, with 13 665 free-text medications (n=803 patients). The automated algorithm demonstrated a sensitivity of 80% (95% CI: 75%, 85%) and specificity of 92% (95% CI: 90%, 94%). Positive likelihood ratio was 10 (95% CI: 8, 14), negative likelihood ratio was 0.21 (95% CI: 0.16, 0.28) and F1 score was 0.81. Evaluation of mismatches revealed that the algorithm performed correctly against the gold standard in most cases. The developed algorithm was then deployed on records from an additional 15 865 patients, which took 18 hours for data extraction and 1 hour to deploy., Discussion: An automated algorithm for risk stratification has several advantages including reducing clinician time for manual review to allow more time for direct care, improving efficiency and increasing transparency in individual patient communication. It has the potential to be adapted for future public health initiatives that require prompt automated review of hospital outpatient letters., Competing Interests: Competing interests: ASK is the former Chief Medical Officer of Intelligent Medical Objects (IMO) and owns a stock in IMO. WGD has received consultancy fees from Google unrelated to the project. Other authors have no conflicts of interest., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ on behalf of EULAR.)

Background: Opioids administered in hospital during the immediate postoperative period are likely to influence post-surgical outcomes, but inpatient prescribing during the admission is challenging to access. Modified-release(MR) preparations have been especially associated with harm, whilst certain populations such as the elderly or those with renal impairment may be vulnerable to complications. This study aimed to assess postoperative opioid utilisation patterns during hospital stay for people admitted for major/orthopaedic surgery., Methods: Patients admitted to a teaching hospital in the North-West of England between 2010-2021 for major/orthopaedic surgery with an admission for ≥1 day were included. We examined opioid administrations in the first seven days post-surgery in hospital, and "first 48 hours" were defined as the initial period. Proportions of MR opioids, initial immediate-release(IR) oxycodone and initial morphine milligram equivalents (MME)/day were calculated and summarised by calendar year. We also assessed the proportion of patients prescribed an opioid at discharge., Results: Among patients admitted for major/orthopaedic surgery, 71.1% of patients administered opioids during their hospitalisation. In total 50,496 patients with 60,167 hospital admissions were evaluated. Between 2010-2017 MR opioids increased from 8.7% to 16.1% and dropped to 11.6% in 2021. Initial use of oxycodone IR among younger patients (≤70 years) rose from 8.3% to 25.5% (2010-2017) and dropped to 17.2% in 2021. The proportion of patients on ≥50MME/day ranged from 13% (2021) to 22.9% (2010). Of the patients administered an opioid in hospital, 26,920 (53.3%) patients were discharged on an opioid., Conclusions: In patients hospitalised with major/orthopaedic surgery, 4 in 6 patients were administered an opioid. We observed a high frequency of administered MR opioids in adult patients and initial oxycodone IR in the ≤70 age group. Patients prescribed with ≥50MME/day ranged between 13-22.9%. This is the first published study evaluating UK inpatient opioid use, which highlights opportunities for improving safer prescribing in line with latest recommendations., Competing Interests: WGD has received consultancy fees from Google unrelated to this work. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening., Objective: This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app., Methods: Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used. Predictors were several summary features of the daily symptom scores (eg, pain and fatigue) collected in the week leading up to the flare question. We fitted 3 binary classifiers: logistic regression with and without elastic net regularization, a random forest, and naive Bayes. Performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. For the best-performing model, we considered sensitivity and specificity for different thresholds in order to illustrate different ways in which the predictive model could behave in a clinical setting., Results: The data comprised an average of 60.6 daily reports and 10.5 weekly reports per participant. Participants reported a median of 2 (IQR 0.75-4.25) flares each over a median follow-up time of 81 (IQR 79-82) days. AUCs were broadly similar between models, but logistic regression with elastic net regularization had the highest AUC of 0.82. At a cutoff requiring specificity to be 0.80, the corresponding sensitivity to detect flares was 0.60 for this model. The positive predictive value (PPV) in this population was 53%, and the negative predictive value (NPV) was 85%. Given the prevalence of flares, the best PPV achieved meant only around 2 of every 3 positive predictions were correct (PPV 0.65). By prioritizing a higher NPV, the model correctly predicted over 9 in every 10 non-flare weeks, but the accuracy of predicted flares fell to only 1 in 2 being correct (NPV and PPV of 0.92 and 0.51, respectively)., Conclusions: Predicting self-reported flares based on daily symptom scorings in the preceding week using machine learning methods was feasible. The observed predictive accuracy might improve as we obtain more data, and these exploratory results need to be validated in an external cohort. In the future, analysis of frequently collected patient-generated data may allow us to predict flares before they unfold, opening opportunities for just-in-time adaptative interventions. Depending on the nature and implication of an intervention, different cutoff values for an intervention decision need to be considered, as well as the level of predictive certainty required., (©Julie Gandrup, David A Selby, William G Dixon. Originally published in JMIR Formative Research (https://formative.jmir.org), 14.05.2024.)

