Beyond fitness tracking: The use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research
Open accessAbstract
The use of consumer-grade wearables for purposes beyond fitness tracking has not been comprehensively explored. We generated and analyzed multidimensional data from 233 normal volunteers, integrating wearable data, lifestyle questionnaires, cardiac imaging, sphingolipid profiling, and multiple clinical-grade cardiovascular and metabolic disease markers. We show that subjects can be stratified into distinct clusters based on daily activity patterns and that these clusters are marked by distinct demographic and behavioral patterns. While resting heart rates (RHRs) performed better than step counts in being associated with cardiovascular and metabolic disease markers, step counts identified relationships between physical activity and cardiac remodeling, suggesting that wearable data may play a role in reducing overdiagnosis of cardiac hypertrophy or dilatation in active individuals. Wearable-derived activity levels can be used to identify known and novel activity-modulated sphingolipids that are in turn associated with insulin sensitivity. Our findings demonstrate the potential for wearables in biomedical research and personalized health.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Algorithms have been developed to automatically process and interpret wearables data to present evidence-based health insights such as detection of inflammation or prediction of cardiometabolic health based on activity habits.
“To address this data deluge, we and others have recently developed algorithms to automatically process and interpret wearables data in order to present evidence-based health insights such as detection of inflammation or prediction of cardiometabolic health based on activity habits [40-43].”
From Wearables and the Medical Revolution (2018) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works
A formula to estimate the approximate surface area if height and weight be known. 1916.
Bois, D. D., & Bois, E. F. D. (1989) · PubMed · Cited by 5,303 · see citing works
Bois, D. D. (1916) · Archives of Internal Medicine · doi:10.1001/archinte.1916.00080130010002 · Cited by 4,355 · see citing works
Lack of Exercise Is a Major Cause of Chronic Diseases
Booth, F. W., Roberts, C. K., & Laye, M. J. (2012) · Comprehensive Physiology · doi:10.1002/cphy.c110025 · Cited by 2,575 · see citing works
Resting Heart Rate in Cardiovascular Disease
Fox, K., Borer, J. S., Camm, A. J., Danchin, N., Ferrari, R., Lopez Sendon, J. L., Steg, P. G., Tardif, J.-C., Tavazzi, L., & Tendera, M. (2007) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2007.04.079 · Cited by 1,062 · see citing works
Large-scale physical activity data reveal worldwide activity inequality
Althoff, T., Sosič, R., Hicks, J. L., King, A. C., Delp, S. L., & Leskovec, J. (2017) · Nature · doi:10.1038/nature23018 · Cited by 1,000 · see citing works
Gender differences in insulin resistance, body composition, and energy balance
Geer, E. B., & Shen, W. (2009) · Gender Medicine · doi:10.1016/j.genm.2009.02.002 · Cited by 913 · see citing works
Exercise-induced right ventricular dysfunction and structural remodelling in endurance athletes
La Gerche, A., Burns, A. T., Mooney, D. J., Inder, W. J., Taylor, A. J., Bogaert, J., MacIsaac, A. I., Heidbüchel, H., & Prior, D. L. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehr397 · Cited by 776 · see citing works
Laaksonen, R., Ekroos, K., Sysi‐Aho, M., Hilvo, M., Vihervaara, T., Kauhanen, D., Suoniemi, M., Hurme, R., März, W., Scharnagl, H., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw148 · Cited by 679 · see citing works
Haus, J. M., Kashyap, S. R., Kasumov, T., Zhang, R., Kelly, K. R., DeFronzo, R. A., & Kirwan, J. P. (2008) · Diabetes · doi:10.2337/db08-1228 · Cited by 665 · see citing works
A Ceramide-Centric View of Insulin Resistance
