Abstract
BackgroundAdherence to medication regimens and patient rest are two important factors in the well-being of patients with serious mental illness. Both of these behaviors are traditionally difficult to record objectively in unsupervised populations.ObjectiveA digital medicine system that provides objective time-stamped medication ingestion records was used by patients with serious mental illness. Accelerometer data from the digital medicine system was used to assess rest quality and thus allow for investigation into correlations between rest and medication ingestion.MethodsLongest daily rest periods were identified and then evaluated using a k-means clustering algorithm and distance metric to quantify the relative quality of patient rest during these periods. This accelerometer-derived quality-of-rest metric, along with other accepted metrics of rest quality, such as duration and start time of the longest rest periods, was compared to the objective medication ingestion records. Overall medication adherence classification based on rest features was not performed due to a lack of patients with poor adherence in the sample population.ResultsExplorations of the relationship between these rest metrics and ingestion did seem to indicate that patients with poor adherence experienced relatively low quality of rest; however, patients with better adherence did not necessarily exhibit consistent rest quality. This sample did not contain sufficient patients with poor adherence to draw more robust correlations between rest quality and ingestion behavior. The correlation of temporal outliers in these rest metrics with daily outliers in ingestion time was also explored.ConclusionsThis result demonstrates the ability of digital medicine systems to quantify patient rest quality, providing a framework for further work to expand the participant population, compare these rest metrics to gold-standard sleep measurements, and correlate these digital medicine biomarkers with objective medication ingestion data.
Highlights
Lack of adherence to medication regimens is a significant public health issue that contributes to increased health care utilization [1,2]
Explorations of the relationship between these rest metrics and ingestion did seem to indicate that patients with poor adherence experienced relatively low quality of rest; patients with better adherence did not necessarily exhibit consistent rest quality
The 4 features chosen by the aforementioned agglomeration technique were the mean of the circular deviation in the y–z acceleration plane, the mean of the posture angle, the mean of the 3D acceleration norm, and the SD of the x acceleration, which were all calculated across 3-minute rolling windows
Summary
Lack of adherence to medication regimens is a significant public health issue that contributes to increased health care utilization [1,2]. Adherence is of particular concern in patients with serious mental illness (SMI), including schizophrenia, bipolar disorder, and major depressive disorder, with estimates of nonadherence as high as 60% [1,3]. Within this population, effective pharmacotherapy is critical for mitigating the risk of serious adverse events, such as psychosis, symptom recurrence, poor social functions, hospitalizations, and suicide attempts [4,5]. There is a clear, unmet clinical need for adherence monitoring that digital medicine is ideally suited to address In this context, digital medicine refers to a system that combines an active pharmaceutical and an ingestible sensor that https://formative.jmir.org/2021/3/e17993. Both of these behaviors are traditionally difficult to record objectively in unsupervised populations
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