Cloudy with a Chance of Pain: Engagement and Subsequent Attrition of Daily Data Entry in a Smartphone Pilot Study Tracking Weather, Disease Severity, and Physical Activity in Patients With Rheumatoid Arthritis

User engagement in a randomised controlled trial for a digital health intervention for early psychosis (Actissist 2.0 trial)

Digital Health Interventions (DHIs) can help support people with mental health problems. Achieving satisfactory levels of patient engagement is a crucial, yet often underexplored, pre-requisite for health improvement. Actissist is a co-produced DHI delivered via a smartphone app for people with early psychosis, based on Cognitive Behaviour Therapy principles. This study describes and compares engagement patterns among participants in the two arms of the Actissist 2.0 randomised controlled trial. Engagement frequency and duration were measured among participants using the Actissist app in the intervention arm (n = 87) and the ClinTouch symptom monitoring only app used as the control condition (n = 81). Overall, 47.1 % of Actissist and 45.7 % of ClinTouch users completed at least a third of scheduled alerts while active in the study. The mean frequency (77.1 versus 60.2 total responses) and the median duration (80 versus 75 days until last response) of engagement were not significantly higher among Actissist users compared to ClinTouch users. Older age, White ethnicity, using their own smartphone device and, among Actissist users, an increased sense of therapeutic alliance were significantly associated with increased engagement. Through exploiting detailed usage data, this study identifies possible participant-level and DHI-level predictors of engagement to inform the practical implementation of future DHIs.

The Clinical Impact of Electronic Patient-Reported Outcome Measures in the Remote Monitoring of Inflammatory Arthritis: A Systematic Review and Meta-analysis

OBJECTIVE

The inflammatory arthritides (IAs) make up a significant proportion of conditions followed up in rheumatology clinics. These patients require regular monitoring, but this is increasingly difficult with rising patient numbers and demand on clinics. Our objective is to evaluate the clinical impact of electronic patient-reported outcome measures (ePROMs) as a digital remote-monitoring intervention on disease activity, treatment decisions, and health care resource use in patients with IA.

METHODS

Five databases (MEDLINE, Embase, PubMed, Cochrane Library, and Web of Science) were searched, with randomized controlled trials and (nonrandomized) controlled clinical trials included, and meta-analysis and forest plots conducted for each outcome. Risk of bias was assessed using the Risk of Bias-2 tool and Risk of Bias in Nonrandomized Studies of Interventions.

RESULTS

Eight studies were included with a total of 4,473 patients, with seven studies assessing patients with rheumatoid arthritis. Compared with control, the disease activity in the ePROM group was lower (standardized mean difference [SMD] -0.15; 95% confidence interval [CI] -0.27 to -0.03) and rates of remission/low disease activity were higher (odds ratio1.65; 95% CI 1.02-2.68), but five of eight studies provided additional combined interventions (e.g., disease education). Fewer face to face visits were needed in the remote ePROM group (SMD -0.93; 95% CI -2.14-0.28).

CONCLUSION

Most studies were at high risk of bias with significant heterogeneity in design, but our results suggest there is an advantage in using ePROM monitoring in patients with IAs, with the potential for reduction in health care resource use without detrimental impact in disease outcomes.

Impact of humid climate on rheumatoid arthritis faecal microbiome and metabolites

Studies have shown that high humidity is a condition that aggravates the pain of rheumatoid arthritis (RA), but the relevant mechanism is controversial. Currently, there is a lack of experimental animal studies on high humidity as an adverse factor related to the pathogenesis of RA. We used healthy SD rats and collagen-induced arthritis (CIA) rats to investigate the effects of high humidity on arthritis. Integrated metabolomics analyses of faeces and 16S rRNA sequencing of the faecal microbiota were performed to comprehensively assess the diversity of the faecal microbiota and metabolites in healthy and CIA rats. In this study, high humidity aggravated arthritis in CIA rats, which manifested as articular cartilage lesions, increased arthritis scores, and an increase in proinflammatory cytokines. High humidity had a certain effect on the articular cartilage extent, arthritis score and proinflammatory cytokines of healthy rats as well. Furthermore, high humidity caused significant changes in faecal microbes and metabolites in both healthy and CIA rats. 16S rRNA sequencing of faecal samples showed that high humidity increased the amount of inflammation-related bacteria in healthy and CIA rats. Faecal metabolomics results showed that high humidity significantly altered the level of faecal metabolites in healthy rats and CIA rats, and the changes in biological functions were mainly related to the inflammatory response and oxidative stress. Combined analysis showed that there was a strong correlation between the faecal microbiota and faecal metabolites. High humidity is an adverse factor for the onset and development of RA, and its mechanism is related to the inflammatory response and oxidative stress. However, the question of how high humidity impacts RA pathogenesis needs to be further investigated.

A comparison of experience sampled hay fever symptom severity across rural and urban areas of the UK

Hay fever affects people differently and can change over a lifetime, but data is lacking on how environmental factors may influence this. This study is the first to combine atmospheric sensor data with real-time, geo-positioned hay fever symptom reports to examine the relationship between symptom severity and air quality, weather and land use. We study 36145 symptom reports submitted over 5 years by over 700 UK residents using a mobile application. Scores were recorded for nose, eyes and breathing. Symptom reports are labelled as urban or rural using land-use data from the UK's Office for National Statistics. Reports are compared with AURN network pollution measurements and pollen and meteorological data taken from the UK Met Office. Our analysis suggests urban areas record significantly higher symptom severity for all years except 2017. Rural areas do not record significantly higher symptom severity in any year. Additionally, symptom severity correlates with more air quality markers in urban areas than rural areas, indicating that differences in allergy symptoms may be due to variations in the levels of pollutants, pollen counts and seasonality across land-use types. The results suggest that a relationship exists between urban surroundings and hay fever symptoms.

Engagement With mHealth COVID-19 Digital Biomarker Measurements in a Longitudinal Cohort Study: Mixed Methods Evaluation

BACKGROUND

The COVID-19 pandemic accelerated the interest in implementing mobile health (mHealth) in population-based health studies, but evidence is lacking on engagement and adherence in studies. We conducted a fully remote study for ≥6 months tracking COVID-19 digital biomarkers and symptoms using a smartphone app nested within an existing cohort of adults.

