Variability of the Skin Temperature from Wrist-Worn Device for Definition of Novel Digital Biomarkers of Glycemia
Open accessAbstract
This study exploited the skin temperature signal derived from a wrist-worn wearable device to define potential digital biomarkers for glycemia levels. Characterization of the skin temperature signal measured through the Empatica E4 device was obtained in 16 subjects (data taken from a dataset freely available on PhysioNet) by deriving standard metrics and a set of novel metrics describing both the current and the retrospective behavior of the signal. For each subject and for each metric, values that correspond to when glycemia was inside the tight range (70–140 mg/dL) were compared through the Wilcoxon rank-sum test against those above or below the range. For hypoglycemia characterization (below range), retrospective behavior of skin temperature described by the metric CVT SD (standard deviation of the series of coefficient of variation) proved to be the most effective both in daytime and nighttime (100% and 50% of the analyzed subjects, respectively). On the other side, for hyperglycemia characterization (above range), differences were observed between daytime and nighttime, with current behavior of skin temperature, described by M2T (deviation from the reference value of 32 °C), being the most informative during daytime, whereas retrospective behavior, described by SDT hhmm (standard deviation of the series of means), showed the highest effectiveness during nighttime. Proposed variability features outperformed standard metrics, and in future studies, their integration with other digital biomarkers of glycemia could improve the performance of applications devoted to non-invasive detection of glycemic events.
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Changes in statistical measures of distal body temperature can be used for identifying hypoglycemic events and predicting blood glucose concentration.
“These works analyzed how changes in statistical measures of distal body temperature can be used for identifying hypoglycemic events and prediction blood glucose concentration29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [32]
The Results report that wrist skin-temperature metrics differed significantly during hypoglycemic conditions, supporting their potential to identify such events. The Conclusions describe possible use in non-invasive glycemia assessment, but this study did not demonstrate prediction of blood glucose concentration; the Discussion (4.3) calls for further evaluation of hypoglycemia detection. (LLM, conf=0.87)
Experimental — accuracy not yet measuredCommonly used features are supported by prior work to be relevant to the effects of DM.
“commonly used features supported by prior work to be relevant to the effects of DM31–33,35–38,45,49”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [32]
The Introduction (p. unavailable) reports prior evidence linking skin temperature to glycemic variation, and Discussion §4.2 says earlier studies used standard statistical temperature features. But the paper describes its engineered features as novel and studied people at risk of diabetes, not people with overt diabetes; it does not establish those features as commonly used measures of the effects of DM. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrior work chiefly assessed statistical aggregation functions of wearable data streams (primary features), which are nonspecific to the effects of DM within data streams that are constantly fluctuating in natural settings.
“Third, prior work chiefly assessed statistical aggregation functions of wearable data streams (primary features)5,26–32, which are nonspecific to the effects of DM within data streams that are constantly fluctuating in natural settings.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [32]
The Discussion (§4.2; page unavailable) says previous studies using the same dataset relied on raw skin-temperature signals or standard statistical features such as the mean and standard deviation. Section 4.1 finds those standard metrics only partially effective at capturing glycemic changes. This supports the point about prior use and limitations of aggregation features, but does not establish that prior wearable-data work generally used such features or that they are wholly nonspecific to diabetes effects. (LLM, conf=0.85)
Experimental — accuracy not yet measuredNeither prior literature was able to test their efficacy in large, real-world cohorts representing a practical assessment.
“Though interesting, neither literature was able to test their efficacy in large, real-world cohorts representing a practical assessment.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [32]
The Discussion (section 4.3, page unavailable) says this study evaluated hypoglycemia metrics in only six of fifteen subjects and calls for larger populations and further evaluation for real applications. That supports the lack of a large-cohort practical assessment for this study, but the passage does not establish the same limitation for both pieces of prior literature described in the claim. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSkin temperature collected from wrist-worn wearables has been assessed for changes with hyperglycemic events in cohorts ranging from 10 to 20 individuals.
“Skin temperature was additionally collected from wrist-worn wearables was assessed for changes with hyperglycemic events in cohorts ranging from 10 to 20 individuals29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [32]
Our data is more representative of real-world performance than prior work because we captured a broader population segment with larger cohorts.
“While prior work reported higher performance, our data is more representative of real-world performance as we captured a broader population segment with our larger cohorts (balanced cohort is 5× and imbalanced cohort is 91× the largest prior cohort of wearable data)5,29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [32]
The balanced cohort is 5 times and the imbalanced cohort is 91 times the largest prior cohort of wearable data.
