1
|
Gao Y, Zhang J, Zou C, Bi L, Huang C, Nie J, Yan Y, Yu X, Zhang F, Yao F, Ding L. A method for calculating vector forces at human-mattress interface during sleeping positions utilizing image registration. Sci Rep 2024; 14:15238. [PMID: 38956282 PMCID: PMC11220148 DOI: 10.1038/s41598-024-66035-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 06/26/2024] [Indexed: 07/04/2024] Open
Abstract
The vector forces at the human-mattress interface are not only crucial for understanding the distribution of vertical and shear forces exerted on the human body during sleep but also serves as a significant input for biomechanical models of sleeping positions, whose accuracy determines the credibility of predicting musculoskeletal system loads. In this study, we introduce a novel method for calculating the interface vector forces. By recording indentations after supine and lateral positions using a vacuum mattress and 3D scanner, we utilize image registration techniques to align body pressure distribution with the mattress deformation scanning images, thereby calculating the vector force values for each unit area (36.25 mm × 36.25 mm). This method was validated through five participants attendance from two perspectives, revealing that (1) the mean summation of the vertical force components is 98.67% ± 7.21% body weight, exhibiting good consistency, and mean ratio of horizontal component force to body weight is 2.18% ± 1.77%. (2) the predicted muscle activity using the vector forces as input to the sleep position model aligns with the measured muscle activity (%MVC), with correlation coefficient over 0.7. The proposed method contributes to the vector force distribution understanding and the analysis of musculoskeletal loads during sleep, providing valuable insights for mattress design and evaluation.
Collapse
Affiliation(s)
- Ying Gao
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Jing Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Chengzhao Zou
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Liwen Bi
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Chengzhen Huang
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Jiachen Nie
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Yongli Yan
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Xinli Yu
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Fujun Zhang
- De Rucci Healthy Sleep Co., Ltd, Dongguan, 523960, Guangdong, China
| | - Fanglai Yao
- De Rucci Healthy Sleep Co., Ltd, Dongguan, 523960, Guangdong, China
| | - Li Ding
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
| |
Collapse
|
2
|
Kawasaki Y, Kitamura E, Kasai T. Impact of Body Composition on Sleep and Its Relationship with Sleep Disorders: Current Insights. Nat Sci Sleep 2023; 15:375-388. [PMID: 37220427 PMCID: PMC10200107 DOI: 10.2147/nss.s340946] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 05/04/2023] [Indexed: 05/25/2023] Open
Abstract
Sleep is involved in many physiological processes and is essential for both physical and mental health. Obesity and sleep deprivation due to sleep disorders are major public health issues. Their incidence is increasing, and they have a wide range of adverse health-related consequences, including life-threatening cardiovascular disease. The impact of sleep on obesity and body composition is well-known, and many studies have shown an association between insufficient or excessive sleep duration and obesity, body fat percentage, and weight gain. However, there is growing evidence of the effects of body composition on sleep and sleep disorders (particularly sleep disordered breathing) through anatomical and physiological mechanisms (nocturnal fluid shift, core body temperature, or diet). Although some research has been conducted on the bidirectional effects of sleep-disordered breathing and body composition, the specific effects of obesity and body composition on sleep and the underlying mechanisms that explain these effects remain unclear. Therefore, this review summarizes the findings on the effects of body composition on sleep and draws conclusions and proposals for future research in this field.
Collapse
Affiliation(s)
- Yu Kawasaki
- Department of Obstetrics and Gynecology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Eri Kitamura
- Department of Obstetrics and Gynecology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takatoshi Kasai
- Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Sleep and Sleep-Disordered Breathing Center, Juntendo University Hospital, Tokyo, Japan
- Cardiovascular Respiratory Sleep Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Cardiovascular Management and Remote Monitoring, Juntendo University Graduate School of Medicine, Tokyo, Japan
| |
Collapse
|
3
|
Bioelectrical impedance analysis to estimate one-repetition maximum measurement of muscle strength for leg press in healthy young adults. Sci Rep 2022; 12:17142. [PMID: 36229499 PMCID: PMC9561610 DOI: 10.1038/s41598-022-20526-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 09/14/2022] [Indexed: 01/04/2023] Open
Abstract
Resistance training (RT) progress is determined by an individual's muscle strength, measured by one-repetition maximum (1RM). However, this evaluation is time-consuming and has some safety concerns. Bioelectrical impedance analysis (BIA) is a valid and easy-to-use method to assess skeletal muscle mass (SMM). Although BIA measurements are often correlated with muscle strength, few studies of 1RM for RT and BIA measurements are available. This observational study examined the relationship between 1RM and BIA measurements and developed BIA-based prediction models for 1RM. Thirty-five healthy young Japanese adults were included. SMM and the skeletal muscle mass index (SMI) were measured using the BIA device. In addition, dominant-leg 1RM was measured using a unilateral leg-press (LP) machine. The correlations between BIA measurements and 1RM were calculated, and simple regression analyses were performed to predict 1RM from the BIA variables. The results showed significant correlations between 1RM and dominant-leg SMM (R = 0.845, P = 0.0001) and SMI (R = 0.910, P = 0.0001). The prediction models for 1RM for LP derived from SMM of the dominant leg and SMI were Y = 8.21x + 8.77 (P = 0.0001), R2 = 0.73, and Y = 15.53x - 36.33 (P = 0.0001), R2 = 0.83, respectively. Our results indicated that BIA-based SMI might be used to predict 1RM for LP accurately.
Collapse
|