Exploration of Musculoskeletal Diseases

Table 4. Pearson correlations between HRV parameters, PSS-14, and musculoskeletal pain outcomes (N = 105).

Variable	SDNN	RMSSD	MHRR	Coherence	PSS-14	Pain intensity	Multi-site burden	Pain impact
SDNN	—
RMSSD	0.71**	—
MHRR	0.58**	0.62**	—
Coherence	0.64**	0.59**	0.51**	—
PSS-14	–0.27**	–0.31**	–0.19*	–0.34**	—
Pain intensity	–0.28**	–0.26**	–0.17*	–0.31**	0.41**	—
Multi-site burden	–0.35**	–0.33**	–0.21*	–0.38**	0.46**	0.67**	—
Pain impact	–0.24*	–0.28**	–0.16	–0.29**	0.44**	0.71**	0.63**	—

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*p < 0.05; **p < 0.01. HRV: heart rate variability; MHRR: mean heart rate range; PSS-14: Perceived Stress Scale-14; RMSSD: root mean square of successive differences; SDNN: standard deviation of normal-to-normal intervals.

Supplementary materials

The supplementary materials for this article are available at: https://www.explorationpub.com/uploads/Article/file/1007128_sup_1.pdf.

Declarations

Author contributions

AL: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Visualization, Writing—original draft, Writing—review & editing, Supervision, Project administration. BL: Investigation, Data curation, Formal analysis, Writing—review & editing. Both authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest related to this research. Adrian Low is a HeartMath-certified practitioner; this certification did not influence the design, conduct, analysis, or interpretation of the study. The HeartMath Institute had no involvement in this research.

Ethical approval

The Heart Rate Variability and Work-Related Musculoskeletal Disorder Risk study was approved by the Ethics Committee of the Hong Kong Association of Psychology (Approval No. HKAP-20251015-001, approved October 15, 2025). The study was conducted in accordance with the Declaration of Helsinki.

Consent to participate

Informed consent to participate in the study was obtained from all participants.

Consent to publication

Informed consent to publication was obtained from relevant participants. All participants provided written informed consent for the publication of de-identified aggregated data derived from their participation in the study. No individually identifiable information is included in this manuscript.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to participant confidentiality terms stipulated in the ethics approval but are available from the corresponding author on reasonable request, subject to the participant consent terms.

Funding

This research received no external or internal funding or financial support.

Copyright

Publisher’s note

Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

References

Cieza A, Causey K, Kamenov K, Hanson SW, Chatterji S, Vos T. Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2021;396:2006–17. [DOI] [PubMed] [PMC]

World Health Organization. Mental Health, Brain Health and Substance Use [Internet]. Geneva: WHO; c2026 [cited 2026 Apr 1]. Available from:https://www.who.int/teams/mental-health-and-substance-use/promotion-prevention/mental-health-in-the-workplace

Davis KG, Kotowski SE. Prevalence of Musculoskeletal Disorders for Nurses in Hospitals, Long-Term Care Facilities, and Home Health Care: A Comprehensive Review. Hum Factors. 2015;57:754–92. [DOI] [PubMed]

Yan P, Li F, Zhang L, Yang Y, Huang A, Wang Y, et al. Prevalence of Work-Related Musculoskeletal Disorders in the Nurses Working in Hospitals of Xinjiang Uygur Autonomous Region. Pain Res Manag. 2017;2017:5757108. [DOI] [PubMed] [PMC]

da Costa BR, Vieira ER. Risk factors for work-related musculoskeletal disorders: A systematic review of recent longitudinal studies. Am J Ind Med. 2010;53:285–323. [DOI] [PubMed]

Osborne A, Blake C, Fullen BM, Meredith D, Phelan J, McNamara J, et al. Prevalence of musculoskeletal disorders among farmers: A systematic review. Am J Ind Med. 2012;55:143–58. [DOI] [PubMed]

Wahlström J. Ergonomics, musculoskeletal disorders and computer work. Occup Med (Lond). 2005;55:168–76. [DOI] [PubMed]

Cagnie B, Danneels L, Van Tiggelen D, De Loose V, Cambier D. Individual and work related risk factors for neck pain among office workers: a cross sectional study. Eur Spine J. 2007;16:679–86. [DOI] [PubMed] [PMC]

Buscemi V, Chang WJ, Liston MB, McAuley JH, Schabrun S. The role of psychosocial stress in the development of chronic musculoskeletal pain disorders: protocol for a systematic review and meta-analysis. Syst Rev. 2017;6:224. [DOI] [PubMed] [PMC]

10.

Lundberg U. Psychophysiology of work: stress, gender, endocrine response, and work-related upper extremity disorders. Am J Ind Med. 2002;41:383–92. [DOI] [PubMed]

11.

Wing HP, Low A, Lam J. Effects of perceived stress with musculoskeletal pain in Hong Kong. Heart Mind. 2022;6:173–82. [DOI]

12.

Low A, McCraty R. Emerging dynamics of workplace stress of employees in a large organization in Hong Kong. Public Adm Policy. 2018;21:134–51. [DOI]

13.

Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996;93:1043–65. [PubMed]

14.

McCraty R, Shaffer F. Heart Rate Variability: New Perspectives on Physiological Mechanisms, Assessment of Self-regulatory Capacity, and Health risk. Glob Adv Health Med. 2015;4:46–61. [DOI] [PubMed] [PMC]

15.

