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Tulip wants to create a world where every person has access to
personalized health information using the most advanced technology
in the field to improve their overall health and well-being. We
value sharing the expertise of our research team and their
findings to empower you, so that you can take charge of your
ongoing health.
After you receive the analysis of your data and your customized
Wellness Plan, our ever-growing database of educational articles
and latest research findings are always available (24/7) to help
you find more natural and healthy ways to live a vibrant life. Be
sure to check out our ongoing research studies often, as you may
want to participate in some of the studies that interest you.
By combining cutting-edge technology with natural and integrative
medicine, Tulip aims to empower every person to achieve their
personal health and wellness goals. By incorporating data from
wearable technology, with proven behavioral science principles and
the use of evidence-based natural medicine, we are creating a
health platform that can help everyone build long-lasting
improvements for whole-person health.
For example, our interfaces with your watch and health apps can
assess data that those devices collect such as heart rate (HR),
heart rate variability (HRV), heart rhythms, blood oxygen, steps,
sleep, and so on. Based on studies related to heart function,
movement, and sleep, we can determine where changes can be made to
not only improve sleep, boost energy, or balance blood sugar
levels, but to also improve your overall health.
Integrative Medicine (IM) is federally defined by the National Center for Complementary and Integrative Health (NCCIH) as the use of complementary medicine together with conventional medicine. Complementary medicine includes a diverse group of medical and health care practices that are not generally considered part of conventional medicine. As a branch of the National Institutes of Health (NIH), the NCCIH is well-aligned with the NIH strategic plan to answer important scientific and public health questions, and to initiate and support research that defines the safety and efficacy of IM therapies for improving health and the delivery of healthcare. Research shows that patients have been using IM therapies for decades. The NIH has been researching the use of complementary modalities since 1970’s. However, despite the interest and well-documented widespread use of complementary therapies, there is insufficient data regarding their safety and efficacy. After almost two decades of population-based research focused on the use of alternative medicine, it was determined that the use of unproven practices used in place of conventional medicine was quite rare. The NCCIH continues to conduct ongoing research regarding the safety and efficacy of integrative health modalities.
Natural Products
Includes several items such as herbs or botanicals, vitamins, minerals (data from the 2012 National Health Information Survey and other studies show that natural products are the most frequently used IM modality by both adults and children)
Other Complementary Health Approaches
Includes well known medicinal approaches and traditional healers such as Ayurvedic, Traditional Chinese, Homeopathic, and Naturopathic
Mind Body Practices
Encompasses a diverse group of procedures or techniques including yoga, chiropractic and osteopathic manipulation, meditation, massage therapy, acupuncture and relaxation, and a host of others
Black, L.I., Clarke, C.T., Barnes, P.M., Stussman, B.J., Nahin, R.L. (2015). Use of Complementary Health Approaches Among Children Aged 4–17 Years in the United States: National Health Interview Survey, 2007–2012. National Health Statistics Report, 78, 1-18.
Naing, A., Stephen, S.K., Frenkel, M., Chandhasin, C., Hong, D.S., Lei, X., Falchook, G., Wheler, J.J., Fu, S., Kurzrock, R. (2011). Prevalence of complementary medicine use in a phase 1 clinical trials program. Cancer, 117, 5142–5150.
Sencer, S.F., Kelly, K.M. (2007). Complementary and alternative therapies in pediatric oncology. Pediatric Clinics of North America, 54(6), 1043-1060.
Hewson, M.G., Copeland, H.L., Mascha, E., Arrigain, S., Topol, E., Fox, J.E. (2006). Integrative medicine: implementation and evaluation of a professional development program using experiential learning and conceptual change teaching approaches. Patient Educ. Couns., 62(1), 5-12.
Molassiotis, A., Cubbin, D. (2004). Thinking outside the box: complementary and alternative therapies use in paediatric oncology patients. European Journal of Oncology Nursing, 8(1), 50-60.
McCurdy, E.A., Spangler, J.G., Wofford, M.M, Chauvenet, A.R., McLean, T.W. (2003). Religiosity is associated with the use of complementary medical therapies by pediatric oncology patients. Journal of Pediatric Hematology/Oncology, 25(2), 125–129.
Sparber A, Wootton JC. (2001). Surveys of complementary and alternative medicine. Part II: use of alternative and complementary cancer therapies. J. Altern. Complement. Med., 7(3), 281–287.