OBJECTIVE

We aimed to investigate participant characteristics associated with initial and sustained engagement in digital biomarker collection from a bespoke smartphone app and if engagement changed over time or because of COVID-19 factors and explore participants' reasons for consenting to the smartphone substudy and experiences related to initial and continued engagement.

METHODS

Participants in the Fenland COVID-19 study were invited to the app substudy from August 2020 to October 2020 until study closure (April 30, 2021). Participants were asked to complete digital biomarker modules (oxygen saturation, body temperature, and resting heart rate [RHR]) and possible COVID-19 symptoms in the app 3 times per week. Participants manually entered the measurements, except RHR that was measured using the smartphone camera. Engagement was categorized by median weekly frequency of completing the 3 digital biomarker modules (categories: 0, 1-2, and ≥3 times per week). Sociodemographic and health characteristics of those who did or did not consent to the substudy and by engagement category were explored. Semistructured interviews were conducted with 35 participants who were purposively sampled by sex, age, educational attainment, and engagement category, and data were analyzed thematically; 63% (22/35) of the participants consented to the app substudy, and 37% (13/35) of the participants did not consent.

RESULTS

A total of 62.61% (2524/4031) of Fenland COVID-19 study participants consented to the app substudy. Of those, 90.21% (2277/2524) completed the app onboarding process. Median time in the app substudy was 34.5 weeks (IQR 34-37) with no change in engagement from 0 to 3 months or 3 to 6 months. Completion rates (≥1 per week) across the study between digital biomarkers were similar (RHR: 56,517/77,664, 72.77%; temperature: 56,742/77,664, 73.06%; oxygen saturation: 57,088/77,664, 73.51%). Older age groups and lower managerial and intermediate occupations were associated with higher engagement, whereas working, being a current smoker, being overweight or obese, and high perceived stress were associated with lower engagement. Continued engagement was facilitated through routine and personal motivation, and poor engagement was caused by user error and app or equipment malfunctions preventing data input. From these results, we developed key recommendations to improve engagement in population-based mHealth studies.

CONCLUSIONS

This mixed methods study demonstrated both high initial and sustained engagement in a large mHealth COVID-19 study over a ≥6-month period. Being nested in a known cohort study enabled the identification of participant characteristics and factors associated with engagement to inform future applications in population-based health research.

Seasonal exacerbation of rheumatoid arthritis detected by big claims data analysis: A retrospective population study

OBJECTIVES

The objective of the study was to determine the seasonal changes in the initiation of biological disease-modifying antirheumatic drugs (bDMARDs) and methotrexate (MTX) using big claims data.

METHODS

We counted the monthly number of initial administrations of each bDMARD and MTX in patients with rheumatoid arthritis (RA) between April 2010 and March 2017. Data were collected from the National Database of Health Insurance Claims and Specific Health Checkups of Japan. This database covers more than 95% of Japanese citizens. Seasonal changes in the number of initiations were determined. Patient claims were also classified according to drugs, districts, gender, and ages.

RESULTS

The initiation of bDMARDs and MTX administration varied according to the season in a sine curve shape, with the highest numbers in May to July and the lowest numbers in November to January. The same changing pattern was observed among each bDMARD, district, gender, and age groups particularly when the number was on the higher side.

CONCLUSION

We noted an apparent seasonal change in the number of bDMARDs initiated, with a peak during spring, suggesting an exacerbation of RA in the spring in Japan. These changes are overlooked in daily practice and are only visible using big data.

Engagement and attrition with eHealth tools for remote monitoring in chronic arthritis: a systematic review and meta-analysis

Objectives

Although eHealth tools are potentially useful for remote disease monitoring, barriers include concerns of low engagement and high attrition. We aimed to summarise evidence on patients’ engagement and attrition with eHealth tools for remotely monitoring disease activity/impact in chronic arthritis.

Methods

A systematic literature search was conducted for original articles and abstracts published before September 2022. Eligible studies reported quantitative measures of patients’ engagement with eHealth instruments used for remote monitoring in chronic arthritis. Engagement rates were pooled using random effects meta-analysis.

Results

Of 8246 references, 45 studies were included: 23 using smartphone applications, 13 evaluating wearable activity trackers, 7 using personal digital assistants, 6 including web-based platforms and 2 using short message service. Wearable-based studies mostly reported engagement as the proportion of days the tracker was worn (70% pooled across 6 studies). For other eHealth tools, engagement was mostly reported as completion rates for remote patient-reported outcomes (PROs). The pooled completion rate was 80%, although between-study heterogeneity was high (I² 93%) with significant differences between eHealth tools and frequency of PRO-collection. Engagement significantly decreased with longer study duration, but attrition varied across studies (0%–89%). Several predictors of higher engagement were reported. Data on the influence of PRO-reporting frequency were conflicting.

Conclusion

Generally high patient engagement was reported with eHealth tools for remote monitoring in chronic arthritis. However, we found considerable between-study heterogeneity and a relative lack of real-world data. Future studies should use standardised measures of engagement, preferably assessed in a daily practice setting.

Trial registeration number

The protocol was registered on PROSPERO (CRD42021267936).

Designing Studies to Inform Tobacco Harm Reduction: Learnings From an Oral Nicotine Pouch Actual Use Pilot Study

Background

Introduction of new tobacco products in the United States, including those that may be lower on the risk continuum than traditional combustible cigarettes, requires premarket authorization by the US Food and Drug Administration and information on the potential impact of the products on consumer behaviors. Efficient recruitment and data capture processes are needed to collect relevant information in a near-to-real-world environment.

Objective

The aim of this pilot study was to develop and test a protocol for an actual use study of a new tobacco product. The product included in this study was a commercially available oral nicotine pouch. Through the process of study design and execution, learnings were garnered to inform the design, execution, analysis, and report writing of future full-scale actual use studies with tobacco products.

