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

Feasibility and user acceptability of Breezing™, a mobile indirect calorimetry device, in pregnant women with overweight or obesity

Emerging evidence has suggested that prenatal resting energy expenditure (REE) may be an important determinant of gestational weight gain. Advancements in technology such as the real-time, mobile indirect calorimetry device (Breezing™) have offered the novel opportunity to continuously assess prenatal REE while also potentially capturing fluctuations in REE. The purpose of this study was to examine feasibility and user acceptability of Breezing™ to assess weekly REE from 8-36 weeks gestation in pregnant women with overweight or obesity participating in the Healthy Mom Zone intervention study. Participants (N=27) completed REE assessments once per week from 8-36 gestation using Breezing™. Feasibility of the device was calculated as compliance (# of weeks used/total # of weeks). User acceptability was measured by asking women to report on the device's enjoyability and barriers. Median compliance was 68%. However, when weeks women experienced technical difficulties (11 of 702 total events) and the device was unavailable were removed (13 of 702 total events), median compliance increased to 71%. Over half (56%) of the women reported that the device was enjoyable or they had neutral feelings about it whereas the remaining 44% reported that it was not enjoyable. The most common barrier reported (44%) was the experience of technical issues. Study compliance data suggest the feasibility of using Breezing™ to assess prenatal REE is promising. However, acceptability data suggest future interventionists should develop transparent and informative protocols to address any barriers prior to implementing the device to increase use.

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).

Health disparities in systemic lupus erythematosus-a narrative review

PURPOSE OF REVIEW

To describe root causes of health disparities by reviewing studies on incidence and outcomes of systemic lupus erythematosus (SLE) related to ethnic, race, gender, or socioeconomic differences and to propose solutions.

RECENT FINDINGS

SLE outcomes have steadily improved over the past 40 years but are not uniformly distributed across various racial and ethnic groups. Belonging to racial and ethnic minority has been cited as a risk factor for more severe disease and poor outcome in SLE. Population-based registries have demonstrated that Black patients with SLE have significantly lower life expectancy compared to White patients. Lower socioeconomic status has been shown to be one of the strongest predictors of progression to end stage renal disease in lupus nephritis. An association between patient experiences of racial discrimination, increased SLE activity, and damage has also been described. The lack of representation of marginalized communities in lupus clinical trials further perpetuates these disparities. To that end, the goal of a rheumatology workforce that resembles the patients it treats has emerged as one of many solutions to current shortfalls in care. Disparities in SLE incidence, treatment, and outcomes have now been well established. The root causes of these disparities are multifactorial including genetic, epigenetic, and socioeconomic. The underrepresentation of marginalized communities in lupus clinical trials further worsen these disparities. Efforts have been made recently to address disparities in a more comprehensive manner, but systemic causes of disparities must be acknowledged and political will is required for a sustained positive change.

Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs)

Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.

Enrollment and retention of participants in remote digital health studies: a scoping review and framework proposal (Preprint)

Background

Objective

Methods

Results

Conclusions

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.

Converting from face-to-face to postal follow-up and its effects on participant retention, response rates and errors: lessons from the EQUAL study in the UK

BACKGROUND

Prospective cohort studies are challenging to deliver, with one of the main difficulties lying in retention of participants. The need to socially distance during the COVID-19 pandemic has added to this challenge. The pre-COVID-19 adaptation of the European Quality (EQUAL) study in the UK to a remote form of follow-up for efficiency provides lessons for those who are considering changing their study design.

METHODS

The EQUAL study is an international prospective cohort study of patients ≥65 years of age with advanced chronic kidney disease. Initially, patients were invited to complete a questionnaire (SF-36, Dialysis Symptom Index and Renal Treatment Satisfaction Questionnaire) at research clinics every 3-6 months, known as "traditional follow-up" (TFU). In 2018, all living patients were invited to switch to "efficient follow-up" (EFU), which used an abbreviated questionnaire consisting of SF-12 and Dialysis Symptom Index. These were administered centrally by post. Response rates were calculated using returned questionnaires as a proportion of surviving invitees, and error rates presented as the average percentage of unanswered questions or unclear answers, of total questions in returned questionnaires. Response and error rates were calculated 6-monthly in TFU to allow comparisons with EFU.

