Assessing Mobile Usage, Physical Activity and Sleep Through Smartphone Sensing: A Digital Phenotype Study

Vaghela, Maitri; Sasidhar, Kalyan; Parikh, Alka; Wagani, Rekha

doi:10.1007/s42979-022-01221-x

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…We consider data from 14:00 of one day to 14:00 of the next day. Other studies have already used these data sources to model sleep-awake cycles [33,43,44].…”

Section: Data Descriptionmentioning

confidence: 99%

Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data

Martínez-García¹,

Moreno-Pino²,

Olmos³

et al. 2023

Preprint

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Sleep constitutes a key indicator of human health, performance, and quality of life. Sleep deprivation has long been related to the onset, development, and worsening of several mental and metabolic disorders, constituting an essential marker for preventing, evaluating, and treating different health conditions. Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. In this work, we propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner, in contrast to traditional sleep assessment methods that usually rely on intrusive and subjective procedures. A Heterogeneous Hidden Markov Model is used to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by tested wearables, proving the effectiveness of the proposed approach and advocating its use to assess sleep without more reliable sources.

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“…We consider data from 14:00 of one day to 14:00 of the next day. Other studies have already used these data sources to model sleep-awake cycles [33,43,44].…”

Section: Data Descriptionmentioning

confidence: 99%

Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data

Martínez-García¹,

Moreno-Pino²,

Olmos³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This led to the mobile sensing method [10]. The past decade has seen a number of works on mobile sensing for human activity recognition and health [11][12][13][14]. An extension of mobile sensing is digital phenotyping, which involves understanding the human (phenotype) characteristics from data collected insitu passively through digital devices.…”

Section: Introductionmentioning

confidence: 99%

Smartphone Mediated Tracking and Analysis of Sleep Patterns in Indian College Students

Vaghela

Sasidhar

2022

Hum-Cent Intell Syst

Self Cite

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Sleep is one of the essential bio-makers for human health. Poor sleep is associated with reduced cognitive performance. With most smartphone users in India being college students, the focus is now on exploring smartphone usage’s impact on students’ sleep. Umpteen news articles in India have reported binge-watching, social media use during the night, and other mobile phone-related interruptions as causes of improper sleep and irregular sleep patterns. However, such studies may involve bias while self-reporting and are limited to a one-time exercise. To understand the reality, we need to accurately quantify the sleep duration, patterns, mobile usage before and after bedtime, number and duration of interruptions. In this first-of-its-kind study in India, we infer novel insights into the sleep patterns of a cohort of 40 college students. We implement a mobile sensing-based approach for the study by installing a custom-developed mobile app on all phones. We extract sleep activity and infer the sleep duration, bed-in and wake-up times, and interruption duration from the sensor data collected from the phone’s built-in sensors. The study brings about new insights into college student sleep patterns and, interestingly, shows that students have a regular sleep cycle and good sleep quality. Only one-fourth of the students revealed irregular sleep patterns, and we did not observe any mobile-related interruptions 30 min past bedtime.

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Smartphone based on-device computing for societal applications

Sasidhar

2023

Proceedings of the 25th International Conference on Mobile Human-Computer Interaction

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Assessing Mobile Usage, Physical Activity and Sleep Through Smartphone Sensing: A Digital Phenotype Study

Cited by 3 publications

References 23 publications

Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data

Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data

Smartphone Mediated Tracking and Analysis of Sleep Patterns in Indian College Students

Smartphone based on-device computing for societal applications

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