Usability of a telehealth-nurse supported home-based walking training for peripheral arterial disease - The Keep Pace! pilot study.

Background: Guidelines recommend walking trainings for peripheral arterial disease (PAD) management. Supervised walking training is superior to walking advise to improve the walking distance. Telehealth service with nurse support may close this gap. Patients and methods: This study introduces a telehealth service, "Keep pace!", which has been developed for patients with symptomatic PAD (Fontaine stage IIa and IIb), enabling a structured home-based walking training while monitoring progress via an app collecting unblinded account of steps and walking distance in self-paced 6-minute-walking-tests by geolocation tracking to enhance intrinsic motivation. Supervision by nurses via telephone calls was provided for 8 weeks, followed by 4 weeks of independent walking training. Patient satisfaction, walking distance and health-related quality of life were assessed. Results: 19 patients completed the study. The analysis revealed an overall high satisfaction with the telehealth service (95.4%), including system quality (95.1%), information quality (94.4%), service quality (95.6%), intention to use (92.8%), general satisfaction with the program (98.4%) and health benefits (95.8%). 78.9% asserted that the telehealth service lacking nurse calls would be less efficacious. Pain-free walking distance (76.3±36.8m to 188.4±81.2m, +112.2%, p<0.001) as well as total distance in 6-minute-walking test (308.8±82.6m to 425.9±107.1m, +117.2%, p<0.001) improved significantly. The telehealth service significantly reduced discomfort by better pain control (+15.5%, p=0.015) and social participation (+10.5%, p=0.042). Conclusions: In conclusion, patients were highly satisfied with the telehealth service. The physical well-being of the PAD patients improved significantly post vs. prior the telehealth program.

Correlation between timed up and go test and skeletal muscle tensiomyography in female nursing home residents.

OBJECTIVES: Tensiomyography (TMG) derived contraction time (Tc) and amplitude (Dm) are related to muscle fibre composition and to muscle atrophy/tone, respectively. However, the link between mobility and TMG-derived skeletal muscle contractile properties in older persons is unknown. The aim of the study was to correlate lower limb skeletal muscle contractile properties with balance and mobility measures in senior female residents of retirement homes in Austria. METHODS: Twenty-eight female participants (aged from 67-99 years) were included in measurements of contractile properties (TMG) of four skeletal muscles: vastus lateralis, vastus medialis, biceps femoris and gastrocnemius medialis. Their balance and mobility performance was measured using a timed up and go test (TUG). RESULTS: Time needed to complete TUG is negatively correlated to biceps femoris (r= -0.490; p= 0.008), vastus lateralis (r= -0.414; p=0.028) and vastus medialis (r= -0.353; p=0.066) Dm and positively correlated to vastus lateralis Tc (r=0.456; p=0.015). Overall, vastus lateralis Tc and vastus medialis Dm explained 37% of TUG time variance. CONCLUSIONS: Our study demonstrates that TMG-derived quadriceps muscle contractile parameters are correlated with the balance and mobility function in female nursing home residents.

Quantification of the Link between Timed Up-and-Go Test Subtasks and Contractile Muscle Properties.

Frailty and falls are a major public health problem in older adults. Muscle weakness of the lower and upper extremities are risk factors for any, as well as recurrent falls including injuries and fractures. While the Timed Up-and-Go (TUG) test is often used to identify frail members and fallers, tensiomyography (TMG) can be used as a non-invasive tool to assess the function of skeletal muscles. In a clinical study, we evaluated the correlation between the TMG parameters of the skeletal muscle contraction of 23 elderly participants (22 f, age 86.74 ± 7.88) and distance-based TUG test subtask times. TUG tests were recorded with an ultrasonic-based device. The sit-up and walking phases were significantly correlated to the contraction and delay time of the muscle vastus medialis (ρ = 0.55-0.80, p < 0.01). In addition, the delay time of the muscles vastus medialis (ρ = 0.45, p = 0.03) and gastrocnemius medialis (ρ = -0.44, p = 0.04) correlated to the sit-down phase. The maximal radial displacements of the biceps femoris showed significant correlations with the walk-forward times (ρ = -0.47, p = 0.021) and back (ρ = -0.43, p = 0.04). The association of TUG subtasks to muscle contractile parameters, therefore, could be utilized as a measure to improve the monitoring of elderly people's physical ability in general and during rehabilitation after a fall in particular. TUG test subtask measurements may be used as a proxy to monitor muscle properties in rehabilitation after long hospital stays and injuries or for fall prevention.

