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NPJ Digital Medicine Jun 2024Exergaming is a combination of exercise and gaming. Evidence shows an association between exercise and cognition in older people. However, previous studies showed... (Review)
Review
Exergaming is a combination of exercise and gaming. Evidence shows an association between exercise and cognition in older people. However, previous studies showed inconsistent results on the cognitive benefits of exergaming in people with cognitive impairment. Therefore, this study aims to examine the effect of exergaming intervention on cognitive functions in people with MCI or dementia. A systematic literature search was conducted via OVID databases. Randomized controlled trials (RCTs) examined the effect of an exergaming intervention on cognitive functions in people with MCI or dementia were included. Subgroup analyses were conducted according to the type of intervention and training duration. Twenty RCTs with 1152 participants were identified, including 14 trials for MCI and 6 trials for dementia. In people with MCI, 13 studies used virtual-reality (VR)-based exergaming. Those who received VR-based exergaming showed significantly better global cognitive function [SMD (95%CI) = 0.67 (0.23-1.11)], learning and memory [immediate recall test: 0.79 (0.31-1.27); delayed recall test: 0.75 (0.20-1.31)], working memory [5.83 (2.27-9.39)], verbal fluency [0.58 (0.12-1.03)], and faster in executive function than the controls. For people with dementia, all studies used video-based exergaming intervention. Participants with exergaming intervention showed significantly better global cognitive function than the controls [0.38 (0.10-0.67)]. Subgroup analyses showed that longer training duration generated larger effects. The findings suggest that exergaming impacts cognitive functions in people with MCI and dementia. Cognitive benefits are demonstrated for those with a longer training duration. With technological advancement, VR-based exergaming attracts the attention of people with MCI and performs well in improving cognitive functions.
PubMed: 38879695
DOI: 10.1038/s41746-024-01142-4 -
Geriatric Nursing (New York, N.Y.) Jun 2024There exists a deficiency in a distinct understanding of the intervention effects of Traditional Chinese Medicine (TCM) exercise therapies (Tai Chi, Yi Jin Jing, Ba Duan...
OBJECTIVES
There exists a deficiency in a distinct understanding of the intervention effects of Traditional Chinese Medicine (TCM) exercise therapies (Tai Chi, Yi Jin Jing, Ba Duan Jin, Liu Zi Jue, Qigong, Wu Qin Xi etc.) on cognitive function and its moderating variables in the elderly. This study aims to systematically evaluate the effects of TCM exercise therapies on the cognitive function of the elderly and further propose the best exercise intervention programme to delay the cognitive decline of the elderly.
METHODS
PubMed, EBSCO host, Web of Science, EMbase, China National Knowledge Infrastructure and Wan Fang databases were searched for the effects of TCM exercise therapies on the cognitive function in older adults until July 2022. A meta-analysis of the included literature was performed using Stata 12.0 software, with a subgroup analysis of seven moderating variables: subject type, intervention content, intervention duration, intervention frequency, intervention period, study type and sample size. A random effects model was used to combine the overall effect sizes and to test for heterogeneity and publication bias across studies.
RESULTS
A total of 20 publications with 1975 subjects were included. The TCM exercise therapies delayed cognitive decline in older adults (d = 0.83; 95 % CI [0.62-1.04]; P < 0.001). Subgroup analysis found that intervention content, intervention duration, intervention frequency, and intervention period were significant moderating variables influencing the effectiveness of the intervention. Among them, the Ba Duan Jin intervention (d = 0.85; 95 % CI [0.65-1.06]; P < 0.001), the duration of each exercise session of 60 min or more (d = 0.86; 95 % CI [0.71-1.00]; P < 0.001), the frequency of exercise of more than 5 times per week (d = 0.80; 95 % CI [0.64-0.96]; P < 0.001) and exercise cycles of 6-9 months (d = 0.96; 95 % CI [0.80-1.12]; P < 0.001) produced the largest effect sizes.
CONCLUSION
TCM exercise therapies can effectively improve the cognitive function of the elderly. The best effect on the cognitive function of the elderly was achieved by choosing Ba Duan Jin and exercising at least five times a week for at least 60 min each time for a total of 6-9 months. The effect size of the TCM exercise therapy interventions on the cognitive function in older adults may be overestimated because of publication bias. In addition, large-sample, multicenter, high-quality randomised controlled trials should be conducted to validate this result.