Objective: To investigate opioid prescribing trends and assess the impact of the COVID-19 pandemic on opioid prescribing in rheumatic and musculoskeletal diseases (RMDs)., Methods: Adult patients with RA, PsA, axial spondyloarthritis (AxSpA), SLE, OA and FM with opioid prescriptions between 1 January 2006 and 31 August 2021 without cancer in UK primary care were included. Age- and gender-standardized yearly rates of new and prevalent opioid users were calculated between 2006 and 2021. For prevalent users, monthly measures of mean morphine milligram equivalents (MME)/day were calculated between 2006 and 2021. To assess the impact of the pandemic, we fitted regression models to the monthly number of prevalent opioid users between January 2015 and August 2021. The time coefficient reflects the trend pre-pandemic and the interaction term coefficient represents the change in the trend during the pandemic., Results: The study included 1 313 519 RMD patients. New opioid users for RA, PsA and FM increased from 2.6, 1.0 and 3.4/10 000 persons in 2006 to 4.5, 1.8 and 8.7, respectively, in 2018 or 2019. This was followed by a fall to 2.4, 1.2 and 5.9, respectively, in 2021. Prevalent opioid users for all RMDs increased from 2006 but plateaued or dropped beyond 2018, with a 4.5-fold increase in FM between 2006 and 2021. In this period, MME/day increased for all RMDs, with the highest for FM (≥35). During COVID-19 lockdowns, RA, PsA and FM showed significant changes in the trend of prevalent opioid users. The trend for FM increased pre-pandemic and started decreasing during the pandemic., Conclusion: The plateauing or decreasing trend of opioid users for RMDs after 2018 may reflect the efforts to tackle rising opioid prescribing in the UK. The pandemic led to fewer people on opioids for most RMDs, providing reassurance that there was no sudden increase in opioid prescribing during the pandemic., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Introduction: Many people worldwide suffer from chronic pain. Improving our knowledge on chronic pain prevalence and management requires methods to collect pain self-reports in large populations. Smartphone-based tools could aid data collection by allowing people to use their own device, but the measurement properties of such tools are largely unknown., Objectives: To assess the reliability, validity, and responsiveness of a smartphone-based manikin to support pain self-reporting., Methods: We recruited people with fibromyalgia, rheumatoid arthritis, and/or osteoarthritis and access to a smartphone and the internet. Data collection included the Global Pain Scale at baseline and follow-up, and 30 daily pain drawings completed on a 2-dimensional, gender-neutral manikin. After deriving participants' pain extent from their manikin drawings, we evaluated convergent and discriminative validity, test-retest reliability, and responsiveness and assessed findings against internationally agreed criteria for good measurement properties., Results: We recruited 131 people; 104 were included in the full sample, submitting 2185 unique pain drawings. Manikin-derived pain extent had excellent test-retest reliability (intraclass correlation coefficient, 0.94), moderate convergent validity (ρ, 0.46), and an ability to distinguish fibromyalgia and osteoarthritis from rheumatoid arthritis (F statistics, 30.41 and 14.36, respectively; P < 0.001). Responsiveness was poor (ρ, 0.2; P , 0.06) and did not meet the respective criterion for good measurement properties., Conclusion: Our findings suggest that smartphone-based manikins can be a reliable and valid method for pain self-reporting, but that further research is warranted to explore, enhance, and confirm the ability of such manikins to detect a change in pain over time., Competing Interests: B.J. is a cofounder and staff member of uMotif, the company that developed the Manchester Digital Pain Manikin. None of the other authors has any conflicts of interest to declare.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The International Association for the Study of Pain.)