Deciphering lipid structures based on platform-independent decision rules.

We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2). Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete.

Towards an Electronic Health Prevention Record Based on HL7 FHIR and the OMOP Common Data Model.

BACKGROUND: Approximately 40% of all recorded deaths in Austria are due to behavioral risks. These risks could be avoided with appropriate measures. OBJECTIVES: Extension of the concept of EHR and EMR to an electronic prevention record, focusing on primary and secondary prevention. METHODS: The concept of a structured prevention pathway, based on the principles of P4 Medicine, was developed for a multidisciplinary prevention network. An IT infrastructure based on HL7 FHIR and the OHDSI OMOP common data model was designed. RESULTS: An IT solution supporting a structured and modular prevention pathway was conceptualized. It contained a personalized management of prevention, risk assessment, diagnostic and preventive measures supported by a modular, interoperable IT infrastructure including a health app, prevention record web-service, decision support modules and a smart prevention registry, separating primary and secondary use of data. CONCLUSION: A concept was created on how an electronic health prevention record based on HL7 FHIR and the OMOP common data model can be implemented.

Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data.

BACKGROUND: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality. OBJECTIVES: The primary objective was to correlate wearable device data with subjective sleep quality perceptions. METHODS: Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier. RESULTS: Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%. DISCUSSION: More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior.

Identifying Accuracy-related Outliers in a 6-Minute Walk Test Application for Monitoring Cardiovascular Disease Patients.

The 6-Minute Walk Test (6-MWT) is frequently used to evaluate functional physical capacity of patients with cardiovascular diseases. To determine reliability in remote care, outlier classification of a mobile Global Navigation Satellite System (GNSS) based 6-MWT App had to be investigated. The raw data of 53 measurements were Kalman filtered and afterwards layered with a Butterworth high-pass filter to find correlation between the resulting Root Mean Square value (RMS) outliers to relative walking distance errors using the test. The analysis indicated better performance in noise detection using all 3 GNSS dimensions with a high Pearson correlation of r = 0.77, than sole usage of elevation data with r = 0.62. This approach helps with the identification between accurate and unreliable measurements and opens a path that allows usage of the 6-MWT in remote disease management settings.Clinical Relevance- The 6-MWT is an important assessment tool of walking performance for patients with cardiovascular diseases. Using a sufficiently accurate application would enable unsupervised and easy remote usage, which could potentially reduce the demand for in-clinic visits and facilitate a more convenient and reliable monitoring method in telehealth settings.

Development of Algorithms for Automated Timed Up-and-Go Test Subtask and Step Frequency Analysis.

Frailty is one of the major problems associated with an aging society. Therefore, frailty assessment tools which support early detection and autonomous monitoring of the frailty status are heavily needed. One of the most used tests for functional assessment of the elderly is the "Timed Up-and-Go" test. In previous projects, we have developed an ultrasound-based device that enables performing the test autonomously. This paper described the development and validation of algorithms for detection of subtasks (stand up, walk, turn around, walk, sit down) and for step frequency estimation from the Timed Up-and-Go signals. The algorithms have been tested with an annotated test set recorded in 8 healthy subjects. The mean error for the developed subtask transition detection algorithms was in between 0.22 and 0.35 s. The mean step frequency error was 0.15 Hz. Future steps will include prospective evaluation of the algorithms with elderly people.

Investigation of the time shift between wearable photoplethysmography sensors used for continuous heart rate monitoring.

In this study, we investigated the effect of time shift in heartrate measurement by wearables, which might to be used in telehealth applications for patients suffering from heart failure. Six wearables commercially available on the market were tested in a 14-hour measurement. Each wearable was tested three times by five different test persons. A reference sensor was used to test the accuracy of the wearables. We found that different types of time shifts are common in the sensors we tested: time shifts of full days, time shifts of full hours (most probably due to incorrect or unspecified time zones) and time shifts in the range of seconds to minutes (most likely stemming from averaging, data transmission, etc.). We conclude that time shifts of all manufacturers need to be corrected prior comparison of a photoplethysmography signal with other signals. However, even after correction of the time shift, the reliability of the sensors seems to be too low for application in telehealth settings. Clinical relevance- This study shows that signals from state-of-the-art wearable photoplethysmography heart rate measurements show significant time shifts and marked differences even if time shifts were corrected. This limits their utility for clinical applications.