Methods

A small sample (n=100) of healthy adult daily smokers of 7 or more cigarettes per day were recruited to participate in an 8-week prospective observational study conducted at 4 geographically dispersed sites in the United States. A smartphone-based customized electronic diary (eDiary) was employed to capture daily tobacco product use, including 1 week of baseline smoking and 6 weeks during which participants were provided with oral nicotine pouches for use as desired.

Results

Online screening procedures with follow-up telephone interviews and on-site enrollment were successfully implemented. Of 100 participants, 97 completed the study, with more than half (59/99, 60%) identifying as dual- or poly-users of cigarettes and other types of tobacco products at baseline. There was more than 90% (91-93/99, 92%-94%) compliance with daily eDiary reporting, and the majority (92/99, 93%) of participants expressed satisfaction with the study processes. Product use data from the eDiary indicated that after an initial period of trial use, pouches per day increased among those continuing to use the products, while per day average cigarette consumption decreased for 82% (79/97) of all study participants. At the end of the week 6, 16% (15/97) of participants had reduced their cigarette consumption by more than half.

Conclusions

The design of this study, including recruiting, enrollment, eDiary use, and oversight, was successfully implemented through the application of a detailed protocol, a user-friendly eDiary, electronically administered questionnaires, and remote monitoring procedures. High-resolution information was obtained on prospective changes in tobacco product use patterns in the context of availability of a new tobacco product. Future, larger actual use studies will provide important evidence supporting the role that alternatives to combustible cigarettes may play in smoking reduction and/or cessation and lowering the population health burden of tobacco and nicotine-containing products.

Smartphone-based behavioral monitoring and patient-reported outcomes in adults with rheumatic and musculoskeletal disease

BACKGROUND

Rheumatic and musculoskeletal diseases (RMD) are associated with depression, fatigue, and disturbed sleep - symptoms that often impact behavior and activity. Patient reported outcomes (PROs) are a way of collecting information on the patient symptom experience directly from the individual. The purpose of this study was to measure and compare user smartphone sensor and activity data in adults with RMDs and assess associations with PROs.

METHODS

We invited adults with RMDs enrolled in the FORWARD Databank to participate by installing a custom app on their smartphone and answering PROs (pain, global, HAQ-II) questions daily and weekly over 3 years. Passive data collected included mobility distance, unique calls and text messages, call durations, and number of missed calls. Confounders included sociodemographic, clinical, passive phone behavior, and seasonal factors. Kappa statistics between PRO and flares were computed to measure agreement. The agreement between daily and weekly VAS pain was estimated using the intraclass (ICC) correlation of a two-way random effect model. The relationship between the weekly PRO outcomes and the passive phone data was analyzed with a linear mixed-effect model (LMM), including a random intercept for participant and slope for time in the study with an unstructured covariate structure.

RESULTS

Of the 446 participants, the mean (SD) age was 54 (12) years, most (65.5%) had rheumatoid arthritis (RA), the vast majority (91%) were female, and the US Northeast has the least representation (12%). Longer reaction times, interaction diversity, and higher mobility were associated with worse PROs while longer text messages were associated with better PROs. Participants in this study showed good levels of adherence which holds promise for future interventions using passive behavior measures in self-management and clinical follow-up.

CONCLUSION

This is the first study to examine passive smartphone behavior with PROs in RMDs and we found significant associations between these behaviors and important health outcomes of pain and function. As smartphone usage continues to change, future studies should validate and expand on our findings with a goal of finding changes in patient symptoms passively through mobile device monitoring.

Smartphones for musculoskeletal research - hype or hope? Lessons from a decennium of mHealth studies

BACKGROUND

Smartphones provide opportunities for musculoskeletal research: they are integrated in participants' daily lives and can be used to collect patient-reported outcomes as well as sensor data from large groups of people. As the field of research with smartphones and smartwatches matures, it has transpired that some of the advantages of this modern technology are in fact double-edged swords. BODY: In this narrative review, we illustrate the advantages of using smartphones for data collection with 18 studies from various musculoskeletal domains. We critically appraised existing literature, debunking some myths around the advantages of smartphones: the myth that smartphone studies automatically enable high engagement, that they reach more representative samples, that they cost little, and that sensor data is objective. We provide a nuanced view of evidence in these areas and discuss strategies to increase engagement, to reach representative samples, to reduce costs and to avoid potential sources of subjectivity in analysing sensor data.

CONCLUSION

If smartphone studies are designed without awareness of the challenges inherent to smartphone use, they may fail or may provide biased results. Keeping participants of smartphone studies engaged longitudinally is a major challenge. Based on prior research, we provide 6 actions by researchers to increase engagement. Smartphone studies often have participants that are younger, have higher incomes and high digital literacy. We provide advice for reaching more representative participant groups, and for ensuring that study conclusions are not plagued by bias resulting from unrepresentative sampling. Costs associated with app development and testing, data storage and analysis, and tech support are substantial, even if studies use a 'bring your own device'-policy. Exchange of information on costs, collective app development and usage of open-source tools would help the musculoskeletal community reduce costs of smartphone studies. In general, transparency and wider adoption of best practices would help bringing smartphone studies to the next level. Then, the community can focus on specific challenges of smartphones in musculoskeletal contexts, such as symptom-related barriers to using smartphones for research, validating algorithms in patient populations with reduced functional ability, digitising validated questionnaires, and methods to reliably quantify pain, quality of life and fatigue.

What Works Best to Engage Participants in Mobile App Interventions and e-Health: A Scoping Review

Background:

Despite the growing popularity of mobile app interventions, specific engagement components of mobile apps have not been well studied.

Methods:

The objectives of this scoping review are to determine which components of mobile health intervention apps encouraged or hindered engagement, and examine how studies measured engagement.

Results:

A PubMed search on March 5, 2020 yielded 239 articles that featured the terms engagement, mobile app/mobile health, and adult. After applying exclusion criteria, only 54 studies were included in the final analysis.

Discussion:

Common app components associated with increased engagement included: personalized content/feedback, data visualization, reminders/push notifications, educational information/material, logging/self-monitoring functions, and goal-setting features. On the other hand, social media integration, social forums, poor app navigation, and technical difficulties appeared to contribute to lower engagement rates or decreased usage. Notably, the review revealed a great variability in how engagement with mobile health apps is measured due to lack of established processes.

