Exploring the relationship between biometric data measured by sensors and psychological health outcomes in student management
Abstract
This study focuses on analyzing the correlation between automatically collected biometric data obtained through sensors and psychological well-being in relation to student management with the purpose of identifying how physiology can inform on student’s mental state. Heart rate interval, temp, and electrodermal activity (EDA) are the integral body parameters correlated with psychological state indicators, including stress, anxiety, and mood stability. Using a triangulation design of research, this study combines the quantitative academic performance data collected from the smart space biometric sensors with the qualitative data from a survey and interviews. The study adopted a stratified random sampling technique to identify two hundred students from disperse fields of study to increase the variability of the sample. Ad-hoc physiological data was captured using wearable sensors over a three months’ duration and related to psychological health assessment conducted using validated self-report questionnaires. Descriptive and correlation statistics were employed to determine the extent of relationship between the biometric variables and depression. Standard procedures of ethical conduct were observed as the participants signed informed consent, and their data was protected. The presented study is focused on revealing the possibility of using biometric data as an effective nonintrusive method to evaluate and improve the efficiency of student’s mental health management.
References
1. Y. Amanvermez et al., “Feasibility and acceptability of a guided internet-based stress management intervention for university students with high levels of stress: Protocol for an open trial,” Internet Interventions, vol. 24, 2021, doi: 10.1016/j.invent.2021.100369.
2. J. Galante, J. Stochl, G. Dufour, M. Vainre, A. P. Wagner, and P. B. Jones, “Effectiveness of providing university students with a mindfulness-based intervention to increase resilience to stress: 1-year follow-up of a pragmatic randomised controlled trial,” Journal of Epidemiology and Community Health, vol. 75, no. 2, 2021, doi: 10.1136/jech-2020-214390.
3. R. A. M. Darkhawaja et al., “Exploring the role of social capital, self-efficacy and social contagion in shaping lifestyle and mental health among students representing the future healthcare workforce in Palestine: Social cohort study protocol,” BMJ Open, vol. 12, no. 1, 2022, doi: 10.1136/bmjopen-2021-049033.
4. F. Khademian, A. Aslani, R. Ravangard, P. Bastani, M. Nami, and P. Jafari, “Efficacy of a web application for stress management among Iranian college students during COVID-19 outbreak: a study protocol for randomized controlled trials,” Trials, vol. 21, no. 1, 2020, doi: 10.1186/s13063-020-04949-0.
5. N. Imran, A. Rahman, N. Chaudhry, and A. Asif, “Effectiveness of a school-based mental health intervention for school teachers in urban Pakistan: a randomized controlled trial,” Child and Adolescent Psychiatry and Mental Health, vol. 16, no. 1, 2022, doi: 10.1186/s13034-022-00470-1.
6. C. C. G. da Silva, C. V. Bolognani, F. F. Amorim, and A. M. Imoto, “Effectiveness of training programs based on mindfulness in reducing psychological distress and promoting well-being in medical students: a systematic review and meta-analysis,” Systematic Reviews, vol. 12, no. 1, 2023, doi: 10.1186/s13643-023-02244-y.
7. K. Ligus, E. Fritzson, E. A. Hennessy, R. L. Acabchuk, and K. Bellizzi, “Disruptions in the management and care of university students with preexisting mental health conditions during the COVID-19 pandemic,” Translational Behavioral Medicine, vol. 11, no. 3, 2021, doi: 10.1093/tbm/ibab020.
8. M. Flaucher et al., “Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review,” Oncologist, vol. 28, no. 10. 2023. doi: 10.1093/oncolo/oyad217.
9. J. Y. Choi et al., “Health-Related Indicators Measured Using Earable Devices: Systematic Review,” JMIR mHealth and uHealth, vol. 10, no. 11. 2022. doi: 10.2196/36696.
10. G. L. Bate et al., “The Role of Wearable Sensors to Monitor Physical Activity and Sleep Patterns in Older Adult Inpatients: A Structured Review,” Sensors, vol. 23, no. 10. 2023. doi: 10.3390/s23104881.
11. S. Buchman et al., “Different combinations of mobility metrics derived from a wearable sensor are associated with distinct health outcomes in older adults,” Journals of Gerontology - Series A Biological Sciences and Medical Sciences, vol. 75, no. 6, 2020, doi: 10.1093/GERONA/GLZ160.
12. H. Gerlach, S. R. Chaudoir, and P. M. Zebrowski, “Relationships between stigma-identity constructs and psychological health outcomes among adults who stutter,” Journal of Fluency Disorders, vol. 70, 2021, doi: 10.1016/j.jfludis.2021.105842.
13. M. Hybholt, “Psychological and social health outcomes of physical activity around menopause: A scoping review of research,” Maturitas, vol. 164. 2022. doi: 10.1016/j.maturitas.2022.07.014.