Weeks, J., Layton, R. (1998). Integration As Community Organizing: Toward A Model for Optimizing Relationships Between Networks of Conventional and Alternative Providers. Integrative Medicine, 1(1), 15-25.
Lin, Y.C., Lee, A.C., Kemper, K.J., Berde, C.B. (2005). Use of complementary and alternative medicine in pediatric pain management service: A survey. Pain Med., 6, 452–458.
Kemper, K.J., Gardiner P., Birdee G.S. (2013). Use of complementary and alternative medical therapies among youth with mental health concerns. Acad. Pediatr., 13(6),540–545.
Newman, D.J., Cragg, G.M. (2012). Natural products as sources of new drugs over the 30 years from 1981 to 2010. J. Nat. Prod., 75, 311–335.
Zheng, Z., Guo, R.J., Helme, R.D., Muir, A., Costa, C., Xue, C.C. (2008). The effect of electroacupuncture on opioid-like medication consumption by chronic pain patients: A pilot randomized controlled clinical trial. Eur. J. Pain, 12, 671–676.
Tulip wearable technology research is informed by the latest
research in medicine, behavioral health, wearable technology, and
machine learning. Consider this — According to the AHA over 80
million Americans suffer from cardiovascular disease (the leading
cause of death in both men and women); but what if you could use
your watch and your phone to change the course of your heart
disease? Depending on your age, what if you could avoid heart
disease altogether? Tulip research helps show you how to do just
that!
Scientific research has shown that variations in heart rate and
sleep are related to all-cause mortality. While your watch and
phone collect data about your heart rate and sleep patterns, they
don’t put that information “together” for you. For instance, if
your heart rate is increased for longer periods of time while you
sleep, that not only tells Tulip how much REM sleep you had but
also over time tells Tulip how that is affecting your heart
function. Tulip can use daily variability together with long term
disparities to determine what behavioral and nutrition changes you
need to make to help improve your overall cardiac health. This
scientific approach can also be applied to other problems as well,
such as autoimmune disease and chronic illnesses.
Below is a list of abstracts (short summaries) of some notable Tulip studies. Currently, Tulip has multiple ongoing studies assessing the use and efficacy of wearable health technology combined with integrative medicine interventions to improve the symptoms of chronic illness related to chronic fatigue syndrome, fibromyalgia, diabetes and long-COVID 19. Studies that you can enroll in are noted for each abstract.
Heart Rate Variability as an Indicator of Fatigue Severity in CFS/ ME
Introduction: Chronic fatigue syndrome (CFS) or myalgic encephalomyelitis (ME) is a complex, often debilitating disease with no known etiology, characterized by severe fatigue. In addition to fatigue, patients also suffer from sleep disorders, cognitive dysfunction, muscle pain and post-exertional malaise. TULIP Health conducted an observational study to assess the accuracy of wearable health technology in identifying patients with CFS/ ME.
Methods: This observational study enrolled participants that reported severe fatigue. Healthy participants without fatigue or pain were enrolled as controls. Validated CDC CFS/ME surveys were administered to all participants. Each participant was given an Oura Ring to wear twenty-four hours per day, seven days per week for thirty days. Data was collected from the ring for thirty days including heart rate (HR), heart rate variability (HRV), body temperature, activity, and sleep. Participants did not change their daily habits or routines.
Results: Data from wearable technology was able to discern between participants and healthy controls. Heart rate variability was the strongest variable associated with disease. Participants with CFS /ME had significantly lower HRV than healthy controls. Additionally, HRV scores were correlated with severity of disease.
Conclusion: The present study demonstrates that patients with CFS/ ME have lower HRV than healthy subjects. HRV can also be an indicator of disease severity. Wearable health technology is a novel and more accessible technique of assessing disease in this population.
