A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort

Physiological Data Collected From Wearable Devices Identify and Predict Inflammatory Bowel Disease Flares

BACKGROUND & AIMS

Wearable devices capture physiological signals noninvasively and passively. Many of these parameters have been linked to inflammatory bowel disease (IBD) activity. We evaluated the associative ability of several physiological metrics with IBD flares and how they change before the development of flare.

METHODS

Participants throughout the United States answered daily disease activity surveys and wore an Apple Watch (Apple), Fitbit (Google), or Oura Ring (Oura Health). These devices collected longitudinal heart rate (HR), resting heart rate (RHR), heart rate variability (HRV), steps, and oxygenation. C-reactive protein, erythrocyte sedimentation rate, and fecal calprotectin were collected as standard of care. Linear mixed-effect models were implemented to analyze HR, RHR, steps, and oxygenation, and cosinor mixed-effect models were applied to HRV circadian features. Mixed-effect logistic regression was used to determine the predictive ability of physiological metrics.

RESULTS

Three hundred and nine participants were enrolled across 36 states. Circadian patterns of HRV differed significantly between periods of inflammatory flare and remission and symptomatic flare and remission. Marginal means for HR and RHR were higher during periods of inflammatory flare and symptomatic flare. There were fewer daily steps during inflammatory flares. HRV, HR, and RHR differentiated whether participants with symptoms had inflammation. HRV, HR, RHR, steps, and oxygenation were significantly altered up to 7 weeks before inflammatory and symptomatic flares.

CONCLUSIONS

Longitudinally collected physiological metrics from wearable devices can identify and change before IBD flares, suggesting their feasibility to monitor and predict IBD activity.

Biomedical data analytics for better patient outcomes

Medical professionals today have access to immense amounts of data, which enables them to make decisions that enhance patient care and treatment efficacy. This innovative strategy can improve global health care by bridging the divide between clinical practice and medical research. This paper reviews biomedical developments aimed at improving patient outcomes by addressing three main questions regarding techniques, data sources and challenges. The review includes peer-reviewed articles from 2018 to 2023, found via systematic searches in PubMed, Scopus and Google Scholar. The results show diverse disease-specific applications. Challenges such as data quality and ethics are discussed, underscoring data analytics' potential for patient-focused health care. The review concludes that successful implementation requires addressing gaps, collaboration and innovation in biomedical science and data analytics.

Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review

BACKGROUND

The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers.

OBJECTIVE

This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs).

METHODS

A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies.

RESULTS

The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements.

CONCLUSIONS

With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation.

Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study

BACKGROUND

Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach.

OBJECTIVE

The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being.

METHODS

To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV.

RESULTS

In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant.

CONCLUSIONS

In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.

Understanding the impact of sisu on workforce and well-being: A machine learning-based analysis

This study investigates the construct of sisu, a Finnish attribute representing mental resilience and fortitude when confronted with difficult situations. By leveraging advanced analytical methods and explainable Artificial Intelligence, we gain insights into how sisu factors influence well-being, work efficiency, and overall health. We investigate how the beneficial aspects of sisu contribute significantly to mental and physical health, satisfaction, and professional accomplishments. Conversely, we analyze the harmful sisu and its adverse impacts on the same domains. Our findings, including intriguing trends related to age, educational level, emotional states, and gender, pave the way for developing tailored solutions and initiatives to nurture the beneficial aspects of sisu and curtail the damaging consequences of sisu within professional settings and personal welfare.