Chavez, J. A., & Summers, S. A. (2012) · Cell Metabolism · doi:10.1016/j.cmet.2012.04.002 · Cited by 617 · see citing works
Wearable technologies: The role of usefulness and visibility in smartwatch adoption
Chuah, S. H., Rauschnabel, P. A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.07.047 · Cited by 546 · see citing works
Li, H., Wu, J., Gao, Y., & Shi, Y. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.12.010 · Cited by 439 · see citing works
Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W., Schüssler-Fiorenza Rose, S. M., Perelman, D., Colbert, E., Runge, R., Rego, S., et al. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2001402 · Cited by 431 · see citing works
A wellness study of 108 individuals using personal, dense, dynamic data clouds
Price, N. D., Magis, A. T., Earls, J. C., Glusman, G., Levy, R., Lausted, C., McDonald, D. T., Kusebauch, U., Moss, C. L., Zhou, Y., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3870 · Cited by 427 · see citing works
Cooney, M. T., Vartiainen, E. A., Laakitainen, T., Juolevi, A., Dudina, A. L., & Graham, I. M. (2010) · American Heart Journal · doi:10.1016/j.ahj.2009.12.029 · Cited by 416 · see citing works
Finkelstein, E. A., Haaland, B. A., Bilger, M., Sahasranaman, A., Sloan, R. A., Nang, E. E. K., & Evenson, K. R. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(16)30284-4 · Cited by 402 · see citing works
Duffy, J. F., Zitting, K.-M., & Chinoy, E. D. (2015) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2015.08.002 · Cited by 367 · see citing works
Jensen, M. T., Suadicani, P., Hein, H. O., & Gyntelberg, F. (2013) · Heart · doi:10.1136/heartjnl-2012-303375 · Cited by 297 · see citing works
Athlete's Heart and Cardiovascular Care of the Athlete
Baggish, A. L., & Wood, M. J. (2011) · Circulation · doi:10.1161/circulationaha.110.981571 · Cited by 294 · see citing works
High heart rate: a cardiovascular risk factor?
Cook, S., Togni, M., SCHAUB, M. C., Wenaweser, P., & Hess, O. M. (2006) · European Heart Journal · doi:10.1093/eurheartj/ehl259 · Cited by 276 · see citing works
Self-citationDubé, J. J., Amati, F., Toledo, F. G. S., Stefanović-Račić, M., Rossi, A. P., Coen, P. M., & Goodpaster, B. H. (2011) · Diabetologia · doi:10.1007/s00125-011-2065-0 · Cited by 272 · see citing works
Petersen, S. E., Matthews, P. M., Bamberg, F., Bluemke, D. A., Francis, J. M., Friedrich, M. G., Leeson, P., Nagel, E. C., Plein, S., Rademakers, F. E., et al. (2013) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/1532-429x-15-46 · Cited by 266 · see citing works
Strączkowski, M., Kowalska, I., Nikołajuk, A., Dzienis-Straczkowska, S., Kinalska, I., Baranowski, M., Żendzian‐Piotrowska, M., Brzezinska, Z., & Górski, J. (2004) · Diabetes · doi:10.2337/diabetes.53.5.1215 · Cited by 258 · see citing works
Tarassov, K., Ekroos, K., Suoniemi, M., Kauhanen, D., Sylvänne, T., Hurme, R., Gouni‐Berthold, I., Berthold, H. K., Kleber, M. E., Laaksonen, R., et al. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-2559 · Cited by 254 · see citing works
A global quantification of “normal” sleep schedules using smartphone data
Walch, O., Cochran, A. L., & Forger, D. B. (2016) · Science Advances · doi:10.1126/sciadv.1501705 · Cited by 227 · see citing works
Chuah, S. H., Rauschnabel, P. A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016) · Rowan Digitals Works (Rowan University) · Cited by 213 · see citing works
Tan, C., Emmanuel, S. C., Tan, B. Y., & Jacob, E. B. (1999) · Diabetes Care · doi:10.2337/diacare.22.2.241 · Cited by 210 · see citing works
Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background
Bamberg, F., Kauczor, H., Weckbach, S., Schlett, C. L., Forsting, M., Ladd, S. C., Greiser, K. H., Weber, M. D., Schulz‐Menger, J., Niendorf, T., et al. (2015) · Radiology · doi:10.1148/radiol.2015142272 · Cited by 204 · see citing works
Could Ceramides Become the New Cholesterol?
