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Accepted for/Published in: JMIR Formative Research

Date Submitted: Jan 28, 2020
Date Accepted: Jan 17, 2021

The final, peer-reviewed published version of this preprint can be found here:

A Rest Quality Metric Using a Cluster-Based Analysis of Accelerometer Data and Correlation With Digital Medicine Ingestion Data: Algorithm Development

Heidary Z, Cochran JM, Peters-Strickland T, Knights J

A Rest Quality Metric Using a Cluster-Based Analysis of Accelerometer Data and Correlation With Digital Medicine Ingestion Data: Algorithm Development

JMIR Form Res 2021;5(3):e17993

DOI: 10.2196/17993

PMID: 33650981

PMCID: 7967235

Development of a rest quality metric using a cluster-based analysis of accelerometer data and correlation with digital medicine ingestion data

  • Zahra Heidary; 
  • Jeffrey Martin Cochran; 
  • Timothy Peters-Strickland; 
  • Jonathan Knights

ABSTRACT

Background:

Adherence 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.

Objective:

A digital medicine system that provides objective time-stamped medication ingestion records was utilized in 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.

Methods:

Longest daily rest periods were identified and then evaluated using a k-means clustering-based 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 poorly adherent patients in the sample population.

Results:

Explorations of the relationship between these rest metrics and ingestion did seem to indicate that low-adherence patients 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.

Conclusions:

This result demonstrates the ability of digital medicine systems to quantify patient rest quality, providing a framework for further work to expand the subject population, compare these rest metrics to gold-standard sleep measurements, and correlate these digital medicine biomarkers with objective medication ingestion data. Clinical Trial: All data used in this manuscript came from registered trials.


 Citation

Please cite as:

Heidary Z, Cochran JM, Peters-Strickland T, Knights J

A Rest Quality Metric Using a Cluster-Based Analysis of Accelerometer Data and Correlation With Digital Medicine Ingestion Data: Algorithm Development

JMIR Form Res 2021;5(3):e17993

DOI: 10.2196/17993

PMID: 33650981

PMCID: 7967235

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