Conclusion:

There is a critical need for controlled studies to provide guidelines and standards to help facilitate engagement and its measurement in research and clinical trial work using mobile health intervention apps.

Outcomes of a Mobile App to Monitor Patient-Reported Outcomes in Rheumatoid Arthritis: A Randomized Controlled Trial

OBJECTIVE

To examine the effects of a smartphone application (app) to monitor longitudinal electronic patient-reported outcomes (ePROs) on patient satisfaction and disease activity in patients with rheumatoid arthritis (RA).

METHODS

We conducted a 6-month randomized controlled trial of care coordination along with an app (intervention) versus care coordination alone (control) in 191 RA patients. Participants in the intervention group were prompted to provide information daily using ePROs. In both the intervention and control groups, a care coordinator contacted participants at 6 and 18 weeks to assess for flares. The main outcome measures were the global satisfaction score from the Treatment Satisfaction Questionnaire for Medication (TSQM), the score from the Perceived Efficacy in Patient-Physician Interactions (PEPPI) Questionnaire, and the Clinical Disease Activity Index (CDAI) score.

RESULTS

Groups were similar at baseline. The median TSQM score at 6 months was 83.3 in both groups, and the median PEPPI score at 6 months was 50 in both groups. The median CDAI score at 6 months was 8 in the intervention group versus 10 in the control group. No statistically significant group differences in the medians of TSQM, PEPPI, or CDAI scores at 6 months were detected. Of the 67 intervention participants who completed the exit survey, 90% rated their likelihood of recommending the app as ≥7 of 10. Of the 11 physicians who completed the exit survey, 73% agreed/strongly agreed that they wanted to continue offering the app to patients.

CONCLUSION

A mobile app designed to collect ePRO data on RA symptoms did not significantly improve patient satisfaction or disease activity compared to care coordination alone. However, both patients and physicians reported positive experiences with the app.

Remote Measurement in Rheumatoid Arthritis: Qualitative Analysis of Patient Perspectives

BACKGROUND

Rheumatoid arthritis (RA) is characterized by recurrent fluctuations in symptoms such as joint pain, swelling, and stiffness. Remote measurement technologies (RMTs) offer the opportunity to track symptoms continuously and in real time; therefore, they may provide a more accurate picture of RA disease activity as a complement to prescheduled general practitioner appointments. Previous research has shown patient interest in remote symptom tracking in RA and has provided evidence for its clinical validity. However, there is a lack of co-design in the current development of systems, and the features of RMTs that best promote optimal engagement remain unclear.

OBJECTIVE

This study represents the first in a series of work that aims to develop a multiparametric RMT system for symptom tracking in RA. The objective of this study is to determine the important outcomes for disease management in patients with RA and how these can be best captured via remote measurement.

METHODS

A total of 9 patients (aged 23-77 years; mean 55.78, SD 17.54) with RA were recruited from King's College Hospital to participate in two semistructured focus groups. Both focus group discussions were conducted by a facilitator and a lived-experience researcher. The sessions were recorded, transcribed, independently coded, and analyzed for themes.

RESULTS

Thematic analysis identified a total of four overarching themes: important symptoms and outcomes in RA, management of RA symptoms, views on the current health care system, and views on the use of RMTs in RA. Mobility and pain were key symptoms to consider for symptom tracking as well as symptom triggers. There is a general consensus that the ability to track fluctuations and transmit such data to clinicians would aid in individual symptom management and the effectiveness of clinical care. Suggestions for visually capturing symptom fluctuations in an app were proposed.

CONCLUSIONS

The findings support previous work on the acceptability of RMT with RA disease management and address key outcomes for integration into a remote monitoring system for RA self-management and clinical care. Clear recommendations for RMT design are proposed. Future work will aim to take these recommendations into a user testing phase.

Feasibility of Self-Monitoring Rheumatoid Arthritis With a Smartphone App: Results of Two Mixed-Methods Pilot Studies

Background

Several mobile apps that monitor symptoms of rheumatoid arthritis (RA) exist, but a recent systematic review indicated that high-quality apps are lacking. When patients self-monitor their own disease with patient-reported outcomes (PROs) and self-initiate care at the right moment, it may be possible to reduce the frequency of their clinic visits, which would reduce health care burden and costs. We developed an app, that is, the MijnReuma Reade app, for this purpose and performed 2 pilot tests with weekly self-monitoring.

Objective

The primary objective of this study was to design, develop, and evaluate the usability, satisfaction, and usage of the MijnReuma Reade app—an app that allows patients with RA to monitor their own disease. The secondary objective was to review the patients’ perspectives on app usage and its intended purpose.

Methods

This app was designed in collaboration with patients with RA, rheumatologists, and information technology experts. Two 1-month pilot studies were performed, after which satisfaction (0-10 scale), usability (system usability scale, 0-100), and usage (proportion of completed questionnaires) of this app were assessed. After the second pilot study, semistructured interviews were performed to determine patients’ perspectives and the promoters and barriers of app usage.

Results

In the first and second pilot study, 42 and 27 patients were included, respectively. Overall, the patients were satisfied (medians, 8 and 7) and found the app usable (mean system usability scores, 76 and 71) in pilot studies 1 and 2, respectively. App usage declined over time in both the pilot studies; 61% (17/28) and 37% (10/27) of the patients who disclosed their usage statistics completed the final weekly questionnaire in pilot study 1 and pilot study 2, respectively. Approximately 81% (25/31) of the patients indicated they would like to skip hospital visits if the self-monitored disease activity is low. In the semistructured interviews, technical problems, internal resistance (respondent fatigue, the app reminded them of their disease), and a lack of symptoms were identified as barriers for usage. Patients reported that “experiencing more grip on their disease” and “improved communication with their physician” were promoters for usage. Patients reported that pain positively mediated usage, that is, more pain promoted and less pain discouraged app usage.

Conclusions

This study illustrates the feasibility of the MijnReuma Reade app that enables self-monitoring of the disease activity in patients with RA with the overarching aim to allocate clinical consultations according to need. Satisfaction with the app and usability of the app were found to be high; however, app usage declined over time. Patients acknowledged the potential of the app to self-monitor their own disease and would like to be able to skip clinic visits if the monitored disease activity is low. To evaluate this strategy, a randomized controlled trial is underway.

