Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records.

OBJECTIVE: This study aimed to develop and validate predictive models using electronic health records (EHR) data to determine whether hospitalized COVID-19-positive patients would be admitted to alternative medical care or discharged home. METHODS: We conducted a retrospective cohort study using deidentified data from the University of Florida Health Integrated Data Repository. The study included 1,578 adult patients (≥18 years) who tested positive for COVID-19 while hospitalized, comprising 960 (60.8%) female patients with a mean (SD) age of 51.86 (18.49) years and 618 (39.2%) male patients with a mean (SD) age of 54.35 (18.48) years. Machine learning (ML) model training involved cross-validation to assess their performance in predicting patient disposition. RESULTS: We developed and validated six supervised ML-based prediction models (logistic regression, Gaussian Naïve Bayes, k-nearest neighbors, decision trees, random forest, and support vector machine classifier) to predict patient discharge status. The models were evaluated based on the area under the receiver operating characteristic curve (ROC-AUC), precision, accuracy, F1 score, and Brier score. The random forest classifier exhibited the highest performance, achieving an accuracy of 0.84 and an AUC of 0.72. Logistic regression (accuracy: 0.85, AUC: 0.71), k-nearest neighbor (accuracy: 0.84, AUC: 0.63), decision tree (accuracy: 0.84, AUC: 0.61), Gaussian Naïve Bayes (accuracy: 0.84, AUC: 0.66), and support vector machine classifier (accuracy: 0.84, AUC: 0.67) also demonstrated valuable predictive capabilities. SIGNIFICANCE: This study's findings are crucial for efficiently allocating healthcare resources during pandemics like COVID-19. By harnessing ML techniques and EHR data, we can create predictive tools to identify patients at greater risk of severe symptoms based on their medical histories. The models developed here serve as a foundation for expanding the toolkit available to healthcare professionals and organizations. Additionally, explainable ML methods, such as Shapley Additive Explanations, aid in uncovering underlying data features that inform healthcare decision-making processes.

A Natural Language Processing Algorithm for Classifying Suicidal Behaviors in Alzheimer's Disease and Related Dementia Patients: Development and Validation Using Electronic Health Records Data.

This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) and Deep Learning (DL) techniques to identify and classify documentation of suicidal behaviors in patients with Alzheimer's disease and related dementia (ADRD). We utilized MIMIC-III and MIMIC-IV datasets and identified ADRD patients and subsequently those with suicide ideation using relevant International Classification of Diseases (ICD) codes. We used cosine similarity with ScAN (Suicide Attempt and Ideation Events Dataset) to calculate semantic similarity scores of ScAN with extracted notes from MIMIC for the clinical notes. The notes were sorted based on these scores, and manual review and categorization into eight suicidal behavior categories were performed. The data were further analyzed using conventional ML and DL models, with manual annotation as a reference. The tested classifiers achieved classification results close to human performance with up to 98% precision and 98% recall of suicidal ideation in the ADRD patient population. Our NLP model effectively reproduced human annotation of suicidal ideation within the MIMIC dataset. These results establish a foundation for identifying and categorizing documentation related to suicidal ideation within ADRD population, contributing to the advancement of NLP techniques in healthcare for extracting and classifying clinical concepts, particularly focusing on suicidal ideation among patients with ADRD. Our study showcased the capability of a robust NLP algorithm to accurately identify and classify documentation of suicidal behaviors in ADRD patients.

Utilization of the percutaneous left ventricular support as bridge to heart transplantation across the United States: In-depth UNOS database analysis.

