The effectiveness of HeartMath biofeedback intervention to improve blood glucose levels among type 2 diabetes mellitus patients
DOI:
https://doi.org/10.18203/2320-6012.ijrms20260947Keywords:
Type 2 diabetes mellitus, Biofeedback, Heart rate variability blood glucose, HeartMathAbstract
Background: Biofeedback interventions are widely used among naturopathic practitioners to treat and manage various health conditions, such as mental health-related disorders, hypertension, and diabetes mellitus. The aim of the study was to evaluate the efficacy of the biofeedback HeartMath emWave Pro intervention in patients with type 2 diabetes mellitus (T2DM).
Methods: A total of 25 adults [male (n=14) and female (n=11)] participants (age: 51.32±4.13 years) were recruited in this quasi-experimental pilot study. Heart rate variability (HRV) and breathing analysis were trained and measured using HeartMath emWave Pro during biofeedback training. Blood glucose parameters [haemoglobin A1c (A1c)], fasting blood glucose, and post-prandial blood sugar) were measured pre- and post-intervention using a biochemistry auto-analyzer. Data were analyzed via the Wilcoxon Signed Rank test and Spearman correlation.
Results: Post-intervention findings revealed a significant difference in HbA1c, fasting blood sugar, post-prandial blood sugar, and cortisol (all, p<0.05). Post intervention, thyroid-stimulating hormone (TSH) (r=-0.408) and breathing (r=0.465) showed significant correlation with HRV (all, p<0.05).
Conclusions: These findings suggest that HeartMath biofeedback training may improve blood glucose control in individuals with T2DM.
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References
WHO. Diabetes. Geneva: WHO, 2023. Available at: https://www.who.int/newsroom/factsheets/detail/diabetes. Accessed on 14 February 2024.
Rahman MS, Hossain KS, Das S, Kundu S, Adegoke EO, Rahman MA,et al. Role of insulin in health and disease: an update. Int J Mol Sci. 2021;22:6403. DOI: https://doi.org/10.3390/ijms22126403
International Diabetes Federation. IDF diabetes atlas. 10th ed. Brussels: IDF; 2021.
Akhtar S, Nasir JA, Ali A, Asghar M, Majeed R, Sarwar A. Prevalence of type-2 diabetes and prediabetes in malaysia: a systematic review and meta-analysis. PLoS One. 2022;17:e0263139. DOI: https://doi.org/10.1371/journal.pone.0263139
Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. DOI: https://doi.org/10.1016/j.diabres.2021.109119
International Diabetes Federation. IDF diabetes atlas. 8th ed. Brussels: IDF; 2017.
Pradeepa R, Mohan V. Epidemiology of type 2 diabetes in india. Indian J Ophthalmol. 2021;69:2932-8. DOI: https://doi.org/10.4103/ijo.IJO_1627_21
Ogbonna SU, Ezeani IU. Risk factors of thyroid dysfunction in patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne). 2019;10:440. DOI: https://doi.org/10.3389/fendo.2019.00440
Ortiz R, Kluwe B, Odei JB, Echouffo Tcheugui JB, Sims M, et al. The association of morning serum cortisol with glucose metabolism and diabetes: the jackson heart study. Psychoneuroendocrinology. 2019;103:25-32. DOI: https://doi.org/10.1016/j.psyneuen.2018.12.237
Kalra S, Sahay R. Diabetes fatigue syndrome. Diabetes Ther. 2018;9:1421-9. DOI: https://doi.org/10.1007/s13300-018-0453-x
Shi Q, Nong K, Vandvik PO, Guyatt GH, Schnell O, Rydén L, Marx N, et al. Benefits and harms of drug treatment for type 2 diabetes: systematic review and network meta-analysis of randomised controlled trials. BMJ. 2023;381:e074068. DOI: https://doi.org/10.1136/bmj-2022-074068
Stevenson-Hoare J, Leonenko G, Escott-Price V. Comparison of long-term effects of metformin on longevity between people with type 2 diabetes and matched non-diabetic controls. BMC Public Health. 2023;23:804. DOI: https://doi.org/10.1186/s12889-023-15764-y
Benichou T, Pereira B, Mermillod M, Tauveron I, Pfabigan D, Maqdasy S, et al. Heart rate variability in type 2 diabetes mellitus: A systematic review and meta-analysis. PLoS One. 2018 Apr 2;13(4):e0195166. DOI: https://doi.org/10.1371/journal.pone.0195166
Hufnagel C, Chambres P, Bertrand PR, Dutheil F. The need for objective measures of stress in autism. Front Psychol. 2017;8:64. DOI: https://doi.org/10.3389/fpsyg.2017.00064
Eckstein ML, Brockfeld A, Haupt S, Schierbauer JR, Zimmer RT, Wachsmuth NB, et al. Acute changes in heart rate variability to glucose and fructose supplementation in healthy individuals: a double-blind randomized crossover placebo-controlled trial. Biology (Basel). 2022;11:338. DOI: https://doi.org/10.3390/biology11020338
Sielski R, Rief W, Glombiewski JA. Biofeedback in chronic pain: a systematic review. Appl Psychophysiol Biofeedback. 2017;42:1-15.