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., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: WGD has received consultancy fees from Google., (© The Author(s) 2024.)

Objectives: Epidemiological estimates of psoriatic arthritis (PsA) underpin the provision of healthcare, research, and the work of government, charities and patient organizations. Methodological problems impacting prior estimates include small sample sizes, incomplete case ascertainment, and representativeness. We developed a statistical modelling strategy to provide contemporary prevalence and incidence estimates of PsA from 1991 to 2020 in the UK., Methods: Data from Clinical Practice Research Datalink (CPRD) were used to identify cases of PsA between 1st January 1991 and 31st December 2020. To optimize ascertainment, we identified cases of Definite PsA (≥1 Read code for PsA) and Probable PsA (satisfied a bespoke algorithm). Standardized annual rates were calculated using Bayesian multilevel regression with post-stratification to account for systematic differences between CPRD data and the UK population, based on age, sex, socioeconomic status and region of residence., Results: A total of 26293 recorded PsA cases (all definitions) were identified within the study window (77.9% Definite PsA). Between 1991 and 2020 the standardized prevalence of PsA increased twelve-fold from 0.03 to 0.37. The standardized incidence of PsA per 100,000 person years increased from 8.97 in 1991 to 15.08 in 2020, an almost 2-fold increase. Over time, rates were similar between the sexes, and across socioeconomic status. Rates were strongly associated with age, and consistently highest in Northern Ireland., Conclusion: The prevalence and incidence of PsA recorded in primary care has increased over the last three decades. The modelling strategy presented can be used to provide contemporary prevalence estimates for musculoskeletal disease using routinely collected primary care data., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Because people with chronic pain feel uncertain about their future pain, a pain-forecasting model could support individuals to manage their daily pain and improve their quality of life. We conducted two patient and public involvement activities to design the content of a pain-forecasting model by learning participants' priorities in the features provided by a pain forecast and understanding the perceived benefits that such forecasts would provide. The first was a focus group of 12 people living with chronic pain to inform the second activity, a survey of 148 people living with chronic pain. Respondents prioritized forecasting of pain flares (100, or 68%) and fluctuations in pain severity (94, or 64%), particularly the timing of the onset and the severity. Of those surveyed, 75% (or 111) would use a future pain forecast and 80% (or 118) perceived making plans (e.g., shopping, social) as a benefit. For people with chronic pain, the timing of the onset of pain flares, the severity of pain flares and fluctuations in pain severity were prioritized as being key features of a pain forecast, and making plans was prioritized as being a key benefit., Competing Interests: Coauthor Will Dixon has received consultancy fees from Google, and David Schultz has received consultancy fees from Palta, both unrelated to this work. All other authors have no conflicts of interest to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Little et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article., (© The Author(s) 2023.)

