Participant Engagement and Adherence to Providing Smartwatch and Patient-Reported Outcome Data: Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Real-World Study

A systematic review of digital and imaging technologies for measuring fatigue in immune mediated inflammatory diseases

Chronic fatigue greatly impacts the quality of life in individuals with immune-mediated inflammatory disease (IMID). Currently, fatigue assessment relies on patient-reported outcome (PRO) questionnaires. A systematic review following PRISMA guidelines was conducted to explore how digital and imaging technologies have been used to measure fatigue. PubMed and Cochrane Library were searched from 2003 to June 2023. Study quality was assessed using the STROBE checklist for observational studies. The database search identified 1541 studies; 16 were selected for inclusion, including three clinical trial reports. Disease cohorts included in this review were rheumatoid arthritis, primary Sjögren's syndrome, systemic lupus erythematosus, and inflammatory bowel disease. The majority of the studies found significant associations between fatigue, as assessed by PROs, and various digital and imaging endpoints. However, the studies were limited by a small sample size and short duration. This review stresses the need for additional research on fatigue using innovative digital and imaging modalities.

The real experience of reporting electronic patient-reported outcomes in patients with inflammatory arthritis and factors influencing participation: a scoping review

Inflammatory arthritis can result in pain, stiffness, fatigue, and reduce quality of life. Frequent monitoring of disease activity is necessary for patients with inflammatory arthritis, and electronic patient-reported outcomes (ePROs) play a crucial role in this process. This study aimed to investigate the real experience of reporting ePROs in patients with inflammatory arthritis, as well as to identify factors influencing participation. The ultimate goal was to inform targeted strategies and develop interventions to enhance the utilization of ePROs in clinical settings. A scoping review was performed using PubMed, Web of science, Embase, and the Cochrane library from 2000 to the present and the literature search focused on the experience of reporting ePROs in inflammatory arthritis and the factors that influence participation. Screening articles based on inclusion and exclusion criteria. A total of 1478 studies were identified, out of which 26 were included in the review. The top experience of applications/platforms in patients was that they were easy to use and that the applications were clear, logical and intuitive. A summary of 18 potential influencing factors were identified and there was inconsistent evidence for five of these factors. The participation of reporting ePROs is influenced by various factors, and the experience is a crucial aspect in patients with inflammatory arthritis. Analyzing patients' experience and influencing factors provides a theoretical basis for future interventions to facilitate the clinical application of ePRO. However, further research is needed to fully understand the association between influencing factors and intervention outcomes.

Generating Real-World Evidence From the Excellence Network in Rheumatology

PURPOSE

The Excellence Network in RheumatoloGY (ENRGY) was founded in 2021 and encompasses data from more than 700 private practice rheumatology providers throughout the United States, forming a practice-based research network (PBRN).

METHODS

Electronic health record (EHR) data from participating practices are aggregated, including structured data (e.g., clinical assessments) and unstructured data from two different EHR platforms. Targeted data quality efforts ensure capture of high-quality clinical data and reduce missingness. ENRGY network membership also provides participating sites access to technology services that enhance patient care. Centralized ethics approval and pre-existing legal agreements along with electronic tools to curate data and contact eligible study participants improves efficiency of multi-center prospective studies.

RESULTS

The ENRGY data warehouse includes linked administrative claims data for commercial and government-provided insurance (31% of patients linked to commercial claims) and patient-generated health data from both in-office and out-of-office settings via a smartphone app as well as biosensor data. ENRGY data and infrastructure can be employed to identify the highest yield sites for prospective studies; identify patients meeting study eligibility criteria; pre-screen individual patient's willingness to participate in specific studies; centralize study data monitoring; and assist in the conduct of prospective studies.

CONCLUSION

ENRGY data have been used to generate a growing number of scientific publications and may serve as a model for PBRNs in other specialties seeking to harness the potential of data linkages, patient-generated data capture, and centralized study infrastructure for observational and interventional research.

Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies

BACKGROUND

Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor.

OBJECTIVE

The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs.

METHODS

Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported.

RESULTS

The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies.

CONCLUSIONS

Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.

Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population

Aims

Wearable health technologies are increasingly popular. Yet, wearable monitoring only works when devices are worn as intended, and adherence reporting lacks standardization. In this study, we aimed to explore the long-term adherence to a wrist-worn activity tracker in the prospective SafeHeart study and identify patient characteristics associated with adherence.

Methods and results

This study enrolled 303 participants, instructed to wear a wrist-worn accelerometer day and night for 6 months. Long-term adherence was defined as valid days (≥22 h of wear time) divided by expected days, and daily adherence as mean hours of wear time per 24 h period. Optimal, moderate, and low long-term and daily adherence groups were defined as long-term adherence above or below 95 and 75% and daily adherence above or below 90 and 75%. Regression models were used to identify patient characteristics associated with long-term adherence. In total, 296 participants [median age 64 years; interquartile range (IQR) 57-72; 19% female] were found eligible, yielding 44 003 days for analysis. The median long-term adherence was 88.2% (IQR 74.6-96.5%). A total of 83 (28%), 127 (42.9%), and 86 (29.1%) participants had optimal, moderate, and low long-term adherence, and 163 (55.1%), 87 (29.4%), and 46 (15.5%) had optimal, moderate, and low daily adherence, respectively. Age and smoking habits differed significantly between adherence levels, and increasing changeover intervals improved the degree of long-term adherence.

Conclusion

Long-term adherence to a wearable activity tracker was 88.2% over a 6-month period. Older age and longer changeover interval were positively associated with long-term adherence. This serves as a benchmark for future studies that rely on wearable devices.

Trial registration number

The National Trial Registration number: NL9218 (https://onderzoekmetmensen.nl/).