Yoichi M Ito

Background/Aims Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations. Methods As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov—a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords “ActiGraph,”“Apple Watch,”“Empatica,”“Fitbit,”“Garmin,” and “wearable devices” to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables. Results Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations. Conclusions Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.

Aim This study aimed to develop and assess the validity and reliability of the Care Transitions Scale for Patients with Heart Failure (CTS‐HF) as a nurse‐reported measure for evaluating patients' readiness for hospital discharge. Methods We conducted a cross‐sectional study of cardiovascular ward nurses from 163 hospitals across Japan. Structural validity was assessed using exploratory factor analysis with development participants and confirmatory factor analysis with validation participants. Convergent validity was assessed by correlation with the Discharge Planning of Ward Nurses scale (DPWN). Hypotheses testing for construct validity was performed as comparisons between subgroups of transitional care practice. Results Valid responses were obtained from 704 nurses (development participants, n = 352; validation participants, n = 352). The final scale comprised 21 items divided into six factors: “Clear preparation for how to manage health at home,” “Adjusting to home care/support system,” “Transitions of medication management from hospital to home,” “Dealing with patients' concerns and questions,” “Transitions of disease management from hospital to home,” and “Family support.” Indices of fit supported these results (comparative fit index = 0.944, root mean square error of approximation = 0.057). The CTS‐HF was significantly correlated with the DPWN. The nurses' subgroup with higher transitional care practice had higher CTS‐HF scores. Cronbach's alpha was .93 for the CTS‐HF. Conclusions The CTS‐HF showed sufficient reliability and validity for use in evaluating discharge care. Further studies are needed regarding the usefulness of this scale in nursing practice.