Summers, S. A. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2017.12.003 · Cited by 184 · see citing works
Serum sphingolipids: relationships to insulin sensitivity and changes with exercise in humans
Bergman, B. C., Brozinick, J. T., Strauss, A., Bacon, S. D., Kerege, A. A., Bui, H. H., Sanders, P., Siddall, P., Kuo, M. S., & Perreault, L. (2015) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00134.2015 · Cited by 161 · see citing works
Maron, B. J. (2005) · Heart · doi:10.1136/hrt.2005.060962 · Cited by 148 · see citing works
Muscle sphingolipids during rest and exercise: a C18:0 signature for insulin resistance in humans
Bergman, B. C., Brozinick, J. T., Strauss, A., Bacon, S., Kerege, A., Bui, H. H., Sanders, P., Siddall, P., Wei, T., Thomas, M. K., et al. (2016) · Diabetologia · doi:10.1007/s00125-015-3850-y · Cited by 148 · see citing works
Right and Left Ventricular Function and Mass in Male Elite Master Athletes
Bohm, P., Schneider, G. K., Linneweber, L., Rentzsch, A., Krämer, N., Abdul‐Khaliq, H., Kindermann, W., Meyer, T., & Scharhag, J. (2016) · Circulation · doi:10.1161/circulationaha.115.020975 · Cited by 142 · see citing works
Chu, A. H. Y., Ng, S. H.-X., Paknezhad, M., Gauterin, A., Koh, D. S. Q., Brown, M. S., & Müller‐Riemenschneider, F. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172535 · Cited by 121 · see citing works
Kasumov, T., Solomon, T. P. J., Hwang, C. E., Huang, H., Haus, J. M., Zhang, R., & Kirwan, J. P. (2015) · Obesity · doi:10.1002/oby.21117 · Cited by 110 · see citing works
Cardiovascular magnetic resonance reference ranges for the heart and aorta in Chinese at 3T
Le, T., Tan, R., Deyn, M. D., Goh, E., Han, Y., Leong, B. R., Cook, S. A., & Chin, C. W.-L. (2016) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-016-0236-3 · Cited by 106 · see citing works
Self-citationDaily Exercise Reduces Fat, Protein and Body Mass in Male but not Female Rats
Cortright, R. N., Chandler, M. P., Lemon, P. W. R., & DiCarlo, S. E. (1997) · Physiology & Behavior · doi:10.1016/s0031-9384(97)00148-0 · Cited by 91 · see citing works
Wearables for Promoting Physical Activity
Strath, S. J., & Rowley, T. W. (2017) · Clinical Chemistry · doi:10.1373/clinchem.2017.272369 · Cited by 74 · see citing works
Moderate Physical Activity in Healthy Adults Is Associated With Cardiac Remodeling
Dawes, T. J. W., Corden, B., Cotter, S., de Marvao, A., Walsh, R., Ware, J. S., Cook, S. A., & O’Regan, D. P. (2016) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.116.004712 · Cited by 69 · see citing works
Self-citationPlasma dihydroceramide species associate with waist circumference in Mexican American families
Mamtani, M., Meikle, P. J., Kulkarni, H. R., Weir, J. M., Barlow, C. K., Jowett, J. B. M., Bellis, C., Dyer, T. D., Almasy, L. A., Mahaney, M. C., et al. (2013) · Obesity · doi:10.1002/oby.20598 · Cited by 38 · see citing works
Skeletal muscle ceramide species in men with abdominal obesity
de la Maza, M., Rodríguez, J., Hirsch, S., Leiva, L., Barrera, G., & Bunout, D. (2014) · The Journal of Nutrition, Health & Aging · doi:10.1007/s12603-014-0548-7 · Cited by 38 · see citing works
Resting Heart Rate as a Predictor of Body Weight Gain in the Early Stage of Hypertension
Palatini, P., Mos, L., Santonastaso, M., Zanatta, N., Mormino, P., Saladini, F., Bortolazzi, A., Cozzio, S., Garavelli, G., & On behalf of the HARVEST Study Group (2010) · Obesity · doi:10.1038/oby.2010.191 · Cited by 37 · see citing works
Fabbri, E., Yang, A., Simonsick, E., Chia, C. W., Zoli, M., Haughey, N., Mielke, M., Ferrucci, L., & Coen, P. M. (2016) · Aging Cell · doi:10.1111/acel.12491 · Cited by 33 · see citing works
