Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience

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.

Effect of Modifiable Lifestyle Factors on Biological Aging

Biological age is a concept that uses bio-physiological parameters to account for individual heterogeneity in the biological processes driving aging and aims to enhance the prediction of age-related clinical conditions compared to chronological age. Although engaging in healthy lifestyle behaviors has been linked to a lower mortality risk and a reduced incidence of chronic diseases, it remains unclear to what extent these health benefits result from slowing the pace of the biological aging process. This short review summarized how modifiable lifestyle factors - including diet, physical activity, smoking, alcohol consumption, and the aggregate of multiple healthy behaviors - were associated with established estimates of biological age based on clinical or cellular/molecular markers, including Klemera-Doubal Method biological age, homeostatic dysregulation, phenotypic age, DNA methylation age, and telomere length. In brief, the available studies tend to show a consistent association of lifestyle factors with physiological measures of biological age, while findings regarding molecular-based metrics vary. The limited evidence highlights the need for further research in this field, particularly with a life-course approach.

Biological age estimation using circulating blood biomarkers

Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767-0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739-0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual's mortality risk. Values ranged between 20-years younger and 20-years older than individuals' chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.

Biomarkers of aging for the identification and evaluation of longevity interventions

With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.

Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014

Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.

Progress in biological age research

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

Use of Personal Resources May Influence the Rate of Biological Aging Depending on Individual Typology

Individual hobbies and interests, the ways of spending leisure time develop personal resources influencing health and wellbeing. The literature analysis helped selecting thirteen personal resources that also affect the rate of aging: sports, order, creativity, intellect, handwork, kindness, Humor, spirituality, risk, nature, achievements, optimism, communication. In 1632 people, (840 women and 792 men) personal resources were assessed using a questionnaire developed in-house. Biological age was determined by health indicators. The personal typology was determined by testing functional asymmetry, physique, interaction style, emotionality, profession, marital status, gender, age, and place of residence. The data were processed by correlation and cluster analysis and methods of automatic artificial neural networks (ANN). Personal resources were used as input continuous variables. Personality types were used as input categorical variables. The index of relative biological aging (RBA) was applied as an output continuous variable. We also calculated the correlation between the RBA index and the applied personal resources in different types of personalities. For most female types including investigative occupations, psychomotor emotionality, living in urban areas, asthenic physique, negative correlations were found between most personal resources and the aging index. In men, resources that slow down aging are found only for certain types: enterprising and conventional professions, ambidexter and left-handed, intellectual emotionality, athletic physique. In conclusion, with the help of the trained ANN, we selected personal resources that slow down aging. For women of all types, there are common resources reducing RBA index including nature, intellect, and achievements. For men, ANN was unable to find common resources that slow down aging. However, with an individual selection of resources, a trained neural network gives a favorable forecast of the ability to slow down the biological aging of a particular man by changing his hobbies and interests and ways of spending free time.

Unsupervised learning of aging principles from longitudinal data

Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the "dynamic frailty indicator" (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.

Biological Age Prediction From Wearable Device Movement Data Identifies Nutritional and Pharmacological Interventions for Healthy Aging

Intervening in aging processes is hypothesized to extend healthy years of life and treat age-related disease, thereby providing great benefit to society. However, the ability to measure the biological aging process in individuals, which is necessary to test for efficacy of these interventions, remains largely inaccessible to the general public. Here we used NHANES physical activity accelerometer data from a wearable device and machine-learning algorithms to derive biological age predictions for individuals based on their movement patterns. We found that accelerated biological aging from our “MoveAge” predictor is associated with higher all-cause mortality. We further searched for nutritional or pharmacological compounds that associate with decelerated aging according to our model. A number of nutritional components peak in their association to decelerated aging later in life, including fiber, magnesium, and vitamin E. We additionally identified one FDA-approved drug associated with decelerated biological aging: the alpha-blocker doxazosin. We show that doxazosin extends healthspan and lifespan in C. elegans. Our work demonstrates how a biological aging score based on relative mobility can be accessible to the wider public and can potentially be used to identify and determine efficacy of geroprotective interventions.

