A Remote Patient Monitoring System With Feedback Mechanisms Using a Smartwatch: Concept, Implementation, and Evaluation Based on the activeDCM Randomized Controlled Trial

Abstract

Background: Technological advances allow for recording and sharing health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable close management of individual health trajectories, monitoring of risk factors, and asynchronous feedback. However, most remote patient monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack a patient-centric design.

Objective: The objective of this study was to conceptualize and implement a user-friendly, reusable, interoperable, and secure RPM system incorporating asynchronous feedback mechanisms using a broadly available consumer wearable (Apple Watch). In addition, this study sought to evaluate factors influencing patient acceptance of such systems.

Methods: The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was used as part of the activeDCM prospective randomized interventional study focusing on dilated cardiomyopathy. Each patient used the system for at least 12 months. The System Usability Scale was used to measure usability from a subjective patient perspective. In addition, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data classified into sensor-based health data (SHD) and patient-reported outcome measures (PROMs). Descriptive statistics using box plots and bootstrapped multiple linear regression with 95% CIs were used for evaluation analyzing the influence of age, sex, device experience, and intervention group membership.

Results: The RPM system comprised 4 interoperable components: patient devices, a data server, a data viewer, and a notification service. The system was evaluated with 95 consecutive patients with dilated cardiomyopathy (28/95, 29% female; mean age 50, SD 12 y) who completed the activeDCM study protocol. The system's app achieved a mean System Usability Scale score of 78 (SD 17), which was most influenced by device experience. In total, 87% (83/95) of the patients could integrate the use of the app well or very well into their daily routine, and 71% (67/95) saw a benefit of the RPM system for management of their health condition. On average, patients interacted with the wearable on 61% (SD 26%) of days enrolled in the study. SHD were available on average for 78% (SD 23%) of days, and PROM data were available on 64% (SD 27%) of weeks enrolled in the study. Wearable interaction frequency, SHD, and PROM completeness were most influenced by intervention group membership.

Conclusions: Our results mark a first step toward integrating RPM systems based on a consumer wearable device for primary patient input into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable, and secure RPM system that can be integrated into patients' daily routines.

Keywords: HL7 FHIR; Health Level 7 Fast Healthcare Interoperability Resources; cardiology; consumer device; dilated cardiomyopathy; heart failure; mobile health; mobile phone; remote patient monitoring; telemedicine; usability; wearable.

Figure 1

Use of the remote patient monitoring (RPM) system in the activeDCM study. After onboarding, patients are equipped with devices whose RPM system app is activated by scanning a patient-specific QR code. Patient-generated data are then continuously recorded and transmitted to the study center until the end of follow-up. Physicians analyze the data and send feedback messages to the patients’ devices if appropriate. IG+: intervention group with feedback messages.

Figure 2

Architecture of the remote patient monitoring system with asynchronous feedback mechanisms describing the 4 main components and their interaction. Black writing shows the initial concept, and colored writing shows concepts realized subsequently as part of the implementation. The services, frameworks, and protocols of the Apple ecosystem are shown in green; the standardized data model based on Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) are shown in orange; and the authentication mechanisms are shown in blue. PROM: patient-reported outcome measure; SHD: sensor-based health data.

Figure 3

Screenshots of the Apple Watch app. (A) and (B) show the daily notification to remind patients about answering 1 question of the 7-part weekly patient-reported outcome measure (PROM) questionnaire. (C) and (D) show the user interface to enter 1 of 2 different answer types for the PROM questionnaire—(C) allows for entering a star rating between 1 and 4, and (D) allows for entering a Boolean answer using yes or no. (E) shows the user interface during the transmission of the patient-generated data to the study center.

Figure 4

Screenshots of the data viewer application for 1 patient; (A) shows the recorded and transmitted heart rate data of a single day as a scatter plot; (B) shows the recorded and transmitted heart rate data over 2 weeks as daily box plots; (C) shows the recorded and transmitted step counts for all days included in the study as a zoomable bar plot, where asterisks indicate days without any recordings or transmissions, for example, because the wearable was not worn.

Figure 5

Results of the wearable and smartphone app usability analysis using the System Usability Scale (SUS) displayed as box plots. All data combined are shown in blue, experience using Apple devices is shown in green, and study arm membership is shown in orange. Triangles show mean values. CG: control group; Exp–: without previous device experience; Exp+: with previous device experience; IG–: intervention group without feedback messages; IG+: intervention group with feedback messages.

Figure 6

Results of the 5 self-designed questions regarding the patients’ attitudes toward using the remote patient monitoring system as well as their participation in the activeDCM study. Responses to the statements were provided by patients using a 5-point ordinal Likert scale ranging from strongly disagree in orange to strongly agree in blue.

Figure 7

Results of patients’ wearable interaction frequency as well as completeness of recorded and transmitted patient-generated data classified into sensor-based health data (SHD) and electronic patient-reported outcome measures (PROMs) of the wearable and smartphone app as box plots. All data combined are shown in blue, experience using Apple devices is shown in green, and study arm membership is shown in orange. Triangles show mean values. CG: control group; Exp–: without previous device experience; Exp+: with previous device experience; IG–: intervention group without feedback messages; IG+: intervention group with feedback messages.