JMIR neurotechnology最新文献

{"title":"Improving Alzheimer Disease Diagnosis via a Custom Convolutional Neural Network and Pretrained Models: Development and Evaluation of a Diagnostic Tool (NeuroFusionNet).","authors":"Abraham Varghese, Andres Ricaurte Fajardo, Prasanth Gowda Parameshwara, Manesh Thankappan, Balakrishnan Kannan","doi":"10.2196/68839","DOIUrl":"10.2196/68839","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer disease (AD) is a progressive neurodegenerative disorder that impairs cognitive function, memory, and behavior. Early and accurate diagnosis is essential for effective management; however, traditional cognitive tests often lack the sensitivity and specificity required for early detection.</p><p><strong>Objective: </strong>This study aims to develop and evaluate NeuroFusionNet-a diagnostic tool that integrates a custom convolutional neural network (CNN) with a pretrained VGG16 model-to improve the accuracy and reliability of AD diagnosis from neuroimaging data across multiple cognitive classes.</p><p><strong>Methods: </strong>A comprehensive preprocessing pipeline, including brain region segmentation, was implemented to isolate regions of interest and reduce noise. NeuroFusionNet extracts multilevel features by combining a custom CNN with VGG16, while Local Interpretable Model-Agnostic Explanations enhances interpretability. Data were obtained from the Alzheimer's Disease Neuroimaging Initiative database, comprising 600 test samples (120 per class for AD, cognitively normal, early mild cognitive impairment, late mild cognitive impairment, and mild cognitive impairment). Given the multiclass nature of the study, odds ratios were not applied. Statistical significance was assessed using the McNemar test for paired predictions.</p><p><strong>Results: </strong>NeuroFusionNet achieved an overall accuracy of 0.81 (95% CI 0.779-0.841; <i>P</i><.001). Per-class performance metrics were as follows: AD: precision 0.90 (95% CI 0.85-0.95), recall 0.78 (95% CI 0.72-0.84), <i>F</i> ₁-score 0.84; cognitively normal: precision 0.67 (95% CI 0.60-0.74), recall 0.97 (95% CI 0.94-1.00), <i>F</i> ₁-score 0.79; early mild cognitive impairment: precision 0.90 (95% CI 0.84-0.96), recall 0.82 (95% CI 0.76-0.88), <i>F</i> ₁-score 0.86; late mild cognitive impairment: precision 0.95 (95% CI 0.90-1.00), recall 0.87 (95% CI 0.81-0.93), <i>F</i> ₁-score 0.90; and mild cognitive impairment: precision 0.71 (95% CI 0.64-0.78), recall 0.61 (95% CI 0.53-0.69), <i>F</i> ₁-score 0.65. Training and validation curves over 50 epochs indicated robust learning with minimal overfitting.</p><p><strong>Conclusions: </strong>NeuroFusionNet demonstrated robust performance in a multiclass diagnostic setting, achieving high accuracy and balanced per-class performance. The combination of a custom CNN and fine-tuned VGG16, along with the interpretability provided by Local Interpretable Model-Agnostic Explanations, yields a reliable tool for early AD detection with significant potential to enhance clinical decision-making. Further validation on larger datasets is warranted.</p>","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"4 ","pages":"e68839"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Game-Based Mechatronic Device for Digital Rehabilitation of Hand Function After a Stroke: Design, Prototyping, and Feasibility Study.","authors":"Anuprita Kanitkar, Nariman Sepehri, Ariel Lezen, Sanjay Tejraj Parmar, Cherry Kit-Fong Hin, Tony Joseph Szturm","doi":"10.2196/67779","DOIUrl":"10.2196/67779","url":null,"abstract":"<p><strong>Background: </strong>This paper presents an easy-to-use, affordable robotic manipulandum device (RMD) equipped with smart monitoring and assistive technologies to engage in game-based exercise and repetitive task practice. The RMD has been designed to enhance a wide range of fine motor manual dexterity skills, including thumb, finger, and wrist movements. By focusing on finger and hand functions, it extends its utility beyond basic reaching or object transfer movements. Various interchangeable 3D-printed therapy handles of different shapes and sizes can be easily attached to the RMD drive shaft. These handle movements can be used to engage with numerous affordable, commercially available computer games, allowing patients to practice tasks that involve varying movement amplitudes, speeds, precision, and cognitive challenges. Additionally, the device is capable of automatically recording and storing the patient's real-time performance data on any given computer, integrating assessment into treatment.</p><p><strong>Objective: </strong>A pilot study was conducted with 5 patients with stroke to examine the feasibility and benefits of a 6-week game-based exercise program using the proposed device.