PERSONAL: Feasibility Study Protocol for Placebo-Controlled, Randomized n-of-1 Trials of Tamsulosin for Lower Urinary Tract Symptoms

Background: Lower urinary tract symptoms (LUTS) affect more than half of men over age 70 and contribute to both poor health-related quality of life and polypharmacy. Tamsulosin hydrochloride, a selective α₁-blocker, is the most common medication used to treat LUTS due to presumed benign prostatic hyperplasia and is often prescribed indefinitely, although not all men benefit from long-term therapy. N-of-1 trials allow for individualized estimates of benefit and harm and could facilitate decisions regarding chronic tamsulosin therapy for LUTS, particularly among older men. Our team developed the PERSONAL (PlacEbo-controlled, Randomized, patient-Selected Outcomes, N-of-1 triALs) app to track daily urinary symptoms and medication side effects for n-of-1 trials among older men with LUTS.

Materials and Methods: We will conduct a feasibility study of 20 individual randomized n-of-1 trials using the PERSONAL app to compare tamsulosin (0.4 or 0.8 mg) vs. placebo among older men taking tamsulosin for LUTS. We will include men over age 65 with a smartphone for whom temporary discontinuation of tamsulosin is safe, (e.g., no history of acute retention). Participants will work with research staff to prospectively identify the most important urinary symptoms and medication side effects that they would like to digitally track. Men will then be randomized to 2-week treatment periods of tamsulosin or placebo followed by a 1-week wash-out with placebo, for 4 distinct treatment periods and 3 wash-out periods, totaling 11 weeks. Study medications will be blinded using over-encapsulation of tamsulosin pills and matching placebo. Our primary outcomes for this study will be recruitment and retention of eligible men, completion rates of n-of-1 trials and daily questionnaires using the PERSONAL app, and participants' perceived usefulness of their n-of-1 trial for determining whether tamsulosin is effective for them. Linear mixed effects models with individual-specific intercepts and intervention effects will also be used to estimate within-individual effects of tamsulosin.

Discussion: The goal of this innovative study is to establish feasibility and acceptability of using a mobile health app and n-of-1 trials to provide older men with individualized estimates of benefits and harms of chronic tamsulosin therapy for LUTS.

Evaluating the Feasibility of Frequent Cognitive Assessment Using the Mezurio Smartphone App: Observational and Interview Study in Adults With Elevated Dementia Risk

Background

By enabling frequent, sensitive, and economic remote assessment, smartphones will facilitate the detection of early cognitive decline at scale. Previous studies have sustained participant engagement with remote cognitive assessment over a week; extending this to a period of 1 month clearly provides a greater opportunity for measurement. However, as study durations are increased, the need to understand how participant burden and scientific value might be optimally balanced also increases.

Objective

This study explored the little but often approach to assessment employed by the Mezurio app when prompting participants to interact every day for over a month. Specifically, this study aimed to understand whether this extended duration of remote study is feasible, and which factors promote sustained participant engagement over such periods.

Methods

A total of 35 adults (aged 40-59 years) with no diagnosis of cognitive impairment were prompted to interact with the Mezurio smartphone app platform for up to 36 days, completing short, daily episodic memory tasks in addition to optional executive function and language tests. A subset (n=20) of participants completed semistructured interviews focused on their experience of using the app.

Results

Participants complied with 80% of the daily learning tasks scheduled for subsequent tests of episodic memory, with 88% of participants still actively engaged by the final task. A thematic analysis of the participants’ experiences highlighted schedule flexibility, a clear user interface, and performance feedback as important considerations for engagement with remote digital assessment.

Conclusions

Despite the extended study duration, participants demonstrated high compliance with the schedule of daily learning tasks and were extremely positive about their experiences. Long durations of remote digital interaction are therefore definitely feasible but only when careful attention is paid to the design of the users’ experience.

How the weather affects the pain of citizen scientists using a smartphone app

Patients with chronic pain commonly believe their pain is related to the weather. Scientific evidence to support their beliefs is inconclusive, in part due to difficulties in getting a large dataset of patients frequently recording their pain symptoms during a variety of weather conditions. Smartphones allow the opportunity to collect data to overcome these difficulties. Our study Cloudy with a Chance of Pain analysed daily data from 2658 patients collected over a 15-month period. The analysis demonstrated significant yet modest relationships between pain and relative humidity, pressure and wind speed, with correlations remaining even when accounting for mood and physical activity. This research highlights how citizen-science experiments can collect large datasets on real-world populations to address long-standing health questions. These results will act as a starting point for a future system for patients to better manage their health through pain forecasts.

Simplification of Validated Patient-Reported Outcome Instruments in Inflammatory Bowel Disease

Objectives

Improving health-related quality of life (HRQoL) is a major treatment goal for inflammatory bowel disease (IBD) patients. Tools to measure HRQoL, including the Pittsburgh Sleep Quality Index and the Short Inflammatory Bowel Diseases Questionnaire, are lengthy and rely on recall. This prospective, proof-of-concept pilot study assessed the feasibility, reliability, and validity of daily, simplified HRQoL and sleep quality data collection using mobile technologies in patients with IBD.

Methods

Adult IBD patients were recruited from our center. Daily HRQoL and sleep quality were assessed using visual analog scale (VAS) surveys, and pain was assessed using the Wong-Baker FACES Pain Rating Scale (WBS). HRQoL and sleep were assessed on days 1, 14, and 28 of the study using the short IBD Questionnaire (SIBDQ) and Pittsburgh Sleep Quality Index (PSQI). Correlation between the daily instruments and the biweekly validated instruments were assessed using the Pearson correlation coefficient.

Results

One hundred patients were enrolled. The correlation between mean global assessment VAS score over 2 weeks and PSQI score taken at 14 days was moderate (r = 0.62, P < 0.0001). The correlation over the same time interval between mean WBS score and SIBDQ score was strong (r = −0.71, P < 0.0001), and the correlation between mean sleep VAS score and PSQI score was moderate (r = −0.55, P < 0.0001).

