Prospective Validation of 2B-Cool: Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries

Laxminarayan, Swamy; Hornby, Samantha; Belval, Luke N.; Giersch, Gabrielle E.W.; Morrissey, Margaret C.; Casa, Douglas J.; Reifman, Jaques

doi:10.1249/mss.0000000000003093

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Artificial intelligence embedded in smart wearables still requires developing reliability for decision making on physiological-stressrelated information classification. However, studies at the edge of this systematic review demonstrate good parameters, such as cross-validation accuracy of 99.7%, sensitivity of 100%, precision of 97% [89], and a scientific background on heart illness prevention validation protocols [90].…”

Section: How Do Wearables Help People Manage Stress?mentioning

confidence: 88%

Wearables for Stress Management: A Scoping Review

González Ramírez,

García Vázquez,

Rodríguez

et al. 2023

Healthcare

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In recent years, wearable devices have been increasingly used to monitor people’s health. This has helped healthcare professionals provide timely interventions to support their patients. In this study, we investigated how wearables help people manage stress. We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) standard to address this question. We searched studies in Scopus, IEEE Explore, and Pubmed databases. We included studies reporting user evaluations of wearable-based strategies, reporting their impact on health or usability outcomes. A total of 6259 studies were identified, of which 40 met the inclusion criteria. Based on our findings, we identified that 21 studies report using commercial wearable devices; the most common are smartwatches and smart bands. Thirty-one studies report significant stress reduction using different interventions and interaction modalities. Finally, we identified that the interventions are designed with the following aims: (1) to self-regulate during stress episodes, (2) to support self-regulation therapies for long-term goals, and (3) to provide stress awareness for prevention, consisting of people’s ability to recall, recognize and understand their stress.

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Section: How Do Wearables Help People Manage Stress?mentioning

confidence: 88%

Wearables for Stress Management: A Scoping Review

González Ramírez,

García Vázquez,

Rodríguez

et al. 2023

Healthcare

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External auricle temperature enhances ear-based wearable accuracy during physiological strain monitoring in the heat

Tan,

Tran,

Chiang

et al. 2024

Sci Rep

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Body core temperature (Tc) monitoring is crucial for minimizing heat injury risk. However, validated strategies are invasive and expensive. Although promising, aural canal temperature (Tac) is susceptible to environmental influences. This study investigated whether incorporation of external auricle temperature (Tea) into an ear-based Tc algorithm enhances its accuracy during multiple heat stress conditions. Twenty males (mean ± SD; age = 25 ± 3 years, BMI = 21.7 ± 1.8, body fat = 12 ± 3%, maximal aerobic capacity (VO2max) = 64 ± 7 ml/kg/min) donned an ear-based wearable and performed a passive heating (PAH), running (RUN) and brisk walking trial (WALK). PAH comprised of immersion in hot water (42.0 ± 0.3 °C). RUN (70 ± 3%VO2max) and WALK (50 ± 10%VO2max) were conducted in an environmental chamber (Tdb = 30.0 ± 0.2 °C, RH = 71 ± 2%). Several Tc models, developed using Tac, Tea and heart rate, were validated against gastrointestinal temperature. Inclusion of Tea as a model input improved the accuracy of the ear-based Tc algorithm. Our best performing model (Trf3) displayed good group prediction errors (mean bias error = − 0.02 ± 0.26 °C) but exhibited individual prediction errors (percentage target attainment ± 0.40 °C = 88%) that marginally exceeded our validity criterion. Therefore, Trf3 demonstrates potential utility for group-based Tc monitoring, with additional refinement needed to extend its applicability to personalized heat strain monitoring.

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Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

Ghomrawi,

O’Brien,

Carter

et al. 2023

npj Digit. Med.

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When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3–17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.

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Prospective Validation of 2B-Cool: Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries

Cited by 4 publications

References 36 publications

Wearables for Stress Management: A Scoping Review

Wearables for Stress Management: A Scoping Review

External auricle temperature enhances ear-based wearable accuracy during physiological strain monitoring in the heat

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

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