Day-to-day variability of knee pain and the relationship with physical activity in people with knee osteoarthritis: an observational, feasibility study using consumer smartwatches.

OBJECTIVE: To assess the feasibility of using smartwatches in people with knee osteoarthritis (OA) to determine the day-to-day variability of pain and the relationship between daily pain and step count. DESIGN: Observational, feasibility study. SETTING: In July 2017, the study was advertised in newspapers, magazines and, on social media. Participants had to be living/willing to travel to Manchester. Recruitment was in September 2017 and data collection was completed in January 2018. PARTICIPANTS: 26 participants aged>50 years with self-diagnosed symptomatic knee OA were recruited. OUTCOME MEASURES: Participants were provided with a consumer cellular smartwatch with a bespoke app that triggered a series of daily questions including two times per day questions about level of knee pain and one time per month question from the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire. The smartwatch also recorded daily step counts. RESULTS: Of the 25 participants, 13 were men and their mean age was 65 years (standard deviation (SD) 8 years). The smartwatch app was successful in simultaneously assessing and recording data on knee pain and step count in real time. Knee pain was categorised into sustained high/low or fluctuating levels, but there was considerable day-to-day variation within these categories. Levels of knee pain in general correlated with pain assessed by KOOS. Those with sustained high/low levels of pain had a similar daily step count average (mean 3754 (SD 2524)/4307 (SD 2992)), but those with fluctuating pain had much lower step count levels (mean 2064 (SD 1716)). CONCLUSIONS: Smartwatches can be used to assess pain and physical activity in knee OA. Larger studies may help inform a better understanding of causal links between physical activity patterns and pain. In time, this could inform development of personalised physical activity recommendations for people with knee OA.

Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study.

BACKGROUND: Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. OBJECTIVE: The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. METHODS: We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants' time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. RESULTS: For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data. CONCLUSIONS: The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants.

Public Perspectives of Using Social Media Data to Improve Adverse Drug Reaction Reporting: A Mixed-Methods Study.

INTRODUCTION: Information on suspected adverse drug reactions (ADRs) voluntarily submitted by patients can be a valuable source of information for improving drug safety; however, public awareness of reporting mechanisms remains low. Whilst methods to automatically detect ADR mentions from social media posts using text mining techniques have been proposed to improve reporting rates, it is unclear how acceptable these would be to social media users. OBJECTIVE: The objective of this study was to explore public opinion about using automated methods to detect and report mentions of ADRs on social media to enhance pharmacovigilance efforts. METHODS: Users of the online health discussion forum HealthUnlocked participated in an online survey (N = 1359) about experiences with ADRs, knowledge of pharmacovigilance methods, and opinions about using automated data mining methods to detect and report ADRs. To further explore responses, five qualitative focus groups were conducted with 20 social media users with long-term health conditions. RESULTS: Participant responses indicated a low awareness of pharmacovigilance methods and ADR reporting. They showed a strong willingness to share health-related social media data about ADRs with researchers and regulators, but were cautious about automated text mining methods of detecting and reporting ADRs. CONCLUSIONS: Social media users value public-facing pharmacovigilance schemes, even if they do not understand the current framework of pharmacovigilance within the UK. Ongoing engagement with users is essential to understand views, share knowledge and respect users' privacy expectations to optimise future ADR reporting from online health communities.

Multinational comparison of new antidepressant use in older adults: a cohort study.

OBJECTIVES: We used an international pharmacosurveillance network to estimate the rate and characteristics of antidepressant use in older adults in countries with more conservative (UK) and liberal depression guidelines (Canada, USA). SETTING: Electronic health records and population-based administrative data from six jurisdictions in four countries (UK, Taiwan, USA and Canada). PARTICIPANTS: A historical cohort of older adults (≥65 years) who had a new episode of antidepressant use between 2009 and 2014. OUTCOME MEASURES: The age and sex-standardised cumulative incidence of new episodes of antidepressant use in older adults was measured. Descriptive statistics were used to compare the proportion of new users by the antidepressant prescribed, therapeutic class, potential treatment indication and country, as well as the characteristics of the first treatment episode (standardised daily doses, duration and changes). RESULTS: The incidence of antidepressant use between 2009 and 2014 varied from 4.7% (Montreal and Quebec City) to 18.6% (Taiwan). Tricyclic antidepressants (TCAs) were the most commonly used class in the UK (48.8%) and Taiwan (52.4%) compared with selective serotonin reuptake inhibitors (SSRIs) in North American jurisdictions (42.3%-53.3%). Chronic pain was the most common potential treatment indication (41.2%-68.2%). Among users with chronic pain, TCAs were used most frequently in the UK and Taiwan (55.2%-60.4%), whereas SSRIs were used most frequently in North America (33.5%-46.4%). Treatment was longer (252-525 vs 169-437 days), standardised doses were higher (0.7-1.3 vs 0.5-1.0) and treatment was more likely to be changed (31%-46% vs 21%-34%) among patients with depression (9.1%-43%) than those with chronic pain. CONCLUSION: Antidepressant use in older adults varied 24-fold by country, with the UK, which has the most conservative treatment guidelines, being among the lowest. Chronic pain was the most common potential treatment indication. Evaluation of real-world risks of TCAs is a priority for future research, given high rates of use and the potential for increased toxicity in older adults because of potent anticholinergic effects.

