How Physical Exercise Level Affects Sleep Quality? Analyzing Big Data Collected from Wearables

Liu, Xiaoli; Tamminen, Satu; Korhonen, Tapani; Röning, Juha

doi:10.1016/j.procs.2019.08.035

Cited by 4 publications

(14 citation statements)

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“…Although sleep quality assessment may sound straightforward using clinical standards [ 39 ], it remains challenging if personal differences in sleep needs should be taken into consideration. Studies in this direction are limited, and we identified only 2 relevant studies [ 37 , 40 ].…”

Section: Resultsmentioning

confidence: 99%

“…However, the authors acknowledged that this method was not reliable because not everyone had the habit of using smartphones until they fell asleep. Most studies (9/14, 64%) used commercial wearable devices such as Fitbit [ 16 , 33 , 35 , 37 , 44 ], Microsoft Band (MS Band) [ 34 , 43 ], Garmin [ 42 ], and Polar [ 40 ]. Although all 3 methods are noninvasive, easy to use, and allow longitudinal collection of sleep data, each method has some limitations.…”

Section: Resultsmentioning

confidence: 99%

“…Along with sleep quality metrics, researchers have considered a wide range of contextual factors in their studies. As presented in Table 3 , the contextual factors of interest include the sleep environment [ 16 , 20 , 42 ], daily activities [ 16 , 19 , 20 , 31 , 36 , 37 , 40 , 42 , 43 ], physiological states [ 16 , 35 , 36 , 43 , 44 ], and mental states [ 16 , 19 ]. It was a common practice to collect demographic information (eg, age, gender, and BMI) and medical history using questionnaires at the beginning of a sleep tracking study [ 16 , 35 , 36 , 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…These studies developed their own tools to track users’ web-based behavior and linked them to sleep quality metrics. Using consumer wearable trackers such as Fitbit, MS Band, and Garmin, researchers were able to expand their list of factors to include, for example, bedtime, steps, distance, hours of exercise, and calories burned [ 16 , 19 , 20 , 31 , 36 , 37 , 40 , 42 , 43 ]. Biosignals such as heart rate during sleep and daytime activities were included in the studies by Faust et al [ 35 ], Farajtabar et al [ 43 ], and Choi et al [ 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…For example, exercise events <5 or >180 minutes were removed in the study by Farajtabar et al [ 43 ]. Exercises with calorie consumption per hour <50 or >2000 calories or with a duration of <10 minutes were excluded in the study by Liu et al [ 40 ]. In addition, data records with steps <1000 and those with a sedentary time of 0 minute were removed in the study by Liang et al [ 16 ].…”

Section: Resultsmentioning

confidence: 99%

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Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review

Hoang¹,

Liang²

2023

JMIR Mhealth Uhealth

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Background Over the past few decades, there has been a rapid increase in the number of wearable sleep trackers and mobile apps in the consumer market. Consumer sleep tracking technologies allow users to track sleep quality in naturalistic environments. In addition to tracking sleep per se, some sleep tracking technologies also support users in collecting information on their daily habits and sleep environments and reflecting on how those factors may contribute to sleep quality. However, the relationship between sleep and contextual factors may be too complex to be identified through visual inspection and reflection. Advanced analytical methods are needed to discover new insights into the rapidly growing volume of personal sleep tracking data. Objective This review aimed to summarize and analyze the existing literature that applies formal analytical methods to discover insights in the context of personal informatics. Guided by the problem-constraints-system framework for literature review in computer science, we framed 4 main questions regarding general research trends, sleep quality metrics, contextual factors considered, knowledge discovery methods, significant findings, challenges, and opportunities of the interested topic. Methods Web of Science, Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Fitbit Research Library, and Fitabase were searched to identify publications that met the inclusion criteria. After full-text screening, 14 publications were included. Results The research on knowledge discovery in sleep tracking is limited. More than half of the studies (8/14, 57%) were conducted in the United States, followed by Japan (3/14, 21%). Only a few of the publications (5/14, 36%) were journal articles, whereas the remaining were conference proceeding papers. The most used sleep metrics were subjective sleep quality (4/14, 29%), sleep efficiency (4/14, 29%), sleep onset latency (4/14, 29%), and time at lights off (3/14, 21%). Ratio parameters such as deep sleep ratio and rapid eye movement ratio were not used in any of the reviewed studies. A dominant number of the studies applied simple correlation analysis (3/14, 21%), regression analysis (3/14, 21%), and statistical tests or inferences (3/14, 21%) to discover the links between sleep and other aspects of life. Only a few studies used machine learning and data mining for sleep quality prediction (1/14, 7%) or anomaly detection (2/14, 14%). Exercise, digital device use, caffeine and alcohol consumption, places visited before sleep, and sleep environments were important contextual factors substantially correlated to various dimensions of sleep quality. Conclusions This scoping review shows that knowledge discovery methods have great potential for extracting hidden insights from a flux of self-tracking data and are considered more effective than simple visual inspection. Future research should address the challenges related to collecting high-quality data, extracting hidden knowledge from data while accommodating within-individual and between-individual variations, and translating the discovered knowledge into actionable insights.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review

Hoang¹,

Liang²

2023

JMIR Mhealth Uhealth

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Running Pace Adjustment and Training Distance Fitting with Fuzzy Logic and Machine Learning

Dziomdziora

Taibi

2022

2022 21st International Symposium on Communications and Information Technologies (ISCIT)

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Investigating the Effect of a Multicomponent Exercise Program on Adropin, Endothelial Function, Insulin Resistance, and Sleep Quality in Overweight Older Adults (a Link With Physiological Indexes and Sleep Quality): Results of a Randomized Controlled Study

Ghasemi,

Cheraghbirjandi

2024

Journal of Aging and Physical Activity

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The aim of the present study was to investigate the effects of multicomponent training on adropin, endothelial function, insulin resistance, and sleep quality in overweight older adults. In this randomized controlled study, 40 overweight older adults were randomly divided into training and control groups. The multicomponent training program including aerobic, resistance, and balance exercise was followed for 8 weeks, 3 days a week. Study variables were measured 48 hr before and after the intervention. After 8 weeks of multicomponent training, adropin (p = .01), nitric oxide (p = .01), and maximal oxygen uptake (VO2max; p = .002) increased, and glucose (p = .001), insulin (p = .001), insulin resistance (p = .01), systolic blood pressure (p = .01), and sleep disorders (p = .01) decreased significantly. Also, Pearson’s test results showed a significant inverse relationship between adropin level (p = .01 and r = −.55) and glucose (p = .01 and r = −.51) with sleep disorders. It seems that multicomponent training increases adropin and improves insulin resistance, endothelial function, and sleep quality in older adults.

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How Physical Exercise Level Affects Sleep Quality? Analyzing Big Data Collected from Wearables

Cited by 4 publications

References 10 publications

Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review

Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review

Running Pace Adjustment and Training Distance Fitting with Fuzzy Logic and Machine Learning

Investigating the Effect of a Multicomponent Exercise Program on Adropin, Endothelial Function, Insulin Resistance, and Sleep Quality in Overweight Older Adults (a Link With Physiological Indexes and Sleep Quality): Results of a Randomized Controlled Study

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