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Updated: Oct 17, 2025

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation.

Habiballah Rahimi-Eichi^1,2,3, Garth Coombs Iii¹, Constanza M Vidal Bustamante¹

¹Department of Psychology, Harvard University, Cambridge, MA, United States.

JMIR Mhealth and Uhealth

|October 6, 2021

Summary

This summary is machine-generated.

A new open-source pipeline, DPSleep, analyzes wearable accelerometer data to infer sleep onset, duration, and quality. This deep sleep phenotyping method shows associations with self-reported sleep quality and integrates with other device data for comprehensive health insights.

Keywords:

accelerometer actigraphy deep-phenotyping mobile phone sleep smartphone

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Published on: August 8, 2019

Area of Science:

Behavioral science
Biomedical engineering
Sleep science

Background:

Wearable devices offer continuous objective behavioral and sleep data collection.
Objective sleep measurement is crucial for understanding health and disease dynamics.

Purpose of the Study:

Introduce a novel pipeline (DPSleep) for deep sleep phenotyping using accelerometer data.
Infer sleep onset, duration, and quality from raw wearable sensor data.
Quantify relationships between sleep metrics and other variables.

Main Methods:

Developed DPSleep, an open-source software package for sleep analysis.
Utilized a stepwise algorithm for data processing and spectral power analysis.
Employed sliding windows for sleep episode onset and offset detection.
Incorporated modules for manual quality control and time zone correction.

Main Results:

Actigraphy-derived sleep duration correlated with self-reported sleep quality.
Smartphone and GPS data validated sleep timing inferences.
Demonstrated differences between phone-based and actigraphy sleep timing.

Conclusions:

DPSleep provides a robust method for deep sleep phenotyping from wearable data.
The pipeline supports multi-dimensional, longitudinal health studies.
Integration with personal electronic device data enhances individual behavioral variation analysis.