BACKGROUND: Intra-aortic balloon pump (IABP) and Impella device utilization as a bridge to heart transplantation (HTx) have risen exponentially. We aimed to explore the influence of device selection on HTx outcomes, considering regional practice variation. METHODS: A retrospective longitudinal study was performed on a United Network for Organ Sharing (UNOS) registry dataset. We included adult patients listed for HTx between October 2018 and April 2022 as status 2, as justified by requiring IABP or Impella support. The primary end-point was successful bridging to HTx as status 2. RESULTS: Of 32,806 HTx during the study period, 4178 met inclusion criteria (Impella n = 650, IABP n = 3528). Waitlist mortality increased from a nadir of 16 (in 2019) to a peak of 36 (in 2022) per thousand status 2 listed patients. Impella annual use increased from 8% in 2019 to 19% in 2021. Compared to IABP, Impella patients demonstrated higher medical acuity and lower success rate of transplantation as status 2 (92.1% vs 88.9%, p < 0.001). The IABP:Impella utilization ratio varied widely between regions, ranging from 1.77 to 21.31, with high Impella use in Southern and Western states. However, this difference was not justified by medical acuity, regional transplant volume, or waitlist time and did not correlate with waitlist mortality. CONCLUSIONS: The shift in utilizing Impella as opposed to IABP did not improve waitlist outcomes. Our results suggest that clinical practice patterns beyond mere device selection determine successful bridging to HTx. There is a critical need for objective evidence to guide tMCS utilization and a paradigm shift in the UNOS allocation system to achieve equitable HTx practice across the United States.

Developing and validating a natural language processing algorithm to extract preoperative cannabis use status documentation from unstructured narrative clinical notes.

OBJECTIVE: This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) techniques to identify and classify documentation of preoperative cannabis use status. MATERIALS AND METHODS: We developed and applied a keyword search strategy to identify documentation of preoperative cannabis use status in clinical documentation within 60 days of surgery. We manually reviewed matching notes to classify each documentation into 8 different categories based on context, time, and certainty of cannabis use documentation. We applied 2 conventional ML and 3 deep learning models against manual annotation. We externally validated our model using the MIMIC-III dataset. RESULTS: The tested classifiers achieved classification results close to human performance with up to 93% and 94% precision and 95% recall of preoperative cannabis use status documentation. External validation showed consistent results with up to 94% precision and recall. DISCUSSION: Our NLP model successfully replicated human annotation of preoperative cannabis use documentation, providing a baseline framework for identifying and classifying documentation of cannabis use. We add to NLP methods applied in healthcare for clinical concept extraction and classification, mainly concerning social determinants of health and substance use. Our systematically developed lexicon provides a comprehensive knowledge-based resource covering a wide range of cannabis-related concepts for future NLP applications. CONCLUSION: We demonstrated that documentation of preoperative cannabis use status could be accurately identified using an NLP algorithm. This approach can be employed to identify comparison groups based on cannabis exposure for growing research efforts aiming to guide cannabis-related clinical practices and policies.

Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review.

Introduction: Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence. Methods: We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines. Results: Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities. Conclusion: Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.

Feasibility of a Smartwatch Platform to Assess Ecological Mobility: Real-Time Online Assessment and Mobility Monitor.

BACKGROUND: Early detection of mobility decline is critical to prevent subsequent reductions in quality of life, disability, and mortality. However, traditional approaches to mobility assessment are limited in their ability to capture daily fluctuations that align with sporadic health events. We aim to describe findings from a pilot study of our Real-time Online Assessment and Mobility Monitor (ROAMM) smartwatch application, which uniquely captures multiple streams of data in real time in ecological settings. METHODS: Data come from a sample of 31 participants (Mage = 74.7, 51.6% female) who used ROAMM for approximately 2 weeks. We describe the usability and feasibility of ROAMM, summarize prompt data using descriptive metrics, and compare prompt data with traditional survey-based questionnaires or other established measures. RESULTS: Participants were satisfied with ROAMM's function (87.1%) and ranked the usability as "above average." Most were highly engaged (average adjusted compliance = 70.7%) and the majority reported being "likely" to enroll in a 2-year study (77.4%). Some smartwatch features were correlated with their respective traditional measurements (eg, certain GPS-derived life-space mobility features (r = 0.50-0.51, p < .05) and ecologically measured pain (r = 0.72, p = .01), but others were not (eg, ecologically measured fatigue). CONCLUSIONS: ROAMM was usable, acceptable, and effective at measuring mobility and risk factors for mobility decline in our pilot sample. Additional work with a larger and more diverse sample is necessary to confirm associations between smartwatch-measured features and traditional measures. By monitoring multiple data streams simultaneously in ecological settings, this technology could uniquely contribute to the evolution of mobility measurement and risk factors for mobility loss.

