Development and Pilot Study of an iOS Smartphone Application for Perioperative Functional Capacity Assessment

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity

<b><i>Background:</i></b> Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. <b><i>Methods:</i></b> We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. <b><i>Results:</i></b> Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69–74) years, with a body mass index of 31 (27–32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102–114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108–118) versus 106 (96–114) steps/min; <i>p</i> = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden’s index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87). <b><i>Conclusions:</i></b> Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

TEST OF PHYSIOLOGICAL PERFORMANCE: RATIONALE AND FEASIBILITY

Rigorous clinical evaluation of the physiological performance is currently performed with complex and long procedures which need expensive technology and skilled operators. In a wide range of situations (frail patients, daily clinical practice, etc.), these approaches are difficult to be applied and simpler tests, with a lack of scientific background, are mandatory. To avoid these problems, we propose a test (test of physiological performance (TOPP)) to evaluate the physiological behavior of a subject, in a really easy and safe clinical setting, measuring only the heart rate. The subject is submitted to an active standing-up test and then two submaximal exercises (with a low power load) on a cycle-ergometer. The heart rate modifications due to each submaximal step are analyzed by exponential interpolation to calculate the ascending and descending time constants and evaluate the way each subject adapts his heart rate to work. The standard deviation of the RR for each stationary phase (warm-up, load, recovery) was calculated as an index of short-term variability. Then a standard Fourier analysis of the stationary periods of the standing-up procedures allows to quickly and easily evaluate the autonomic nervous activation. We tested the protocol on five healthy subjects to verify the feasibility and the acceptance of the procedure. The five subjects demonstrated a good tolerance of the entire procedure. The standing-up showed a behavior of the autonomic system consistent with the physiology (with an increase in sympathetic activation in the passage to standing position). The analysis of the two submaximal steps highlights how younger and trained subjects present lower heart rates (both in the ascending phase and in the recovery) with a quicker adaptation ability (smaller time constants) consistent with what is expected. The short-term variability of heart rate is greater in young and trained subjects, thus confirming how the sympatho-vagal balance, in these subjects, is more dynamic. The proposed test is well tolerated by the subjects and the results, albeit in a small cohort of healthy volunteers, are consistent with what is expected from physiology and is already present in the literature. Our work aims to be a proposal with a feasibility check of a method for evaluating performance. The work to be done for the clinical validation of the TOPP is still long, but we are aware that it can give important results and that the TOPP can become an effective tool for the assessment of the physiological performance even of fragile subjects.

Mobile Applications in Clinical and Perioperative Care for Anesthesia: Narrative Review

BACKGROUND

The increasing use of smartphones by providers and patients alike demonstrates that digital health utilizing mobile applications has the potential to transform perioperative care and education in anesthesia.

OBJECTIVE

This literature review describes the current scope of the use of mobile applications in anesthesiology.

METHODS

Literature was searched using PubMed, Scopus, and clinicaltrials.gov for articles published from January 1, 2010, through April 1, 2020. Only English language studies were included. Articles were included if they examined the use of a mobile health application in the setting of anesthesia or the perioperative (immediate preoperative, intraoperative, and postoperative) period. Studies were excluded if they explored video interventions or did not examine the feasibility or efficacy of the mobile app.

RESULTS

We included 29 articles, and three areas of clinical functionality were identified: patient-centered care (preoperative, intraoperative, and postoperative), systems-based improvement, and medical education. Several studies demonstrate the feasibility and reliability of mobile apps in these areas, but many are only tested for efficacy in simulated environments or with small patient samples.

CONCLUSIONS

Mobile health applications show promise in improving communication between anesthesiologists, improving workflow efficiency, enhancing medical education, and reducing hospital costs. However, there is a need for validation and improvement before full implementation by the provider, patients, and hospital systems. Future studies are needed to demonstrate meaningful health outcomes to create guidelines and recommendations specific to the application of mobile technology to health care.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Using Medical Big Data to Develop Personalized Medicine for Dry Eye Disease

Dry eye disease (DED) is a chronic, multifactorial ocular surface disorder with multiple etiologies that results in tear film instability. Globally, the prevalence of DED is expected to increase with an aging society and daily use of digital devices. Unfortunately, the medical field is currently unprepared to meet the medical needs of patients with DED. Noninvasive, reliable, and readily reproducible biomarkers have not yet been identified, and the current mainstay treatment for DED relies on symptom alleviation using eye drops with no effective preventative therapies available. Medical big data analyses, mining information from multiomics studies and mobile health applications, may offer a solution for managing chronic conditions such as DED. Omics-based data on individual physiologic status may be leveraged to prevent high-risk diseases, accurately diagnose illness, and improve patient prognosis. Mobile health applications enable the portable collection of real-world medical data and biosignals through personal devices. Together, these data lay a robust foundation for personalized treatments for various ocular surface diseases and other pathologies that currently lack the components of precision medicine. To fully implement personalized and precision medicine, traditional aggregate medical data should not be applied directly to individuals without adjustments for personal etiology, phenotype, presentation, and symptoms.