circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking
Objective To identify the differences between circadian rhythm (CR) metrics characterized by different mobile sensors and computational methods. Methods We used smartphone tracking and daily survey data from 225 college student participants, applied four methods (survey construct automation, cosinor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-07-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076221114201 |
| _version_ | 1811292851026264064 |
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| author | Congyu Wu Megan McMahon Hagen Fritz David M. Schnyer |
| author_facet | Congyu Wu Megan McMahon Hagen Fritz David M. Schnyer |
| author_sort | Congyu Wu |
| collection | DOAJ |
| description | Objective To identify the differences between circadian rhythm (CR) metrics characterized by different mobile sensors and computational methods. Methods We used smartphone tracking and daily survey data from 225 college student participants, applied four methods (survey construct automation, cosinor regression, non-parametric method, Fourier analysis) on two types of smartphone sensor data (GPS, accelerometer) to characterize CR. We explored the inter-relations among the extracted circadian metrics as well as between the circadian metrics and participants’ self-reported mood and sleep outcomes. Results Compared to GPS signals, smartphone accelerometer activity follows an intradaily distribution that starts earlier in the day, winds down later, reaches half cumulative activity about the same time, conforms less to a sinusoidal wave, and exhibits more intradaily fragmentation but higher CR strength and lower interdaily disruption. We found a notable negative correlation between intradaily variability and CR strength especially pronounced in GPS activity. Self-reported sleep and mood outcomes showed significant correlations with particular CR metrics. Conclusions We revealed significant inter-relations and discrepancies in the circadian metrics discovered from two smartphone sensors and four CR algorithms and their bearings on wellbeing indicators such as sleep quality and loneliness. |
| first_indexed | 2024-04-13T04:51:52Z |
| format | Article |
| id | doaj.art-cb65e15e99f343e5b4246f6675cd62a7 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-04-13T04:51:52Z |
| publishDate | 2022-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-cb65e15e99f343e5b4246f6675cd62a72022-12-22T03:01:38ZengSAGE PublishingDigital Health2055-20762022-07-01810.1177/20552076221114201circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life trackingCongyu Wu0Megan McMahon1Hagen Fritz2David M. Schnyer3 Department of Psychology, , USA; Department of Psychology, , USA; Department of Civil, Environmental, and Architectural Engineering, , USA Department of Psychology, , USA;Objective To identify the differences between circadian rhythm (CR) metrics characterized by different mobile sensors and computational methods. Methods We used smartphone tracking and daily survey data from 225 college student participants, applied four methods (survey construct automation, cosinor regression, non-parametric method, Fourier analysis) on two types of smartphone sensor data (GPS, accelerometer) to characterize CR. We explored the inter-relations among the extracted circadian metrics as well as between the circadian metrics and participants’ self-reported mood and sleep outcomes. Results Compared to GPS signals, smartphone accelerometer activity follows an intradaily distribution that starts earlier in the day, winds down later, reaches half cumulative activity about the same time, conforms less to a sinusoidal wave, and exhibits more intradaily fragmentation but higher CR strength and lower interdaily disruption. We found a notable negative correlation between intradaily variability and CR strength especially pronounced in GPS activity. Self-reported sleep and mood outcomes showed significant correlations with particular CR metrics. Conclusions We revealed significant inter-relations and discrepancies in the circadian metrics discovered from two smartphone sensors and four CR algorithms and their bearings on wellbeing indicators such as sleep quality and loneliness.https://doi.org/10.1177/20552076221114201 |
| spellingShingle | Congyu Wu Megan McMahon Hagen Fritz David M. Schnyer circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking Digital Health |
| title | circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking |
| title_full | circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking |
| title_fullStr | circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking |
| title_full_unstemmed | circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking |
| title_short | circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking |
| title_sort | circadian rhythms are not captured equal exploring circadian metrics extracted by differentcomputational methods from smartphone accelerometer and gps sensors in daily life tracking |
| url | https://doi.org/10.1177/20552076221114201 |
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