PubMed: 38878735
DOI: 10.1016/j.gerinurse.2024.06.001 -
Scientific Reports Jun 2024Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and...
Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80-20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson's Disease.
Topics: Parkinson Disease; Humans; Machine Learning; Male; Female; Middle Aged; Aged; Neural Networks, Computer; Voice; Deep Learning
PubMed: 38877028
DOI: 10.1038/s41598-024-64004-9 -
JMIR Biomedical Engineering Mar 2024The digital era has witnessed an escalating dependence on digital platforms for news and information, coupled with the advent of "deepfake" technology. Deepfakes,...
BACKGROUND
The digital era has witnessed an escalating dependence on digital platforms for news and information, coupled with the advent of "deepfake" technology. Deepfakes, leveraging deep learning models on extensive data sets of voice recordings and images, pose substantial threats to media authenticity, potentially leading to unethical misuse such as impersonation and the dissemination of false information.
OBJECTIVE
To counteract this challenge, this study aims to introduce the concept of innate biological processes to discern between authentic human voices and cloned voices. We propose that the presence or absence of certain perceptual features, such as pauses in speech, can effectively distinguish between cloned and authentic audio.
METHODS
A total of 49 adult participants representing diverse ethnic backgrounds and accents were recruited. Each participant contributed voice samples for the training of up to 3 distinct voice cloning text-to-speech models and 3 control paragraphs. Subsequently, the cloning models generated synthetic versions of the control paragraphs, resulting in a data set consisting of up to 9 cloned audio samples and 3 control samples per participant. We analyzed the speech pauses caused by biological actions such as respiration, swallowing, and cognitive processes. Five audio features corresponding to speech pause profiles were calculated. Differences between authentic and cloned audio for these features were assessed, and 5 classical machine learning algorithms were implemented using these features to create a prediction model. The generalization capability of the optimal model was evaluated through testing on unseen data, incorporating a model-naive generator, a model-naive paragraph, and model-naive participants.
RESULTS
Cloned audio exhibited significantly increased time between pauses (P<.001), decreased variation in speech segment length (P=.003), increased overall proportion of time speaking (P=.04), and decreased rates of micro- and macropauses in speech (both P=.01). Five machine learning models were implemented using these features, with the AdaBoost model demonstrating the highest performance, achieving a 5-fold cross-validation balanced accuracy of 0.81 (SD 0.05). Other models included support vector machine (balanced accuracy 0.79, SD 0.03), random forest (balanced accuracy 0.78, SD 0.04), logistic regression, and decision tree (balanced accuracies 0.76, SD 0.10 and 0.72, SD 0.06). When evaluating the optimal AdaBoost model, it achieved an overall test accuracy of 0.79 when predicting unseen data.
CONCLUSIONS
The incorporation of perceptual, biological features into machine learning models demonstrates promising results in distinguishing between authentic human voices and cloned audio.
PubMed: 38875685
DOI: 10.2196/56245 -
JMIR AI Sep 2023The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are...
BACKGROUND
The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of extracting and summarizing relevant information in the most meticulous manner from various search systems. An artificial intelligence (AI)-based intelligent search system that can significantly bring down the manual efforts in the existing processes of the RA department while maintaining and improving the quality of final outcomes is desirable. We proposed a "frequently asked questions" component and its utility in an AI-based intelligent search system in this paper. The scenario is further complicated by the lack of publicly available relevant data sets in the RA domain to train the machine learning models that can facilitate cognitive search systems for regulatory authorities.
OBJECTIVE
In this study, we aimed to use AI-based intelligent computational models to automatically recognize semantically similar question pairs in the RA domain and evaluate the Recognizing Question Entailment-based system.
METHODS
We used transfer learning techniques and experimented with transformer-based models pretrained on corpora collected from different resources, such as Bidirectional Encoder Representations from Transformers (BERT), Clinical BERT, BioBERT, and BlueBERT. We used a manually labeled data set that contained 150 question pairs in the pharmaceutical regulatory domain to evaluate the performance of our model.
RESULTS
The Clinical BERT model performed better than other domain-specific BERT-based models in identifying question similarity from the RA domain. The BERT model had the best ability to learn domain-specific knowledge with transfer learning, which reached the best performance when fine-tuned with sufficient clinical domain question pairs. The top-performing model achieved an accuracy of 90.66% on the test set.
CONCLUSIONS
This study demonstrates the possibility of using pretrained language models to recognize question similarity in the pharmaceutical regulatory domain. Transformer-based models that are pretrained on clinical notes perform better than models pretrained on biomedical text in recognizing the question's semantic similarity in this domain. We also discuss the challenges of using data augmentation techniques to address the lack of relevant data in this domain. The results of our experiment indicated that increasing the number of training samples using back translation and entity replacement did not enhance the model's performance. This lack of improvement may be attributed to the intricate and specialized nature of texts in the regulatory domain. Our work provides the foundation for further studies that apply state-of-the-art linguistic models to regulatory documents in the pharmaceutical industry.
PubMed: 38875534
DOI: 10.2196/43483 -
Medicine Jun 2024To explore the effects of focused solution nursing combined with cognitive intervention combined with functional training on negative emotions, compliance and quality of...
Focus on the effect of nursing care combined with cognitive intervention and functional training on negative emotion, compliance and quality of life in elderly patients with sudden deafness: A retrospective study.
To explore the effects of focused solution nursing combined with cognitive intervention combined with functional training on negative emotions, compliance and quality of life in elderly patients with sudden deafness. A total of 160 patients with sudden deafness in the elderly who were treated in our hospital from January 2019 to May 2021 were selected as the objects of this retrospective study. Based on different treatment approaches, subjects were divided into a control group and an observation group. Due to reasons such as the COVID-19 pandemic and transfers, 10 cases were lost to follow-up. In total, 75 cases were ultimately lost from both the control and observation groups. The control group implements cognitive intervention and functional training, and the observation group adopts focused solution nursing care on the basis of the control group. Observe and compare the effects of negative emotions, psychological distress, air conduction threshold level, compliance and quality of life of the 2 groups of patients. The air conduction hearing threshold level of the 2 groups of patients after nursing was lower than that before nursing and the observation group was lower than the control group. The positive coping scores of the 2 groups were significantly increased, and the negative coping scores were both significantly reduced and the observation group was in the 2 indicators. The degree of change was greater than that of the control group (P < .05). After nursing, the self-rating anxiety scale (SAS) and self-rating depression scale (SDS) of the observation group were lower than those of the control group (P < .05). The mental vitality score, social interaction score, emotional restriction score, and mental status of the observation group were significantly higher than those of the control group. The observation group's psychological compliance, activity compliance, dietary compliance, and treatment protocol compliance were significantly higher than those of the control group (P < .05). Adopting the focused solution model of nursing care can provide a better nursing recovery for elderly patients with sudden deafness, significantly improve the patient quality of life and anxiety and depression, improve patient compliance with treatment, and provide a certain reference for the nursing of elderly patients with sudden deafness.
Topics: Humans; Quality of Life; Retrospective Studies; Male; Female; Aged; Hearing Loss, Sudden; Patient Compliance; COVID-19; Emotions; Cognitive Behavioral Therapy; Aged, 80 and over; Adaptation, Psychological
PubMed: 38875427
DOI: 10.1097/MD.0000000000038283 -
Science Advances Jun 2024The optical memory effect in complex scattering media including turbid tissue and speckle layers has been a critical foundation for macroscopic and microscopic imaging...
The optical memory effect in complex scattering media including turbid tissue and speckle layers has been a critical foundation for macroscopic and microscopic imaging methods. However, image reconstruction from strong scattering media without the optical memory effect has not been achieved. Here, we demonstrate image reconstruction through scattering layers where no optical memory effect exists, by developing a multistage convolutional optical neural network (ONN) integrated with multiple parallel kernels operating at the speed of light. Training this Fourier optics-based, parallel, one-step convolutional ONN with the strong scattering process for direct feature extraction, we achieve memory-less image reconstruction with a field of view enlarged by a factor up to 271. This device is dynamically reconfigurable for ultrafast multitask image reconstruction with a computational power of 1.57 peta-operations per second (POPS). Our achievement establishes an ultrafast and high energy-efficient optical machine learning platform for graphic processing.
PubMed: 38875337
DOI: 10.1126/sciadv.adn2205 -
Science Advances Jun 2024We present the fabrication of 4 K-scale electrochemical random-access memory (ECRAM) cross-point arrays for analog neural network training accelerator and an electrical...
We present the fabrication of 4 K-scale electrochemical random-access memory (ECRAM) cross-point arrays for analog neural network training accelerator and an electrical characteristic of an 8 × 8 ECRAM array with a 100% yield, showing excellent switching characteristics, low cycle-to-cycle, and device-to-device variations. Leveraging the advances of the ECRAM array, we showcase its efficacy in neural network training using the Tiki-Taka version 2 algorithm (TTv2) tailored for non-ideal analog memory devices. Through an experimental study using ECRAM devices, we investigate the influence of retention characteristics on the training performance of TTv2, revealing that the relative location of the retention convergence point critically determines the available weight range and, consequently, affects the training accuracy. We propose a retention-aware zero-shifting technique designed to optimize neural network training performance, particularly in scenarios involving cross-point devices with limited retention times. This technique ensures robust and efficient analog neural network training despite the practical constraints posed by analog cross-point devices.
PubMed: 38875324
DOI: 10.1126/sciadv.adl3350 -
European Journal of Sport Science Jun 2024Perception of Velocity (PV) is the ability to estimate single repetition velocity during resistance training (RT) exercises. The main purpose of the study was to...
Perception of Velocity (PV) is the ability to estimate single repetition velocity during resistance training (RT) exercises. The main purpose of the study was to evaluate the effects of Mental Fatigue (MF) on the accuracy of barbell PV. The secondary aims were to evaluate whether MF affected RT performance and ratings of perceived exertion (RPE; OMNI-RES) in the back squat. Twenty-four (14 Females, 10 Males) resistance-trained participants underwent 2 familiarization sessions and 1RM test for the back squat. In two separate sessions, PV was tested for light, medium, and heavy loads in 2 conditions in random order: at rest (REST) and in MF condition (POST-MF) induced by previous incongruent Stroop color-word task. MF and Motivation were assessed through visual analog scales (VAS; 0-100) before and after the Stroop task. For each load subjects performed 2 repetitions and reported the RPE value. Mean propulsive velocity (Vr) of the barbell was recorded with a linear encoder, while the perceived velocity (Vp) of the subjects was self-reported using the Squat-PV scale. The PV accuracy was calculated through the delta score (ds: Vp-Vr). Following the Stroop task MF increased significantly (p < 0.001; F (1, 23) = 52.572), while motivation decreased (p < 0.05; F (1, 23) = 7.401). Ds, Vr, and RPE did not show significant differences between conditions (p > 0.05) for the three loads analyzed. MF induced by previous demanding cognitive task did not affect PV accuracy. Furthermore, subjects maintained unchanged both RT performance and RPE values associated with each load, even when mentally fatigued.
Topics: Humans; Male; Female; Resistance Training; Mental Fatigue; Young Adult; Adult; Perception; Physical Exertion; Stroop Test; Motivation; Weight Lifting
PubMed: 38874957
DOI: 10.1002/ejsc.12105 -
Turkish Neurosurgery Aug 2023Apathy is a newly recognized non-motor symptom and has a high impact on the quality of life in Parkinson's Disease (PD). The effect of subthalamic deep brain stimulation...
AIM
Apathy is a newly recognized non-motor symptom and has a high impact on the quality of life in Parkinson's Disease (PD). The effect of subthalamic deep brain stimulation (STN DBS) on apathy is controversial. This study aimed to investigate the impact of STN DBS on apathy and the possible relationship between apathy, depression, and levodopa equivalent dosage (LED) in PD patients.
MATERIAL AND METHODS
A total of 26 patients have been evaluated via the Unified Parkinson Disease Rating Scale (UPDRS), Beck Depression Inventory (Beck D), and Beck Anxiety Inventory (Beck A), Montreal Cognitive Assessment (MoCA), Parkinson Disease Questionnaire (PDQ-39) just before and 6 months after DBS.
RESULTS
Apathy scores (AES) showed a slight decrease from 54.00±10.30 to 52.69±8.88 without any statistical significance (p= 0.502) after DBS therapy. No correlation was detected between the post-treatment changes in apathy and UPDRS scores, Beck D, Beck A. Although the direction of the correlation between changes in AES scores and LED values was negative, the results did not reach statistical significance.
CONCLUSION
STN DBS therapy does not have a negative effect on apathy in PD Patients. Despite the satisfactory motor improvement, conservative dopaminergic dose reduction after surgery seems to be the main point to prevent apathy increase in PD patients after STN DBS.
PubMed: 38874248
DOI: 10.5137/1019-5149.JTN.43415-23.3