14. M. Buultjens, A. Farouque, L. Karimi, L. Whitby, J. Milgrom, and B. Erbas, “The contribution of group prenatal care to maternal psychological health outcomes: A systematic review,” Women and Birth, vol. 34, no. 6. 2021. doi: 10.1016/j.wombi.2020.12.004.
15. P. M. Bamonti, S. A. Robinson, E. S. Wan, and M. L. Moy, “Improving Physiological, Physical, and Psychological Health Outcomes: A Narrative Review in US Veterans with COPD,” International Journal of COPD, vol. 17. 2022. doi: 10.2147/COPD.S339323.
16. L. yin Liang and H. bo Che, “Fear of COVID-19 and Adverse Psychological Health Outcomes Among Chinese Elderly: a Serial Mediation Model of Social Participation and Loneliness,” Applied Research in Quality of Life, vol. 18, no. 5, 2023, doi: 10.1007/s11482-023-10182-z.
17. N. Mostafa and S. D. Smith, “Improving Psychological Health Outcomes in Children with Atopic Dermatitis,” Clinical, Cosmetic and Investigational Dermatology, vol. 16. 2023. doi: 10.2147/CCID.S393254.
18. Adedeji, T. Y. Akintunde, F. Metzner, and E. Idemudia, “Psychological health outcome of Sub-Saharan African migrants in Germany,” Electronic Journal of General Medicine, vol. 20, no. 1, 2023, doi: 10.29333/ejgm/12602.
19. D. M. Hilty, C. M. Armstrong, D. D. Luxton, M. T. Gentry, and E. A. Krupinski, “A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions,” Journal of Technology in Behavioral Science, vol. 6, no. 2. 2021. doi: 10.1007/s41347-021-00199-2.
20. H. Saner et al., “Potential of Ambient Sensor Systems for Early Detection of Health Problems in Older Adults,” Frontiers in Cardiovascular Medicine, vol. 7, 2020, doi: 10.3389/fcvm.2020.00110.
21. S. Munasinghe et al., “The Impact of Physical Distancing Policies During the COVID-19 Pandemic on Health and Well-Being Among Australian Adolescents,” Journal of Adolescent Health, vol. 67, no. 5, 2020, doi: 10.1016/j.jadohealth.2020.08.008.
22. Poudyal et al., “Wearable digital sensors to identify risks of postpartum depression and personalize psychological treatment for adolescent mothers: Protocol for a mixed methods exploratory study in rural Nepal,” JMIR Research Protocols, vol. 8, no. 9, 2019, doi: 10.2196/14734.
23. K. A. Kercher et al., “Association Between Head Impact Exposure, Psychological Needs, and Indicators of Mental Health Among U.S. High School Tackle Football Players,” Journal of Adolescent Health, vol. 72, no. 4, 2023, doi: 10.1016/j.jadohealth.2022.11.247.
24. M. Alneyadi, N. Drissi, M. Almeqbaali, and S. Ouhbi, “Biofeedback-based connected mental health interventions for anxiety: Systematic literature review,” JMIR mHealth and uHealth, vol. 9, no. 4. 2021. doi: 10.2196/26038.
25. G. Liyanaarachchi, G. Viglia, and F. Kurtaliqi, “Privacy in hospitality: managing biometric and biographic data with immersive technology,” International Journal of Contemporary Hospitality Management, 2023, doi: 10.1108/IJCHM-06-2023-0861.
26. K. L. Jacobsen, “Biometric data flows and unintended consequences of counterterrorism,” International Review of the Red Cross, vol. 103, no. 916–917, 2021, doi: 10.1017/S1816383121000928.
27. M. Griniuk, “Performance Art Using Biometric Data,” Art History and Criticism, vol. 17, no. 1, 2021, doi: 10.2478/mik-2021-0009.
28. Ross, S. Banerjee, and A. Chowdhury, “Deducing health cues from biometric data,” Computer Vision and Image Understanding, vol. 221, 2022, doi: 10.1016/j.cviu.2022.103438.
29. F. Rosique, P. J. Navarro, L. Miller, and E. Salas, “Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data,” Sensors, vol. 23, no. 4, 2023, doi: 10.3390/s23042009.
30. S. G. L. Persiani, B. Kobas, S. C. Koth, and T. Auer, “Biometric data as real-time measure of physiological reactions to environmental stimuli in the built environment,” Energies, vol. 14, no. 1, 2021, doi: 10.3390/en14010232.
31. Ross, S. Banerjee, and A. Chowdhury, “Security in smart cities: A brief review of digital forensic schemes for biometric data,” Pattern Recognition Letters, vol. 138, 2020, doi: 10.1016/j.patrec.2020.07.009.
32. S. F. Jacoby, A. J. Robinson, J. L. Webster, C. N. Morrison, and T. S. Richmond, “The feasibility and acceptability of mobile health monitoring for real-time assessment of traumatic injury outcomes,” mHealth, vol. 7, 2021, doi: 10.21037/mhealth-19-200.
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