Interrelation of Individual-Personal Anti-Aging Strategies with Biological Age

<p>The definition of anti-aging is given as a condition that reduces the biological age, improves health or increases life expectancy. Based on the meta-analysis, 13 anti-aging strategies were identified: sports, control, creative, intellectual, subject, altruistic, humor, self-improvement, risk, communication, interaction with nature, achievement, optimism. An empirical study of the effectiveness of these strategies has been carried out. Subjects: persons of retirement age, men &mdash; 61&mdash;70, women &mdash; 56&mdash;70 years. The following methods were used: diagnostics of biological age according to Voitenko, questionnaire of personal resources, assessment of individual typological features, correlation analysis. It was found out that the relationship of biological aging with personal resources depends on the socio-demographic characteristics of the individual. Conclusions: to develop an individual-personal anti-aging strategy, it is necessary to take into account the totality of data: gender, age, place of residence, family, children, physique, emotionality, functional asymmetry, interaction style. An effective anti-aging strategy is selected individually for each respondent.</p>

Applications of deep learning methods in digital biomarker research using noninvasive sensing data

Introduction: Noninvasive digital biomarkers are critical elements in digital healthcare in terms of not only the ease of measurement but also their use of raw data. In recent years, deep learning methods have been put to use to analyze these diverse heterogeneous data; these methods include representation learning for feature extraction and supervised learning for the prediction of these biomarkers. Methods: We introduce clinical cases of digital biomarkers and various deep-learning methods applied according to each data type. In addition, deep learning methods for the integrated analysis of multidimensional heterogeneous data are introduced, and the utility of these data as an integrated digital biomarker is presented. The current status of digital biomarker research is examined by surveying research cases applied to various types of data as well as modeling methods. Results: We present a future research direction for using data from heterogeneous sources together by introducing deep learning methods for dimensionality reduction and mode integration from multimodal digital biomarker studies covering related domains. The integration of multimodality has led to advances in research through the improvement of performance and complementarity between modes. Discussion: The integrative digital biomarker will be more useful for research on diseases that require data from multiple sources to be treated together. Since delicate signals from patients are not missed and the interaction effects between signals are also considered, it will be helpful for immediate detection and more accurate prediction of symptoms.

Association between housing type and accelerated biological aging in different sexes: moderating effects of health behaviors

Introduction: Despite associated with multiple geriatric disorders, whether housing type, an indicator of socioeconomic status (SES) and environmental factors, is associated with accelerated biological aging is unknown. Furthermore, although individuals with low-SES have higher body mass index (BMI) and are more likely to smoke, whether BMI and smoking status moderate the association between SES and biological aging is unclear. We examined these questions in urbanized low-SES older community-dwelling adults.

Methods: First, we analyzed complete blood count data using the cox proportional hazards model and derived measures for biological age (BA) and biological age acceleration (BAA, the higher the more accelerated aging) (N = 376). Subsequently, BAA was regressed on housing type, controlling for covariates, including four other SES indicators. Interaction terms between housing type and BMI/smoking status were separately added to examine their moderating effects. Total sample and sex-stratified analyses were performed.

Results: There were significant differences between men and women in housing type and BAA. Compared to residents in ≥3 room public or private housing, older adults resided in 1–2 room public housing had a higher BAA. Furthermore, BMI attenuated the association between housing type and BAA. In sex-stratified analyses, the main and interaction effects were only significant in women. In men, smoking status instead aggravated the association between housing type and BAA.

Conclusion: Controlling for other SES indicators, housing type is an independent socio-environmental determinant of BA and BAA in a low-SES urbanized population. There were also sex differences in the moderating effects of health behaviors on biological aging.

Acceleration of Biological Aging and Underestimation of Subjective Age Are Risk Factors for Severe COVID-19

In an epidemic, it is important to have methods for reliable and rapid assessment of risk groups for severe forms of the disease for their priority vaccination and for the application of preventive lockdown measures. The aim of this study was to investigate risk factors for severe forms of COVID-19 in adults using indicators of biological and subjective aging. Longitudinal studies evaluated the severity of the disease and the number of cases. Respondents (447) were divided into "working group" and "risk group" (retirees with chronic diseases). During the lockdown period (in mid-2020), accelerated aging was observed in the group of workers (by 3.9-8 years for men and an increase at the tendency level for women). However, the respondents began to feel subjectively younger (by 3.3-7.2 years). In the risk group, there were no deviations from the expected biopsychological aging. The number of cases at the end of 2020 was 31% in workers and 0% in the risk group. Reasonably, the risk group followed the quarantine rules more strictly by 1.5 times. In working men, indicators of relative biological and relative subjective aging (measured in both 2019 and mid-2020) significantly influenced the incidence at the end of 2020. In women, only the indicators obtained in mid-2020 had a significant impact. The relative biological aging of an individual tested in the middle of 2020 had a direct impact on the risk of infection (p < 0.05) and on the probability of death (p < 0.0001). On the contrary, an increase in the relative subjective (psychological) aging index reduced the risk of infection (at the tendency level, p = 0.06) and the risk of death (p < 0.0001). Both the risk of infection and the risk of death increased with calendar age at the tendency level. Conclusions: Indicators of individual relative biological and subjective aging affect the probability of getting COVID-19 and its severity. The combination of high indicators of biological aging and underestimated indicators of subjective aging is associated with increased chances of developing severe forms of the disease.