</p><p><strong>Methods: </strong>A feasibility study was conducted with 5 participants. Data were collected using the computer game-based upper extremity assessment of manual dexterity and Wolf Motor Function Test (WMFT) before and after the intervention lasting 6 weeks.</p><p><strong>Results: </strong>The pilot study demonstrated that clients' expectations related to manual dexterity were met. The average improvement in the functional ability score of the WMFT was 14 (SD 3) points, with all participants exceeding the minimal clinically important difference. The average reduction in total time was 30 (SD 14) seconds, with 4 of 5 participants surpassing the minimal clinically important difference. For the computer game-based upper extremity assessment, the average improvement in success rate was 23% (SD 12%), and the average decrease in response time was 105 (SD 44) milliseconds.</p><p><strong>Conclusions: </strong>Findings revealed acceptable, engaging, game-based, and task-oriented training with a high level of compliance. Substantial improvements from pre- to postintervention were observed using the WMFT and assessments of manual dexterity.</p>","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"4 ","pages":"e67779"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Speech Biosignatures for Traumatic Brain Injury and Neurodegeneration: Pilot Machine Learning Study.","authors":"Rahmina Rubaiat, John Michael Templeton, Sandra L Schneider, Upeka De Silva, Samaneh Madanian, Christian Poellabauer","doi":"10.2196/64624","DOIUrl":"10.2196/64624","url":null,"abstract":"<p><strong>Background: </strong>Speech features are increasingly linked to neurodegenerative and mental health conditions, offering the potential for early detection and differentiation between disorders. As interest in speech analysis grows, distinguishing between conditions becomes critical for reliable diagnosis and assessment.</p><p><strong>Objective: </strong>This pilot study explores speech biosignatures in two distinct neurodegenerative conditions: (1) mild traumatic brain injuries (eg, concussions) and (2) Parkinson disease (PD) as the neurodegenerative condition.</p><p><strong>Methods: </strong>The study included speech samples from 235 participants (97 concussed and 94 age-matched healthy controls, 29 PD and 15 healthy controls) for the PaTaKa test and 239 participants (91 concussed and 104 healthy controls, 29 PD and 15 healthy controls) for the Sustained Vowel (/ah/) test. Age-matched healthy controls were used. Young age-matched controls were used for concussion and respective age-matched controls for neurodegenerative participants (15 healthy samples for both tests). Data augmentation with noise was applied to balance small datasets for neurodegenerative and healthy controls. Machine learning models (support vector machine, decision tree, random forest, and Extreme Gradient Boosting) were employed using 37 temporal and spectral speech features. A 5-fold stratified cross-validation was used to evaluate classification performance.</p><p><strong>Results: </strong>For the PaTaKa test, classifiers performed well, achieving <i>F</i> ₁-scores above 0.9 for concussed versus healthy and concussed versus neurodegenerative classifications across all models. Initial tests using the original dataset for neurodegenerative versus healthy classification yielded very poor results, with <i>F</i> ₁-scores below 0.2 and accuracy under 30% (eg, below 12 out of 44 correctly classified samples) across all models. This underscored the need for data augmentation, which significantly improved performance to 60%-70% (eg, 26-31 out of 44 samples) accuracy. In contrast, the Sustained Vowel test showed mixed results; <i>F</i> ₁-scores remained high (more than 0.85 across all models) for concussed versus neurodegenerative classifications but were significantly lower for concussed versus healthy (0.59-0.62) and neurodegenerative versus healthy (0.33-0.77), depending on the model.</p><p><strong>Conclusions: </strong>This study highlights the potential of speech features as biomarkers for neurodegenerative conditions. The PaTaKa test exhibited strong discriminative ability, especially for concussed versus neurodegenerative and concussed versus healthy tasks, whereas challenges remain for neurodegenerative versus healthy classification. These findings emphasize the need for further exploration of speech-based tools for differential diagnosis and early identification in neurodegenerative health.</p>","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"4 ","pages":"e64624"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming Perceptions: Exploring the Multifaceted Potential of Generative AI for People With Cognitive Disabilities.","authors":"Dorit Hadar Souval, Yuval Haber, Amir Tal, Tomer Simon, Tal Elyoseph, Zohar Elyoseph","doi":"10.2196/64182","DOIUrl":"10.2196/64182","url":null,"abstract":"<p><strong>Background: </strong>The emergence of generative artificial intelligence (GenAI) presents unprecedented opportunities to redefine conceptions of personhood and cognitive disability, potentially enhancing the inclusion and participation of individuals with cognitive disabilities in society.