RESULTS

Of the 504 patients initially recruited, 236 were still alive at the time of conversion to EFU; 111 of these (47%) consented to the change in follow-up. In those who consented, median TFU was 34 months, ranging from 0 to 42 months. Their response rates fell steadily from 88% (98/111) at month 0 of TFU, to 20% (3/15) at month 42. The response rate for the first EFU questionnaire was 60% (59/99) of those alive from TFU. With this improvement in response rates, the first EFU also lowered errors to baseline levels seen in early follow-up, after having almost trebled throughout traditional follow-up.

CONCLUSIONS

Overall, this study demonstrates that administration of shorter follow-up questionnaires by post rather than in person does not negatively impact patient response or error rates. These results may be reassuring for researchers who are trying to limit face-to-face contact with patients during the COVID-19 pandemic.

A Systematic Review of Methods used to Conduct Decentralised Clinical Trials

Aims

To evaluate, using quantitative and qualitative approaches, published data on the design and conduct of decentralised clinical trials (DCTs).

Methods

We searched MEDLINE, EMBASE, CENTRAL, PsycINFO, ProQuest Dissertations and Theses, ClinicalTrials.gov, OpenGrey and Google Scholar for publications reporting, discussing, or evaluating decentralised clinical research methods. Reports of randomised clinical trials using decentralised methods were included in a focused quantitative analysis with a primary outcome of number of randomised participants. All publications discussing or evaluating DCTs were included in a wider qualitative analysis to identify advantages, disadvantages, facilitators, barriers and stakeholder opinions of decentralised clinical trials. Quantitative data were summarised using descriptive statistics, and qualitative data analysed using a thematic approach.

Results

Initial searches identified 19 704 articles. After removal of duplicates, 18 553 were screened, resulting in 237 eligible for full‐text assessment. Forty‐five trials were included in the quantitative analysis; 117 documents were included in the qualitative analysis. Trials were widely heterogeneous in design and reporting, precluding meta‐analysis of the effect of DCT methods on the primary recruitment outcome. Qualitative analysis formulated 4 broad themes: value, burden, safety and equity. Participant and stakeholder experiences of DCTs were incompletely represented.

Conclusion

DCTs are developing rapidly. However, there is insufficient evidence to confirm which methods are most effective in trial recruitment, retention, or overall cost. The identified advantages, disadvantages, facilitators and barriers should inform the development of DCT methods. We recommend further research on how DCTs are experienced and perceived by participants and stakeholders to maximise potential benefits.

The ideal mHealth-application for rheumatoid arthritis: qualitative findings from stakeholder focus groups

Background

Shifts in treatment strategies for rheumatoid arthritis (RA) have made ambulatory care more labour-intensive. These developments have prompted innovative care models, including mobile health (mHealth) applications. This study aimed to explore the perceptions of mHealth-inexperienced stakeholders concerning these applications in RA care.

Methods

We performed a qualitative study by focus group interviews of stakeholders including RA patients, nurses specialised in RA care and rheumatologists. The qualitative analysis guide of Leuven (QUAGOL), which is based on grounded theory principles, was used to thematically analyse the data. In addition, the Persuasive Systems Design (PSD) model was used to structure recommended app-features.

Results

In total, 2 focus groups with nurses (total n = 16), 2 with patients (n = 17) and 2 with rheumatologists (n = 25) took place. Six overarching themes emerged from the analysis. Efficiency of care and enabling patient empowerment were the two themes considered as expected benefits of mHealth-use in practice by the stakeholders. In contrast, 4 themes emerged as possible barriers of mHealth-use: the burden of chronic app-use, motivational aspects, target group aspects, and legal and organisational requirements. Additionally, recommendations for an ideal mHealth-app could be structured into 4 domains (Primary Task Support, Dialogue Support, Social Support and System Credibility) according to the PSD-framework. Most recommended features were related to improving ease of use (Task Support) and System Credibility.

Conclusions

Although mHealth-apps were expected to improve care efficiency and stimulate patient empowerment, stakeholders were concerned that mHealth-app use could reinforce negative illness behaviour. For mHealth-apps to be successful in practice, challenges according to stakeholders were avoiding long-term poor compliance, finding the target audience and tailoring a legal and organisational framework. Finally, the ideal mHealth-application should above all be trustworthy and easy to use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04624-8.