“balanced cohort is 5× and imbalanced cohort is 91× the largest prior cohort of wearable data”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [32]
The Results report 15 analyzed subjects, and Discussion §4.2 mentions other wearable datasets, but neither section identifies a largest prior wearable cohort or compares it with balanced and imbalanced cohorts. Searches for the 5× and 91× ratios and for cohort-size comparisons found no supporting passage in our copy; page numbers were unavailable. (LLM, conf=0.73)
Experimental — accuracy not yet measured
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Cites
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809 · see citing works
Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., et al. (2019) · Diabetes Care · doi:10.2337/dci19-0028 · Cited by 4,032 · see citing works
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069 · see citing works
Continuous glucose monitoring and metrics for clinical trials: an international consensus statement
Battelino, T., Alexander, C. M., Amiel, S. A., Arreaza-Rubin, G., Beck, R. W., Bergenstal, R. M., Buckingham, B. A., Carroll, J., Ceriello, A., Chow, E., et al. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00319-9 · Cited by 831 · see citing works
Gonzales, W. V., Mobashsher, A. T., & Abbosh, A. M. (2019) · Sensors · doi:10.3390/s19040800 · Cited by 788 · see citing works
Skin Temperature Monitoring Reduces the Risk for Diabetic Foot Ulceration in High-risk Patients
Armstrong, D. G., Holtz-Neiderer, K., Wendel, C. S., Mohler, M. J., Kimbriel, H. R., & Lavery, L. A. (2007) · The American Journal of Medicine · doi:10.1016/j.amjmed.2007.06.028 · Cited by 440 · see citing works
The M‐Value, an Index of Blood‐sugar Control in Diabetics
Schlichtkrull, J., Munck, O., & Jersild, M. (1965) · Acta Medica Scandinavica · doi:10.1111/j.0954-6820.1965.tb01810.x · Cited by 386 · see citing works
Current and Emerging Technology for Continuous Glucose Monitoring
Chen, C., Zhao, X.-L., Li, Z.-H., Zhu, Z., Qian, S., & Flewitt, A. J. (2017) · Sensors · doi:10.3390/s17010182 · Cited by 293 · see citing works
Tura, A., Maran, A., & Pacini, G. (2007) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2006.10.027 · Cited by 283 · see citing works
Self-citationIndividual differences in normal body temperature: longitudinal big data analysis of patient records
Obermeyer, Z., Samra, J., & Mullainathan, S. (2017) · BMJ · doi:10.1136/bmj.j5468 · Cited by 190 · see citing works
“J”-Index. A New Proposition of the Assessment of Current Glucose Control in Diabetic Patients
Wojcicki, J. (1995) · Hormone and Metabolic Research · doi:10.1055/s-2007-979906 · Cited by 156 · see citing works
The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020.
Marling, C., & Bunescu, R. (2020) · PubMed · Cited by 127 · see citing works
Manikandan, S. (2010) · Journal of Pharmacology and Pharmacotherapeutics · doi:10.4103/0976-500x.72373 · Cited by 111 · see citing works
Benchmark on a large cohort for sleep-wake classification with machine learning techniques
Palotti, J., Mall, R., Aupetit, M., Rueschman, M., Singh, M., Sathyanarayana, A., Taheri, S., & Fernandez-Luque, L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0126-9 · Cited by 104 · see citing works
Review of non-invasive continuous glucose monitoring based on impedance spectroscopy
Huang, J., Zhang, Y., & Wu, J. J. (2020) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2020.112103 · Cited by 102 · see citing works
Interpreting blood GLUcose data with R package iglu
Broll, S., Urbanek, J., Buchanan, D., Chun, E., Muschelli, J., Punjabi, N., & Gaynanova, I. (2020) · bioRxiv · doi:10.1371/journal.pone.0248560 · Cited by 102 · see citing works
Dubosson, F., Ranvier, J.-E., Bromuri, S., Calbimonte, J.-P., Ruiz, J., & Schumacher, M. (2018) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2018.09.003 · Cited by 101 · see citing works
Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
Bent, B., Cho, P. J., Henriquez, M., Wittmann, A., Thacker, C., Feinglos, M. N., Crowley, M. J., & Dunn, J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00465-w · Cited by 99 · see citing works
Use of Continuous Glucose Monitors by People Without Diabetes: An Idea Whose Time Has Come?