Conclusions

This study demonstrates the potential for electronic quality of life, sleep quality, and pain assessments as feasible, reliable, and valid tools in IBD patients. Intermittent administration of these simplified electronic assessments may be useful in further reducing patient survey burden without significantly compromising their utility.

Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study

Background

Rheumatoid arthritis (RA) is a condition with symptoms that vary over time. The typical 3- to 6-month interval between physician visits may lead to patients failing to recall or underreporting symptoms experienced during the interim. Wearable digital technology enables the regular passive collection of patients’ biometric and activity data. If it is shown to be strongly related to data captured by patient-reported outcome (PRO) measures, information collected passively from wearable digital technology could serve as an objective proxy or be complementary to patients’ subjective experience of RA symptoms.

Objective

The goal of this study is to characterize the extent to which digital measures collected from a consumer-grade smartwatch agree with measures of RA disease activity and other PROs collected via a smartphone app.

Methods

This observational study will last 6 months for each participant. We aim to recruit 250 members of the ArthritisPower registry with an RA diagnosis who will receive a smartwatch to wear for the period of the study. From the ArthritisPower mobile app on their own smartphone device, participants will be prompted to answer daily and weekly electronic PRO (ePRO) measures for the first 3 months.

Results

The study was launched in December 2018 and will require up to 18 months to complete. Study results are expected to be published by the end of 2021.

Conclusions

The completion of this study will provide important data regarding the following: (1) the relationship between passively collected digital measures related to activity, heart rate, and sleep collected from a smartwatch with ePROs related to pain, fatigue, physical function, and RA flare entered via smartphone app; (2) determine predictors of adherence with smartwatch and smartphone app technology; and (3) assess the effect of study-specific reminders on adherence with the smartwatch.

International Registered Report Identifier (IRRID)

DERR1-10.2196/14665

Mobile Apps for Individuals With Rheumatoid Arthritis: A Systematic Review

BACKGROUND

Mobile health applications (apps) have the potential to help individuals with chronic illnesses learn about, monitor, and manage their condition, but these apps are largely unexamined, with the state and direction of development unclear.

OBJECTIVE

We performed a systematic review of publicly available apps, directed toward individuals with rheumatoid arthritis (RA); described their current features; and determined areas of unmet need.

METHODS

We searched the iTunes and Google Play App Stores for the term "arthritis" and reviewed the descriptions of these apps for specific mentions of RA. Applications that met inclusion criteria were downloaded and reviewed. Using a set of quality measures identified from literature review, we assessed each app for 4 features: basic characteristics, content source, functionality, and security. Frequencies for each feature were recorded, and percentages were calculated.

RESULTS

Twenty apps intended for use by RA patients were identified in December 2016. Fifty percent of apps (n = 10) offered only symptom tracking. Five (20%) provided only information about RA, and 5 (20%) engaged patients by providing both symptom tracking and educational information. Fewer than 50% of apps provided means to contact health care providers or link to an online community, and only 6 (30%) offered security protection for the user.

CONCLUSIONS

Most current RA apps do not provide a comprehensive experience for individuals with RA. Areas for optimization include the implementation of smartphone accessibility features and secure methods of protecting individual health information.

Music-Enhanced Analgesia and Antiseizure Activities in Animal Models of Pain and Epilepsy: Toward Preclinical Studies Supporting Development of Digital Therapeutics and Their Combinations With Pharmaceutical Drugs

Digital therapeutics (software as a medical device) and mobile health (mHealth) technologies offer a means to deliver behavioral, psychosocial, disease self-management and music-based interventions to improve therapy outcomes for chronic diseases, including pain and epilepsy. To explore new translational opportunities in developing digital therapeutics for neurological disorders, and their integration with pharmacotherapies, we examined analgesic and antiseizure effects of specific musical compositions in mouse models of pain and epilepsy. The music playlist was created based on the modular progression of Mozart compositions for which reduction of seizures and epileptiform discharges were previously reported in people with epilepsy. Our results indicated that music-treated mice exhibited significant analgesia and reduction of paw edema in the carrageenan model of inflammatory pain. Among analgesic drugs tested (ibuprofen, cannabidiol (CBD), levetiracetam, and the galanin analog NAX 5055), music intervention significantly decreased paw withdrawal latency difference in ibuprofen-treated mice and reduced paw edema in combination with CBD or NAX 5055. To the best of our knowledge, this is the first animal study on music-enhanced antinociceptive activity of analgesic drugs. In the plantar incision model of surgical pain, music-pretreated mice had significant reduction of mechanical allodynia. In the corneal kindling model of epilepsy, the cumulative seizure burden following kindling acquisition was lower in animals exposed to music. The music-treated group also exhibited significantly improved survival, warranting further research on music interventions for preventing Sudden Unexpected Death in Epilepsy (SUDEP). We propose a working model of how musical elements such as rhythm, sequences, phrases and punctuation found in K.448 and K.545 may exert responses via parasympathetic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis. Based on our findings, we discuss: (1) how enriched environment (EE) can serve as a preclinical surrogate for testing combinations of non-pharmacological modalities and drugs for the treatment of pain and other chronic diseases, and (2) a new paradigm for preclinical and clinical development of therapies leading to drug-device combination products for neurological disorders, depression and cancer. In summary, our present results encourage translational research on integrating non-pharmacological and pharmacological interventions for pain and epilepsy using digital therapeutics.

Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study

BACKGROUND

The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research.

OBJECTIVE

The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies.

METHODS

A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance.

RESULTS

Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019.

CONCLUSIONS

KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/10238.

A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review

Background

There is mixed evidence to support current ambitions for mobile health (mHealth) apps to improve chronic health and well-being. One proposed explanation for this variable effect is that users do not engage with apps as intended. The application of analytics, defined as the use of data to generate new insights, is an emerging approach to study and interpret engagement with mHealth interventions.

Objective

This study aimed to consolidate how analytic indicators of engagement have previously been applied across clinical and technological contexts, to inform how they might be optimally applied in future evaluations.