Adaptive Symptom Monitoring Using Hidden Markov Models - An Application in Ecological Momentary Assessment

Wearable and mobile technology provides new opportunities to manage health conditions remotely and unobtrusively. For example, healthcare providers can repeatedly sample a person's condition to monitor progression of symptoms and intervene if necessary. There is usually a utility-tolerability trade-off between collecting information at sufficient frequencies and quantities to be useful, and over-burdening the user or the underlying technology, particularly when active input is required from the user. Selecting the next sampling time adaptively using previous responses, so that people are only sampled at high frequency when necessary, can help to manage this trade-off. We present a novel approach to adaptive sampling using clustered continuous-time hidden Markov models. The model predicts, at any given sampling time, the probability of moving to an 'alert' state, and the next sample time is scheduled when this probability has exceeded a given threshold. The clusters, each representing a distinct sub-model, allow heterogeneity in states and state transitions. The work is illustrated using longitudinal mental-health symptom data in 49 people collected using ClinTouch, a mobile app designed to monitor people with a diagnosis of schizophrenia. Using these data, we show how the adaptive sampling scheme behaves under different model parameters and risk thresholds, and how the average sampling can be substantially reduced whilst maintaining a high sampling frequency during high-risk periods.

Perspectives of Trial Staff on the Barriers to Recruitment in a Digital Intervention for Psychosis and How to Work Around Them: Qualitative Study Within a Trial

Background

Recruitment processes for clinical trials of digital interventions for psychosis are seldom described in detail in the literature. Although trial staff have expertise in describing barriers to and facilitators of recruitment, a specific focus on understanding recruitment from the point of view of trial staff is rare, and because trial staff are responsible for meeting recruitment targets, a lack of research on their point of view is a key limitation.

Objective

The primary aim of this study was to understand recruitment from the point of view of trial staff and discover what they consider important.

Methods

We applied pluralistic ethnographic methods, including analysis of trial documents, observation, and focus groups, and explored the recruitment processes of the EMPOWER (Early Signs Monitoring to Prevent Relapse in Psychosis and Promote Well-being, Engagement, and Recovery) feasibility trial, which is a digital app–based intervention for people diagnosed with schizophrenia.

Results

Recruitment barriers were categorized into 2 main themes: service characteristics (lack of time available for mental health staff to support recruitment, staff turnover, patient turnover [within Australia only], management styles of community mental health teams, and physical environment) and clinician expectations (filtering effects and resistance to research participation). Trial staff negotiated these barriers through strategies such as emotional labor (trial staff managing feelings and expressions to successfully recruit participants) and trying to build relationships with clinical staff working within community mental health teams.

Conclusions

Researchers in clinical trials for digital psychosis interventions face numerous recruitment barriers and do their best to work flexibly and to negotiate these barriers and meet recruitment targets. The recruitment process appeared to be enhanced by trial staff supporting each other throughout the recruitment stage of the trial.

Heartrate variability biofeedback for migraine using a smartphone application and sensor: A randomized controlled trial

INTRODUCTION

Although hand temperature and electromyograph biofeedback have evidence for migraine prevention, to date, no study has evaluated heartrate variability (HRV) biofeedback for migraine.

METHODS

2-arm randomized trial comparing an 8-week app-based HRV biofeedback (HeartMath) to waitlist control. Feasibility/acceptability outcomes included number and duration of sessions, satisfaction, barriers and adverse events. Primary clinical outcome was Migraine-Specific Quality of Life Questionnaire (MSQv2).

RESULTS

There were 52 participants (26/arm). On average, participants randomized to the Hearthmath group completed 29 sessions (SD = 29, range: 2-86) with an average length of 6:43 min over 36 days (SD = 27, range: 0, 88) before discontinuing. 9/29 reported technology barriers. 43% said that they were likely to recommend Heartmath to others. Average MSQv2 decreases were not significant between the Heartmath and waitlist control (estimate = 0.3, 95% CI = -3.1 - 3.6). High users of Heartmath reported a reduction in MSQv2 at day 30 (-12.3 points, p = 0.010) while low users did not (p = 0.765).

DISCUSSION

App-based HRV biofeedback was feasible and acceptable on a time-limited basis for people with migraine. Changes in the primary clinical outcome did not differ between biofeedback and control; however, high users of the app reported more benefit than low users.