</p><p><strong>Objective: </strong>We aim to explore the transformative potential of GenAI in reshaping perceptions of cognitive disability, dismantling societal barriers, and promoting social participation for individuals with cognitive disabilities.</p><p><strong>Methods: </strong>This study is a critical review of current literature in disability studies, artificial intelligence (AI) ethics, and computer science, integrating insights from disability theories and the philosophy of technology. The analysis focused on 2 key aspects: GenAI as a social mirror reflecting societal values and biases, and GenAI as a cognitive partner for individuals with cognitive disabilities.</p><p><strong>Results: </strong>This paper proposes a theoretical framework for understanding the impact of GenAI on perceptions of cognitive disability. It introduces the concepts of GenAI as a \"social mirror\" that reflects and potentially amplifies societal biases and as a \"cognitive copilot\" providing personalized assistance in daily tasks, social interactions, and environmental navigation. This paper also presents a novel protocol for developing AI systems tailored to the needs of individuals with cognitive disabilities, emphasizing user involvement, ethical considerations, and the need to address both the opportunities and challenges posed by GenAI.</p><p><strong>Conclusions: </strong>Although GenAI has great potential for promoting the inclusion and empowerment of individuals with cognitive disabilities, realizing this potential requires a change in societal attitudes and development practices. This paper calls for interdisciplinary collaboration and close partnership with the disability community in the development and implementation of GenAI technologies. Realizing the potential of GenAI for promoting the inclusion and empowerment of individuals with cognitive disabilities requires a multifaceted approach. This involves a shift in societal attitudes, inclusive AI development practices that prioritize the needs and perspectives of the disability community, and ongoing interdisciplinary collaboration. This paper emphasizes the importance of proceeding with caution, recognizing the ethical complexities and potential risks alongside the transformative possibilities of GenAI technology.</p>","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"4 ","pages":"e64182"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13151972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of Study Procedures to Estimate Sensitivity and Reliability of a Virtual Physical Assessment Developed for Workplace Concussions: Method-Comparison Feasibility Study.","authors":"Keely Barnes, Heidi Sveistrup, Mark Bayley, Mary Egan, Martin Bilodeau, Michel Rathbone, Monica Taljaard, Motahareh Karimijashni, Shawn Marshall","doi":"10.2196/57661","DOIUrl":"10.2196/57661","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Remote approaches to workplace concussion assessment have demonstrated value to end users. The feasibility of administering physical concussion assessment measures in a remote context has been minimally explored, and there is limited information on important psychometric properties of physical assessment measures used in remote contexts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objectives of this feasibility study were to determine recruitment capability for a future larger-scale study aimed at determining sensitivity and reliability of the remote assessment, time required to complete study assessments, and acceptability of remote assessment to people with brain injuries and clinicians; document preliminary results of the sensitivity of the remote assessment when compared to the in-person assessment; and estimate the preliminary interrater and intrarater reliability of the remote assessments to inform procedures of a future larger-scale study that is adequately powered to reliably estimate these parameters of interest.