Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review Article Swipe
Conor M. Dolson
,
Ethan R. Harlow
,
Dermot Phelan
,
Tim J. Gabbett
,
Benjamin T. Gaal
,
Christopher J. McMellen
,
Benjamin J. Geletka
,
Jacob G. Calcei
,
James E. Voos
,
Dhruv R. Seshadri
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22197639
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22197639
Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22197639
- https://www.mdpi.com/1424-8220/22/19/7639/pdf?version=1665455347
- OA Status
- gold
- Cited By
- 70
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4303982459
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4303982459Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s22197639Digital Object Identifier
- Title
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Wearable Sensor Technology to Predict Core Body Temperature: A Systematic ReviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-10-09Full publication date if available
- Authors
-
Conor M. Dolson, Ethan R. Harlow, Dermot Phelan, Tim J. Gabbett, Benjamin T. Gaal, Christopher J. McMellen, Benjamin J. Geletka, Jacob G. Calcei, James E. Voos, Dhruv R. SeshadriList of authors in order
- Landing page
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https://doi.org/10.3390/s22197639Publisher landing page
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https://www.mdpi.com/1424-8220/22/19/7639/pdf?version=1665455347Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/22/19/7639/pdf?version=1665455347Direct OA link when available
- Concepts
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Wearable computer, Computer science, Machine learning, Wearable technology, Heat illness, Artificial intelligence, Embedded system, Physics, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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70Total citation count in OpenAlex
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2025: 31, 2024: 26, 2023: 12, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.meeting | 177 |
| abstract_inverted_index.methods | 62, 213 |
| abstract_inverted_index.predict | 52, 135, 155 |
| abstract_inverted_index.prevent | 106, 275 |
| abstract_inverted_index.process | 294 |
| abstract_inverted_index.studies | 146 |
| abstract_inverted_index.usually | 28 |
| abstract_inverted_index.workout | 237 |
| abstract_inverted_index.accuracy | 166 |
| abstract_inverted_index.athletic | 36, 296 |
| abstract_inverted_index.clinical | 179, 292 |
| abstract_inverted_index.critical | 287 |
| abstract_inverted_index.distinct | 152 |
| abstract_inverted_index.efficacy | 127 |
| abstract_inverted_index.existing | 252 |
| abstract_inverted_index.failure. | 43 |
| abstract_inverted_index.illness. | 140 |
| abstract_inverted_index.increase | 278 |
| abstract_inverted_index.invasive | 78 |
| abstract_inverted_index.learning | 212 |
| abstract_inverted_index.military | 304 |
| abstract_inverted_index.mitigate | 138 |
| abstract_inverted_index.physical | 34 |
| abstract_inverted_index.records, | 272 |
| abstract_inverted_index.settings | 74 |
| abstract_inverted_index.systems, | 268 |
| abstract_inverted_index.trainers | 297 |
| abstract_inverted_index.validity | 180 |
| abstract_inverted_index.wearable | 90, 129, 161, 228 |
| abstract_inverted_index.accurate, | 220 |
| abstract_inverted_index.analytics | 133 |
| abstract_inverted_index.assessing | 125 |
| abstract_inverted_index.athletic, | 109 |
| abstract_inverted_index.excessive | 83 |
| abstract_inverted_index.improving | 290 |
| abstract_inverted_index.including | 231, 259 |
| abstract_inverted_index.military, | 110 |
| abstract_inverted_index.officers. | 305 |
| abstract_inverted_index.potential | 97 |
| abstract_inverted_index.practical | 65 |
| abstract_inverted_index.reliable, | 221 |
| abstract_inverted_index.severity, | 57 |
| abstract_inverted_index.thousands | 10 |
| abstract_inverted_index.treatment | 257 |
| abstract_inverted_index.worldwide | 13 |
| abstract_inverted_index.additional | 232 |
| abstract_inverted_index.algorithms | 94, 153, 176, 183, 226, 250 |
| abstract_inverted_index.continuous | 99 |
| abstract_inverted_index.electronic | 270 |
| abstract_inverted_index.employers, | 302 |
| abstract_inverted_index.exhaustion | 6 |
| abstract_inverted_index.identified | 144 |
| abstract_inverted_index.illnesses, | 1 |
| abstract_inverted_index.individual | 185, 201 |
| abstract_inverted_index.intensity, | 238 |
| abstract_inverted_index.literature | 124 |
| abstract_inverted_index.management | 267 |
| abstract_inverted_index.monitoring | 101 |
| abstract_inverted_index.multiorgan | 42 |
| abstract_inverted_index.prediction | 168, 194, 225, 249 |
| abstract_inverted_index.predictive | 93, 132 |
| abstract_inverted_index.prevention | 255 |
| abstract_inverted_index.standards. | 181 |
| abstract_inverted_index.systematic | 142 |
| abstract_inverted_index.technology | 91 |
| abstract_inverted_index.Noninvasive | 85 |
| abstract_inverted_index.algorithms, | 195 |
| abstract_inverted_index.eventually, | 41 |
| abstract_inverted_index.heatstroke, | 8 |
| abstract_inverted_index.individuals | 12 |
| abstract_inverted_index.integration | 244 |
| abstract_inverted_index.measurement | 45, 61 |
| abstract_inverted_index.obstructive | 80 |
| abstract_inverted_index.physicians, | 299 |
| abstract_inverted_index.predictions | 86 |
| abstract_inverted_index.scientists, | 301 |
| abstract_inverted_index.situational | 204 |
| abstract_inverted_index.surrounding | 241 |
| abstract_inverted_index.technology. | 162 |
| abstract_inverted_index.temperature | 26, 159, 193 |
| abstract_inverted_index.Heat-related | 0 |
| abstract_inverted_index.availability | 280 |
| abstract_inverted_index.dysfunction, | 39 |
| abstract_inverted_index.environment. | 242 |
| abstract_inverted_index.heat-related | 53, 139, 253 |
| abstract_inverted_index.hyperthermia | 21 |
| abstract_inverted_index.incorporated | 184 |
| abstract_inverted_index.intervention | 104 |
| abstract_inverted_index.performance, | 37 |
| abstract_inverted_index.personalized | 223 |
| abstract_inverted_index.representing | 147 |
| abstract_inverted_index.characterized | 18 |
| abstract_inverted_index.environmental | 187, 206 |
| abstract_inverted_index.environments. | 114 |
| abstract_inverted_index.peer-reviewed | 123 |
| abstract_inverted_index.decision-making | 293 |
| abstract_inverted_index.characteristics, | 236 |
| abstract_inverted_index.interoperability | 246 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5044348811 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I2799343972, https://openalex.org/I58956616 |
| citation_normalized_percentile.value | 0.98601865 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |