Technology and physical activity for preventing cognitive and physical decline in older adults: Protocol of a pilot RCT.

BACKGROUND: Cognitive frailty, defined as having both physical frailty and cognitive impairment that does not satisfy the criteria for Major Neurocognitive Disorder, represents an elevated risk for morbidity. Hence, it is crucial to mitigate such risks. Physical activity interventions have been found effective in protecting against physical frailty and cognitive deterioration. This pilot RCT examines if smartwatches and mobile phone applications can help to increase physical activity, thereby improving physical and cognitive outcomes. METHODS: Older individuals (n = 60) aged 60 to 85 years old will have their physical activity tracked using a smartwatch. The subjects will be randomized into two arms: one group will receive daily notification prompts if they did not reach the recommended levels of PA; the control group will not receive prompts. Outcome variables of physical activity level, neurocognitive scores, and physical frailty scores will be measured at baseline, T1 (3 months), and T2 (6 months). Sleep quality, levels of motivation, anxiety, and depression will be controlled for in our analyses. We hypothesize that the intervention group will have higher levels of physical activity resulting in improved cognitive and physical outcomes at follow-up. This study was approved by the National University of Singapore's Institutional Review Board on 17 August 2020 (NUS-IRB Ref. No.: H-20-038). DISCUSSION: Wearable sensors technology could prove useful by facilitating self-management in physical activity interventions. The findings of this study can justify the use of technology in physical activity as a preventive measure against cognitive frailty in older adults. This intervention also complements the rapidly rising use of technology, such as smartphones and wearable health devices, in our lives today. REGISTRATION DETAILS: This study has been retrospectively registered on clinicaltrials.gov on 5th January 2021 (NCT Identifier: NCT04692974), after the first participant was recruited.

An Exploratory Clinical Study on an Automated, Speed-Sensing Treadmill Prototype With Partial Body Weight Support for Hemiparetic Gait Rehabilitation in Subacute and Chronic Stroke Patients.

Impairments in walking speed, capacity, and endurance are commonly seen after stroke. Treadmill training improves endurance and gait speed. However, the lack of variable training speed and automated speed progression increases the risk of backward displacement and falling. An automated, speed-sensing treadmill prototype with partial body weight support, the Variable Automated Speed and Sensing Treadmill II (VASST II), was tested in an outpatient rehabilitation setting. Eleven subacute or chronic hemiplegics who could ambulate at > 0.2 m/s for >50 m participated in the study. All subjects underwent physiotherapist-supervised training on VASST II for 60 min daily, 3 times per week, for 5 weeks (total 15 h). Outcome measures at Week 3 (mid-VASST II training), Week 6 (post-VASST II training), Week 12 (first follow-up), and Week 24 (second follow-up) included the 6 minute walk test (6 MWT), 10 meter walk test (10 MWT), Berg Balance Scale (BBS) score, and Functional Ambulation category (FAC) score. User acceptability of VASST II for both study subjects and physiotherapists were also assessed. All subjects [median (IQR) age: 53.0 (22) years; median (IQR) duration post-stroke: 524 (811) days] completed VASST II training. At baseline, mean ± SD 6 MWT was 114 ± 50.9 m; mean ± SD 10 MWT was 0.37 ± 0.18 m/s; mean ± SD BBS score was 40 ± 10; and, mean ± SD FAC score was 4 ± 1. At Week 6, there were significant improvements in the 6 MWT [158.91 ± 88.69 m; P = 0.003], 10 MWT [0.49 ± 0.30 m/s; P = 0.016], and BBS score [42 ± 10; P = 0.003]. Improvements in 6 MWT and BBS scores were sustained at Week 24, but not in the 10 MWT. No VASST II-training related falls were reported. All subjects rated their VASST II training positively and indicated that it improved their current walking ability. VASST II training was effective, feasible, and safe in patients with subacute or chronic post-stroke hemiparetic gait, with sustained gains in distance walked (6 MWT) and functional balance (BBS score) up to 19 weeks post-intervention.

A Novel Automated Visual Acuity Test Using a Portable Head-mounted Display.