Methods

We conducted a scoping review to catalog the range of analytic indicators being used in evaluations of consumer mHealth apps for chronic conditions. We categorized studies according to app structure and application of engagement data and calculated descriptive data for each category. Chi-square and Fisher exact tests of independence were applied to calculate differences between coded variables.

Results

A total of 41 studies met our inclusion criteria. The average mHealth evaluation included for review was a two-group pretest-posttest randomized controlled trial of a hybrid-structured app for mental health self-management, had 103 participants, lasted 5 months, did not provide access to health care provider services, measured 3 analytic indicators of engagement, segmented users based on engagement data, applied engagement data for descriptive analyses, and did not report on attrition. Across the reviewed studies, engagement was measured using the following 7 analytic indicators: the number of measures recorded (76%, 31/41), the frequency of interactions logged (73%, 30/41), the number of features accessed (49%, 20/41), the number of log-ins or sessions logged (46%, 19/41), the number of modules or lessons started or completed (29%, 12/41), time spent engaging with the app (27%, 11/41), and the number or content of pages accessed (17%, 7/41). Engagement with unstructured apps was mostly measured by the number of features accessed (8/10, P=.04), and engagement with hybrid apps was mostly measured by the number of measures recorded (21/24, P=.03). A total of 24 studies presented, described, or summarized the data generated from applying analytic indicators to measure engagement. The remaining 17 studies used or planned to use these data to infer a relationship between engagement patterns and intended outcomes.

Conclusions

Although researchers measured on average 3 indicators in a single study, the majority reported findings descriptively and did not further investigate how engagement with an app contributed to its impact on health and well-being. Researchers are gaining nuanced insights into engagement but are not yet characterizing effective engagement for improved outcomes. Raising the standard of mHealth app efficacy through measuring analytic indicators of engagement may enable greater confidence in the causal impact of apps on improved chronic health and well-being.

Patient understanding of two commonly used patient reported outcome measures for primary care: a cognitive interview study

Background

Standardised generic patient-reported outcome measures (PROMs) which measure health status are often unresponsive to change in primary care. Alternative formats, which have been used to increase responsiveness, include individualised PROMs (in which respondents specify the outcomes of interest in their own words) and transitional PROMs (in which respondents directly rate change over a period). The objective of this study was to test qualitatively, through cognitive interviews, two PROMs, one using each respective format.

Methods

The individualised PROM selected was the Measure Yourself Medical Outcomes Profile (MYMOP). The transitional PROM was the Patient Enablement Instrument (PEI). Twenty patients who had recently attended the GP were interviewed while completing the questionnaires. Interview data was analysed using a modification of Tourangeau’s model of cognitive processing: comprehension, response, recall and face validity.

Results

Patients found the PEI simple to complete, but for some it lacked face validity. The transitional scale was sometimes confused with a status scale and was problematic in situations when the relevant GP appointment was part of a longer episode of care. Some patients reported a high enablement score despite verbally reporting low enablement but high regard for their GP, which suggested hypothesis-guessing. The interpretation of the PEI items was inconsistent between patients.

MYMOP was more difficult for patients to complete, but had greater face validity than the PEI. The scale used was open to response-shift: some patients suggested they would recalibrate their definition of the scale endpoints as their illness and expectations changed.

Conclusions

The study provides information for both users of PEI/MYMOP and developers of individualised and transitional questionnaires.

Users should heed the recommendation that MYMOP should be interview-administered, and this is likely to apply to other individualised scales. The PEI is open to hypothesis-guessing and may lack face-validity for a longer episode of care (e.g. in patients with chronic conditions). Developers should be cognisant that transitional scales can be inconsistently completed: some patients forget during completion that they are measuring change from baseline. Although generic questionnaires require the content to be more general than do disease-specific questionnaires, developers should avoid questions which allow broad and varied interpretations.

Electronic supplementary material

The online version of this article (10.1186/s12875-018-0850-2) contains supplementary material, which is available to authorized users.

Using a ResearchKit Smartphone App to Collect Rheumatoid Arthritis Symptoms From Real-World Participants: Feasibility Study

Background

Using smartphones to enroll, obtain consent, and gather self-reported data from patients has the potential to enhance our understanding of disease burden and quantify physiological impact in the real world. It may also be possible to harness integral smartphone sensors to facilitate remote collection of clinically relevant data.

Objective

We conducted the Patient Rheumatoid Arthritis Data From the Real World (PARADE) observational study using a customized ResearchKit app with a bring-your-own-device approach. Our objective was to assess the feasibility of using an entirely digital approach (social media and smartphone app) to conduct a real-world observational study of patients with rheumatoid arthritis.

Methods

We conducted this observational study using a customized ResearchKit app with a bring-your-own-device approach. To recruit patients, the PARADE app, designed to guide patients through a series of tasks, was publicized via social media platforms and made available for patients in the United States to download from the Apple App Store. We collected patient-reported data, such as medical history, rheumatoid arthritis-related medications (past and present), and a range of patient-reported outcome measures. We included in the assessment a joint-pain map and a novel objective assessment of wrist range of movement, measured by the smartphone-embedded gyroscope and accelerometer.

Results

Within 1 month of recruitment via social media campaigns, 399 participants self-enrolled, self-consented, and provided complete demographic data. Joint pain was the most frequently reported rheumatoid arthritis symptom to bother study participants (344/393, 87.5%). Severe patient-reported wrist pain appeared to be inversely linked with the range of wrist movement measured objectively by the app. At study entry, 292 of 399 participants (73.2%) indicated a preference for participating in a mobile app–based study. The number of participants in the study declined to 45 of 399 (11.3%) at week 12.

Conclusions

Despite the declining number of participants over time, the combination of social media and smartphone app with sensor integration was a feasible and cost-effective approach for the collection of patient-reported data in rheumatoid arthritis. Integral sensors within smartphones can be harnessed to provide novel end points, and the novel wrist range of movement test warrants further clinical validation.

Physical Activity Assessment Using an Activity Tracker in Patients with Rheumatoid Arthritis and Axial Spondyloarthritis: Prospective Observational Study

Background

Physical activity can be tracked using mobile devices and is recommended in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) management. The World Health Organization (WHO) recommends at least 150 min per week of moderate to vigorous physical activity (MVPA).