Incorporating patient generated health data into pharmacoepidemiological research

Epidemiology and pharmacoepidemiology frequently employ Real-World Data (RWD) from healthcare teams to inform research. These data sources usually include signs, symptoms, tests, and treatments, but may lack important information such as the patient's diet or adherence or quality of life. By harnessing digital tools a new fount of evidence, Patient (or Citizen/Person) Generated Health Data (PGHD), is becoming more readily available. This review focusses on the advantages and considerations in using PGHD for pharmacoepidemiological research. New and corroborative types of data can be collected directly from patients using digital devices, both passively and actively. Practical issues such as patient engagement, data linking, validation, and analysis are among important considerations in the use of PGHD. In our ever increasingly patient-centric world, PGHD incorporated into more traditional Real-Word data sources offers innovative opportunities to expand our understanding of the complex factors involved in health and the safety and effectiveness of disease treatments. Pharmacoepidemiologists have a unique role in realizing the potential of PGHD by ensuring that robust methodology, governance, and analytical techniques underpin its use to generate meaningful research results.

Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis

Background

Chronic disease represents a large and growing burden to the health care system worldwide. One method of managing this burden is the use of app-based interventions; however attrition, defined as lack of patient use of the intervention, is an issue for these interventions. While many apps have been developed, there is some evidence that they have significant issues with sustained use, with up to 98% of people only using the app for a short time before dropping out and/or dropping use down to the point where the app is no longer effective at helping to manage disease.

Objective

Our objectives are to systematically appraise and perform a meta-analysis on dropout rates in apps for chronic disease and to qualitatively synthesize possible reasons for these dropout rates that could be addressed in future interventions.

Methods

MEDLINE (Medical Literature Analysis and Retrieval System Online), PubMed, Cochrane CENTRAL (Central Register of Controlled Trials), and Embase were searched from 2003 to the present to look at mobile health (mHealth) and attrition or dropout. Studies, either randomized controlled trials (RCTs) or observational trials, looking at chronic disease with measures of dropout were included. Meta-analysis of attrition rates was conducted in Stata, version 15.1 (StataCorp LLC). Included studies were also qualitatively synthesized to examine reasons for dropout and avenues for future research.

Results

Of 833 studies identified in the literature search, 17 were included in the review and meta-analysis. Out of 17 studies, 9 (53%) were RCTs and 8 (47%) were observational trials, with both types covering a range of chronic diseases. The pooled dropout rate was 43% (95% CI 29-57), with observational studies having a higher dropout rate (49%, 95% CI 27-70) than RCTs in more controlled scenarios, which only had a 40% dropout rate (95% CI 16-63). The studies were extremely varied, which is represented statistically in the high degree of heterogeneity (I²>99%). Qualitative synthesis revealed a range of reasons relating to attrition from app-based interventions, including social, demographic, and behavioral factors that could be addressed.

Conclusions

Dropout rates in mHealth interventions are high, but possible areas to minimize attrition exist. Reducing dropout rates will make these apps more effective for disease management in the long term.

Trial Registration

International Prospective Register of Systematic Reviews (PROSPERO) CRD42019128737; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019128737

Smartphone based behavioral therapy for pain in multiple sclerosis (MS) patients: A feasibility acceptability randomized controlled study for the treatment of comorbid migraine and ms pain

BACKGROUND

Multiple Sclerosis (MS) and Migraine are comorbid neurologic conditions. Migraine prevalence is three times higher in the MS clinic population compared to the general population, and patients with MS and migraine are more symptomatic than patients with MS without migraine.

OBJECTIVE

We sought to conduct a pilot feasibility and acceptability study of the RELAXaHEAD app in MS-Migraine patients and to assess whether there was any change in migraine disability and MS pain-related disability.

METHODS

Randomized controlled study of patients with MS-migraine ages 18-80 years with 4+ headache days/ month who were willing to engage in smartphone based behavioral therapy. Half received the RELAXaHEAD app with progressive muscle relaxation (PMR) and the other half received the app without the PMR. Data was collected for 90 days on measures of recruitment, retention, engagement, and adherence to RELAXaHEAD. Preliminary data was also collected on migraine disability (MIDAS) and MS pain (PES).

RESULTS

Sixty-two subjects with MS-migraine were enrolled in the study (34 in PMR arm, 28 in monitored usual care arm). On average, during the 90 days, participants played the PMR on average 1.8 times per week, and for 12.9 min on days it was played. Forty-one percent (14/34) of the participants played the PMR two or more times weekly on average. Data was entered into the daily diaries, on average, 49% (44/90) of the days. There were major challenges in reaching subjects in follow-up for the efficacy data, and there was no significant change in migraine disability (MIDAS) scores or MS Pain (PES) scores from baseline to the endpoints. During the six-month follow-up, most patients felt either positively or neutral about the relaxation therapy.