Klonoff, D. C., Nguyen, K. T., Xu, N. Y., Gutierrez, A., Espinoza, J. C., & Vidmar, A. P. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221110830 · Cited by 93 · see citing works
Data quality evaluation in wearable monitoring
Böttcher, S., Vieluf, S., Bruno, E., Joseph, B., Epitashvili, N., Biondi, A., Zabler, N., Glasstetter, M., Dümpelmann, M., Van Laerhoven, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25949-x · Cited by 91 · see citing works
Bent, B., Wang, K., Grzesiak, E., Jiang, C., Qi, Y., Jiang, Y., Cho, P., Zingler, K., Ogbeide, F. I., Zhao, A., et al. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.511 · Cited by 85 · see citing works
Prendin, F., Favero, S. D., Vettoretti, M., Sparacino, G., & Facchinetti, A. (2021) · Sensors · doi:10.3390/s21051647 · Cited by 77 · see citing works
Definitions of digital biomarkers: a systematic mapping of the biomedical literature
Alonso, A. K. M., Hirt, J., Woelfle, T., Janiaud, P., & Hemkens, L. G. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100914 · Cited by 75 · see citing works
FLIRT: A feature generation toolkit for wearable data
Föll, S., Maritsch, M., Spinola, F., Mishra, V., Barata, F., Kowatsch, T., Fleisch, E., & Wortmann, F. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106461 · Cited by 72 · see citing works
Gu, W., Zhou, Y., Zhou, Z., Liu, X., Zou, H., Zhang, P., Spanos, C. J., & Zhang, L. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130919 · Cited by 54 · see citing works
Ahmed, A., Aziz, S., Qidwai, U. A., Abd-Alrazaq, A. A., & Sheikh, J. I. (2023) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2023.100094 · Cited by 47 · see citing works
Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data
Lehmann, V., Föll, S., Maritsch, M., van Weenen, E., Kraus, M., Lagger, S., Odermatt, K., Albrecht, C., Fleisch, E., Zueger, T., et al. (2023) · Diabetes Care · doi:10.2337/dc22-2290 · Cited by 47 · see citing works
Human thermal perception and time of day: A review
Vellei, M., Chinazzo, G., Zitting, K.-M., & Hubbard, J. (2021) · Temperature · doi:10.1080/23328940.2021.1976004 · Cited by 45 · see citing works
Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?
Zahalka, S. J., Galindo, R. J., Shah, V. N., & Wang, C. C. L. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241242487 · Cited by 45 · see citing works
Skin Temperature Rhythms in Humans Respond to Changes in the Timing of Sleep and Light
Cuesta, M., Boudreau, P., Cermakian, N., & Boivin, D. B. (2017) · Journal of Biological Rhythms · doi:10.1177/0748730417702974 · Cited by 44 · see citing works
Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept
Bent, B., Cho, P. J., Wittmann, A., Thacker, C., Muppidi, S., Snyder, M. P., Crowley, M. J., Feinglos, M. N., & Dunn, J. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-002027 · Cited by 43 · see citing works
Dave, D., Vyas, K., Branan, K. L., McKay, S. V., DeSalvo, D. J., Gutiérrez‐Osuna, R., Coté, G. L., & Erraguntla, M. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221116393 · Cited by 40 · see citing works
Hypoglycemia and Body Temperature
Molnar, G. W. (1974) · JAMA · doi:10.1001/jama.1974.03230210028006 · Cited by 33 · see citing works
Machine-Learning-Based Diabetes Prediction Using Multisensor Data
Site, A., Nurmi, J., & Lohan, E. S. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3319360 · Cited by 33 · see citing works
Hypo- and Hyperglycemic Alarms
Howsmon, D. P., & Bequette, B. W. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815583507 · Cited by 31 · see citing works
Artificial Intelligence-Based Digital Biomarkers for Type 2 Diabetes: A Review.
Jabara, M., Kose, O., Perlman, G., Corcos, S., Pelletier, M., Possik, E., Tsoukas, M., & Sharma, A. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.028 · Cited by 31 · see citing works
Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data
Piersanti, A., Giurato, F., Göbl, C. S., Burattini, L., Tura, A., & Morettini, M. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0237 · Cited by 29 · see citing works
Self-citationCappon, G., Prendin, F., Facchinetti, A., Sparacino, G., & Favero, S. D. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3276193 · Cited by 29 · see citing works
Chowdhury, M. H., Chowdhury, M. E. H., & Alqahtani, A. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.105975 · Cited by 27 · see citing works
Investigation of finite-sample properties of robust location and scale estimators
Park, C., Kim, H., & Wang, M. (2020) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2019.1699114 · Cited by 24 · see citing works
A review of non-invasive blood glucose monitoring through breath acetone and body surface
Liu, H., Liu, W., Sun, C., Huang, W.-Z., & Cui, X. (2024) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2024.115500 · Cited by 24 · see citing works
