Javad Razjouyan

Comparison of Posthospitalization Function and Community Mobility in Hospital Mobility Program and Usual Care Patients: A Randomized Clinical Trial

IMPORTANCE Low mobility is common during hospitalization and associated with loss or declines in ability to perform activities of daily living (ADL) and limitations in community mobility. OBJECTIVE To examine the effect of an in-hospital mobility program (MP) on posthospitalization function and community mobility. DESIGN, SETTING, AND PARTICIPANTS This single-blind randomized clinical trial used masked assessors to compare a MP with usual care (UC). Patients admitted to the medical wards of the Birmingham Veterans Affairs Medical Center from January 12, 2010, through June 29, 2011, were followed up throughout hospitalization with 1-month posthospitalization telephone follow-up. One hundred hospitalized patients 65 years or older were randomly assigned to the MP or UC groups. Patients were cognitively intact and able to walk 2 weeks before hospitalization. Data analysis was performed from November 21, 2012, to March 14, 2016. INTERVENTIONS Patients in the MP group were assisted with ambulation up to twice daily, and a behavioral strategy was used to encourage mobility. Patients in the UC group received twice-daily visits. MAIN OUTCOMES AND MEASURES Changes in self-reported ADL and community mobility were assessed using the Katz ADL scale and the University of Alabama at Birmingham Study of Aging Life-Space Assessment (LSA), respectively. The LSA measures community mobility based on the distance through which a person reports moving during the preceding 4 weeks. RESULTS Of 100 patients, 8 did not complete the study (6 in the MP group and 2 in the UC group). Patients (mean age, 73.9 years; 97 male [97.0%]; and 19 black [19.0%]) had a median length of stay of 3 days. No significant differences were found between groups at baseline. For all periods, groups were similar in ability to perform ADL; however, at 1-month after hospitalization, the LSA score was significantly higher in the MP (LSA score, 52.5) compared with the UC group (LSA score, 41.6) (P = .02). For the MP group, the 1-month posthospitalization LSA score was similar to the LSA score measured at admission. For the UC group, the LSA score decreased by approximately 10 points. CONCLUSIONS AND RELEVANCE A simple MP intervention had no effect on ADL function. However, the MP intervention enabled patients to maintain their prehospitalization community mobility, whereas those in the UC group experienced clinically significant declines. Lower life-space mobility is associated with increased risk of death, nursing home admission, and functional decline, suggesting that declines such as those observed in the UC group would be of great clinical importance. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00715962.

Smarter Sole Survival: Will Neuropathic Patients at High Risk for Ulceration Use a Smart Insole-Based Foot Protection System?

Objective: This study examined adherence to alert-based cues for plantar pressure offloading in patients with diabetic foot disease. Method and Design: Participants (n = 17) with in diabetic foot remission (history of neuropathic ulceration) were instructed to wear a smart insole system (the SurroSense Rx, Orpyx Medical Technologies Inc, Calgary, Canada) over a three-month period. This device is designed to cue offloading to manage unprotected sustained plantar pressures in an effort to prevent foot ulceration. A successful response to an alert was defined as pressure offloading, which occurred within 20 minutes of the alert onset. Patient adherence, defined as daily hours of device wear, was determined using sensor data and patient questionnaires. Changes in these parameters were assessed monthly. Results: Patients demonstrating increased adherence over the course of the study received more alerts (0.82 ± 0.31 alerts/hour) than patients whose adherence did not improve (0.36 ± 0.46 alerts/hour, P = .156). By the final stages of the study, participants who had received at least one alert every two hours were more adherent with offloading than participants who received fewer alerts (52.5 ± 4.1% vs 24.7 ± 22.4%, P = .043). Furthermore, duration of time from alert generation to successful offloading was significantly greater in the group receiving fewer alerts. This was measured in the third month with an effect size Cohen’s d = 1.739, P = .043. Conclusion: The results suggest a minimum number of alerts (one every two hours) is required for patients with diabetic neuropathy to optimally respond to offloading cues from a smart insole system.

Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography

STUDY OBJECTIVES To improve sleep quality assessment using a single chest-worn sensor by extracting body acceleration and sleep position changes. METHODS Sleep patterns of 21 participants (50.8 ± 12.8 years, 47.8% female) with self-reported sleep problems were simultaneously recorded using a chest sensor (Chest), an Actiwatch (Wrist), and polysomnography (PSG) during overnight sleep laboratory assessment. An algorithm for Chest was developed to detect sleep/wake epochs based on body acceleration and sleep position/postural changes data, which were then used to estimate sleep parameters of interest. Comparisons between Chest and Wrist with respect to PSG were performed. Identification of sleep/wake epochs was assessed by estimating sensitivity, specificity, and accuracy. Agreement between sensor-derived sleep parameters and PSG was assessed using correlation coefficients and Bland-Altman analysis. RESULTS Chest identified sleep/wake epochs with an accuracy of on average 6% higher than Wrist (85.8% versus 79.8%). Similar trends were observed for sensitivity/specificity values. Correlation between Wrist and PSG was poor for most of the sleep parameters of interest (r = 0.0-0.3); however, Chest and PSG correlation showed moderate to strong agreement (r = 0.4-0.8) with relatively low bias and high precision bias (precision): 9.2 (13.2) minutes for sleep onset latency; 17.3(34.8) minutes for total sleep time; 7.5 (29.8) minutes for wake after sleep onset; and 2.0 (7.3)% for sleep efficacy. CONCLUSIONS Combination of sleep postural/position changes and body acceleration improved detection of sleep/wake epochs compared to wrist acceleration alone. The chest sensors also improved estimation of sleep parameters of interest with stronger agreement with PSG. Our findings may expand the application of wearable sensors to clinically assess sleep outside of a sleep laboratory.

