Capturing Rest-Activity Profiles in Schizophrenia Using Wearable and Mobile Technologies: Development, Implementation, Feasibility, and Acceptability of a Remote Monitoring Platform

Abstract

BackgroundThere is growing interest in the potential for wearable and mobile devices to deliver clinically relevant information in real-world contexts. However, there is limited information on their acceptability and barriers to long-term use in people living with psychosis.ObjectiveThis study aimed to describe the development, implementation, feasibility, acceptability, and user experiences of the Sleepsight platform, which harnesses consumer wearable devices and smartphones for the passive and unobtrusive capture of sleep and rest-activity profiles in people with schizophrenia living in their homes.MethodsA total of 15 outpatients with a diagnosis of schizophrenia used a consumer wrist-worn device and smartphone to continuously and remotely gather rest-activity profiles over 2 months. Once-daily sleep and self-rated symptom diaries were also collected via a smartphone app. Adherence with the devices and smartphone app, end-of-study user experiences, and agreement between subjective and objective sleep measures were analyzed. Thresholds for acceptability were set at a wear time or diary response rate of 70% or greater.ResultsOverall, 14 out of 15 participants completed the study. In individuals with a mild to moderate symptom severity at baseline (mean total Positive and Negative Syndrome Scale score 58.4 [SD 14.4]), we demonstrated high rates of engagement with the wearable device (all participants meeting acceptability criteria), sleep diary, and symptom diary (93% and 86% meeting criteria, respectively), with negative symptoms being associated with lower diary completion rate. The end-of-study usability and acceptability questionnaire and qualitative analysis identified facilitators and barriers to long-term use, and paranoia with study devices was not a significant barrier to engagement. Comparison between sleep diary and wearable estimated sleep times showed good correspondence (ρ=0.50, P<.001).ConclusionsExtended use of wearable and mobile technologies are acceptable to people with schizophrenia living in a community setting. In the future, these technologies may allow predictive, objective markers of clinical status, including early markers of impending relapse.

Adverse event monitoring in mHealth for psychosis interventions provides an important opportunity for learning

“…None of those studies described having undertaken adverse events monitoring. We identified nine further studies of mHealth for psychosis that either did not meet the inclusion criteria for the Bell et al (2017) review or which were published subsequent to the review (Ainsworth et al, 2013;Barnett et al, 2018;Bucci et al, 2018;Eisner et al, in press;Kumar et al, 2018;Meyer et al, 2018;Palmier-Claus et al, 2012;Španiel et al, 2018) Of these just one described adverse event monitoring and in that instance it was limited to the identification of serious adverse events 1 , with none noted over the twelve-month period of the Actissist trial (Bucci et al, 2018). There was no indication of whether non-serious events were monitored or whether the relatedness of events to the digital intervention was assessed.…”

“…One participant also asked for text messaging to be terminated early because the questions led to unhelpful rumination. Meyer and colleagues asked 14 participants, who were using a combination of wearable and smartphone devices capturing rest and activity data, to complete a post study usability assessment (Meyer et al, 2018). Through this, concerns were expressed about the potential for the intervention to generate false alarms, discomfort from the wearable device and frustration at the repetitive nature of the integrated Clintouch symptom tracking component.…”

Deep Learning-Based Human Activity Recognition for Continuous Activity and Gesture Monitoring for Schizophrenia Patients With Negative Symptoms

“…Previous studies have investigated the feasibility and use of wearable devices in schizophrenia (10,31). Cella et al examined the use of a novel mobile health method using wearable technology to determine illness severity in patients with schizophrenia.…”

“…Meyer et al utilized a combination of wrist-worn devices and smartphones to continuously monitor sleep and restactivity profiles in people with schizophrenia over a 2-month period. All study participants exceeded the 70% threshold for feasibility of the wearable device with a mean wear time of 21.8 h per day or 91% of the total study duration (31). Passive monitoring has also been used to quantify behavioral changes in several neurological conditions, including Parkinson's disease (32,33), multiple sclerosis (29), and Huntington's disease (34,35).…”

An Image-Based Mobile Health App for Postdrainage Monitoring: Usability Study

“…In this study, the feasibility was assessed as in past studies [ 31 ]. The feasibility was defined as the proportion of participants using each element of the system for at least 70% of the period.…”