Chronic fatigue syndrome (CFS) or myalgic encephalomyelitis (ME) is a complex disease characterized by severe fatigue that is not relieved by rest. Previous studies have identified heart rate variability (HRV) analysis as a possible target to assess autonomic nervous system dysfunction in CFS/ME. The autonomic nervous system is part of the peripheral nervous system which is responsible for sustaining involuntary functions such as blood pressure, heart rate, respiration, and temperature. The two branches, sympathetic and parasympathetic branches, influence most bodily functions to maintain homeostasis in the body. Disruption to homeostasis activates the sympathetic nervous system; if homeostasis is not restored, chronic illnesses can arise from constitutive activation. Research has shown that pain and fatigue are often correlated to autonomic dysfunction related to persistent sympathetic nervous system stimulation. In the current study we used HRV analysis with data collected from wearable health technology to correlate patient-reported symptoms and severity. Our findings, not unlike other studies, demonstrate that HRV analysis from wearable health technology can be used to identify and predict fatigue severity in patients with CFS / ME. Low HRV was consistently associated with patients suffering from symptoms of CFS / ME, independent of heart rate. Additionally, low HRV was also correlated to severity of fatigue in CFS / ME. Our results indicate that wearable health technology can be used to decrease the incidence of post- exertional malaise related to activities of daily living (ADL) or exercise, which would not only greatly improve the quality of life for patients with this disease, but contribute to the understanding of the pathophysiology of CFS /ME. Tulip will continue to collect ongoing data to broaden our understanding of how measurable health parameters can predict and prevent symptoms related to CFS / ME.
Ni Z, Sun F, Li Y. Heart Rate Variability-Based Subjective Physical Fatigue Assessment. Sensors (Basel). 2022 Apr 21;22(9):3199. doi:10.3390/s22093199.
Capdevila,L.; Castro-Marrero, J.; Alegre, J.; Ramos-Castro, J.; Escorihuela, R.M. Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology. Sensors 2021, 21, 3746. https://doi.org/10.3390/s21113746
Escorihuela, R.M., Capdevila, L., Castro, J.R. et al. Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis. J Transl Med 18, 4 (2020). https://doi.org/10.1186/s12967-019-02184-z
Hongyu Luo, Pierre-Alexandre Lee and Ieuan Clay et al. Assessment of Fatigue Using Wearable Sensors: A Pilot Study. Digit Biomark. 2020;4(suppl 1):59-72. DOI: 10.1159/000512166
Heart Rate Variability as a Biomarker of Pain in Fibromyalgia Syndrome
Introduction: Fibromyalgia is a chronic pain syndrome with no known etiology. It is characterized by widespread pain which is often accompanied by fatigue, sleep disorders, anxiety and depression. TULIP Health is conducting an observational study to assess the use of wearable health technology in identifying patients with Fibromyalgia (FM).
Methods: This observational study enrolled participants that reported significant pain. Healthy participants without pain were enrolled as controls. Validated FIQR surveys were administered to all participants. Each participant was given an Oura Ring to wear twenty-four hours per day, seven days per week for thirty days. Data was collected from the ring for thirty days including heart rate (HR), heart rate variability (HRV), body temperature, activity, and sleep. Participants did not change their daily habits or routines.
Results: Data from wearable technology was able to discern between participants and healthy controls. Heart rate variability was the strongest variable associated with disease. Participants with FM had significantly lower HRV than healthy controls. Additionally, HRV scores were correlated with severity of disease.
Conclusion: The present study demonstrates that patients with FM have lower HRV than healthy subjects. HRV can also be an indicator of disease severity. Wearable health technology is a novel and more accessible technique of assessing disease in this population.
Fibromyalgia (FM) is a chronic pain syndrome characterized by widespread pain accompanied by fatigue, sleep disturbance, as well as mental and emotional symptoms. Currently there are no laboratory tests or imaging techniques to differentiate patients suffering from FM. Research has shown that patients who suffer from FM, display signs of autonomic dysfunction. The autonomic nervous system is part of the peripheral nervous system which is responsible for sustaining involuntary functions such as blood pressure, heart rate, respiration, and temperature. The two branches, sympathetic and parasympathetic branches, influence most bodily functions to maintain homeostasis in the body. Disruption to homeostasis activates the sympathetic nervous system; if homeostasis is not restored, chronic illnesses can arise from constitutive activation. Research has shown that pain and fatigue are often correlated to autonomic dysfunction related to persistent sympathetic nervous system stimulation. In the current study we used HRV analysis with data collected from wearable health technology to correlate patient-reported symptoms and severity. Our findings, not unlike other studies, including our CFS / ME study, demonstrate that HRV analysis from wearable health technology can be used to identify and predict pain severity in patients with FM. Low HRV was consistently associated with patients suffering from symptoms of FM independent of heart rate. Our results indicate that wearable health technology can be used to decrease the incidence of post- exertional malaise related to activities of daily living (ADL), which would not only greatly improve the quality of life for patients with this disease. Tulip will continue to collect ongoing data to broaden our understanding of how measurable health parameters can predict and prevent symptoms related to FM.