CONCLUSION

There was interest in scalable accessible forms of behavioral therapy to treat migraine and MS-related pain in patients with MS and comorbid migraine. Similar to prior studies, a significant minority were willing to practice the PMR at least twice weekly. In the societal shift from telephone to more text and internet-based interactions, follow up was challenging, but those reached indicated that they appreciated the PMR and would recommend it to others. Future work should focus on engagement and efficacy.

Providing 'the bigger picture': benefits and feasibility of integrating remote monitoring from smartphones into the electronic health record

Objectives

To establish the acceptability and feasibility of collecting daily patient-generated health data (PGHD) using smartphones and integrating PGHD into the electronic health record, using the example of RA.

Methods

The Remote Monitoring of RA smartphone app was co-designed with patients, clinicians and researchers using qualitative semi-structured interviews and focus groups, including selection of question sets for symptoms and disease impact. PGHD were integrated into the electronic health record of one hospital and available in graphical form during consultations. Acceptability and feasibility were assessed with 20 RA patients and two clinicians over 3 months. A qualitative evaluation included semi-structured interviews with patients and clinicians before and after using the app, and audio-recordings of consultations to explore impact on the consultation. PGHD completeness was summarized descriptively, and qualitative data were analysed thematically.

Results

Patients submitted data on a median of 91% days over 3 months. Qualitative analysis generated three themes: RA as an invisible disease; providing the bigger picture of RA; and enabling person-centred consultations. The themes demonstrated that the system helped render patients’ RA more visible by providing the ‘bigger picture’, identifying real-time changes in disease activity and capturing symptoms that would otherwise have been missed. Graphical summaries during consultations enabled a more person-centred approach whereby patients felt better able to participate in consultations and treatment plans.

Conclusion

Remote Monitoring of RA has uniquely integrated daily PGHD from smartphones into the electronic health record. It has delivered proof-of-concept that such integrated remote monitoring systems are feasible and can transform consultations for clinician and patient benefit.

Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

Digital technologies such as smartphones are transforming the way scientists conduct biomedical research. Several remotely conducted studies have recruited thousands of participants over a span of a few months allowing researchers to collect real-world data at scale and at a fraction of the cost of traditional research. Unfortunately, remote studies have been hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of outcomes. We report the findings regarding recruitment and retention from eight remote digital health studies conducted between 2014-2019 that provided individual-level study-app usage data from more than 100,000 participants completing nearly 3.5 million remote health evaluations over cumulative participation of 850,000 days. Median participant retention across eight studies varied widely from 2-26 days (median across all studies = 5.5 days). Survival analysis revealed several factors significantly associated with increase in participant retention time, including (i) referral by a clinician to the study (increase of 40 days in median retention time); (ii) compensation for participation (increase of 22 days, 1 study); (iii) having the clinical condition of interest in the study (increase of 7 days compared with controls); and (iv) older age (increase of 4 days). Additionally, four distinct patterns of daily app usage behavior were identified by unsupervised clustering, which were also associated with participant demographics. Most studies were not able to recruit a sample that was representative of the race/ethnicity or geographical diversity of the US. Together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.

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.

Associations Between Engagement With an Online Health Community and Changes in Patient Activation and Health Care Utilization: Longitudinal Web-Based Survey

BACKGROUND

Participation in online health communities (OHCs) is a popular trend in the United Kingdom. However, so far, no evidence exists to indicate an association between participation in OHCs and improved health outcomes.

OBJECTIVE

This study aimed to (1) determine changes in patient activation over 3 months in new users of an OHC, (2) describe patterns of engagement with an OHC, (3) examine whether patients' characteristics at baseline were associated with subsequent patterns of engagement, and (4) determine if patterns of engagement during the 3 months were associated with changes in patient activation, health care utilization, and health status.

METHODS

Active new OHC users on HealthUnlocked (HU) were surveyed to measure demographics, levels of patient activation (describing a person's confidence in managing their own health; scale 0-100 with 4 categories), health care utilization, and health status using a Web-based survey at baseline and 3 months. Patient activation at baseline and 3 months was compared (aim 1). Alongside, for a sample of HU users and survey responders, daily OHC website usage data were automatically captured. This was used to identify clusters of engagement with HU (aim 2). For survey responders, baseline characteristics, patient activation, health care utilization, and health status were compared at baseline and 3 months, overall, and between engagement clusters using t tests and chi-square tests (aims 3 and 4).

