Driven by science.

At HealthQb, we’re committed to using research and data to advance better long-term outcomes for those living with chronic illness.

HealthQb was developed by scientists, researchers, and practitioners. The following key scientific concepts inform our work and drive our mission.

Heart Rate Variability

Heart Rate Variability (HRV) is our heart’s ability to change the rate at which it pumps blood based on our constantly changing state of being. It significantly impacts how our body can react to and recover from stressful situations.

HRV is a non-invasive measurement of our Autonomic Nervous System. So whether our body enters a “fight or flight” response during a high-stress situation, or it begins to “rest and digest” when the threat has disappeared, these changes are reflected in our HRV.

Having high HRV is a sign of a healthy and resilient autonomic nervous system, which is an important component of overall physical and mental health. A resilient autonomic nervous system bounces back from stressors quickly and regulates our bodies efficiently in light of changes in our environment. When measured every night, HRV provides a reliable indication of our nervous system status.

The BioPsychoSocial (BPS) Model

While traditional models of clinical medicine focus on biological approaches to disease, the BioPsychoSocial (BPS) model emphasizes the importance of understanding human health and illness in their holistic context. 

The BPS model systematically considers biological, psychological, and social factors and their complex interactions in understanding illness, health, and well-being. 

We now know that chronic health conditions manifest from multiple factors. So in order to manage these conditions appropriately, each major factor has to be addressed individually. The BPS model is a way to acknowledge impact of social, psychological, and biological influences on one’s unique state of health and well-being, and to leverage the three forces to increase resilience.

The Autonomic Nervous System

The Autonomic Nervous System (ANS) is a part of our peripheral nervous system. It plays a crucial role in the maintenance of homeostasis, or balance, by switching our body between two basic states of our existence: “fight or flight” and “rest and digest”.

The ANS regulates many bodily functions, such as heart rate, blood pressure, digestion, temperature, and more. It is constantly making adjustments to these functions based on information it receives from the brain.

With advances in the technology of wearable devices, we now have the ability to objectively measure the ANS. Through Heart Rate Variability, we can determine if a person is in more of a “flight or flight” state, or a “rest and digest” state and make educated guesses about the resilience of their nervous system.

Taken together, these concepts illuminate a new option for the sustainable management of chronic illness—one that has both objective and subjective markers of progress.

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Explore The Research

+ HRV and health

Gang, Y., & Malik, M. (2003). Heart rate variability analysis in general medicine. Indian pacing and electrophysiology journal, 3(1), 34–40.

Young, H. A., & Benton, D. (2018). Heart-rate variability: a biomarker to study the influence of nutrition on physiological and psychological health?. Behavioural pharmacology, 29(2 and 3-Spec Issue), 140–151. https://doi.org/10.1097/FBP.0000000000000383

Coutts, L. V., Plans, D., Brown, A. W., & Collomosse, J. (2020). Deep learning with wearable based heart rate variability for prediction of mental and general health. Journal of Biomedical Informatics, 112, 103610. https://doi.org/10.1016/j.jbi.2020.103610

Harvard Health Publishing Staff. (2021, December 1). Heart rate variability: How it might indicate well-being. Harvard Health Blog.

+ HRV and disease

Benichou, T., Pereira, B., Mermillod, M., Tauveron, I., Pfabigan, D., Maqdasy, S., & Dutheil, F. (2018). Heart rate variability in type 2 diabetes mellitus: A systematic review and meta-analysis. PloS one, 13(4), e0195166. https://doi.org/10.1371/journal.pone.0195166

Kallio, M., Suominen, K., Bianchi, A. M., Mäkikallio, T., Haapaniemi, T., Astafiev, S., Sotaniemi, K. A., Myllyä, V. V., & Tolonen, U. (2002). Comparison of heart rate variability analysis methods in patients with Parkinson's disease. Medical & biological engineering & computing, 40(4), 408–414. https://doi.org/10.1007/BF02345073