Does Physiological Stress Slow Down Wound Healing in Patients With Diabetes

Background: Poor healing is an important contributing factor to amputation among patients with diabetic foot ulcers (DFUs). Physiological stress may slow wound healing and increase susceptibility to infection. Objectives: The objective was to examine the association between heart rate variability (HRV) as an indicator of physiological stress response and healing speed (HealSpeed) among outpatients with active DFUs. Design and Methods: Ambulatory patients with diabetes with DFUs (n = 25, age: 59.3 ± 8.3 years) were recruited. HRV during pre–wound dressing was measured using a wearable sensor attached to participants’ chest. HRVs were quantified in both time and frequency domains to assess physiological stress response and vagal tone (relaxation). Change in wound size between two consecutive visits was used to estimate HealSpeed. Participants were then categorized into slow healing and fast healing groups. Between the two groups, comparisons were performed for demographic, clinical, and HRV derived parameters. Associations between different descriptors of HRV and HealSpeed were also assessed. Results: HealSpeed was significantly correlated with both vagal tone (r = –.705, P = .001) and stress response (r = .713, P = .001) extracted from frequency domain. No between-group differences were observed except those from HRV-derived parameters. Models based on HRVs were the highest predictors of slow/fast HealSpeed (AUC > 0.90), while models based on demographic and clinical information had poor classification performance (AUC = 0.44). Conclusion: This study confirms an association between stress/vagal tone and wound healing in patients with DFUs. In particular, it highlights the importance of vagal tone (relaxation) in expediting wound healing. It also demonstrates the feasibility of assessing physiological stress responses using wearable technology in outpatient clinic during routine clinic visits.

Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study

Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty components are of key importance to enable targeted interventions and remediation. We hypothesize that sensor-derived parameters, measured by a pendant accelerometer device in the home setting, are sensitive to identifying pre-frailty. Methods: Using the Fried frailty phenotype criteria, 153 community-dwelling, ambulatory older adults were classified as pre-frail (51%), frail (22%), or non-frail (27%). A pendant sensor was used to monitor the at home physical activity, using a chest acceleration over 48 h. An algorithm was developed to quantify physical activity pattern (PAP), physical activity behavior (PAB), and sleep quality parameters. Statistically significant parameters were selected to discriminate the pre-frail from frail and non-frail adults. Results: The stepping parameters, walking parameters, PAB parameters (sedentary and moderate-to-vigorous activity), and the combined parameters reached and area under the curve of 0.87, 0.85, 0.85, and 0.88, respectively, for identifying pre-frail adults. No sleep parameters discriminated the pre-frail from the rest of the adults. Conclusions: This study demonstrates that a pendant sensor can identify pre-frailty via daily home monitoring. These findings may open new opportunities in order to remotely measure and track frailty via telehealth technologies.

Activity Monitoring and Heart Rate Variability as Indicators of Fall Risk: Proof-of-Concept for Application of Wearable Sensors in the Acute Care Setting

Growing concern for falls in acute care settings could be addressed with objective evaluation of fall risk. The current proof-of-concept study evaluated the feasibility of using a chest-worn sensor during hospitalization to determine fall risk. Physical activity and heart rate variability (HRV) of 31 volunteers admitted to a 29-bed adult inpatient unit were recorded using a single chest-worn sensor. Sensor data during the first 24-hour recording were analyzed. Participants were stratified using the Hendrich II fall risk assessment into high and low fall risk groups. Univariate analysis revealed age, daytime activity, nighttime side lying posture, and HRV were significantly different between groups. Results suggest feasibility of wearable technology to consciously monitor physical activity, sleep postures, and HRV as potential markers of fall risk in the acute care setting. Further study is warranted to confirm the results and examine the efficacy of the proposed wearable technology to manage falls in hospitals. [Journal of Gerontological Nursing, 43(7), 53-62.].

Feature Importance and Predictive Modeling for Multi-source Healthcare Data with Missing Values

With rapid development of sensor technologies and the internet of things, research in the area of connected health is increasing in importance and complexity with wide-reaching impacts for public health. As data sources such as mobile (wearable) sensors get cheaper, smaller, and smarter, important research questions can be answered by combining information from multiple data sources. However, integration of multiple heterogeneous data streams often results in a dataset with several empty cells or missing values. The challenge is to use such sparsely populated integrated datasets without compromising model performance. Naïve approaches for dataset modification such as discarding observations or ad-hoc replacement of missing values often lead to misleading results. In this paper, we discuss and evaluate current best-practices for modeling such data with missing values and then propose an ensemble-learning based sparse-data modeling framework. We develop a predictive model using this framework and compare it with existing models using a study in a healthcare setting. Instead of generating a single score on variable/feature importance, our framework enables the user to understand the importance of a variable based on the existing data values and their localized impact on the outcome.

