Gait can reveal sleep quality with machine learning models

Liu, Xingyun; Sun, Bingli; Zhang, Zhan; Wang, Yameng; Tang, Haina; Zhu, Tingshao

doi:10.1371/journal.pone.0223012

Cited by 16 publications

(30 citation statements)

References 36 publications

(36 reference statements)

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“…Our findings are in line with previous studies that also show a negative association between sleep disturbances and gait performance, 41 , 42 as well as an association between lower sleep efficiency and decreased gait speed and increased gait variability under dual-task conditions. 20 Furthermore, several studies have shown that self-reported daytime sleepiness, often a sign of disturbed sleep, was associated with increased pace variability in the DT condition.…”

Section: Discussionsupporting

confidence: 93%

Why Do They Fall? The Impact of Insomnia on Gait of Older Adults: A Case–Control Study

Kirshner¹,

Kizony²,

Gil³

et al. 2021

NSS

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Study Objectives To compare gait and cognitive performance conducted separately as a single- (ST) and simultaneously as a dual-task (DT), ie, when a cognitive task was added, among community-dwelling older adults with and without insomnia. Methods Participants included: 39 (28 females) community-dwelling older adults with insomnia, 34 (21 females) controls without insomnia. Subject groups were matched for age, gender, and education. Sleep quality was evaluated based on two-week actigraphy. Gait speed and cognition were assessed as ST and DT performance. DT costs (DTCs) were calculated for both tasks. Outcomes were compared via independent samples t -tests or Mann–Whitney U -tests. Results Older adults with insomnia demonstrated significantly slower gait speed during ST (1 ± 0.29 vs 1.27 ± 0.17 m/s, p <0.001) and DT (0.77 ± 0.26 vs 1.14 ± 0.20 m/s, p <0.001) and fewer correct responses in the cognitive task during ST (21 ± 7 vs 27 ± 11, p =0.009) and DT (19 ± 7 vs 23 ± 9, p =0.015) compared to control group. DTC for the gait task was higher among older adults with insomnia (18.32%, IQR: 9.48–30.93 vs 7.81% IQR: 4.43–14.82, p<0.001). However, no significant difference was observed in DTC for the cognitive task (14.71%, IQR: −0.89–38.84 vs 15%, IQR: −0.89–38.84%, p=0.599). Conclusion Older adults with insomnia have lower gait speed and poorer cognitive performance during ST and DT and an inefficient pattern of task prioritization during walking, compared to counterparts without insomnia. These findings may explain the higher risk of falls among older adults with insomnia. Geriatric professionals should be aware of potential interrelationships between sleep and gait.

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Section: Discussionsupporting

confidence: 93%

Why Do They Fall? The Impact of Insomnia on Gait of Older Adults: A Case–Control Study

Kirshner¹,

Kizony²,

Gil³

et al. 2021

NSS

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“… 9 , 10 Since both sleep quality and gait are major contributors to health, one could expect that they influence each other. 11 Previous evidence has highlighted, that the association between sleep quality and gait is bidirectional. 12–17 For example, a study by Goldman et al 12 has shown, that the gait speed of those who sleep less than 6 hours per night is 3.5% slower, compared to those who sleep 6–8 hours.…”

Section: Introductionmentioning

confidence: 99%

The Associations Between Objectively Measured Gait Speed and Subjective Sleep Quality in First-Year University Students, According to Gender

Kasović¹,

Štefan²,

Štefan³

2021

NSS

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Purpose To examine the associations between gait speed and sleep quality in first-year university students, according to gender. Methods In this cross-sectional study, we recruited 193 first-year university students [mean age±standard deviation (SD): 19.6±1.1 years; mean height: 178.0±10.5 cm; mean weight: 74.0±11.0 kg; 26.9% women). Sleep quality was assessed using the Pittsburgh Sleep Quality questionnaire, with a lower score indicating “better” sleep quality. Gait speed was measured using the Zebris pressure platform. The associations were examined with generalized linear models and multiple regression analysis. Results In the unadjusted model, faster participants had significantly “better” sleep quality ( β =−3.15, 95% CI −3.82 to −2.47, p <0.001). When the model was adjusted for sex, age, body-mass index, self-rated health, smoking status, and psychological distress, faster participants remained having “better” sleep quality ( β =−2.88, 95% CI −3.53 to −2.22, p <0.001). Conclusion This study shows that sleep quality can be predicted by gait speed in the first-year university students.

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“…Therefore, all trajectories had be normalized in some way. A basic method to standardize trajectories is to use the z-score of p (the time series of coordinates for x, y, and z): where μ is the mean value and σ is the standard deviation of p. This method has been used for motion feature normalization in classification tasks 46 , 47 . Figures 2 to 6 were normalized using this method.…”

Section: Technical Validationmentioning

confidence: 99%

Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes

2021

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Human motion capture is commonly used in various fields, including sport, to analyze, understand, and synthesize kinematic and kinetic data. Specialized computer vision and marker-based optical motion capture techniques constitute the gold-standard for accurate and robust human motion capture. The dataset presented consists of recordings of 37 Kyokushin karate athletes of different ages (children, young people, and adults) and skill levels (from 4th dan to 9th kyu) executing the following techniques: reverse lunge punch (Gyaku-Zuki), front kick (Mae-Geri), roundhouse kick (Mawashi-Geri), and spinning back kick (Ushiro-Mawashi-Geri). Each technique was performed approximately three times per recording (i.e., to create a single data file), and under three conditions where participants kicked or punched (i) in the air, (ii) a training shield, or (iii) an opponent. Each participant undertook a minimum of two trials per condition. The data presented was captured using a Vicon optical motion capture system with Plug-In Gait software. Three dimensional trajectories of 39 reflective markers were recorded. The resultant dataset contains a total of 1,411 recordings, with 3,229 single kicks and punches. The recordings are available in C3D file format. The dataset provides the opportunity for kinematic analysis of different combat sport techniques in attacking and defensive situations.

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Gait can reveal sleep quality with machine learning models

Cited by 16 publications

References 36 publications

Why Do They Fall? The Impact of Insomnia on Gait of Older Adults: A Case–Control Study

Why Do They Fall? The Impact of Insomnia on Gait of Older Adults: A Case–Control Study

The Associations Between Objectively Measured Gait Speed and Subjective Sleep Quality in First-Year University Students, According to Gender

Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes

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