Noninvasive Estimation of Hydration Status in Athletes Using Wearable Sensors and a Data-Driven Approach Based on Orthostatic Changes

Dehydration beyond 2% bodyweight loss should be monitored to reduce the risk of heat-related injuries during exercise. However, assessments of hydration in athletic settings can be limited in their accuracy and accessibility. In this study, we sought to develop a data-driven noninvasive approach to measure hydration status, leveraging wearable sensors and normal orthostatic movements. Twenty participants (10 males, 25.0 ± 6.6 years; 10 females, 27.8 ± 4.3 years) completed two exercise sessions in a heated environment: one session was completed without fluid replacement. Before and after exercise, participants performed 12 postural movements that varied in length (up to 2 min). Logistic regression models were trained to estimate dehydration status given their heart rate responses to these postural movements. The area under the receiver operating characteristic curve (AUROC) was used to parameterize the model’s discriminative ability. Models achieved an AUROC of 0.79 (IQR: 0.75, 0.91) when discriminating 2% bodyweight loss. The AUROC for the longer supine-to-stand postural movements and shorter toe-touches were similar (0.89, IQR: 0.89, 1.00). Shorter orthostatic tests achieved similar accuracy to clinical tests. The findings suggest that data from wearable sensors can be used to accurately estimate mild dehydration in athletes. In practice, this method may provide an additional measurement for early intervention of severe dehydration.

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Differentiating COVID-19 and dengue from other febrile illnesses in co-epidemics: Development and internal validation of COVIDENGUE scores

10.1101/2021.10.14.21264897 ◽

2021 ◽

Author(s):

Patrick Gerardin ◽

Olivier Maillard ◽

Lea Bruneau ◽

Frederic Accot ◽

Florian Legrand ◽

...

Keyword(s):

Goodness Of Fit ◽

Multinomial Logistic Regression ◽

Characteristic Curve ◽

Severe Dengue ◽

Delayed Presentation ◽

Upper Respiratory Tract ◽

Discriminative Ability ◽

Internal Validation ◽

Logistic Regression Models ◽

Testing Center

Background. In a retrospective cohort study, we previously distinguished the factors associated with coronavirus 2019 (COVID-19) or dengue from those associated with other febrile illnesses (OFIs). In this study, we developed a scoring system to discriminate both infectious diseases. Methods. Predictors of both infections were sought using multinomial logistic regression models (OFIs as controls) in all subjects suspected of COVID-19 who attended the SARS-CoV-2 testing center of Saint-Pierre teaching hospital, Reunion Island, between March 23 and May 10, 2020. Two COVIDENGUE scores were developed and internally validated by bootstrapping for predicting each infection after weighting the odd ratios according to a predefined rule. The discriminative ability of each score was assessed using the area under the receiver operating characteristic curve (AUC). Their calibration was assessed using goodness-of-fit statistics. Results. Over 49 days, 80 COVID-19, 60 non-severe dengue and 872 OFI cases were diagnosed. The translation of the best fit model yielded two COVIDENGUE scores composed of 11 criteria: contact with a COVID-19 positive case (+3 points for COVID-19; 0 point for dengue), return from travel abroad within 15 days (+3/-1), previous individual episode of dengue (+1/+3), active smoking (-3/0), body ache (0/+5), cough (0/-2), upper respiratory tract infection symptoms (-1/-1), anosmia (+7/-1), headache (0/+5), retro-orbital pain (-1/+5), and delayed presentation (>3 days) to hospital (+1/0). The AUC was of 0.79 (95%CI 0.76-0.82) for COVID-19 score and of 0.88 (95%CI 0.85-0.90) for dengue score. Calibration was satisfactory for COVID-19 score and excellent for dengue score. For predicting COVID-19, sensitivity was of 97% at the 0-point cut-off and specificity approximated 99% at the 10-point cut-off. For predicting dengue, sensitivity approximated 97% at the 3-point cut-off and specificity 98% at the 11-point cut-off. Conclusions. In conclusion, the COVIDENGUE scores proved discriminant to differentiate COVID-19 and dengue from other febrile illnesses in the context of SARS-CoV-2 testing center during a co-epidemic. Further studies are needed to validate or refine these scores in other settings.

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A Generalizable, Data-Driven Approach to Predict Daily Risk ofClostridium difficileInfection at Two Large Academic Health Centers

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2018.16 ◽

2018 ◽

Vol 39 (4) ◽

pp. 425-433 ◽

Cited By ~ 54

Author(s):

Jeeheh Oh ◽

Maggie Makar ◽

Christopher Fusco ◽

Robert McCaffrey ◽

Krishna Rao ◽

...