SIGNIFICANCE: We developed a head-mounted display (HMD) as an automated way of testing visual acuity (VA) to increase workplace efficiency. This study raises its potential utility and advantages, analyzes reasons for its current limitations, and discusses areas of improvement in the development of this device. PURPOSE: Manual VA testing is important but labor-intensive in ophthalmology and optometry clinics. The purpose of this exploratory study is to assess the performance and identify potential limitations of an automated HMD for VA testing. METHODS: Sixty patients from National University Hospital, Singapore, were enrolled in a prospective observational study. The HMD was constructed based on the Snellen chart, with single optotypes displayed at a time. Each subject underwent VA testing of both eyes with the manual Snellen chart tested at 6 m from the subject and the HMD. RESULTS: Fifty-three subjects were included in the final analysis, with an incompletion rate of 11.7% (n = 7). The mean difference in estimated acuity between the HMD and Snellen chart was 0.05 logMAR. However, 95% limits of agreement were large at ±0.33 logMAR. The HMD overestimated vision in patients with poorer visual acuities. In detecting VA worse than 0.30 logMAR (6/12), sensitivity was 63.6% (95% confidence interval, 0.31 to 0.89%), and specificity was 81.0% (95% confidence interval, 0.66 to 0.91%). No significant correlation existed between mean difference and age (r = -0.15, P = .27) or education level (r = 0.04, P = .76). CONCLUSIONS: Advantages of our novel HMD technology include its fully automated nature and its portability. However, the device in its current form is not ready for widespread clinical use primarily because of its low accuracy, which is limited by both technical and user factors. Future studies are needed to improve its accuracy and completion rate and to evaluate for test-retest reliability in a larger population.

Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies.

Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.

Automated extraction of retinal vasculature.

PURPOSE: The authors propose an algorithm that automatically extracts retinal vasculature and provides a simple measure to correct the extraction. The output of the method is a network of salient points, and blood vessels are drawn by connecting the salient points using a centripetal parameterized Catmull-Rom spline. METHODS: The algorithm starts by background correction. The corrected image is filtered with a bank of Gabor kernels, and the responses are consolidated to form a maximal image. After that, the maximal image is thinned to get a network of 1-pixel lines, analyzed and pruned to locate forks and form branches. Finally, the Ramer-Douglas-Peucker algorithm is used to determine salient points. When extraction is not satisfactory, the user simply shifts the salient points to edit the segmentation. RESULTS: On average, the authors' extractions cover 93% of ground truths (on the Drive database). CONCLUSIONS: By expressing retinal vasculature as a series of connected points, the proposed algorithm not only provides a means to edit segmentation but also gives knowledge of the shape of the blood vessels and their connections.

Pectoral muscle segmentation: a review.

Mammograms are X-ray images of breasts which are used to detect breast cancer. The pectoral muscle is a mass of tissue on which the breast rests. During routine mammographic screenings, in medio-lateral oblique (MLO) views, the pectoral muscle turns up in the mammograms along with the breast tissues. The pectoral muscle has to be segmented from the mammogram for an effective automated computer aided diagnosis (CAD). This is due to the fact that pectoral muscles have pixel intensities and texture similar to that of breast tissues which can result in awry CAD results. As a result, a lot of effort has been put into the segmentation of pectoral muscles and finding its contour with the breast tissues. To the best of our knowledge, currently there is no definitive literature available which provides a comprehensive review about the current state of research in this area of pectoral muscle segmentation. We try to address this shortcoming by providing a comprehensive review of research papers in this area. A conscious effort has been made to avoid deviating into the area of automated breast cancer detection.

Application of higher order spectra to identify epileptic EEG.

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Analysis and automatic identification of sleep stages using higher order spectra.

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.

Application of higher order statistics/spectra in biomedical signals--a review.

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second-order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.

Automated identification of diabetic type 2 subjects with and without neuropathy using wavelet transform on pedobarograph.

Diabetes is a disorder of metabolism-the way our bodies use digested food for growth and energy. The most common form of diabetes is Type 2 diabetes. Abnormal plantar pressures are considered to play a major role in the pathologies of neuropathic ulcers in the diabetic foot. The purpose of this study was to examine the plantar pressure distribution in normal, diabetic Type 2 with and without neuropathy subjects. Foot scans were obtained using the F-scan (Tekscan USA) pressure measurement system. Various discrete wavelet coefficients were evaluated from the foot images. These extracted parameters were extracted using the discrete wavelet transform (DWT) and presented to the Gaussian mixture model (GMM) and a four-layer feed forward neural network for classification. We demonstrated a sensitivity of 100% and a specificity of more than 85% for the classifiers.