La Rovere, M. T., Pinna, G. D., Hohnloser, S. H., Marcus, F. I., Mortara, A., Nohara, R., Bigger, J. T., Jr, Camm, A. J., Schwartz, P. J., & ATRAMI Investigators. Autonomic Tone and Reflexes After Myocardial Infarcton (2001). Baroreflex sensitivity and heart rate variability in the identification of patients at risk for life-threatening arrhythmias: implications for clinical trials. Circulation, 103(16), 2072–2077. https://doi.org/10.1161/01.cir.103.16.2072

Boveda, S., Galinier, M., Pathak, A., Fourcade, J., Dongay, B., Benchendikh, D., Massabuau, P., Fauvel, J. M., Senard, J. M., & Bounhoure, J. P. (2001). Prognostic value of heart rate variability in time domain analysis in congestive heart failure. Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing, 5(2), 181–187. https://doi.org/10.1023/a:1011485609838

Goto, T., Fukushima, H., Sasaki, G., Matsuo, N., & Takahashi, T. (2001). Evaluation of autonomic nervous system function with spectral analysis of heart rate variability in a case of tetanus. Brain & development, 23(8), 791–795. https://doi.org/10.1016/s0387-7604(01)00259-5

Goldstein, B., DeKing, D., DeLong, D. J., Kempski, M. H., Cox, C., Kelly, M. M., Nichols, D. D., & Woolf, P. D. (1993). Autonomic cardiovascular state after severe brain injury and brain death in children. Critical care medicine, 21(2), 228–233. https://doi.org/10.1097/00003246-199302000-00014

Goldstein, B., Fiser, D. H., Kelly, M. M., Mickelsen, D., Ruttimann, U., & Pollack, M. M. (1998). Decomplexification in critical illness and injury: relationship between heart rate variability, severity of illness, and outcome. Critical care medicine, 26(2), 352–357. https://doi.org/10.1097/00003246-199802000-00040

Roche, F., Gaspoz, J. M., Court-Fortune, I., Minini, P., Pichot, V., Duverney, D., Costes, F., Lacour, J. R., & Barthélémy, J. C. (1999). Screening of obstructive sleep apnea syndrome by heart rate variability analysis. Circulation, 100(13), 1411–1415. https://doi.org/10.1161/01.cir.100.13.1411

Wiklund, U., Olofsson, B. O., Franklin, K., Blom, H., Bjerle, P., & Niklasson, U. (2000). Autonomic cardiovascular regulation in patients with obstructive sleep apnoea: a study based on spectral analysis of heart rate variability. Clinical physiology (Oxford, England), 20(3), 234–241. https://doi.org/10.1046/j.1365-2281.2000.00251.x

Ribeiro, A. L., Lombardi, F., Sousa, M. R., Lins Barros, M. V., Porta, A., Costa Val Barros, V., Gomes, M. E., Santana Machado, F., & Otávio Costa Rocha, M. (2002). Power-law behavior of heart rate variability in Chagas' disease. The American journal of cardiology, 89(4), 414–418. https://doi.org/10.1016/s0002-9149(01)02263-9

+ HRV and arthritis

Cooper, T. M., McKinley, P. S., Seeman, T. E., Choo, T. H., Lee, S., & Sloan, R. P. (2015). Heart rate variability predicts levels of inflammatory markers: Evidence for the vagal anti-inflammatory pathway. Brain, behavior, and immunity, 49, 94–100. https://doi.org/10.1016/j.bbi.2014.12.017

Marita Zimmermann, Elisabeth Vodicka, Andrew J. Holman & Louis P. Garrison Jr. (2018) Heart rate variability testing: could it change spending for rheumatoid arthritis patients in the United States? An exploratory economic analysis, Journal of Medical Economics, 21:7, 712-720, DOI: 10.1080/13696998.2018.1470519

+ HRV, biopsychosocial model, and chronic pain

Forte, G., Troisi, G., Pazzaglia, M., Pascalis, V., & Casagrande, M. (2022). Heart Rate Variability and Pain: A Systematic Review. Brain sciences, 12(2), 153. https://doi.org/10.3390/brainsci12020153

Atkinson, T. M., Mendoza, T. R., Sit, L., Passik, S., Scher, H. I., Cleeland, C., & Basch, E. (2010). The brief pain inventory and its “pain at its worst in the last 24 hours” Item: Clinical trial endpoint considerations. Pain Medicine, 11(3), 337–346. https://doi.org/10.1111/j.1526-4637.2009.00774.x

Bevers, Kelley & Watts, Lynette & Kishino, Nancy & Gatchel, Robert. (2016). The Biopsychosocial Model of the Assessment, Prevention, and Treatment of Chronic Pain. US Neurology. 12. 98. 10.17925/USN.2016.12.02.98.