Sochodolak RC, Schamne JC, Ressetti JC, Costa BM, Antunes EL, Okuno NM. A comparative study of heart rate variability and physical fitness in women with moderate and severe fibromyalgia. J Exerc Rehabil. 2022 Apr 26;18(2):133-140. doi: 10.12965/jer.2244070.035. PMID: 35582683; PMCID: PMC9081406.
Meeus M, Goubert D, De Backer F, Struyf F, Hermans L, Coppieters I, De Wandele I, Da Silva H, Calders P. Heart rate variability in patients with fibromyalgia and patients with chronic fatigue syndrome: a systematic review. Semin Arthritis Rheum. 2013 Oct;43(2):279-87. doi: 10.1016/j.semarthrit.2013.03.004. Epub 2013 Jul 6. PMID: 23838093.
Lerma C, Martinez A, Ruiz N, Vargas A, Infante O, Martinez-Lavin M. Nocturnal heart rate variability parameters as potential fibromyalgia biomarker: correlation with symptoms severity. Arthritis Res Ther. 2011;13(6):R185. doi: 10.1186/ar3513. Epub 2011 Nov 16. PMID: 22087605; PMCID: PMC3334634.
Heart Rate Variability and Blood Sugar Dysregulation
Introduction: Diabetes mellitus is a complex group of diseases affecting how the body uses blood sugar or glucose. It encompasses several diseases including Type I and Type II diabetes, gestational Diabetes, impaired glucose tolerances and impaired fasting glycemia. This suite of chronic illnesses occurs when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces.
It is characterized by hyperglycemia, fatigue, weakness, vision changes and changes in immune system function. TULIP Health is conducting an observational study to assess the accuracy of wearable health technology in identifying patterns of measurable health metrics and changes in blood sugar.
Methods: This observational study is enrolling participants that reported a diagnosis of Diabetes, prediabetes or problems with glucose tolerance. Additional healthy participants without glucose dysregulation were enrolled as controls. Validated CDC Diabetes surveys were administered to all participants. Each participant was given an Oura Ring to wear twenty-four hours per day, seven days per week for thirty days. Data was collected from the ring for thirty days including heart rate (HR), heart rate variability (HRV), body temperature, activity, and sleep. Participants did not change their daily habits or routines.
*This study is ongoing and currently enrolling participants.
Heart Rate Variability as an Indicator of Post-Covid Syndrome
Introduction: Post-COVID 19 Syndrome includes a group of new, returning or ongoing symptoms that patients experience more than four weeks after becoming infected with COVID-19 virus. Symptoms may last weeks, months or years and sometimes cause disability. It is a complex, pervasive syndrome characterized by significant fatigue, sleep disorders, cognitive dysfunction, post-exertional malaise, cardiovascular conditions, digestive disorders, and pain.
Tulip Health is conducting an observational study to assess the accuracy of wearable health technology in identifying patterns of measurable health metrics related to Post-COVID 19 Syndrome symptoms with CFS/ ME.
Methods: This observational study is enrolling participants that reported a diagnosis of Post-COVID 19 Syndrome or reported unexplained symptoms related to Post-COVID 19 Syndrome. Additional healthy participants without glucose dysregulation were enrolled as controls. Validated Post-COVID 19 Functional Status surveys were administered to all participants. Each participant was given an Oura Ring to wear twenty-four hours per day, seven days per week for thirty days. Data was collected from the ring for thirty days including heart rate (HR), heart rate variability (HRV), body temperature, activity, and sleep. Participants did not change their daily habits or routines.
*This study is ongoing and currently enrolling participants.
Research has shown that wearable technology can be used
effectively in numerous clinical settings by allowing novel,
passive, data collection on a 24/7 basis in natural environments
of a patient’s home or work as they go through their daily
routines. Wearable technologies have proven that they can indicate
current or future health problems, including heart conditions,
anxiety and depression. Comparative studies have been performed
using classical devices like polysomnography, actigraphy,
electroencephalogram and electrocardiogram in tandem with wearable
devices proving that wearable devices can perform equivocally or
superior to typical clinic-bound devices. They have also garnered
attention for drug development in early and late-stage clinical
trials due to the flexibility and ease of use of the devices,
whereas traditional data collection requires hospital stays for
participants.