Castro Ribeiro M, et al. Biofeedback interventions for stress and mental health symptoms: a systematic review. Int J Environ Res Public Health. 2023;20:4285.
Aaraji F, Nosratabadi M, Hoseinpourfard M. The impact of biofeedback on diabetic patients’ glycemia. Hosp Pract Res. 2019;4:18-24. DOI: https://doi.org/10.15171/hpr.2019.03
Kumar M, Pandey D, Shrivastva P. Effect of GSR biofeedback relaxation training on blood glucose and anxiety level of type 2 diabetic patients. Int J Indian Psychol. 2016;4:1. DOI: https://doi.org/10.25215/0401.160
McGinnis RA, McGrady A, Cox SA, Grower-Dowling KA. Biofeedback-assisted relaxation in type 2 diabetes. Diabetes Care. 2005;28(9):2145-9. DOI: https://doi.org/10.2337/diacare.28.9.2145
Druschky K, Druschky A. Mobile biofeedback of heart rate variability in patients with diabetic polyneuropathy: a preliminary study. Clin Physiol Funct Imaging. 2015;35:332-7. DOI: https://doi.org/10.1111/cpf.12130
Liu Z, He W, Fang R, Xiao G, Yan F, Qiu Q, et al. Lifetide biofeedback intervention in type 2 diabetic patients: a pilot observation. Nan Fang Yi Ke Da Xue Xue Bao. 2023;43:1622-8.
Sridhar B, Haleagrahara N, Bhat R, Kulur AB, Avabratha S, Adhikary P. Increase in the heart rate variability with deep breathing in diabetic patients after 12-month exercise training. Tohoku J Exp Med. 2010;220(2):107-13. DOI: https://doi.org/10.1620/tjem.220.107
McCraty R, Atkinson M, Lipsenthal L. Emotional self-regulation program enhances psychological health and quality of life in patients with diabetes. Institute of HeartMath. 2000;1-11.
Sharma K, Akre S, Chakole S, Wanjari MB. Stress-Induced Diabetes: A Review. Cureus. 2022 Sep 13;14(9):e29142. DOI: https://doi.org/10.7759/cureus.29142
Kotozaki Y, Takeuchi H, Sekiguchi A, Yamamoto Y, Shinada T, Araki T, et al. Biofeedback-based training for stress management in daily hassles: an intervention study. Brain Behav. 2014;4:566-79. DOI: https://doi.org/10.1002/brb3.241
Makaracı Y, Makaracı M, Zorba E, Lautenbach F. A pilot study of the biofeedback training to reduce salivary cortisol level and improve mental health in highly-trained female athletes. Appl Psychophysiol Biofeedback. 2023;48:357-67. DOI: https://doi.org/10.1007/s10484-023-09589-z
Shokr EAM, Altwiher RA, Alnaam MK, Alsunaitani AF, Alshamery MI. Physiological study on the relation of heart rate variability in ageing and thyroid hormone disorder. Int J Med Res Health Sci. 2016;5:133-8.
Brusseau V, Tauveron I, Bagheri R, Ugbolue UC, Magnon V, Bouillon-Minois JB, et al. Heart rate variability in hyperthyroidism: a systematic review and meta-analysis. Int J Environ Res Public Health. 2022;19:3606. DOI: https://doi.org/10.3390/ijerph19063606
Chaitanya S, Datta A, Bhandari B, Sharma VK. Effect of resonance breathing on heart rate variability and cognitive functions in young adults: a randomised controlled study. Cureus. 2022;14:e22187. DOI: https://doi.org/10.7759/cureus.22187
Steffen PR, Austin T, DeBarros A, Brown T. The impact of resonance frequency breathing on measures of heart rate variability, blood pressure, and mood. Front Public Health. 2017;5:222. DOI: https://doi.org/10.3389/fpubh.2017.00222
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