Effects of office workstation type on physical activity and stress

Objective Office environments have been causally linked to workplace-related illnesses and stress, yet little is known about how office workstation type is linked to objective metrics of physical activity and stress. We aimed to explore these associations among office workers in US federal office buildings. Methods We conducted a wearable, sensor-based, observational study of 231 workers in four office buildings. Outcome variables included workers’ physiological stress response, physical activity and perceived stress. Relationships between office workstation type and these variables were assessed using structural equation modelling. Results Workers in open bench seating were more active at the office than those in private offices and cubicles (open bench seating vs private office=225.52 mG (31.83% higher on average) (95% CI 136.57 to 314.46); open bench seating vs cubicle=185.13 mG (20.16% higher on average) (95% CI 66.53 to 303.72)). Furthermore, workers in open bench seating experienced lower perceived stress at the office than those in cubicles (−0.27 (9.10% lower on average) (95% CI −0.54 to −0.02)). Finally, higher physical activity at the office was related to lower physiological stress (higher heart rate variability in the time domain) outside the office (−26.12 ms/mG (14.18% higher on average) (95% CI −40.48 to −4.16)). Conclusions Office workstation type was related to enhanced physical activity and reduced physiological and perceived stress. This research highlights how office design, driven by office workstation type, could be a health-promoting factor.

Instrumented Trail-Making Task: Application of Wearable Sensor to Determine Physical Frailty Phenotypes

Background: The physical frailty assessment tools that are currently available are often time consuming to use with limited feasibility. Objective: To address these limitations, an instrumented trail-making task (iTMT) platform was developed using wearable technology to automate quantification of frailty phenotypes without the need of a frailty walking test. Methods: Sixty-one older adults (age = 72.8 ± 9.9 years, body mass index [BMI] = 27.4 ± 4.9 kg/m2) were recruited. According to the Fried Frailty Criteria, 39% of participants were determined as robust and 61% as non-robust (pre-frail or frail). In addition, 17 young subjects (age = 29.0 ± 7.2 years, BMI = 26.2 ± 4.6 kg/m2) were recruited to determine the healthy benchmark. The iTMT included reaching 5 indexed circles (including numbers 1-to-3 and letters A&B placed in random orders), which virtually appeared on a computer-screen, by rotating one’s ankle-joint while standing. By using an ankle-worn inertial sensor, 3D ankle-rotation was estimated and mapped into navigation of a computer-cursor in real-time (100 Hz), allowing subjects to navigate the computer-cursor to perform the iTMT. The ankle-sensor was also used for quantifying ankle-rotation velocity (representing slowness), its decline during the test (representing exhaustion), and ankle-velocity variability (representing movement inefficiency), as well as the power (representing weakness) generated during the test. Comparative assessments included Fried frailty phenotypes and gait assessment. Results: All subjects were able to complete the iTMT, with an average completion time of 125 ± 85 s. The iTMT-derived parameters were able to identify the presence and absence of slowness, exhaustion, weakness, and inactivity phenotypes (Cohen’s d effect size = 0.90–1.40). The iTMT Velocity was significantly different between groups (d = 0.62–1.47). Significant correlation was observed between the iTMT Velocity and gait speed (r = 0.684 p < 0.001). The iTMT-derived parameters and age together enabled significant distinguishing of non-robust cases with area under curve of 0.834, sensitivity of 83%, and specificity of 67%. Conclusion: This study demonstrated a non-gait-based wearable platform to objectively quantify frailty phenotypes and determine physical frailty, using a quick and practical test. This platform may address the hurdles of conventional physical frailty phenotypes methods by replacing the conventional frailty walking test with an automated and objective process that reduces the time of assessment and is more practical for those with mobility limitations.

A Regularization Approach for Identifying Cumulative Lagged Effects in Smart Health Applications

Recent development of wearable sensor technologies have made it possible to capture concurrent data streams for ambient environment and instantaneous physiological stress response at a fine granularity. Characterizing the delay in physiological stress response time to each environment stimulus is as important as capturing the magnitude of the effect. In this paper, we discuss and evaluate a new regularization-based statistical method to determine the ideal lagged effect of five environmental factors-carbon dioxide, temperature, relative humidity, atmospheric pressure and noise levels on instantaneous stress response. Using this method, we infer that the first four environment variables have a cumulative lagged effect, of approximately 60 minutes, on stress response whereas noise level has an instantaneous effect on stress response. The proposed transformations to inputs result in models with better fit and predictive performance. This study not only informs the field of environment-wellbeing research about the cumulative lagged effects of the specified environmental factors, but also proposes a new method for determining optimal feature transformation in similar smart health studies.