Hillebrand S, Gast KB, de Mutsert R, Swenne CA, Jukema JW, Middeldorp S, et al. Heart rate variability and first cardiovascular event in populations without known cardiovascular disease: meta-analysis and dose-response meta-regression. Europace. 2013;15:742–9. [DOI] [PubMed]

16.

Low A, McCraty R. Heart Rate Variability: New Perspectives on Assessment of Stress and Health Risk at the Workplace. Heart Mind. 2018;2:16–27.

17.

Meeus M, Goubert D, De Backer F, Struyf F, Hermans L, Coppieters I, et al. Heart rate variability in patients with fibromyalgia and patients with chronic fatigue syndrome: a systematic review. Semin Arthritis Rheum. 2013;43:279–87. [DOI] [PubMed]

18.

Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev. 2009;33:81–8. [DOI] [PubMed]

19.

Tracy LM, Ioannou L, Baker KS, Gibson SJ, Georgiou-Karistianis N, Giummarra MJ. Meta-analytic evidence for decreased heart rate variability in chronic pain implicating parasympathetic nervous system dysregulation. Pain. 2016;157:7–29. [DOI] [PubMed]

20.

Chen WQ, Yu IT, Wong TW. Impact of occupational stress and other psychosocial factors on musculoskeletal pain among Chinese offshore oil installation workers. Occup Environ Med. 2005;62:251–6. [DOI] [PubMed] [PMC]

21.

Järvelin-Pasanen S, Sinikallio S, Tarvainen MP. Heart rate variability and occupational stress-systematic review. Ind Health. 2018;56:500–11. [DOI] [PubMed] [PMC]

22.

Uusitalo A, Mets T, Martinmäki K, Mauno S, Kinnunen U, Rusko H. Heart rate variability related to effort at work. Appl Ergon. 2011;42:830–8. [DOI] [PubMed]

23.

Boudreau P, Yeh WH, Dumont GA, Boivin DB. Circadian variation of heart rate variability across sleep stages. Sleep. 2013;36:1919–28. [DOI] [PubMed] [PMC]

24.

Togo F, Takahashi M. Heart rate variability in occupational health --a systematic review. Ind Health. 2009;47:589–602. [DOI] [PubMed]

25.

Hansraj KK. Assessment of stresses in the cervical spine caused by posture and position of the head. Surg Technol Int. 2014;25:277–9. [PubMed]

26.

Mahmoud NF, Hassan KA, Abdelmajeed SF, Moustafa IM, Silva AG. The Relationship Between Forward Head Posture and Neck Pain: a Systematic Review and Meta-Analysis. Curr Rev Musculoskelet Med. 2019;12:562–77. [DOI] [PubMed] [PMC]

27.

Hofstede G. Culture’s Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations. In: Culture’s Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations. Sage Publications; 2001.

28.

Ho DYF. On the concept of face. Am J Sociol. 1976;81:867–84. [DOI]

29.

Yang KS. Chinese social orientation: an integrative analysis. In: Lin TY, Tseng WS, Yeh EK, editors. Chinese societies and mental health. Hong Kong: Oxford University Press; 1995. pp. 19–39.

30.

Ryder AG, Yang J, Zhu X, Yao S, Yi J, Heine SJ, et al. The cultural shaping of depression: somatic symptoms in China, psychological symptoms in North America? J Abnorm Psychol. 2008;117:300–13. [DOI] [PubMed]

31.

Yip K. Taoism and its impact on mental health of the Chinese communities. Int J Soc Psychiatry. 2004;50:25–42. [DOI] [PubMed]

32.

Schäfer A, Vagedes J. How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. Int J Cardiol. 2013;166:15–29. [DOI] [PubMed]

33.

McCraty R. New Frontiers in Heart Rate Variability and Social Coherence Research: Techniques, Technologies, and Implications for Improving Group Dynamics and Outcomes. Front Public Health. 2017;5:267. [DOI] [PubMed] [PMC]

34.

Nunan D, Sandercock GR, Brodie DA. A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing Clin Electrophysiol. 2010;33:1407–17. [DOI] [PubMed]

35.

Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24:385–96. [PubMed]

36.

Andreou E, Alexopoulos EC, Lionis C, Varvogli L, Gnardellis C, Chrousos GP, et al. Perceived Stress Scale: reliability and validity study in Greece. Int J Environ Res Public Health. 2011;8:3287–98. [DOI] [PubMed] [PMC]

37.

Figalová N, Charvát M. The Perceived Stress Scale: reliability and validity study in the Czech Republic. Cesk Psychol. 2021;65:46–59. [DOI]

38.

Low A, Chan S. Case study: monitoring the effectiveness of aromatherapeutic breathing with heart rate variability biofeedback. Heart Mind. 2021;5:90–4. [DOI]

39.

IBM Corp. IBM SPSS Statistics for Windows, Version 27.0. Armonk (NY): IBM Corp; 2020.

40.

Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale (NJ): Lawrence Erlbaum Associates; 1988.

41.

Hayes AF. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. 2nd ed. New York: Guilford Press; 2018.

42.

Maxwell SE, Cole DA. Bias in cross-sectional analyses of longitudinal mediation. Psychol Methods. 2007;12:23–44. [DOI] [PubMed]

43.

Farrar JT, Young JP Jr, LaMoreaux L, Werth JL, Poole MR. Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale. Pain. 2001;94:149–58. [DOI] [PubMed]

44.

Low A, Lam WL, Chau KT. Evaluating the Efficacy of EEG Neurofeedback Therapy in Managing ADHD and Anxiety: A Retrospective Analysis of 113 Cases. J Clini Epi Public Health. 2025;3:1–10.