Quality of life, sleep and rheumatoid arthritis (QUASAR): a protocol for a prospective UK mHealth study to investigate the relationship between sleep and quality of life in adults with rheumatoid arthritis.

INTRODUCTION: People with rheumatoid arthritis (RA) frequently report reduced health-related quality of life (HRQoL), the impact one's health has on physical, emotional and social well-being. There are likely numerous causes for poor HRQoL, but people with RA have identified sleep disturbances as a key contributor to their well-being. This study will identify sleep/wake rhythm-associated parameters that predict HRQoL in patients with RA. METHODS AND ANALYSIS: This prospective cohort study will recruit 350 people with RA, aged 18 years or older. Following completion of a paper-based baseline questionnaire, participants will record data on 10 symptoms including pain, fatigue and mood two times a day for 30 days using a study-specific mobile application (app). A triaxial accelerometer will continuously record daytime activity and estimate evening sleep parameters over the 30 days. Every 10 days following study initiation, participants will complete a questionnaire that measures disease specific (Arthritis Impact Measurement Scale 2-Short Form (AIMS2-SF)) and generic (WHOQOL-BREF) quality of life. A final questionnaire will be completed at 60 days after entering the study. The primary outcomes are the AIMS2-SF and WHOQOL-BREF. Structural equation modelling and latent trajectory models will be used to examine the relationship between sleep/wake rhythm-associated parameters and HRQoL, over time. ETHICS AND DISSEMINATION: Results from this study will be disseminated at regional and international conferences, in peer-reviewed journals and Patient and Public Engagement events, as appropriate.

A threshold hazard model for estimating serious infection risk following anti-tumor necrosis factor therapy in rheumatoid arthritis patients.

Over recent years novel biologic agents have been developed for the treatment of rheumatoid arthritis. The most common type of biologic agent in use in the United Kingdom is the anti-tumor necrosis factor inhibitor class. To fully appreciate the potential risks of anti-tumor necrosis factor therapy in patients, knowledge about the baseline hazard (risk pattern) and the characteristics of patients associated with serious infection is important. We propose a nonproportional hazard model for estimating the infection risk, by including the drug exposure history information into the baseline hazard. We found that the infection risk reaches a peak within 1 month after drug exposure starts and then declines steadily for nearly 2 years before stabilizing out.

The safety of anti-tumour necrosis factor therapy in rheumatoid arthritis.

PURPOSE OF REVIEW: Anti-tumour necrosis factor therapy has proven effective as treatment against a series of autoimmune inflammatory diseases, and has displayed a rapidly increasing market penetration. The safety profile of these drugs is, however, both uncertain and debated, in particular with respect to infections and malignancy. Lack of uniform definitions and methods of analysis makes it difficult to directly compare data from randomized controlled trials, meta-analyses of trials, open-label extensions, data from spontaneous reporting, and particularly, observational cohort studies. RECENT FINDINGS: In this review, we provide a summary of currently published data on infection, malignancy, cardiovascular disease, interstitial lung disease, and death in relation to treatment of rheumatoid arthritis with anti-tumour necrosis factor agents. SUMMARY: Superficially contradictory data on infection display a more or less coherent pattern of an increased risk shortly after treatment starts. Available data on malignancy, cardiovascular disease, interstitial lung disease, and death do not exclude clinically important increased risks, nor do they refute beneficial effects. Harmonized methods of reporting safety data would greatly improve the interpretation and comparison of class and drug-specific risks.

Is there an association between anti-TNF monoclonal antibody therapy in rheumatoid arthritis and risk of malignancy and serious infection? Commentary on the meta-analysis by Bongartz et al.

A recent meta-analysis of randomized clinical trials reported by Bongartz and coworkers raised concerns about an increased rate of malignancy and serious infection in rheumatoid arthritis patients treated with anti-tumour necrosis factor monoclonal antibodies. This commentary discusses some of the methodological issues in their analysis and urges caution in interpreting the results.