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 exposure (5619-9832). The distribution of hazard ratios as a function of the decisions ranged from 2.71 (95% CI: 2.31, 3.18) to 3.24 (2.76, 3.82)., Conclusions: Assumptions made during the drug preparation process, especially for those with variability in prescription instructions, can impact results of subsequent risk estimates. The developed R packages can improve transparency related to drug preparation assumptions, in line with best practice advocated by international pharmacoepidemiology guidelines., (© 2023 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Objective: This paper describes the co-production of a training video to support people with RA to self-examine for tender and swollen joints., Methods: The patient and public involvement and engagement (PPIE) group supporting a remote monitoring study elected to develop a video to train people with RA how to self-examine for tender and swollen joints, because nothing appropriate was publicly available to fulfil their needs. A core team of PPIE group members and clinicians developed the video, with input from conception to dissemination from the PPIE group. The video was posted, open access, on a YouTube website in February 2021, alongside supporting materials. The number of monthly hits was tracked and a survey developed to ascertain feedback., Results: The video received 1000 hits in the first week, and >40 000 at 10 months. The top three countries viewing the video were India, the USA and the UK, with a range of ages and gender profile broadly corresponding to those of RA patients. Forty-eight survey responses were received (26 patients and 22 clinicians). Patients reported an improvement in their ability to self-examine after watching this video. Eighty-six per cent of patients and 71% of clinicians would recommend the video. It has been used and disseminated by a number of national organizations within the UK., Conclusion: This co-produced, open-access training video for people with RA, originally intended to support a research study into remote monitoring, has been well received, reflecting an international interest in self-examination., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Background: Culture and ethnicity influence how people communicate about their pain. This makes it challenging to develop pain self-report tools that are acceptable across ethnic groups., Objective: We aimed to inform the development of cross-culturally acceptable digital pain self-report tools by better understanding the similarities and differences between ethnic groups in pain experiences and self-reporting needs., Methods: Three web-based workshops consisting of a focus group and a user requirement exercise with people who self-identified as being of Black African (n=6), South Asian (n=10), or White British (n=7) ethnicity were conducted., Results: Across ethnic groups, participants shared similar lived experiences and challenges in communicating their pain to health care professionals. However, there were differences in beliefs about the causes of pain, attitudes toward pain medication, and experiences of how stigma and gender norms influenced pain-reporting behavior. Despite these differences, they agreed on important aspects for pain self-report, but participants from non-White backgrounds had additional language requirements such as culturally appropriate pain terminologies to reduce self-reporting barriers., Conclusions: To improve the cross-cultural acceptability and equity of digital pain self-report tools, future developments should address the differences among ethnic groups on pain perceptions and beliefs, factors influencing pain reporting behavior, and language requirements., (©Syed Mustafa Ali, Rebecca R Lee, John McBeth, Ben James, Sean McAlister, Alessandro Chiarotto, William G Dixon, Sabine N van der Veer. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 08.02.2023.)

The use of data from smartphones and wearable devices has huge potential for population health research given high device ownership, the range of novel health-relevant data types available from consumer devices, and the frequency and duration over which data are, or could be, collected. Yet the uptake and success of large-scale mobile health research in the last decade has not matched the hyped opportunity. We make the argument that digital person-generated health data is required and necessary to answer many top priority research questions through illustrative examples taken from the James Lind Alliance Priority Setting Partnership. We then summarise the findings from two UK initiatives that considered the challenges and possible solutions for what needs to be done, and in what way, to realise the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas to be addressed to advance the field include digital inequality and addressing possible selection bias, easy access for researchers to the appropriate data collection tools including how best to harmonise data items, analysis methodology for time series data, methods for patient and public involvement and engagement to optimise recruitment, retention and public trust, and providing greater control of their data to research participants. There is also a major opportunity through the linkage of digital persongenerated health data to routinely-collected data to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognise that well conducted studies need a wide range of diverse challenges to be skilfully addressed in unison: for example, epidemiology, data science and biostatistics, psychometrics, behavioural and social science, software engineering, user interface design, information governance, data management and patient and public involvement and engagement. Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow excellence throughout the lifecycle of a research study. This will require a partnership of diverse people, of methods and of technology. Get this right and the synergy has the potential to transform many millions of people's lives for the better., Competing Interests: Competing interests The authors declare no completing non-financial interests but the following completing financial interests: -WGD has received consultancy fees from Google, unrelated to this work.-AD is the recipient of research funding from Novo Nordisk and Swiss Re.