Daskalaki, E., Parkinson, A., Brew‐Sam, N., Hossain, M. Z., O'Neal, D. N., Nolan, C. J., & Suominen, H. (2022) · Journal of Medical Internet Research · doi:10.2196/28901 · Cited by 22 · see citing works
Thirunavukkarasu, U., Umapathy, S., Janardhanan, K., et al. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00886-z · Cited by 21 · see citing works
Layered Meta-Learning Algorithm for Predicting Adverse Events in Type 1 Diabetes
D’Antoni, F., Petrosino, L., Marchetti, A., Bacco, L., Pieralice, S., Vollero, L., Pozzilli, P., Piemonte, V., & Merone, M. (2023) · IEEE Access · doi:10.1109/access.2023.3237992 · Cited by 21 · see citing works
Ali, H., Niazi, I. K., White, D. E., Akhter, M. N., & Madanian, S. (2024) · Electronics · doi:10.3390/electronics13163192 · Cited by 20 · see citing works
Tai, Y., Obayashi, K., Yamagami, Y., & Saeki, K. (2023) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.10590 · Cited by 18 · see citing works
Sivanandam, S. N., Anburajan, M., Venkatraman, B., Menaka, M., & Sharath, D. (2012) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2012.12.017 · Cited by 17 · see citing works
Miller, E. M., & Miller, K. (2024) · Clinical Diabetes · doi:10.2337/cd23-0077 · Cited by 17 · see citing works
Li, T., Wang, Q., An, Y., Guo, L. J., Ren, L., Lei, L., & Chen, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107750 · Cited by 17 · see citing works
Thermoregulatory responses to hyperinsulinemic hypoglycemia and euglycemia in humans
Maggs, D. G., Scott, A. R., & Macdonald, I. A. (1994) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.1994.267.5.r1266 · Cited by 16 · see citing works
Sejling, A., Lange, K. H. W., Frandsen, C. S., Diemar, S. S., Tarnow, L., Faber, J. O., Holst, J. J., Hartmann, B., Hilsted, L. M., Kjær, T. W., et al. (2015) · Diabetologia · doi:10.1007/s00125-015-3616-6 · Cited by 15 · see citing works
Constantinou, A., Oikonomou, S., Konstantinou, C., & Makris, K. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-01180-y · Cited by 11 · see citing works
Model for Glycemic Level Detection using Heart Rate Variability in a Mexican Sample
Guzmán, L., Cazares, A. M. G., & Martínez‐Torteya, A. (2021) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes48179.2021.9398841 · Cited by 10 · see citing works
Mendez, C., Kaykayoglu, C. A., Bähler, T., Künzler, J., Lizoain, A., Rothenbühler, M., Schmidt, M. H., Laimer, M., & Witthauer, L. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251319800 · Cited by 10 · see citing works
Prediabetes detection in unconstrained conditions using wearable sensors
Tatli, D., Papapanagiotou, V., Liakos, A., Τσάπας, Α., & Delopoulos, A. N. (2024) · Clinical Nutrition Open Science · doi:10.1016/j.nutos.2024.09.013 · Cited by 8 · see citing works
Chiacchiaretta, P., Tumini, S., Mascitelli, A., Sacrini, L., Saltarelli, M. A., Carabotta, M., Osmelli, J., Carlo, P. D., & Aruffo, E. (2024) · Climate · doi:10.3390/cli12080121 · Cited by 5 · see citing works
Verdini, F., Mengarelli, A., Chemello, G., et al. (2025) · Biosensors · doi:10.3390/bios15020073 · Cited by 5 · see citing works
Zhu, Y., Aimandi, N. B., & Alam, M. A. U. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10781542 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [1]Mathew T.K. Zubair M. Tadi P. Blood Glucose Monitoring StatPearls Publishing Treasure Island, FL, USA 2024 32310436
- [60]Yousef H. Ramezanpour Ahangar E. Varacallo M. Physiology, Thermal Regulation StatPearls Publishing Treasure Island, FL, USA 2024 29763018
Cited by
Sorted by citations
Viswanath, V. K., Navaneethan, S., Burks, J. H., Soltani, S., Kasl, P., Hartogensis, W., Dilchert, S., Hecht, F. M., Mason, A. E., Wang, E. J., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01501-0
- Authors
- Piersanti, A., Littero, M., Del Giudice, L. L., Marcantoni, I., Burattini, L., Tura, A., & Morettini, M.
- Affiliations
- Marche Polytechnic University (IT)
- Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
- Corresponding author
- Morettini, Micaela
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2025-06-28
- Volume
- 25
- Issue
- 13
- Pages
- 4038
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC12251718
- OpenAlex
- W4411802676
- Cited by
- 5
- References
- 59
- Citations by year
- 2026: 5
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- skin temperature, digital biomarkers, glycemia, hypoglycemia, hyperglycemia, wearable devices, Empatica E4, coefficient of variation, non-invasive glucose monitoring
- MeSH terms
- Wearable Electronic Devices, Adult, Blood Glucose, Female, Humans, Hyperglycemia, Hypoglycemia, Male, Middle Aged, Skin Temperature, Wrist, Biomarkers
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