Recent studies by Massachusetts General Hospital, Harvard Medical
School, Rockefeller Neuroscience Institute, and Johns Hopkins
Ciccarone Center for the Prevention of Cardiovascular Disease have
investigated the accuracy and efficacy of using wearable
technology as a means to measure health related parameters. They
have found that using these devices has enabled them to not only
manage patients with established medical conditions but also has
opened the door to remote screening and diagnosis of common
cardiovascular diseases, such as arrhythmias.
Although your Apple watch, iPhone and Oura Ring collect data about
your health, they don’t put that information together for you —
Tulip does. For instance, if your heart rate is increased for
longer periods of time while you sleep, that not only tells Tulip
how much REM sleep you had but also over time tells Tulip how that
is affecting your overall heart function. Tulip can use daily
variability together with long term disparities to determine what
behavioral and nutritional changes you need to make to improve
cardiac health.
Lu L, Zhang J, Xie Y, Gao F, Xu S, Wu X,Ye Z. Wearable Health Devices in Health Care: Narrative Systematic Review. JMIR Mhealth Uhealth 2020;8(11):e18907. doi: 10.2196/18907
Sharma A, Badea M, Tiwari S, Marty JL. Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring. Molecules. 2021 Feb 1;26(3):748. doi: 10.3390/molecules26030748. PMID: 33535493; PMCID: PMC7867046
Stone JD, Ulman HK, Tran K, Thompson AG, Halter MD, Ramadan JH, Stephenson M, Finomore VS Jr, Galster SM, Rezai AR, Hagen JA. Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability. Front Sports Act Living. 2021 Mar 1;3:585870. doi: 10.3389/fspor.2021.585870. PMID: 33733234; PMCID: PMC7956986.
How does sleep affect overall health?
Introduction: Sleep is essential for good health and is as important as diet and exercise. Not getting enough consistent, quality sleep increases the risk for numerous diseases including heart disease, stroke, obesity, and dementia. The quality of your sleep can affect your cognitive performance, mood, and overall health.
A common misconception about sleep is that it is “downtime” for your body and your brain, but that is not the case. The brain and body are working while we sleep. The brain is active, preparing itself to learn, remember, and create. It also has a drainage system that removes toxins during sleep. The body also uses the time during sleep to repair blood vessels and the immune system and certain repair processes occur mostly, or most effectively, during sleep, so when sleep is disturbed so are those processes.
Methods: Recent advances in wearable sensor technology, digital signal processing, and artificial intelligence have enabled enhanced monitoring of sleep quality which has led to detection of a broader-range of sleep disorders and improved quantification of severity of disease. Now, wearable technology like Oura Ring and Apple Watch are highly sophisticated devices that can collect data from multiple sensors and can extract information about the user’s sleep. These devices are capable of capturing 24/7 bio-signals for determining heart rate, heart rate variability, skin conductance, temperature and activity, offering users’ an unprecedented window on their health.
Mekhael M, Lim CH, El Hajjar AH,Noujaim C, Pottle C, Makan N, Dagher L,Zhang Y, Chouman N, Li DL, Ayoub T,Marrouche N. Studying the Effect of Long COVID-19 Infection on Sleep Quality Using Wearable Health Devices: Observational Study. J Med Internet Res 2022;24(7):e38000 doi: 10.2196/38000
Christopher M Depner, Edward L Melanson, Robert H Eckel, Janine A Higgins, Bryan C Bergman, Leigh Perreault, Oliver A Knauer, Brian R Birks, Kenneth P Wright, Jr., Effects of ad libitum food intake, insufficient sleep and weekend recovery sleep on energy balance. Sleep, Volume 44, Issue 11, November 2021, zsab136, https://doi.org/10.1093/sleep/zsab136
Lujan MR, Perez-Pozuelo I, Grandner MA. Past, Present, and Future of Multisensory Wearable Technology to Monitor Sleep and Circadian Rhythms. Front Digit Health. 2021 Aug 16;3:721919. doi:10.3389/fdgth.2021.721919. PMID: 34713186; PMCID: PMC8521807.
How are Sleep and Blood Sugar Connected?