Introduction: People living with multiple long-term conditions (MLTC-M) (multimorbidity) experience a range of inter-related symptoms. These symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices, and then summarised to provide useful clinical insight., Aim: We aimed to perform an exploratory analysis to summarise the extent and trajectory of multiple symptom ratings tracked via a smartwatch, and to investigate the relationship between these symptom ratings and demographic factors in people living with MLTC-M in a feasibility study., Methods: 'Watch Your Steps' was a prospective observational feasibility study, administering multiple questions per day over a 90 day period. Adults with more than one clinician-diagnosed long-term condition rated seven core symptoms each day, plus up to eight additional symptoms personalised to their LTCs per day. Symptom ratings were summarised over the study period at the individual and group level. Symptom ratings were also plotted to describe day-to-day symptom trajectories for individuals., Results: Fifty two participants submitted symptom ratings. Half were male and the majority had LTCs affecting three or more disease areas (N = 33, 64%). The symptom rated as most problematic was fatigue. Patients with increased comorbidity or female sex seemed to be associated with worse experiences of fatigue. Fatigue ratings were strongly correlated with pain and level of dysfunction., Conclusion: In this study we have shown that it is possible to collect and descriptively analyse self reported symptom data in people living with MLTC-M, collected multiple times per day on a smartwatch, to gain insights that might support future clinical care and research., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: WGD has received consultancy fees from Abbvie and Google., (© The Author(s) 2023.)

Background: Getting knowledge from healthcare research into practice (knowledge mobilisation) remains a global challenge. One way in which researchers may attempt to do this is to develop products (such as toolkits, actionable tools, dashboards, guidance, audit tools, protocols and clinical decision aids) in addition to journal papers. Despite their increasing ubiquity, the development of such products remains under-explored in the academic literature. This study aimed to further this understanding by exploring the development of products from healthcare research and how the process of their development might influence their potential application., Methods: This study compared the data generated from a prospective, longitudinal, comparative case study of four research projects which aimed to develop products from healthcare research. Qualitative methods included thematic analysis of data generated from semi-structured interviews (38), meeting observations (83 h) and project documents (300+). Cases were studied for an average of 11.5 months (range 8-19 months)., Results: Case comparison resulted in the identification of three main themes with the potential to affect the use of products in practice. First, aspects of the product, including the perceived need for the specific product being identified, the clarity of product aim and clarity and range of end-users. Second, aspects of development, whereby different types of stakeholder engagement appear to influence potential product application, which either needs to be 'meaningful', or delivered through the implicit understanding of users' needs by the developing team. The third, overarching theme, relates to the academic context in which products are developed, highlighting how the academic context perpetuates the development of products, which may not always be useful in practice., Conclusions: This study showed that aspects of products from healthcare research (need/aim/end-user) and aspects of their development (stakeholder engagement/implicit understanding of end-users) influence their potential application. It explored the motivation for product development and identifies the influence of the current academic context on product development. It shows that there is a tension between ideal 'systems approaches' to knowledge mobilisation and 'linear approaches', which appear to be more pervasive in practice currently. The development of fewer, high-quality products which fulfil the needs of specified end-users might act to counter the current cynicism felt by many stakeholders in regard to products from healthcare research., (© 2022. The Author(s).)