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;People living with acquired brain injury attended 2 assessments (1 in-person and 1 remote) in a randomized order. The measures administered in these assessments included the finger-to-nose test; balance testing; and the Vestibular/Ocular Motor Screening (VOMS) tool, including documentation of change in symptoms and distance for near point convergence, saccades, cervical spine range of motion, and evaluation of effort. Both assessments occurred at the Ottawa Hospital Rehabilitation Center. After the assessments, a clinician different from the person who completed the original assessments then viewed and documented findings independently on the recordings of the remote assessment. The same second clinician viewed the recording again approximately 1 month following the initial observation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The rate of recruitment was 61% (20/33) of people approached, with a total of 20 patient-participants included in the feasibility study. A total of 3 clinicians participated as assessors. The length of time required to complete the in-person and remote assessment procedures averaged 9 and 13 minutes, respectively. The majority of clinicians and patient-participants agreed or strongly agreed that they were confident in the findings on both in-person and remote assessments. Feedback obtained revolved around technology (eg, screen size), lighting, and fatigue of participants in the second assessment. Preliminary estimates of sensitivity of the remote assessment ranged from poor (finger-to-nose testing: 0.0) to excellent (near point convergence: 1.0). Preliminary estimates of reliability of the remote assessment ranged from poor (balance testing, saccades, and range of motion: κ=0.38-0.49) to excellent (VOMS change in symptoms: κ=1.0).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The results of this feasibility study indicate that our study procedures are feasible and ac","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"3 ","pages":"e57661"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13151979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs.","authors":"Barak Gutman, Amit-Haim Shmilovitch, Dvir Aran, Shahar Shelly","doi":"10.2196/59556","DOIUrl":"10.2196/59556","url":null,"abstract":"<p><p>Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting up to 15% of the world population, with the burden of chronic neurodegenerative diseases having doubled over the last 2 decades. Two decades ago, neurologists relying solely on clinical signs and basic imaging faced challenges in diagnosis and treatment. Today, the integration of artificial intelligence (AI) and bioinformatic methods is changing this landscape. This paper explores this transformative journey, emphasizing the critical role of AI in neurology, aiming to integrate a multitude of methods and thereby enhance the field of neurology. Over the past 25 years, integrating biomedical data science into medicine, particularly neurology, has fundamentally transformed how we understand, diagnose, and treat neurological diseases. Advances in genomics sequencing, the introduction of new imaging methods, the discovery of novel molecular biomarkers for nervous system function, a comprehensive understanding of immunology and neuroimmunology shaping disease subtypes, and the advent of advanced electrophysiological recording methods, alongside the digitalization of medical records and the rise of AI, all led to an unparalleled surge in data within neurology. In addition, telemedicine and web-based interactive health platforms, accelerated by the COVID-19 pandemic, have become integral to neurology practice. The real-world impact of these advancements is evident, with AI-driven analysis of imaging and genetic data leading to earlier and more accurate diagnoses of conditions such as multiple sclerosis, Parkinson disease, amyotrophic lateral sclerosis, Alzheimer disease, and more. Neuroinformatics is the key component connecting all these advances. By harnessing the power of IT and computational methods to efficiently organize, analyze, and interpret vast datasets, we can extract meaningful insights from complex neurological data, contributing to a deeper understanding of the intricate workings of the brain. In this paper, we describe the large-scale datasets that have emerged in neurology over the last 25 years and showcase the major advancements made by integrating these datasets with advanced neuroinformatic approaches for the diagnosis and treatment of neurological disorders. We further discuss challenges in integrating AI into neurology, including ethical considerations in data use, the need for further personalization of treatment, and embracing new emerging technologies like quantum computing. These developments are shaping a future where neurological care is more precise, accessible, and tailored to individual patient needs. We believe further advancements in AI will bridge traditional medical disciplines and cutting-edge technology, navigating the complexities of neurological data and steering medicine toward a future of more precise, accessible, and patient-centric health care.