Treede, R. D., Rief, W., Barke, A., Aziz, Q., Bennett, M. I., Benoliel, R., Cohen, M., Evers, S., Finnerup, N. B., First, M. B., Giamberardino, M. A., Kaasa, S., Kosek, E., Lavand'homme, P., Nicholas, M., Perrot, S., Scholz, J., Schug, S., Smith, B. H., Svensson, P., … Wang, S. J. (2015). A classification of chronic pain for ICD-11. Pain, 156(6), 1003–1007. https://doi.org/10.1097/j.pain.0000000000000160

Driscoll, M. A., Edwards, R. R., Becker, W. C., Kaptchuk, T. J., & Kerns, R. D. (2021). Psychological Interventions for the Treatment of Chronic Pain in Adults. Psychological science in the public interest : a journal of the American Psychological Society, 22(2), 52–95. https://doi.org/10.1177/15291006211008157

Pike, A., Hearn, L., & de C Williams, A. C. (2016). Effectiveness of psychological interventions for chronic pain on health care use and work absence: systematic review and meta-analysis. Pain, 157(4), 777–785. https://doi.org/10.1097/j.pain.0000000000000434

Ashar Y., Gordon A., Schubiner H., et al. Effect of Pain Reprocessing Therapy vs Placebo and Usual Care for Patients With Chronic Back Pain: A Randomized Clinical Trial. JAMA Psychiatry. 2022;79(1):13–23. doi:10.1001/jamapsychiatry.2021.2669

Von Korff, M., DeBar, L., Krebs, E., Kerns, R., Deyo, R., & Keefe, F. (2020). Graded chronic pain scale revised: mild, bothersome, and high-impact chronic pain. Pain, 161(3), 651–661. https://doi.org/10.1097/j.pain.0000000000001758

Rethorn, Z., Pettitt, R., Dykstra, E., & Pettitt, C. (2020). Health and wellness coaching positively impacts individuals with chronic pain and pain-related interference. PloS one, 15(7), e0236734. https://doi.org/10.1371/journal.pone.0236734

+ HRV and autonomic dysregulation/rehabilitation

Atkinson, T., Mendoza, T., Sit, L., et al. (2010). The brief pain inventory and its “pain at its worst in the last 24 hours” Item: Clinical trial endpoint considerations. Pain Medicine, 11(3), 337–346. https://doi.org/10.1111/j.1526-4637.2009.00774.x

Gharbo, R. S. (2020). Autonomic rehabilitation. Physical Medicine and Rehabilitation Clinics of North America, 31(4), 633–648. https://doi.org/10.1016/j.pmr.2020.07.003

+ Wearable validation

Chen, J., Abbod, M., & Shieh, J. S. (2021). Pain and Stress Detection Using Wearable Sensors and Devices - A Review. Sensors (Basel, Switzerland), 21(4), 1030. https://doi.org/10.3390/s21041030

Green, E., van Mourik, R., Wolfus, C. et al. (2019). Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor. npj Digit. Med. 2, 57. https://doi.org/10.1038/s41746-019-0130-0

Selder, J., Proesmans, T., Breukel, L., et al. (2020). Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data. Computer Methods and Programs in Biomedicine (197). https://doi.org/10.1016/j.cmpb.2020.105753.