RESULTS

In 329 people who completed both surveys, baseline activation was most frequently level 3, described as taking action but still lacking confidence. At follow-up, a change of 2.6 points was seen, with the greatest change seen in those at lowest baseline activation levels. In addition, 4 clusters of engagement were identified: low, medium, high, and very high, who were active on HU for a mean of 4, 12, 29, and 59 days, respectively. Survey responders were more commonly high or very high engagers. Baseline activation was highest in low and very high engagers. Overall activation increased over time in all engagement groups. Very high engagers had the greatest improvement in activation (5 points), although the average change was not above what is considered clinically meaningful for any group. Fewer accident and emergency visits were seen at follow-up in those with higher engagement, although this trend was not seen for other health care utilization measures. There was no change in health status at 3 months.

CONCLUSIONS

This observational study provides some insight into how patterns of engagement with OHCs are associated with changes in patient activation, health care utilization, and health status. Over 3 months, overall, the change in activation was not clinically significant, and there were some indications that OHCs may be of benefit to particular groups. However, the study limitations prevent firm conclusions about causal relationships.

Enhanced Self-Efficacy and Behavioral Changes Among Patients With Diabetes: Cloud-Based Mobile Health Platform and Mobile App Service

Background

The prevalence of chronic disease is increasing rapidly. Health promotion models have shifted toward patient-centered care and self-efficacy. Devices and mobile app in the Internet of Things (IoT) have become critical self-management tools for collecting and analyzing personal data to improve individual health outcomes. However, the precise effects of Web-based interventions on self-efficacy and the related motivation factors behind individuals’ behavioral changes have not been determined.

Objective

The objective of this study was to gain insight into patients' self-efficacy with newly diagnosed diabetes (type 2 diabetes mellitus) and analyze the association of patient-centered health promotion behavior and to examine the implications of the results for IoT and mobile health mobile app features.

Methods

The study used data from the electronic health database (n=3128). An experimental design (n=121) and randomized controlled trials were employed to determine patient preferences in the health promotion program (n=62) and mobile self-management education (n=28). The transtheoretical model was used as a framework for observing self-management behavior for the improvement of individual health, and the theory of planned behavior was used to evaluate personal goals, execution, outcome, and personal preferences. A mobile app was used to determine individualized health promotion interventions and to apply these interventions to improve patients’ self-management and self-efficacy.

Results

Mobile questionnaires were administered for pre- and postintervention assessment through mobile app. A dynamic questionnaire allocation method was used to follow up and monitor patient behavioral changes in the subsequent 6 to 18 months. Participants at a high risk of problems related to blood pressure (systolic blood pressure ≥120 mm Hg) and body mass index (≥23 kg/m²) indicated high motivation to change and to achieve high scores in the self-care knowledge assessment (n=49, 95% CI −0.26% to −0.24%, P=.052). The associated clinical outcomes in the case group with the mobile-based intervention were slightly better than in the control group (glycated hemoglobin mean −1.25%, 95% CI 6.36 to 7.47, P=.002). In addition, 86% (42/49) of the participants improved their health knowledge through the mobile-based app and information and communications technology. The behavior-change compliance rate was higher among the women than among the men. In addition, the personal characteristics of steadiness and dominance corresponded with a higher compliance rate in the dietary and wellness intervention (83%, 81/98). Most participants (71%, 70/98) also increased their attention to healthy eating, being active, and monitoring their condition (30% 21/70, 21% 15/70, and 20% 14/70, respectively).

Conclusions

The overall compliance rate was discovered to be higher after the mobile app–based health intervention. Various intervention strategies based on patient characteristics, health care–related word-of-mouth communication, and social media may be used to increase self-efficacy and improve clinical outcomes. Additional research should be conducted to determine the most influential factors and the most effective adherence management techniques.