Tison, G. H., Singh, A. C., Ohashi, D. A., Hsieh, J. T., Ballinger, B. M., Olgin, J. E., Marcus, G. M., & Pletcher, M. J. (2017) · Circulation · doi:10.1161/circ.136.suppl_1.21042 · Cited by 19 · see citing works
Challenges of Cardiac Image Analysis in Large-Scale Population-Based Studies
Medrano−Gracia, P., Cowan, B. R., Suinesiaputra, A., & Young, A. A. (2015) · Current Cardiology Reports · doi:10.1007/s11886-015-0563-2 · Cited by 19 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wearables and the Medical Revolution
Dunn, J., Runge, R., & Snyder, M. (2018) · Personalized Medicine · doi:10.2217/pme-2018-0044 · Cited by 654 · see citing works
The impact of wearable technologies in health research: scoping review
Huhn, S., Axt, M., Gunga, H.-C., Maggioni, M. A., Munga, S., Obor, D., Sié, A., Boudo, V., Bunker, A., Sauerborn, R., et al. (2022) · JMIR mHealth and uHealth · doi:10.2196/34384 · Cited by 379 · see citing works
Rodgers, M. M., Alon, G., Pai, V. M., & Conroy, R. S. (2019) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668319839607 · Cited by 119 · see citing works
Activity Tracker–Based Metrics as Digital Markers of Cardiometabolic Health: Cross-Sectional Study
Rykov, Y., Thach, T.-Q., Dunleavy, G., Roberts, A. C., Christopoulos, G., Soh, C. K., & Car, J. (2020) · JMIR mHealth and uHealth · doi:10.2196/16409 · Cited by 29 · see citing works
Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study
Cho, S., Weng, C., Kahn, M. G., & Natarajan, K. (2021) · JMIR mHealth and uHealth · doi:10.2196/31618 · Cited by 28 · see citing works
Nyenhuis, S. M., Balbim, G. M., Ma, J., Marquez, D. X., Wilbur, J., Sharp, L. K., & Kitsiou, S. (2020) · JMIR Formative Research · doi:10.2196/13900 · Cited by 18 · see citing works
- Authors
- Lim, W. K., Davila, S., Teo, J. X., Yang, C., Pua, C. J., Blöcker, C., Lim, J. Q., Ching, J., Yap, J. J. L., Tan, S. Y., Sahlén, A., Chin, C. W.-L., Teh, B. T., Rozen, S. G., Cook, S. A., Yeo, K. K., & Tan, P.
- Affiliations
- SingHealth (SG)
- National Heart Centre Singapore (SG)
- Duke-NUS Medical School (SG)
- National Cancer Centre Singapore (SG)
- Agency for Science, Technology and Research (SG)
- Corresponding author
- Lim, Weng Khong
- Cook, Stuart Alexander
- Yeo, Khung Keong
- Tan, Patrick
- ORCID
- Venue
- PLOS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-02-27
- Volume
- 16
- Issue
- 2
- Pages
- e2004285
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- PMC
- PMC5828350
- OpenAlex
- W2787945643
- Cited by
- 70
- References
- 47
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 2 · 2023: 5 · 2022: 9 · 2021: 15 · 2020: 8 · 2019: 18 · 2018: 5
- FWCI
- 7.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- Lee Foundation
- Duke-NUS Medical School
- Tanoto Foundation
- Medical Research Council
- National Medical Research Council (NMRC/STaR/0026/2015, NMRC/STaR/0011/2012, STaR/0026/2015, 0026/2015)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- consumer-grade wearable devices, activity monitoring, resting heart rate, step count, physical activity, cardiac remodeling, cardiac hypertrophy, cardiac dilatation, sphingolipids, lipidomics, insulin sensitivity, cardiovascular risk markers, lifestyle questionnaire, activity patterns, overdiagnosis
- MeSH terms
- Medical Overuse, Fitness Trackers, Adult, Cardiovascular Physiological Phenomena, Female, Cardiomegaly, Heart Rate, Humans, Insulin Resistance, Life Style, Male, Middle Aged, Surveys and Questionnaires, Sphingolipids, Exercise, Ventricular Remodeling, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