Using Machine Learning To Define the Impact of Beta-Lactam Early and Cumulative Target Attainment on Outcomes in Intensive Care Unit Patients with Hospital-Acquired and Ventilator-Associated Pneumonia.

Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are the most common intensive care unit (ICU) infections. We aimed to evaluate the association of early and cumulative beta-lactam pharmacokinetic/pharmacodynamic (PK/PD) parameters with therapy outcomes in pneumonia. Adult ICU patients who received cefepime, meropenem, or piperacillin-tazobactam for HAP or VAP and had its concentration measured were included. Beta-lactam exposure was generated for every patient for the entire duration of therapy, and the time free concentration remained above the MIC (fT>MIC) and the time free concentration remained above four multiples of the MIC (fT>4×MIC) were calculated for time frames of 0 to 24 h, 0 to 10 days, and day 0 to end of therapy. Regression analyses and machine learning were performed to evaluate the impact of PK/PD on therapy outcomes. A total of 735 patients and 840 HAP/VAP episodes (47% HAP) were included. The mean age was 56 years, and the mean weight was 80 kg. Sequential organ failure assessment (SOFA), hemodialysis, age, and weight were significantly associated with the clinical outcomes and kept in the final model. In the full cohort including all pneumonia episodes, PK/PD parameters at different time windows were associated with a favorable composite outcome, clinical cure, and mechanical ventilation (MV)-free days. In patients who had positive cultures and reported MICs, almost all PK/PD parameters were significant predictors of therapy outcomes. In the machine learning analysis, PK/PD parameters ranked high and were the primary overall predictors of clinical cure. Early target attainment and cumulative target attainment have a great impact on pneumonia outcomes. Beta-lactam exposure should be optimized early and maintained through therapy duration.

Worsening sleep predicts lower life space mobility during the onset of the COVID-19 pandemic.

BACKGROUND: Poor sleep health is an understudied yet potentially modifiable risk factor for reduced life space mobility (LSM), defined as one's habitual movement throughout a community. The objective of this study was to determine whether recalled changes in sleep traits (e.g., sleep quality, refreshing sleep, sleep problems, and difficulty falling asleep) because of the COVID-19 pandemic were associated with LSM in older adults. METHODS: Data were obtained from a University of Florida-administered study conducted in May and June of 2020 (n = 923). Linear regression models were used to assess the impact of COVID-related change in sleep traits with summary scores from the Life Space Assessment. Analyses were adjusted for demographic, mental, and physical health characteristics, COVID-related avoidant behaviors, and pre-COVID sleep ratings. RESULTS: In unadjusted models, reporting that any sleep trait got "a lot worse" or "a little worse" was associated with a decrease in LSM (all p < 0.05). Results were attenuated when accounting for demographic, mental, and physical health characteristics. In fully adjusted models, reporting that problems with sleep got "a lot worse" or that refreshing sleep got "a little worse" was associated with a lower standardized LSM score (ß = -0.38, 95% CI: -0.74, -0.01, and ß = -0.19, 95% CI: -0.37, -0.00, respectively). CONCLUSIONS: While additional research is needed in diverse people and environments, the results demonstrate an association between sleep traits that worsen in response to a health threat and reduced LSM. This finding suggests that interventions that focus on maintaining sleep health in times of heightened stress could preserve LSM.

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932 ± 0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models' performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.

Comparisons Between GPS-based and Self-reported Life-space Mobility in Older Adults.

Assessments of Life-space Mobility (LSM) evaluate the locations of movement and their frequency over a period of time to understand mobility patterns. Advancements in and miniaturization of GPS sensors in mobile devices like smartwatches could facilitate objective and high-resolution assessment of life-space mobility. The purpose of this study was to compare self-reported measures to GPS-based LSM extracted from 27 participants (44.4% female, aged 65+ years) who wore a smartwatch for 1-2 weeks at two different site locations (Connecticut and Florida). GPS features (e.g., excursion size/span) were compared to self-reported LSM with and without an indicator for needing assistance. Although correlations between self-reported measures and GPS-based LSM were positive, none were statistically significant. The correlations improved slightly when needing assistance was included, but statistical significance was achieved only for excursion size (r=0.40, P=0.04). The poor correlations between GPS-based and self-reported indicators suggest that they capture different dimensions of life-space mobility.