Keyword(s):

Predictive Factors ◽

Prediction Models ◽

Massachusetts General Hospital ◽

Characteristic Curve ◽

The United States ◽

Data Driven ◽

Accurate Identification ◽

Discriminative Performance ◽

Building Models ◽

Data Driven Approach

OBJECTIVEAn estimated 293,300 healthcare-associated cases ofClostridium difficileinfection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH).METHODSWe utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital.RESULTSUsing the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80–0.84) and 0.75 ( 95% CI, 0.73–0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities.CONCLUSIONA data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies.Infect Control Hosp Epidemiol2018;39:425–433

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Modelling performance during repetitive precision tasks using wearable sensors: a data-driven approach

Ergonomics ◽

10.1080/00140139.2020.1759700 ◽

2020 ◽

Vol 63 (7) ◽

pp. 831-849

Author(s):

Liuxing Tsao ◽

Maury A. Nussbaum ◽

Sunwook Kim ◽

Liang Ma

Keyword(s):

Wearable Sensors ◽

Data Driven ◽

Data Driven Approach

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A data-driven approach to modeling physical fatigue in the workplace using wearable sensors

Applied Ergonomics ◽

10.1016/j.apergo.2017.02.001 ◽

2017 ◽

Vol 65 ◽

pp. 515-529 ◽

Cited By ~ 40

Author(s):

Zahra Sedighi Maman ◽

Mohammad Ali Alamdar Yazdi ◽

Lora A. Cavuoto ◽

Fadel M. Megahed

Keyword(s):

Wearable Sensors ◽

Data Driven ◽

Physical Fatigue ◽

Data Driven Approach

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Data Driven Approach to Forecast Building Occupant Complaints

Construction Research Congress 2020 ◽

10.1061/9780784482865.019 ◽

2020 ◽

Author(s):

Sena Assaf ◽

Mohamad Awada ◽

Issam Srour

Keyword(s):

Data Driven ◽

Data Driven Approach

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Analysis and Interpretation of Multi-Source Data at the Hydraulic Fracturing Test Site: A Data-Driven Approach to Improve Well Performance Evaluation in Heterogeneous Formations

Proceedings of the 2020 Latin America Unconventional Resources Technology Conference ◽

10.15530/urtec-2020-1560 ◽

2020 ◽

Author(s):

Shadi Salahshoor

Keyword(s):

Performance Evaluation ◽

Hydraulic Fracturing ◽

Test Site ◽

Data Driven ◽

Well Performance ◽

Source Data ◽

Data Driven Approach ◽

Heterogeneous Formations

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Comparison of PROMIS Anxiety and Depression, PHQ-8, and GAD-7 to screen for anxiety and depression among patients presenting for spine surgery

Journal of Neurosurgery Spine ◽

10.3171/2018.9.spine18521 ◽

2019 ◽

Vol 30 (4) ◽

pp. 524-531

Author(s):

Taylor E. Purvis ◽

Brian J. Neuman ◽

Lee H. Riley ◽

Richard L. Skolasky

Keyword(s):

Major Depression ◽

Spine Surgery ◽

Characteristic Curve ◽

Depression Scale ◽

Data Driven ◽

Anxiety And Depression ◽

Measurement Information ◽

Generalized Anxiety ◽

Cut Points ◽

Patient Reported

OBJECTIVEIn this paper, the authors demonstrate to spine surgeons the prevalence and severity of anxiety and depression among patients presenting for surgery and explore the relationships between different legacy and Patient-Reported Outcomes Measurement Information System (PROMIS) screening measures.METHODSA total of 512 adult spine surgery patients at a single institution completed the 7-item Generalized Anxiety Disorder questionnaire (GAD-7), 8-item Patient Health Questionnaire (PHQ-8) depression scale, and PROMIS Anxiety and Depression computer-adaptive tests (CATs) preoperatively. Correlation coefficients were calculated between PROMIS scores and GAD-7 and PHQ-8 scores. Published reference tables were used to determine the presence of anxiety or depression using GAD-7 and PHQ-8. Sensitivity and specificity of published guidance on the PROMIS Anxiety and Depression CATs were compared. Guidance from 3 sources was compared: published GAD-7 and PHQ-8 crosswalk tables, American Psychiatric Association scales, and expert clinical consensus. Receiver operator characteristic curves were used to determine data-driven cut-points for PROMIS Anxiety and Depression. Significance was accepted as p < 0.05.RESULTSIn 512 spine surgery patients, anxiety and depression were prevalent preoperatively (5% with any anxiety, 24% with generalized anxiety screen-positive; and 54% with any depression, 24% with probable major depression). Correlations were moderately strong between PROMIS Anxiety and GAD-7 scores (r = 0.72; p < 0.001) and between PROMIS Depression and PHQ-8 scores (r = 0.74; p < 0.001). The observed correlation of the PROMIS Depression score was greater with the PHQ-8 cognitive/affective score (r = 0.766) than with the somatic score (r = 0.601) (p < 0.001). PROMIS Anxiety and Depression CATs were able to detect the presence of generalized anxiety screen-positive (sensitivity, 86.0%; specificity, 81.6%) and of probable major depression (sensitivity, 82.3%; specificity, 81.4%). Receiver operating characteristic curve analysis demonstrated data-driven cut-points for these groups.CONCLUSIONSPROMIS Anxiety and Depression CATs are reliable tools for identifying generalized anxiety screen-positive spine surgery patients and those with probable major depression.

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