Gradl, S., Wirth, M., Richer, R., et al. (2019). An Overview of the Feasibility of Permanent, Real-Time, Unobtrusive Stress Measurement with Current Wearables. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth'19). Association for Computing Machinery, New York, NY, USA, 360–365. https://doi.org/10.1145/3329189.3329233

Chelu, M. and Marrouche, N. (2017). Assessment of pulse wave pressure changes for detection of arrhythmia and comorbidities using a wrist-worn wearable device. EP Europace 19(3). https://doi.org/10.1093/ehjci/eux151.027

Magliulo, M., Cella, L., and Pacelli, R. (2015). Bluetooth devices for the optimization of patients' workflow in a radiation oncology department. 1-4. 10.1109/EHB.2015.7391515.

Dur, O., Rhoades, C., Ng, M., et al. (2018). Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder. JMIR Mhealth Uhealth 6(10). DOI: 10.2196/11040

Jarchi, D., Salvi, D., Velardo, C., et al. (2018). Estimation of HRV and SpO2 from wrist-worn commercial sensors for clinical settings. 144-147. In Proceedings of the 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Las Vegas, Nevada, USA. 144-147. 10.1109/BSN.2018.8329679.

Budiarto, A., Febriana, T., Suparyanto, T., et al. (2018). Health Assistant Wearable-Based Data Science System Model: A Pilot Study. In Proceedings of the International Conference on Information Management and Technology. Alam Sutera, Indonesia. 10.1109/ICIMTech.2018.8528102.

Quer, G., Nikzad, N., Lanka S., et al. (2017). Abstract 18610: Preliminary Evaluation of a Wrist Wearable Heart Rate Sensor for the Detection of Undiagnosed Atrial Fibrillation in a Real-World Setting. Circulation (136)1. 2017;136:A18610

The Economist Newspaper. (2020, May 2). Wearable devices are connecting health care to daily life. The Economist.

Our Publications

Dudarev, V., Barral, O., Radaeva, M., Davis, G., Enns, J.T. (in prep). Night time heart rate predicts next day pain in fibromyalgia and primary back pain.

Dudarev, V., Zhang, Ch., Barral, O., Davis, G., Enns, J.T. (2022). Night-time cardiac metrics from a wearable sensor predict intensity of next-day chronic pain. Procedia Computer Science, 206.

Dudarev, V., Barral, O., Zhang, C., Davis, G., & Enns, J. T. (2022). On the reliability of wearable technology: A tutorial on measuring heart rate and heart rate variability in the wild. bioRxiv.

Dudarev, V., Davis, G., & Enns, J. T. (2020, July). Tracking subjective and physiological emotion in daily life. Psychosomatic medicine, 82(6), p. A37.

Our Presentations

Dudarev, V., Barral, O., Davis, G., Enns, J.T.. (2023). Heart metrics from wearable sensors at night predict next-day pain reports: Two studies of primary chronic pain. 79th Annual Meeting of the American Psychosomatic Society. (oral presentation)

Dudarev, V., Radaeva, M., Barral, O., Davis, G., Enns, J.T. (2023). Psychological factors of chronic pain. 79th Annual Meeting of the American Psychosomatic Society. (poster)

Dudarev, V., Zhang, Ch., Barral, O., Davis, G., Enns, J.T. (2022). Heart metrics from wearable sensors at night predict next-day pain reports: A study of primary chronic pain. 2022 world congress on pain, Toronto, Canada. (poster)

Dudarev, V., Barral, O., Davis, G., Enns, J.T. (2022). Daily wearable sensors for psychophysiological research: when and how. 62th annual meeting of the Society for psychophysiological research, Vancouver, Canada. (oral presentation)

Dudarev, V., Barral, O., Davis, G., Enns, J.T. (2022). Night-time cardiac metrics from a wearable sensor predict intensity of next-day chronic pain. 11th scientific meeting of the International Society for Research on Internet Interventions, Pittsburg, USA. (oral presentation)

Dudarev, V., Davis, G., Enns, J.T. (2022). Heart metrics from wearable sensors at night predict next-day pain reports: A study of primary chronic pain. Annual Meeting of the American Psychosomatic Society, Long Beach, USA. (oral presentation)

Dudarev, V., Davis, G., Enns, J.T. (2020). Tracking subjective and physiological emotion in daily life. 78th Annual Meeting of the American Psychosomatic Society, Long Beach, USA. (poster)

Interested in learning more? Get in touch to discover our ongoing research.