</p>","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"3 ","pages":"e59556"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validity of a Smartphone App to Objectively Monitor Performance Outcomes in Degenerative Cervical Myelopathy: Preliminary Findings From a Longitudinal Observational Study.","authors":"Alvaro Yanez Touzet, Tatiana Houhou, Zerina Rahic, Ilya Laufer, Konstantinos Margetis, Allan R Martin, Nicolas Dea, Zoher Ghogawala, Misha Kapushesky, Mark R N Kotter, Benjamin M Davies","doi":"10.2196/52832","DOIUrl":"10.2196/52832","url":null,"abstract":"<p><strong>Background: </strong>Developing new clinical measures for degenerative cervical myelopathy (DCM) is an AO Spine RECODE-DCM research priority. Difficulties detecting DCM, and changes in DCM, cause diagnostic and treatment delays in clinical settings and heightened costs in clinical trials due to elevated recruitment targets. Digital outcome measures can tackle these challenges due to their ability to measure disease remotely, repeatedly, and more economically.</p><p><strong>Objective: </strong>The study aims to assess the validity of MoveMed, a battery of performance outcome measures performed using a smartphone app, in the measurement of DCM.</p><p><strong>Methods: </strong>A prospective observational study in decentralized secondary care was performed in England, United Kingdom. Validity and risk of bias were assessed using criteria from the COSMIN (Consensus-Based Standards for the Selection of Health Measurement Instruments) manual. Each MoveMed outcome was compared with 2 patient-reported comparators, with a priori hypotheses of convergence or divergence tested against consensus thresholds. The primary outcome was the correlation coefficient between the MoveMed outcome and the patient-reported comparators. The secondary outcome was the percentage of correlations that aligned with the a priori hypotheses. The comparators used were the patient-derived modified Japanese Orthopaedic Association score and the World Health Organization Quality of Life Brief Version questionnaire. Thresholds for convergence or divergence were set at ≥0.3 for convergence, <0.3 for divergence, and >0/<0 for directionality.</p><p><strong>Results: </strong>A total of 27 adults aged 60 (SD 11) years who live with DCM and possess an approved smartphone were included in a preliminary analysis. As expected, MoveMed tests of neuromuscular function correlated most with questionnaires of neuromuscular function (≥0.3) and least with questionnaires of quality of life (<0.3). Furthermore, directly related constructs correlated positively to each other (>0), while inversely related constructs correlated negatively (<0). Overall, 74% (67/90) and 47% (8/17) of correlations (unidimensional and multidimensional, respectively) were in accordance with hypotheses. No risk-of-bias factors from the COSMIN Risk of Bias checklist were recorded. Overall, this was equivalent to \"very good\" quality evidence of sufficient construct validity in DCM.</p><p><strong>Conclusions: </strong>MoveMed outcomes and patient-reported questionnaires converge and diverge in accordance with expectations. These findings support the validity of the MoveMed tests in an adult population living with DCM. Criteria from COSMIN provide \"very good\" quality evidence to support this.</p>","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"3 ","pages":"e52832"},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethics and Governance of Neurotechnology in Africa: Lessons From AI.","authors":"Damian Eke","doi":"10.2196/56665","DOIUrl":"10.2196/56665","url":null,"abstract":"<p><p>As a novel technology frontier, neurotechnology is revolutionizing our perceptions of the brain and nervous system. With growing private and public investments, a thriving ecosystem of direct-to-consumer neurotechnologies has also emerged. These technologies are increasingly being introduced in many parts of the world, including Africa. However, as the use of this technology expands, neuroethics and ethics of emerging technology scholars are bringing attention to the critical concerns it raises. These concerns are largely not new but are uniquely amplified by the novelty of technology. They include ethical and legal issues such as privacy, human rights, human identity, bias, autonomy, and safety, which are part of the artificial intelligence ethics discourse. Most importantly, there is an obvious lack of regulatory oversight and a dearth of literature on the consideration of contextual ethical principles in the design and application of neurotechnology in Africa. This paper highlights lessons African stakeholders need to learn from the ethics and governance of artificial intelligence to ensure the design of ethically responsible and socially acceptable neurotechnology in and for Africa.