Accuracy of Wearable Photoplethysmography Sensors for Continuous Heart Rate Monitoring in Telehealth Applications.

The demand for extended care for people suffering from heart failure is omnipresent. Wearables providing continuous heart rate measurement through optical sensors are of great interest due to their ease of use without the need for medical staff and their low cost. In this study, seven wearables were tested in fifteen measurement runs, with a duration of fourteen-hour each, and compared to a reference sensor. By calculating the Pearson correlation and the root mean square error, as well as the graphical representation by a Bland Altman plot, it was found that these wearables lack sufficient accuracy and may not be suitable for medical purposes.

A Smartphone-Based Model of Care to Support Patients With Cardiac Disease Transitioning From Hospital to the Community (TeleClinical Care): Pilot Randomized Controlled Trial.

BACKGROUND: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission. OBJECTIVE: This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission. METHODS: In this pilot, 2-center randomized controlled trial, TCC was applied at discharge along with usual care to intervention arm participants. Control arm participants received usual care alone. Inclusion criteria were current admission with ACS or HF, ownership of a compatible smartphone, age ≥18 years, and provision of informed consent. The primary end point was the incidence of unplanned 30-day readmissions. Secondary end points included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness, and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weight, blood pressure, and physical activity daily plus usual care. The devices automatically transmitted recordings to the patients' smartphones and a central server. Thresholds for blood pressure, heart rate, and weight were determined by the treating cardiologists. Readings outside these thresholds were flagged to a monitoring team, who discussed salient abnormalities with the patients' usual care providers (cardiologists, general practitioners, or HF outreach nurses), who were responsible for further management. The app also provided educational push notifications. Participants were followed up after 6 months. RESULTS: Overall, 164 inpatients were randomized (TCC: 81/164, 49.4%; control: 83/164, 50.6%; mean age 61.5, SD 12.3 years; 130/164, 79.3% men; 128/164, 78% admitted with ACS). There were 11 unplanned 30-day readmissions in both groups (P=.97). Over a mean follow-up of 193 days, the intervention was associated with a significant reduction in unplanned hospital readmissions (21 in TCC vs 41 in the control arm; P=.02), including cardiac readmissions (11 in TCC vs 25 in the control arm; P=.03), and higher rates of cardiac rehabilitation completion (20/51, 39% vs 9/49, 18%; P=.03) and medication adherence (57/76, 75% vs 37/74, 50%; P=.002). The average usability rating for the app was 4.5/5. The intervention cost Aus $6028 (US $4342.26) per cardiac readmission saved. When modeled in a mainstream clinical setting, enrollment of 237 patients was projected to have the same expenditure compared with usual care, and enrollment of 500 patients was projected to save approximately Aus $100,000 (approximately US $70,000) annually. CONCLUSIONS: TCC was feasible and safe for inpatients with either ACS or HF. The incidence of 30-day readmissions was similar; however, long-term benefits were demonstrated, including fewer readmissions over 6 months, improved medication adherence, and improved cardiac rehabilitation completion. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618001547235; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375945.

mHealth 6-minute walk test - accuracy for detecting clinically relevant differences in heart failure patients.

Heart failure is a serious disease which increases mortality as well as hospital admission rates for affected patients. Disease management programs supported by telehealth solutions are cost-effective approaches for reducing all-cause mortality and heart failure hospitalizations. A 6-minute walk test (6MWT) app could help heart failure patients to self-monitor their functional capacity. We have developed such an application capable of tracking the geolocation, guiding users through a 6MWT and providing the walked distance after six minutes. Besides common global navigation satellite system (GNSS) filtering methods like a Kalman filter, we have investigated the impact of positioning the device (tablet) and GNSS reception on the accuracy of the test. In a field experiment, we gathered 166 6MWT recordings with the developed mobile application. Applying the Kalman filter reduced the overall relative error from 35.5 % to 3.7 %. Wearing the tablet on the body led to significantly better results than holding it in the hand (p < .001). The average accuracy of 2.2 % of body-worn measurements was below previously defined thresholds for reliable results. It thus allows to define a procedure on how to perform and integrate an accurate 6MWT in telehealth settings for clinical decision support in heart failure patients.

Experimenting with Generative Adversarial Networks to Expand Sparse Physiological Time-Series Data.