Objective

The objectives of this study were to assess and compare physical activity and its patterns in patients with RA and axSpA using an activity tracker and to assess the feasibility of mobile devices in this population.

Methods

This multicentric prospective observational study (ActConnect) included patients who had definite RA or axSpA, and a smartphone. Physical activity was assessed over 3 months using a mobile activity tracker, recording the number of steps per minute. The number of patients reaching the WHO recommendations was calculated. RA and axSpA were compared, using linear mixed models, for number of steps, proportion of morning steps, duration of total activity, and MVPA. Physical activity trajectories were identified using the K-means method, and factors related to the low activity trajectory were explored by logistic regression. Acceptability was assessed by the mean number of days the tracker was worn over the 3 months (ie, adherence), the percentage of wearing time, and by an acceptability questionnaire.

Results

A total of 157 patients (83 RA and 74 axSpA) were analyzed; 36.3% (57/157) patients were males, and their mean age was 46 (standard deviation [SD] 12) years and mean disease duration was 11 (SD 9) years. RA and axSpA patients had similar physical activity levels of 16 (SD 11) and 15 (SD 12) min per day of MVPA (P=.80), respectively. Only 27.4% (43/157) patients reached the recommendations with a mean MVPA of 106 (SD 77) min per week. The following three trajectories were identified with constant activity: low (54.1% [85/157] of patients), moderate (42.7% [67/157] of patients), and high (3.2% [5/157] of patients) levels of MVPA. A higher body mass index was significantly related to less physical activity (odds ratio 1.12, 95% CI 1.11-1.14). The activity trackers were worn during a mean of 79 (SD 17) days over the 90 days follow-up. Overall, patients considered the use of the tracker very acceptable, with a mean score of 8 out 10.

Conclusions

Patients with RA and axSpA performed insufficient physical activity with similar levels in both groups, despite the differences between the 2 diseases. Activity trackers allow longitudinal assessment of physical activity in these patients. The good adherence to this study and the good acceptability of wearing activity trackers confirmed the feasibility of the use of a mobile activity tracker in patients with rheumatic diseases.

Factors that influence older people's engagement with digital health technology

Digital health technology (DHT) encompasses a wide range of applications and interventions with the potential to address the health needs of an increasingly ageing population. Older people's engagement with DHT depends on many factors, and this article summarises understanding of the barriers and facilitators to DHT uptake and continued use among older people. Older people's confidence in using digital technology, their perceptions of personal benefit from DHT, its design, and the support they receive from health professionals and carers in using DHT all affect their level of engagement. Understanding these barriers and facilitators among the older population creates ways to enable greater numbers to benefit from DHT. This article provides information for those who work with, or design digital health interventions for, older people to help them to influence older people's engagement with these rapidly evolving healthcare innovations.

Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study

BACKGROUND

The huge increase in smartphone use heralds an enormous opportunity for epidemiology research, but there is limited evidence regarding long-term engagement and attrition in mobile health (mHealth) studies.

OBJECTIVE

The objective of this study was to examine how representative the Cloudy with a Chance of Pain study population is of wider chronic-pain populations and to explore patterns of engagement among participants during the first 6 months of the study.

METHODS

Participants in the United Kingdom who had chronic pain (≥3 months) and enrolled between January 20, 2016 and January 29, 2016 were eligible if they were aged ≥17 years and used the study app to report any of 10 pain-related symptoms during the study period. Participant characteristics were compared with data from the Health Survey for England (HSE) 2011. Distinct clusters of engagement over time were determined using first-order hidden Markov models, and participant characteristics were compared between the clusters.

RESULTS

Compared with the data from the HSE, our sample comprised a higher proportion of women (80.51%, 5129/6370 vs 55.61%, 4782/8599) and fewer persons at the extremes of age (16-34 and 75+). Four clusters of engagement were identified: high (13.60%, 865/6370), moderate (21.76%, 1384/6370), low (39.35%, 2503/6370), and tourists (25.44%, 1618/6370), between which median days of data entry ranged from 1 (interquartile range; IQR: 1-1; tourist) to 149 (124-163; high). Those in the high-engagement cluster were typically older, whereas those in the tourist cluster were mostly male. Few other differences distinguished the clusters.

CONCLUSIONS

Cloudy with a Chance of Pain demonstrates a rapid and successful recruitment of a large, representative, and engaged sample of people with chronic pain and provides strong evidence to suggest that smartphones could provide a viable alternative to traditional data collection methods.

Flares assessed weekly in patients with rheumatoid arthritis or axial spondyloarthritis and relationship with physical activity measured using a connected activity tracker: a 3-month study

BACKGROUND

The evolution of rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is marked by flares, although their frequency is unclear. Flares may impact physical activity. Activity can be assessed objectively using activity trackers. The objective was to assess longitudinally the frequency of flares and the association between flares and objective physical activity.

METHODS

This prospective observational study (ActConnect) included patients with definite clinician-confirmed RA or axSpA, owning a smartphone. During 3 months, physical activity was assessed continuously by number of steps/day, using an activity tracker, and disease flares were self-assessed weekly using a specific flare question and, if relevant, the duration of the flare. The relationship between flares and physical activity for each week (time point) was assessed by linear mixed models.

RESULTS

In all, 170/178 patients (91 patients with RA and 79 patients with axSpA; 1553 time points) were analysed: mean age was 45.5±12.4 years, mean disease duration was 10.3±8.7 years, 60 (35.3%) were men and 90 (52.9%) received biologics. The disease was well-controlled (mean Disease Activity Score 28: 2.3±1.2; mean Bath Ankylosing Spondylitis Disease Activity Index score: 3.3±2.1). Patients self-reported flares in 28.2%±28.1% of the weekly assessments. Most flares (78.9%±31.4%) lasted ≤3 days. Persistent flares lasting more than 3 days were independently associated with less weekly physical activity (p=0.03), leading to a relative decrease of 12%-21% and an absolute decrease ranging from 836 to 1462 steps/day.

CONCLUSION

Flares were frequent but usually of short duration in these stable patients with RA and axSpA. Persistent flares were related to a moderate decrease in physical activity, confirming objectively the functional impact of patient-reported flares.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.