Using Smartwatches to Detect Face Touching.

Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identify motion signatures that are mapped accurately to face touching. Participants (n = 10, five women, aged 20-83) performed 10 physical activities classified into face touching (FT) and non-face touching (NFT) categories in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Data features were extracted from consecutive non-overlapping windows varying from 2 to 16 s. We examined the performance of state-of-the-art machine learning methods on face-touching movement recognition (FT vs. NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees, and random forest. While all machine learning models were accurate in recognizing FT categories, logistic regression achieved the best performance across all metrics (accuracy: 0.93 ± 0.08, recall: 0.89 ± 0.16, precision: 0.93 ± 0.08, F1-score: 0.90 ± 0.11, AUC: 0.95 ± 0.07) at the window size of 5 s. IAR models resulted in lower performance, where the random forest classifier achieved the best performance across all metrics (accuracy: 0.70 ± 0.14, recall: 0.70 ± 0.14, precision: 0.70 ± 0.16, F1-score: 0.67 ± 0.15) at the window size of 9 s. In conclusion, wearable devices, powered by machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks as it has the potential to limit face touching as a transmission vector.

Satisfaction, Usability, and Compliance With the Use of Smartwatches for Ecological Momentary Assessment of Knee Osteoarthritis Symptoms in Older Adults: Usability Study.

BACKGROUND: Smartwatches enable physicians to monitor symptoms in patients with knee osteoarthritis, their behavior, and their environment. Older adults experience fluctuations in their pain and related symptoms (mood, fatigue, and sleep quality) that smartwatches are ideally suited to capture remotely in a convenient manner. OBJECTIVE: The aim of this study was to evaluate satisfaction, usability, and compliance using the real-time, online assessment and mobility monitoring (ROAMM) mobile app designed for smartwatches for individuals with knee osteoarthritis. METHODS: Participants (N=28; mean age 73.2, SD 5.5 years; 70% female) with reported knee osteoarthritis were asked to wear a smartwatch with the ROAMM app installed. They were prompted to report their prior night's sleep quality in the morning, followed by ecological momentary assessments (EMAs) of their pain, fatigue, mood, and activity in the morning, afternoon, and evening. Satisfaction, comfort, and usability were evaluated using a standardized questionnaire. Compliance with regard to answering EMAs was calculated after excluding time when the watch was not being worn for technical reasons (eg, while charging). RESULTS: A majority of participants reported that the text displayed was large enough to read (22/26, 85%), and all participants found it easy to enter ratings using the smartwatch. Approximately half of the participants found the smartwatch to be comfortable (14/26, 54%) and would consider wearing it as their personal watch (11/24, 46%). Most participants were satisfied with its battery charging system (20/26, 77%). A majority of participants (19/26, 73%) expressed their willingness to use the ROAMM app for a 1-year research study. The overall EMA compliance rate was 83% (2505/3036 responses). The compliance rate was lower among those not regularly wearing a wristwatch (10/26, 88% vs 16/26, 71%) and among those who found the text too small to read (4/26, 86% vs 22/26, 60%). CONCLUSIONS: Older adults with knee osteoarthritis positively rated the ROAMM smartwatch app and were generally satisfied with the device. The high compliance rates coupled with the willingness to participate in a long-term study suggest that the ROAMM app is a viable approach to remotely collecting health symptoms and behaviors for both research and clinical endeavors.

Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning.

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20-50 years), middle-aged (50-70 years], and older adults (70-89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20-89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost's models were high for sedentary (0.955-0.973), locomotion (0.942-0.964) and lifestyle (0.913-0.949) activity types with no apparent difference across age groups. Low (0.919-0.947), light (0.813-0.828) and moderate (0.846-0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835-1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.

The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study.