Machine Learning research and its application have gained enormous relevance in recent years. Their usage in medical settings could support patients, increase patient safety and assist health professionals in various tasks. However, medical data is often sparse, which renders big data analytics methods like deep learning ineffective. Data synthesis helps to augment small data sets and potentially improves patient data integrity. The presented work illustrates how Generative Adversarial Networks can be applied specifically to small data sets for enlarging sparse data. Following a state-of-the-art analysis is conducted, experimental methods with such networks are documented, which have been applied to three different data sets. Results from all three sets are presented and take-away messages are summarized. Concluding, the results' quality and limitations of the work are discussed.

Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients.

Frailty and falls are the main causes of morbidity and disability in elderly people. The Timed Up-and-Go (TUG) test has been proposed as an appropriate method for evaluating elderly individuals' risk of falling. To analyze the TUG's potential for falls prediction, we conducted a clinical study with participants aged ≥ 65 years, living in nursing homes. We harvested 138 TUG recordings with the information, if patients used a walking aid or not and developed a method to predict the use of walking aids using a Random Forest Classifier for ultrasonic based TUG test recordings. We achieved a high accuracy with an Area Under the Curve (AUC) of 96,9% using a 20% leave out evaluation strategy. Automated collection of structured data from TUG recordings - like the use of a walking aid - may help to improve fall risk tools in future.

Quantitative falls risk assessment in elderly people: results from a clinical study with distance based timed up-and-go test recordings.

OBJECTIVE: A third of people over 65 years experiences at least one fall a year. The Timed Up-and-Go (TUG) test is commonly used to assess gait and balance and to evaluate an individual's risk of falling. APPROACH: We conducted a clinical study with 46 older participants for evaluating the fall risk assessment capabilities of an ultra-sound based TUG test device. The fall protocols over a period of one year were used to classify participants as fallers and non-fallers. For frailty evaluation, state-of-the-art questionnaires were used. Fall recordings were compared to six TUG test measurements that were recorded in fallers and non-fallers. MAIN RESULTS: TUG test data were available for 39 participants (36 f, age 84.2 ± 8.2, BMI 26.0 ± 5.1). Twenty-three participants did fall at least once within the fall screening period. We fitted two different regression and probability models into a region of interest of the distance over time curve as derived from the TUG device. We found that the coefficient of determination for Gaussian bell-shaped curves (p < 0.05, AUC = 0.71) and linear regression lines (p < 0.02, AUC = 0.74) significantly separated fallers from non-fallers. Subtasks of the TUG test like the sit-up time showed near significance (p < 0.07, AUC = 0.67). SIGNIFICANCE: We found that specific features calculated from the TUG distance over time curve were significantly different between fallers and non-fallers in our study population. Automatic recording and analysis of TUG measurements could, therefore, reduce time of measurements and improve precision as compared to other methods currently being used in the assessments of fall risk.

Automated Measurement and Subtask Analysis of the Timed Up-and-Go Test in the Field of Geriatrics.

Multimorbidity and age-physiological functional restrictions can lead to frailty and a loss of a self-determined life in elderly patients. The Timed Up-and-Go test (TUG) is a sensitive and specific measure of frailty and has also many other areas of application, for example in chronic diseases. Besides the measurement of the complete TUG time, the analysis of subtasks may also reveal important information about particular aspects of the health status of test subjects. We developed an ultrasonic-based device for performing the TUG automatically, which can be attached to the backrest of a chair. This device provides the total TUG time as well as the displacement-time data for all included subtasks. To prepare for its use in clinical studies, we performed a field test at a geriatric center. The goal was to confirm feasibility, i.e., to assess its application in real patients. Despite some improvement potential revealed by the field test, the concept turned out to be an appropriate method for monitoring the TUG time and its subtasks.

Timed Up-and-Go Device for Unsupervised Functional Assessment of Elderly Patients.

Functional evaluation of elderly patients is one key component in a comprehensive Geriatric Assessment. The increased workload and high costs associated to close and continuous monitoring in clinical settings may be counterbalanced by the application of ICT-supported remote follow-up. Although clinical parameters and questionnaires can be supported with smartphones and smart gadgets, physical performance trials pose a challenge for community-based approaches with difficulties arising from setting up trials, the lack of guidance and supervision. We developed an ultrasonic-based device to overcome all of these barriers and enable elderly people to perform the Timed Up-and-Go test in an autonomous and unsupervised setting. Moreover, we introduce an algorithm to verify the successful performance of the test in order to increase the reliability of the information provided.