Objective: Clinical trials assessing systemic sclerosis (SSc)-related digital ulcers have been hampered by a lack of reliable outcome measures of healing. Our objective was to assess the feasibility of patients collecting high-quality mobile phone images of their digital lesions as a first step in developing a smartphone-based outcome measure., Methods: Patients with SSc-related digital (finger) lesions photographed one or more lesions each day for 30 days using their smartphone and uploaded the images to a secure Dropbox folder. Image quality was assessed using six criteria: blurriness, shadow, uniformity of lighting, dot location, dot angle and central positioning of the lesion. Patients completed a feedback questionnaire., Results: Twelve patients returned 332 photographs of 18 lesions. Each patient sent a median of 29.5 photographs [interquartile range (IQR) 15-33.5], with a median of 15 photographs per lesion (IQR 6-32). Twenty-two photographs were duplicates. Of the remaining 310 images, 256 (77%) were sufficiently in focus; 268 (81%) had some shadow; lighting was even in 56 (17%); dot location was acceptable in 233 (70%); dot angle was ideal in 107 (32%); and the lesion was centred in 255 (77%). Patient feedback suggested that 6 of 10 would be willing to record images daily in future studies, and 9 of 10 at least one to three times per week., Conclusion: Taking smartphone photographs of digital lesions was feasible for most patients, with most lesions in focus and central in the image. These promising results will inform the next research phase (to develop a smartphone monitoring application incorporating photographs and symptom tracking)., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Objective: The objective of this study was to use daily data collected via a smartphone app for characterization of patient-reported and symptom-based (using an a priori definition) flares in an adult idiopathic inflammatory myopathy (IIM) cohort., Methods: UK adults with an IIM answered patient-reported outcome measurements (PROMs) daily via a smartphone app during a 91-day study. Daily symptom PROMs addressed global activity, overall pain, myalgia, fatigue, and weakness (on a 0-100 visual analogue scale). Patient-reported flares were recorded via a weekly app question. Symptom-based flares were defined via an a priori definition related to increase in daily symptom data from the previous 4-day mean., Results: Twenty participants (65% female) participated. Patient-reported flares occurred on a median of 5 weeks (IQR 3, 7) per participant, out of a possible 13. The mean of each symptom score was significantly higher in flare weeks, compared with non-flare weeks (e.g. mean flare week myalgia score 34/100, vs 21/100 during non-flare week, t test P-value <0.01). Fatigue accounted for the most symptom-based flares [incidence-rate 23/100 person-days (95% CI 19, 27)], and myalgia the fewest [incidence rate 13/100 person-days (95% CI 11, 16)]. Symptom-based flares typically resolved after 3 days, although fatigue-predominant flares lasted 2 days. The majority (69%) of patient-reported flare weeks coincided with at least one symptom-based flare., Conclusions: IIM flares are frequent and associated with increased symptom scores. This study has demonstrated the ability to identify and characterize patient-reported and symptom-based flares (based on an a priori definition), using daily app-collected data., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Background: People with rheumatic diseases experience troublesome fluctuations in fatigue. Debated causes include pain, mood and inflammation. To determine the relationships between these potential causes, serial assessments are required but are methodologically challenging. This mobile health (mHealth) study explored the viability of using a smartphone app to collect patient-reported symptoms with contemporaneous Dried Blood Spot Sampling (DBSS) for inflammation., Methods: Over 30 days, thirty-eight participants (12 RA, 13 OA, and 13 FM) used uMotif, a smartphone app, to report fatigue, pain and mood, on 5-point ordinal scales, twice daily. Daily DBSS, from which C-reactive Protein (CRP) values were extracted, were completed on days 1-7, 14 and 30. Participant engagement was determined based on frequency of data entry and ability to calculate within- and between-day symptom changes. DBSS feasibility and engagement was determined based on the proportion of samples returned and usable for extraction, and the number of days between which between-day changes in CRP which could be calculated (days 1-7)., Results: Fatigue was reported at least once on 1085/1140 days (95.2%). Approximately 65% of within- and between-day fatigue changes could be calculated. Rates were similar for pain and mood. A total of 287/342 (83.9%) DBSS, were returned, and all samples were viable for CRP extraction. Fatigue, pain and mood varied considerably, but clinically meaningful (≥ 5 mg/L) CRP changes were uncommon., Conclusions: Embedding DBSS in mHealth studies will enable researchers to obtain serial symptom assessments with matched biological samples. This provides exciting opportunities to address hitherto unanswerable questions, such as elucidating the mechanisms of fatigue fluctuations., (© 2022. The Author(s).)