Introduction: Hypoglycemia or low blood sugar is common in children and adults with type 1 diabetes (T1D). Sometimes people with diabetes don’t recognize the symptoms of low blood sugar, a problem called impaired awareness of hypoglycemia (IAH). If left untreated, hypoglycemia can become severe and lead to seizures, loss of consciousness or even death.
Methods: Recent studies regarding blood sugar regulation have found that tight control of blood sugar levels have led to death at a higher-than-expected rate in people with type 2 diabetes, which may help to explain why some, otherwise healthy people with T1D, die while sleeping without an apparent cause. Upon closer analysis of the data, these events occurred while patients were asleep. Other studies have demonstrated that hypoglycemia is relatively common and that nocturnal episodes in particular, exhibit a pattern whereby glucose levels drop significantly for several hours while patients are asleep.
These studies also reveal that there were pattern changes in heart
variability (HRV) at the start of the hypoglycemic episodes, which
corresponded to an activated sympathetic nervous system response
and suppressed activity of the parasympathetic nervous system. The
hypoglycemic episodes were also associated with an increased risk
of bradycardia and arrhythmia.
For patients that do not use continuous glucose monitoring (CGM),
wearable technology may help detect drops in HRV which could
decrease the number of hypoglycemic episodes while still allowing
patients to maintain tight glycemic control.
Heller SR, Geybels MS, Iqbal A, Liu L, Wagner L, Chow E. A higher non-severe hypoglycemia rate is associated with an increased risk of subsequent severe hypoglycemia and major adverse cardiovascular events in individuals with type 2 diabetes in the LEADER study. Diabetologia. 2022 Jan;65(1):55-64. doi:10.1007/s00125-021-05556-7. Epub 2021 Oct 26.
Olde Bekkink M, Koeneman M, de Galan BE, Bredie SJ. Early Detection of Hypoglycemia in Type 1 Diabetes Using Heart Rate Variability Measured by a Wearable Device. Diabetes Care. 2019 Apr;42(4):689-692. doi: 10.2337/dc18-1843.
Koeneman M, Olde Bekkink M, Meijel L van, Bredie S, de Galan B. Effect of Hypoglycemia on Heart Rate Variability in People with Type 1 Diabetes and Impaired Awareness of Hypoglycemia. Journal of Diabetes Science and Technology. April 2021. doi:10.1177/19322968211007485
Can Wearable Technology Detect Illness?
Introduction: Wearable technology is an easy-to-use, low-cost way for individuals to track their health and well-being. Wearables can include jewelry, watches, medical devices, and even clothing, but the sophistication among them can vary, based on the category they belong to, such as health, fitness, or entertainment. Many devices have accelerometers to track movement and speed, as well as sensors that can detect heart rate, respiratory rate, skin temperature and sleep cycles.
Research by Stanford University School of Medicine and Case
Western University aimed at detection of COVID-19 during the
asymptomatic or pre-symptomatic stage, discovered that wearable
technology can be used to identify subtle changes in physiological
parameters, thereby distinguishing potential cases prior to
symptom onset to prevent virus transmission. Additional large
multi-site studies such as COVID RED and Warrior Watch Study
demonstrate how these wearables, when combined with artificial
intelligence, were able to discern illness prior to symptom onset,
potentially reducing virus transmission. What they found was that
wearable devices could act as early digital biomarkers of
infection because COVID reduced biological timekeeping signals,
changed how the heart responds to activity, altered resting heart
rate and caused stress signals.
Tulip uses the same types of data collected from your wearable
devices to calculate daily, weekly, and monthly health factors.
Combined with other data from your wearable device, we can assess
additional aspects of your health to identify companion factors
that could be affecting your overall well-being, including
illness.
Ates, H.C., Yetisen, A.K., Güder, F. et al. Wearable devices for the detection of COVID-19. Nat Electron 4, 13–14 (2021). https://doi.org/10.1038/s41928-020-00533-1
Mitratza M, Goodale BM, Shagadatova A, Kovacevic V, van de Wijgert J, Brakenhoff TB, Dobson R, Franks B, Veen D, Folarin AA, Stolk P, Grobbee DE, Cronin M, Downward GS. The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review. Lancet Digit Health. 2022 May;4(5):e370-e383. doi:10.1016/S2589-7500(22)00019-X.
Risch M, Grossmann K, Aeschbacher S, et al. Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP). BMJ Open. Published online June 21, 2022.
