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Li D, Mohanty S, Mavathur R, Vageesh VY, Jain A, Gopi A, Raghuram N. Study Protocol for Mindfulness-Based Yoga Versus Physical Exercise on the Psychological Well-Being in Students With Early Visual Impairment: A Three-Armed, Multi-Centered, Randomized Controlled Trial. Cureus 2024; 16:e69240. [PMID: 39398856 PMCID: PMC11470265 DOI: 10.7759/cureus.69240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2024] [Indexed: 10/15/2024] Open
Abstract
Background People with visual impairment (VI) tend to face more psychological distress than normally sighted individuals due to mobility restrictions, fear of falling, and sleep disturbances. However, research to address these problems is rare. This study aims to investigate the effect of mindfulness-based yoga versus physical exercise on the psychological well-being of individuals with VI. Methods This study will be a single-blinded, three-armed, multicentered, randomized controlled trial (RCT). A total of 132 participants with VI (ages 15-25) will be recruited in the study and will be randomly assigned to either group 1 (mindfulness-based yoga), group 2 (physical exercise), or group 3 (wait-list control). Groups 1 and 2 will receive intervention for 40 hours (eight weeks, weekly five days, one hour/day), whereas group 3 will continue their daily activities as usual. The intervention will take place in the afternoon from Monday to Friday. The timing varies between 4-5 pm according to the different time schedules of the institutions of the blind. Three times, assessments will be conducted at T0 (baseline), T1 (eighth week at the completion of the intervention), and T2 (sixth month following the completion of the intervention). ANOVA will be used to find out the differences between groups; repeated measures ANOVA will be used to check within-group changes. Trial status The study was first screened in December 2021. The recruitment of participants has been completed in two centers covering 62 individuals with VI, and intervention started in August 2022. The data collection is still ongoing due to the nature of the study design, a specific demographic, complex logistics, and administrative bottlenecks. The study incorporates three different groups and a substantial sample size (n=132). The specific demographic, people with visual impairments, are rare and difficult to locate. In addition, a six-month follow-up assessment contributes to complex procedures while coordinating between various institutions and securing necessary authorizations. Discussion This study will be the first comprehensive RCT to investigate the psychological well-being of the VI population with various psychophysiological and hormonal parameters in multiple centers. The presence of physical exercise and a wait-list control group will further elucidate the potential mechanism of Mindfulness-based yoga. Mindfulness-based yoga can be integrated into educational and rehabilitation systems to enhance the well-being of individuals with VI.
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Affiliation(s)
- Danqing Li
- Yoga and Humanity Division, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA) University, Bangalore, IND
| | - Soubhagyalaxmi Mohanty
- Yoga and Humanity Division, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA) University, Bangalore, IND
| | - Ramesh Mavathur
- Yoga and Life Science Division, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA) University, Bangalore, IND
| | - Vijaya Y Vageesh
- Physiology, Jagadguru Sri Shivarathreeshwara (JSS) Medical College and Hospital, Jagadguru Sri Shivarathreeshwara (JSS) Academy of Higher Education and Research, Mysore, IND
| | - Anup Jain
- Yoga and Humanity Division, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA) University, Bangalore, IND
| | - Arun Gopi
- Community Medicine, Jagadguru Sri Shivarathreeshwara (JSS) Medical College and Hospital, Jagadguru Sri Shivarathreeshwara (JSS) Academy of Higher Education and Research, Mysore, IND
| | - Nagarathna Raghuram
- Preventive Medicine, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA) University, Bangalore, IND
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Bolpagni M, Pardini S, Dianti M, Gabrielli S. Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:3221. [PMID: 38794074 PMCID: PMC11126007 DOI: 10.3390/s24103221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/14/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.
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Affiliation(s)
- Marco Bolpagni
- Human Inspired Technology Research Centre, University of Padua, 35121 Padua, Italy
- Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy; (S.P.); (M.D.); (S.G.)
| | - Susanna Pardini
- Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy; (S.P.); (M.D.); (S.G.)
| | - Marco Dianti
- Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy; (S.P.); (M.D.); (S.G.)
| | - Silvia Gabrielli
- Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy; (S.P.); (M.D.); (S.G.)