Environmental Hazards: A Coverage Response Approach

The rapid rise and implementation of Smart Systems (i.e., multi-functional observation and platform systems that depict settings and/or identify situations or features of interest, often in real-time) has inversely paralleled and readily exposed the reduced capacity of human and societal systems to effectively respond to environmental hazards. This overarching review and essay explores the complex set of interactions found among Smart, Societal, and Environmental Systems. The resulting rise in the poorly performing response solutions to environmental hazards that has occurred despite best practices, detailed forecast information, and the use and application of real-time in situ observational platforms are considered. The application of Smart Systems, relevant architectures, and ever-increasing numbers of applications and tools development by individuals as they interact with Smart Systems offers a means to ameliorate and resolve confounding found among all of the interdependent Systems. The interactions of human systems with environmental hazards further expose society’s complex operational vulnerabilities and gaps in response to such threats. An examination of decision-making, the auto-reactive nature of responses before, during, and after environmental hazards; and the lack of scalability and comparability are presented with regard to the prospects of applying probabilistic methods, cross-scale time and space domains; anticipated impacts, and the need to account for multimodal actions and reactions—including psycho-social contributions. Assimilation of these concepts and principles in Smart System architectures, applications, and tools is essential to ensure future viability and functionalities with regard to environmental hazards and to produce an effective set of societal engagement responses. Achieving the promise of Smart Systems relative to environmental hazards will require an extensive transdisciplinary approach to tie psycho-social behaviors directly with non-human components and systems in order to close actionable gaps in response. Pathways to achieve a more comprehensive understanding are given for consideration by the wide diversity of disciplines necessary to move forward in Smart Systems as tied with the societal response to environmental hazards.

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.

Assessment of Health-Related Quality of Life between People with Parkinson's Disease and Non-Parkinson's: Using Data Drawn from the '100 for Parkinson's' Smartphone-Based Prospective Study

Background: This study aims to assess the specific difference of the health-related quality of life between people with Parkinson's and non-Parkinson's. Methods: A total of 1710 people were drawn from a prospective study with a smartphone-based survey named '100 for Parkinson's' to assess health-related quality of life. The EQ-5D-5L descriptive system and the EQ visual analogue scale were used to measure health-related quality of life and a linear mixed model was used to analyze the difference. Results: The mean difference of EQ-5D-5L index values between people with Parkinson's and non-Parkinson's was 0.15 (95%CI: 0.12, 0.18) at baseline; it changed to 0.17 (95%CI: 0.14, 0.20) at the end of study. The mean difference of EQ visual analogue scale scores between them increased from 10.18 (95%CI: 7.40, 12.96) to 12.19 (95%CI: 9.41, 14.97) from baseline to the end of study. Conclusion: Data can be captured from the participants' own smart devices and support the notion that health-related quality of life for people with Parkinson's is lower than non-Parkinson's. This analysis provides useful evidence for the EQ-5D instrument and is helpful for public health specialists and epidemiologists to assess the health needs of people with Parkinson's and indirectly improve their health status.

Digital Technology and Mobile Health in Behavioral Migraine Therapy: a Narrative Review

PURPOSE OF REVIEW

This article reviews the recent research and development of electronic health (eHealth) and, in particular, mobile health (mHealth) strategies to deliver behavioral treatment for migraine. Prospects for future development and research of mobile health in migraine are suggested.

RECENT FINDINGS

Advances in digital technology and mobile technology have led to an era where electronic and mobile approaches are applied to several aspects of healthcare. Electronic behavioral interventions for migraine seem to be acceptable and feasible, but efficacy measures are uncertain. Clinical trials on mHealth-based classical behavioral therapies, such as relaxation, biofeedback, and cognitive behavioral therapy are missing in the literature. Within mHealth, headache diaries are the most researched and scientifically developed. Still, there is a gap between commercially available apps and scientifically validated and developed apps. Digital technology and mobile health has not yet lived out its potential in behavioral migraine therapy. Application of proper usability and functionality designs towards the right market, together with appraisal of medical and technological recommendations, may facilitate rapid development of eHealth and mHealth, while also establishing scientific evidence.

Using technology to support clinical care and research in rheumatoid arthritis

PURPOSE OF REVIEW

As digital technology becomes more ubiquitous, understanding the current state-of-the-art in digital information use for clinical care and research for patients with rheumatoid arthritis (RA) is timely and relevant.

RECENT FINDINGS

The opportunities for recording and utilizing high-quality data from rheumatologists are reviewed, as well as opportunities from collecting, integrating and analysing patient-generated data to deliver a step-change in the support and management of RA.

SUMMARY

Once greater adoption, standardization and implementation of relevant RA measures are in place within electronic health records (EHRs), patient care will improve and the ability to learn from aggregate experiences increases dramatically. Incorporating passive and patient-reported outcomes into self-management apps and integrating such data into the patient's health record will provide more responsive and better treatment results.

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.