BACKGROUND: Research has shown the feasibility of human activity recognition using wearable accelerometer devices. Different studies have used varying numbers and placements for data collection using sensors. OBJECTIVE: This study aims to compare accuracy performance between multiple and variable placements of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. METHODS: In total, 93 participants (mean age 72.2 years, SD 7.1) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary versus nonsedentary, locomotion versus nonlocomotion, and lifestyle versus nonlifestyle activities (eg, leisure walk vs computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on 5 different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used to develop random forest models to assess activity category recognition accuracy and MET estimation. RESULTS: Model performance for both MET estimation and activity category recognition were strengthened with the use of additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03-0.09 MET increase in prediction error compared with wearing all 5 devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for the detection of locomotion (balanced accuracy decrease range 0-0.01), sedentary (balanced accuracy decrease range 0.05-0.13), and lifestyle activities (balanced accuracy decrease range 0.04-0.08) compared with all 5 placements. The accuracy of recognizing activity categories increased with additional placements (accuracy decrease range 0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. CONCLUSIONS: Additional accelerometer devices slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

Prevalence and characteristics of self-reported hypothyroidism and its association with nonorgan-specific manifestations in US sarcoidosis patients: a nationwide registry study.

Little is known about the prevalence, clinical characteristics and impact of hypothyroidism in patients with sarcoidosis. We aimed to determine the prevalence and clinical features of hypothyroidism and its relation to organ involvement and other clinical manifestations in patients with sarcoidosis. We conducted a national registry-based study investigating 3835 respondents to the Sarcoidosis Advanced Registry for Cures Questionnaire between June 2014 and August 2019. This registry is based on a self-reported, web-based questionnaire that provides data related to demographics, diagnostics, sarcoidosis manifestations and treatment. We compared sarcoidosis patients with and without self-reported hypothyroidism. We used multivariable logistic regression and adjusted for potential confounders to determine the association of hypothyroidism with nonorgan-specific manifestations. 14% of the sarcoidosis patients self-reported hypothyroidism and were generally middle-aged white women. Hypothyroid patients had more comorbid conditions and were more likely to have multiorgan sarcoidosis involvement, especially with cutaneous, ocular, joints, liver and lacrimal gland involvement. Self-reported hypothyroidism was associated with depression (adjusted odds ratio (aOR) 1.3, 95% CI 1.01-1.6), antidepressant use (aOR 1.3, 95% CI 1.1-1.7), obesity (aOR 1.7, 95% CI 1.4-2.1), sleep apnoea (aOR 1.7, 95% CI 1.3-2.2), chronic fatigue syndrome (aOR 1.5, 95% CI 1.2-2) and was borderline associated with fibromyalgia (aOR 1.3, 95% CI 1-1.8). Physical impairment was more common in patients with hypothyroidism. Hypothyroidism is a frequent comorbidity in sarcoidosis patients that might be a potentially reversible contributor to fatigue, depression and physical impairment in this population. We recommend considering routine screening for hypothyroidism in sarcoidosis patients especially in those with multiorgan sarcoidosis, fatigue and depression.

The Temporal Relationship Between Ecological Pain and Life-Space Mobility in Older Adults With Knee Osteoarthritis: A Smartwatch-Based Demonstration Study.

