Human circadian rhythms and exercise: Significance and application in real-life situations

Mizuno, Koh

doi:10.7600/jpfsm.3.307

Cited by 6 publications

(5 citation statements)

References 73 publications

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“…FD analysis has been widely used in the signal processing field, especially for signals with periodic characteristics [ 32 ]. People’s behaviors follow a quasiperiodic routine, such as sleeping at night, working on weekdays, and gathering with friends on weekends [ 19 , 37 ]. We therefore leveraged FD analysis to explore the periodic patterns in the NBDC data.…”

Section: Methodsmentioning

confidence: 99%

Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

Zhang

Folarin

Sun

et al. 2021

JMIR Mhealth Uhealth

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Background Research in mental health has found associations between depression and individuals’ behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. Objective This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). Methods The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals’ life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. Results A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). Conclusions Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals’ behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.

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Section: Methodsmentioning

confidence: 99%

Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

Zhang

Folarin

Sun

et al. 2021

JMIR Mhealth Uhealth

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show abstract

“…FD analysis has been widely used in the signal processing field, especially for signals with periodic characteristics [32]. People's behaviors follow a quasiperiodic routine, such as sleeping at night, working on weekdays, and gathering with friends on weekends [19,37]. We therefore leveraged FD analysis to explore the periodic patterns in the NBDC data.…”

Section: Fd Featuresmentioning

confidence: 99%

Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study (Preprint)

Zhang¹,

Folarin²,

Sun³

et al. 2021

Preprint

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BACKGROUND The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals’ proximity information, such as the nearby Bluetooth devices count (NBDC). The continuous NBDC data can partially reflect individuals’ behaviors and status, such as social connections and interactions, working status, mobility, and social isolation and loneliness, which were found to be significantly associated with depression by previous survey-based studies. OBJECTIVE This paper aims to explore the NBDC data’s value in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). METHODS The data used in this paper included 2,886 bi-weekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the UK as part of the EU RADAR-CNS study. From the NBDC data two weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring periodicity and regularity of individuals’ life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. RESULTS A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with the depressive symptoms worsening, one or more of the following changes were found in the preceding two weeks’ NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics, R^2= 0.526, and root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R^2=0.338, RMSE = 4.547). CONCLUSIONS Our statistical results indicate that the NBDC data has the potential to reflect changes in individuals’ behaviors and status concurrent with the changes in the depressive state. The prediction results demonstrate the NBDC data has a significant value in predicting depressive symptom severity. These findings may have utility for mental health monitoring practice in real-world settings.

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“…A natação é um caso peculiar, uma vez que os nadadores realizam sessões de treinamento e provas finais em competições em diferentes horários, como por exemplo, nos Jogos Olímpicos Rio2016, onde as competições semifinais e finais da natação ocorreram a partir das 22 horas. Objetivos: O presente estudo teve como objetivos: (1) apresentar o protocolo de uma intervenção realizada com atletas de elite da equipe de natação brasileira durante os Jogos Olímpicos Rio 2016; (2) identificar se o desempenho dos atletas de natação foi afetado devido aos horários das provas durante a competição. Materiais e Métodos: Participaram do estudo 14 atletas da equipe de natação brasileira (masculino: n= 10; 71% e feminino: n= 4; 29%).…”

Section: Introductionunclassified

“…In human beings, the biological rhythms are observed in different physiological, mental, physical and behavioral processes. [1][2][3] In the physiological process, these rhythms modulate the entire metabolism, influencing the development of strength, hormonal secretions, autonomous nervous system´s activity, among others. 4 Considering that circadian changes implicate in adjustments of the biological clock 2 and the meaning of the circadian rhythms or its diurnal variations of approximately 24 hours, would also have an impact upon the sporting performance of athletes, amateurs or high performance athletes.…”

Section: Introductionmentioning

confidence: 99%

“…4 Considering that circadian changes implicate in adjustments of the biological clock 2 and the meaning of the circadian rhythms or its diurnal variations of approximately 24 hours, would also have an impact upon the sporting performance of athletes, amateurs or high performance athletes. 1,2 The circadian variation on the sporting performance has been researched since the end of the 60´s, with major emphasis from the 90´s. 1 The consensus is that the majority of the physical performance rhythms reaches a plateau between 3:00 -9:00 p.m.. 1,[5][6][7] At the end of afternoon or beginning of night, the body temperature, the strength and flexibility are in acrophase (major peaks), contributing to a better performance at this time.…”

Section: Introductionmentioning

confidence: 99%

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Duration and Quality of Sleep in Sprint and Recovery Performances Among Elite Swimmers

Mello

Simim

Narciso

et al. 2020

Rev Bras Med Esporte

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Introduction: Circadian rhythms can impact athletes' sports performance, where the plateau occurs between 15 and 21 hours. Swimming is a peculiar case, as athletes perform training and final sessions in competitions at different times, as in the Rio2016 Olympic Games for example, where the semifinal and final competitions took place from ten o'clock at night. Objectives: (1) to present the protocol of an intervention performed with elite athletes of the Brazilian swimming team during the 2016 Olympic Games in Rio; (2) to find out whether the time at which the competitions were held affected the swimming performances of these athletes during the competition. Materials and Methods: Fourteen athletes of the Brazilian swimming team (males: n= 10; 71% and females: n= 4; 29%) participated in the study. They were followed up during two preparation periods (baseline and intervention) for the 2016 Olympic Games in Rio during June and July 2016. During the competition, we recorded the Reaction Time (RT) and Competition Time (CT) of each athlete in different modalities. The intervention strategies used were light therapy and sleep hygiene. The values of RT at the starting block and CT were registered and conferred with the official results. Results: The athletes showed a decrease in the total time awake (Δ = −13%; Effect size [ES] = 1.0) and sleep latency (Δ = −33%; ES = 0.7), and an increase in total sleep time (Δ = 13%; ES = 1.1; p = 0.04) between the baseline and the period of the intervention, pre-competition. We identified an improvement in the RT (Δ = −2.2% to −1.0%; ES = 0.2 to 0.5) during the competition only for the athletes who participated in the competition finals. Conclusion: We conclude that the intervention carried out was effective in mitigating any negative influence of competition time on the RT and CT of elite athletes of the Brazilian swimming team. Level of evidence II; Prospective comparative study.

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Human circadian rhythms and exercise: Significance and application in real-life situations

Cited by 6 publications

References 73 publications

Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study (Preprint)

Duration and Quality of Sleep in Sprint and Recovery Performances Among Elite Swimmers

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