Chronic pain is common and disabling. Researchers need robust methods to collect pain data in large populations to enhance knowledge on pain prevalence, causes and treatment. Digital pain manikins address this by enabling self-reporting of location-specific pain. However, it is unknown to what extent pain studies adopted digital manikins for data collection. Therefore, we systematically searched the literature. We included 17 studies. Most were published after 2017, collected pain data cross-sectionally in ≥50 participants, and reported pain distribution and pain extent as manikin-derived summary metrics. Across the studies, 13 unique manikins were used, of which four had been evaluated. Our review shows that adoption of digital pain manikins in research settings has been slow. Harnessing the digital nature of manikins, enabling use of personal devices, and assessing and improving the reliability, validity and responsiveness of digital manikins will expedite their adoption as digital data collection tools for pain research.

Background: Sleep disturbances and poor health-related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of nonrestorative sleep, may be a driver of HRQoL. However, understanding whether these sleep disturbances reduce HRQoL has, to date, been challenging because of the need to collect complex time-varying data at high resolution. Such data collection is now made possible by the widespread availability and use of mobile health (mHealth) technologies., Objective: This mHealth study aimed to test whether sleep disturbance (both absolute values and variability) causes poor HRQoL., Methods: The quality of life, sleep, and RA study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, wore a triaxial accelerometer for 30 days to objectively assess sleep, and provided daily reports via a smartphone app that assessed sleep (Consensus Sleep Diary), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multilevel modeling tested the relationship between sleep variables and the WHOQoL-BREF domains (physical, psychological, environmental, and social)., Results: Of the 268 recruited participants, 254 were included in the analysis. Across all WHOQoL-BREF domains, participants' scores were lower than the population average. Consensus Sleep Diary sleep parameters predicted the WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points (β=1.11, 95% CI 0.07-2.15) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, anxiety levels, sleep quality, or clinical sleep disorders. However, these changes were attenuated and no longer significant when pain, fatigue, and mood were included in the model. Increased variability in total time asleep was associated with poorer physical and psychological domain scores, independent of all covariates. There was no association between actigraphy-measured sleep and WHOQoL-BREF., Conclusions: Optimizing total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing variability in total sleep time could improve HRQoL in people with RA., (©John McBeth, William G Dixon, Susan Mary Moore, Bruce Hellman, Ben James, Simon D Kyle, Mark Lunt, Lis Cordingley, Belay Birlie Yimer, Katie L Druce. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.04.2022.)

Background: When performed in an observational setting, treatment effect modification analyses should account for all confounding, where possible. Often, such studies only consider confounding between the exposure and outcome. However, there is scope for misspecification of the confounding adjustment when estimating moderation as the effects of the confounders may themselves be influenced by the moderator. The aim of this study was to investigate bias in estimates of treatment effect modification resulting from failure to account for an interaction between a binary moderator and a confounder on either treatment receipt or the outcome, and to assess the performance of different approaches to account for such interactions., Methods: The theory behind the reason for bias and factors that impact the magnitude of bias is explained. Monte Carlo simulations were used to assess the performance of different propensity scores adjustment methods and regression adjustment where the adjustment 1) did not account for any moderator-confounder interactions, 2) included moderator-confounder interactions, and 3) was estimated separately in each moderator subgroup. A real-world observational dataset was used to demonstrate this issue., Results: Regression adjustment and propensity score covariate adjustment were sensitive to the presence of moderator-confounder interactions on outcome, whilst propensity score weighting and matching were more sensitive to the presence of moderator-confounder interactions on treatment receipt. Including the relevant moderator-confounder interactions in the propensity score (for methods using this) or the outcome model (for regression adjustment) rectified this for all methods except propensity score covariate adjustment. For the latter, subgroup-specific propensity scores were required. Analysis of the real-world dataset showed that accounting for a moderator-confounder interaction can change the estimate of effect modification., Conclusions: When estimating treatment effect modification whilst adjusting for confounders, moderator-confounder interactions on outcome or treatment receipt should be accounted for., (© 2022. The Author(s).)

Objective: We aimed to explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with RA., Methods: We used data from the Remote Monitoring of RA study, in which patients tracked their daily symptoms and weekly flares on an app. We summarized 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). Fifteen of 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% CI, 1.15, 2.97) and 3.12 (95% CI, 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 might, ultimately, facilitate prediction and more timely management of imminent flares., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.)