BACKGROUND: Older adults who experience pain are more likely to reduce their community and life-space mobility (ie, the usual range of places in an environment in which a person engages). However, there is significant day-to-day variability in pain experiences that offer unique insights into the consequences on life-space mobility, which are not well understood. This variability is complex and cannot be captured with traditional recall-based pain surveys. As a solution, ecological momentary assessments record repeated pain experiences throughout the day in the natural environment. OBJECTIVE: The aim of this study was to examine the temporal association between ecological momentary assessments of pain and GPS metrics in older adults with symptomatic knee osteoarthritis by using a smartwatch platform called Real-time Online Assessment and Mobility Monitor. METHODS: Participants (n=19, mean 73.1 years, SD 4.8; female: 13/19, 68%; male: 6/19, 32%) wore a smartwatch for a mean period of 13.16 days (SD 2.94). Participants were prompted in their natural environment about their pain intensity (range 0-10) at random time windows in the morning, afternoon, and evening. GPS coordinates were collected at 15-minute intervals and aggregated each day into excursion, ellipsoid, clustering, and trip frequency features. Pain intensity ratings were averaged across time windows for each day. A random effects model was used to investigate the within and between-person effects. RESULTS: The daily mean pain intensities reported by participants ranged between 0 and 8 with 40% reporting intensities ≥2. The within-person associations between pain intensity and GPS features were more likely to be statistically significant than those observed between persons. Within-person pain intensity was significantly associated with excursion size, and others (excursion span, total distance, and ellipse major axis) showed a statistical trend (excursion span: P=.08; total distance: P=.07; ellipse major axis: P=.07). Each point increase in the mean pain intensity was associated with a 3.06 km decrease in excursion size, 2.89 km decrease in excursion span, 5.71 km decrease total distance travelled per day, 31.4 km2 decrease in ellipse area, 0.47 km decrease ellipse minor axis, and 3.64 km decrease in ellipse major axis. While not statistically significant, the point estimates for number of clusters (P=.73), frequency of trips (P=.81), and homestay (P=.15) were positively associated with pain intensity, and entropy (P=.99) was negatively associated with pain intensity. CONCLUSIONS: In this demonstration study, higher intensity knee pain in older adults was associated with lower life-space mobility. Results demonstrate that a custom-designed smartwatch platform is effective at simultaneously collecting rich information about ecological pain and life-space mobility. Such smart tools are expected to be important for remote health interventions that harness the variability in pain symptoms while understanding their impact on life-space mobility.

Innovations in Geroscience to enhance mobility in older adults.

Aging is the primary risk factor for functional decline; thus, understanding and preventing disability among older adults has emerged as an important public health challenge of the 21st century. The science of gerontology - or geroscience - has the practical purpose of "adding life to the years." The overall goal of geroscience is to increase healthspan, which refers to extending the portion of the lifespan in which the individual experiences enjoyment, satisfaction, and wellness. An important facet of this goal is preserving mobility, defined as the ability to move independently. Despite this clear purpose, this has proven to be a challenging endeavor as mobility and function in later life are influenced by a complex interaction of factors across multiple domains. Moreover, findings over the past decade have highlighted the complexity of walking and how targeting multiple systems, including the brain and sensory organs, as well as the environment in which a person lives, can have a dramatic effect on an older person's mobility and function. For these reasons, behavioral interventions that incorporate complex walking tasks and other activities of daily living appear to be especially helpful for improving mobility function. Other pharmaceutical interventions, such as oxytocin, and complementary and alternative interventions, such as massage therapy, may enhance physical function both through direct effects on biological mechanisms related to mobility, as well as indirectly through modulation of cognitive and socioemotional processes. Thus, the purpose of the present review is to describe evolving interventional approaches to enhance mobility and maintain healthspan in the growing population of older adults in the United States and countries throughout the world. Such interventions are likely to be greatly assisted by technological advances and the widespread adoption of virtual communications during and after the COVID-19 era.

Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning.

Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was: 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.

A Survey of Healthcare Monitoring Systems for Chronically Ill Patients and Elderly.

The demand of healthcare systems for chronically ill patients and elderly has increased in the last few years. This demand is derived by the necessity to allow patients and elderly to be independent in their homes without the help of their relatives or caregivers. The prosperity of the information technology plays an essential role in healthcare by providing continuous monitoring and alerting mechanisms. In this paper, we survey the most recent applications in healthcare monitoring. We organize the applications into categories and present their common architecture. Moreover, we explain the standards used and challenges faced in this field. Finally, we make a comparison between the presented applications and discuss the possible future research paths.

Classifying obstructive sleep apnea using smartphones.

Obstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the gold standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In this paper, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone's built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory effort, and (3) an accelerometer to detect the body's movement. Finally, we examine our system's ability to screen the disease as compared to the gold standard by testing it on 15 samples. The results showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. These preliminary results demonstrate the effectiveness of the developed system when compared to the gold standard and emphasize the important role of smartphones in healthcare.