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Mao Y, Raju G, Zabidi MA. Association Between Occupational Stress and Sleep Quality: A Systematic Review. Nat Sci Sleep 2023; 15:931-947. [PMID: 38021213 PMCID: PMC10656850 DOI: 10.2147/nss.s431442] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 11/07/2023] [Indexed: 12/01/2023] Open
Abstract
Occupational stress and sleep quality are prevalent issues that can impact the physical and mental well-being of adults. An association between occupational stress and sleep quality has been found. However, this association is not entirely the same across different occupational groups. Additionally, variations are present in the research design and instruments employed.This systematic review aims to investigate the association between these two factors and identify gaps and limitations in current research. Articles published between January 1, 2011, and December 31, 2022, were retrieved from the WOS, Scopus, and PubMed databases. Out of 1225 articles, 38 studies met the predetermined inclusion and exclusion criteria and were included in the review. In the study, research designs, samples, instruments, and associations between occupational stress and sleep quality were statistically analyzed.These studies encompassed a diverse range of occupations, including both blue-collar and white-collar workers. Cross-sectional study is the most common research method. The Pittsburgh Sleep Quality Index (PSQI) was the most frequently utilized tool for assessing sleep quality, while there was a wide variety of measurement tools employed to assess occupational stress. The association between occupational stress and sleep quality consistently demonstrated a negative association, although the specific dimensions varied among studies. Moreover, several other factors were identified to have direct or indirect effects on occupational stress and sleep quality. For future research in this field, we propose four recommendations: (1) Consider utilizing objective measures to assess occupational stress and sleep quality. (2) Employ controlled experiments to further validate the causal relationship between occupational stress and sleep quality. (3) Investigate occupational groups that have received less attention. (4) Take into account the potential influence of other factors on occupational stress and sleep quality.
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Affiliation(s)
- Yongchun Mao
- School of Distance Education, Universiti Sains Malaysia, Penang, Malaysia
- School of Arts and Design, Qilu University of Technology (Shandong Academy of Sciences), Jinan, People’s Republic of China
| | - Gunasunderi Raju
- School of Distance Education, Universiti Sains Malaysia, Penang, Malaysia
| | - Muhammad Azrul Zabidi
- Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Pulau Pinang, 13200, Malaysia
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Yao Q, Gu H, Wang S, Liang G, Zhao X, Li X. Exploring EEG characteristics of multi-level mental stress based on human-machine system. J Neural Eng 2023; 20:056023. [PMID: 37729925 DOI: 10.1088/1741-2552/acfbba] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 09/20/2023] [Indexed: 09/22/2023]
Abstract
Objective.The understanding of cognitive states is important for the development of human-machine systems (HMSs), and one of the fundamental but challenging issues is the understanding and assessment of the operator's mental stress state in real task scenarios.Approach.In this paper, a virtual unmanned vehicle (UAV) driving task with multi-challenge-level was created to explore the operator's mental stress, and the human brain activity during the task was tracked in real time via electroencephalography (EEG). A mental stress analysis dataset for the virtual UAV task was then developed and used to explore the neural activation patterns associated with mental stress activity. Finally, a multiple attention-based convolutional neural network (MACN) was constructed for automatic stress assessment using the extracted stress-sensitive neural activation features.Main Results.The statistical results of EEG power spectral density (PSD) showed that frontal theta-PSD decreased with increasing task difficulty, and central beta-PSD increased with increasing task difficulty, indicating that neural patterns showed different trends under different levels of mental stress. The performance of the proposed MACN was evaluated based on the dimensional model, and results showed that average three-class classification accuracies of 89.49%/89.88% were respectively achieved for arousal/valence.Significance.The results of this paper suggest that objective assessment of mental stress in a HMS based on a virtual UAV scenario is feasible, and the proposed method provides a promising solution for cognitive computing and applications in human-machine tasks.
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Affiliation(s)
- Qunli Yao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Heng Gu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Shaodi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Guanhao Liang
- Center for Cognition and Neuroergonomics, Beijing Normal University, Zhuhai 519087, People's Republic of China
| | - Xiaochuan Zhao
- Institute of Computer Applied Technology of China North Industries Group Corporation Limited, Beijing 100821, People's Republic of China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, People's Republic of China
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Li Z, Xing Y, Pi Y, Jiang M, Zhang L. A novel physiological feature selection method for emotional stress assessment based on emotional state transition. Front Neurosci 2023; 17:1138091. [PMID: 37034171 PMCID: PMC10073504 DOI: 10.3389/fnins.2023.1138091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/20/2023] [Indexed: 04/11/2023] Open
Abstract
The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.
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Affiliation(s)
- Zhen Li
- The School of Electronic and Information Engineering, Tongji University, Shanghai, China
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yun Xing
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yao Pi
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Mingzhe Jiang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Lejun Zhang
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, China
- Research and Development Center for E-Learning, Ministry of Education, Beijing, China
- College of Information Engineering, Yangzhou University, Yangzhou, China
- *Correspondence: Lejun Zhang
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Hao T, Zheng X, Wang H, Xu K, Chen S. Linear and nonlinear analyses of heart rate variability signals under mental load. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103758] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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