Rhythm

Circadian rhythms are crucial to human health and have become a popular topic in clinical research in recent years. In the past, wearable devices (e.g., actigraphy) have been widely used to assess circadian rhythms by monitoring physical activity. While actigraphy is convenient and validated as an accurate research tool, it is less popular than smartphones, which nearly everyone owns. On the other hand, many medical-purpose mobile apps exist, but none can automatically record circadian rhythms. We are interested in using human-smartphone interactions (i.e., digital footprints) to assess circadian rhythms and explore the correlations between sleep, depression, and obesity.

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(A) Timestamps of smartphone events recorded by the Rhythm app.

We developed ‘Rhythm,’ a mobile app to capture digital footprints. This app serves as a foundation for establishing ‘digital biomarkers,’ enabling long-term recording and measurement of daily activity and sleep patterns. There are three variables that reflect circadian rhythm: Intradaily Variability (IV), Relative Amplitude (RA), and Intradaily Stability (IS). Our research shows that ‘Rhythm’ produces results comparable to actigraphy, not only in effectiveness in reading circadian rhythms but also in offering more detailed insights into mental activity.

We found that participants’ mental and physical health scores are related to their circadian rhythms: Intradaily Variability (IV) is significantly correlated with depressive symptom scores, and Interdaily Stability (IS) is significantly correlated with obesity. We believe the ‘Rhythm’ app and its algorithm can assist clinicians in providing more accurate diagnoses and treatment for patients, particularly those with mood disorders. Future research will explore the clinical value of the ‘Rhythm’ app in other areas.

(A) The calculation of interdaily stability. (B) The hourly mean of the app count is calculated from the raw data and computed for each day of the recording period. (C) The mean of all hourly means across days is calculated, resulting in the grand mean.

Publications

1. Rest-Activity Rhythm Differences in Acute Rehabilitation Between Poststroke Patients and Non–Brain Disease Controls: Comparative Study

Liang HW, Wu CH, Lin C, Chang HC, Lin YH, Chen SY, Hsu WC

J Med Internet Res. 2024;26:e49530. doi: 10.2196/49530

2. Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study.

Chuang HH, Lin C, Lee LA, Chang HC, She GJ, Lin YH*.
JMIR Mhealth and Uhealth. 2024 Jun 5:26:e50149. doi: 10.2196/50149.

3. Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study

Lin C, Chen IM, Chuang HH, Wang ZW, Lin HH, Lin YH.
J Med Internet Res. 2023;25:e48044.

4. Validation of the Mobile App-Recorded Circadian Rhythm by a Digital Footprint.

Lin YH, Wong BY, Pan YC, Chiu YC, Lee YH.
JMIR Mhealth and Uhealth. 2019 May 16;7(5):e13421.

Do you have social jet lag?

5. Development of a Mobile Application (App) to Delineate “Digital Chronotype” and the Effects of Delayed Chronotype by Bedtime Smartphone Use.

Lin YH, Wong BY, Lin SH, Chiu YC, Pan YC, Lee YH.
Journal of Psychiatric Research. 2019 Mar;110:9-15.

Does using a smartphone before bed affect sleep patterns?

Rhythm's Infographic Video