</p>","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"3 ","pages":"e56665"},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Role of the Autonomic Nervous System as a Driver of Sleep Quality in Patients With Multiple Sclerosis: Observation Study.","authors":"Max Moebus, Marc Hilty, Pietro Oldrati, Liliana Barrios, Christian Holz","doi":"10.2196/48148","DOIUrl":"10.2196/48148","url":null,"abstract":"<p><strong>Background: </strong>Low sleep quality is a common symptom of multiple sclerosis (MS) and substantially decreases patients' quality of life. The autonomic nervous system (ANS) is crucial to healthy sleep, and the transition from wake to sleep produces the largest shift in autonomic activity we experience every day. For patients with MS, the ANS is often impaired. The relationship between the ANS and perceived sleep quality in patients with MS remains elusive.</p><p><strong>Objective: </strong>In this study, we aim to quantify the impact of the ANS and MS on perceived sleep quality.</p><p><strong>Methods: </strong>We monitored 77 participants over 2 weeks using an arm-worn wearable sensor and a custom smartphone app. Besides recording daily perceived sleep quality, we continuously recorded participants' heart rate (HR) and HR variability on a per-second basis, as well as stress, activity, and the weather (20,700 hours of sensor data).</p><p><strong>Results: </strong>During sleep, we found that reduced HR variability and increased motion led to lower perceived sleep quality in patients with MS (n=53) as well as the age- and gender-matched control group (n=24). An activated stress response (high sympathetic activity and low parasympathetic activity) while asleep resulted in lower perceived sleep quality. For patients with MS, an activated stress response while asleep reduced perceived sleep quality more heavily than in the control group. Similarly, the effect of increased stress levels throughout the day is particularly severe for patients with MS. For patients with MS, we found that stress correlated negatively with minimal observed HR while asleep and might even affect their daily routine. We found that patients with MS with more severe impairments generally recorded lower perceived sleep quality than patients with MS with less severe disease progression.</p><p><strong>Conclusions: </strong>For patients with MS, stress throughout the day and an activated stress response while asleep play a crucial role in determining sleep quality, whereas this is less important for healthy individuals. Besides ensuring an adequate sleep duration, patients with MS might thus work to reduce stressors, which seem to have a particularly negative effect on sleep quality. Generally, however, sleep quality decreases with MS disease progression.</p>","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"3 ","pages":"e48148"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technology-Enabled Recreation and Leisure Programs and Activities for Older Adults With Cognitive Impairment: Rapid Scoping Review.","authors":"Kristina Marie Kokorelias, Josephine McMurray, Charlene Chu, Arlene Astell, Alisa Grigorovich, Pia Kontos, Jessica Babineau, Jessica Bytautas, Ashley Ahuja, Andrea Iaboni","doi":"10.2196/53038","DOIUrl":"10.2196/53038","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Recreational and leisure activities significantly contribute to the well-being of older adults, positively impacting physical, cognitive, and mental health. However, limited mobility and cognitive decline often impede access to these activities, particularly for individuals living with dementia. With the increasing availability of digital technologies, there is a rising interest in using technology to deliver recreation and leisure activities for cognitively impaired individuals, acknowledging its potential to provide diverse experiences. The COVID-19 pandemic further highlighted the need for virtual program delivery, especially for individuals in long-term care settings, leading to the development of tools like the Dementia Isolation Toolkit aimed at supporting compassionate isolation. To better support future implementations of the DIT, our rapid scoping review explores evidence-based, technology-enabled recreation programs for older adults with cognitive impairments, which promote well-being.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We conducted a rapid scoping review of published peer-reviewed literature to answer the following research question: What recreation and leisure programs or activities are being delivered using technology to adults living with dementia or another form of cognitive impairment?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In total, 6 databases were searched by an Information Specialist. Single reviewers performed title or abstract review, full-text screening, data extraction, and study characteristic summarization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 92 documents representing 94 studies were identified. The review identified a variety of technology-enabled delivery methods, including robots, gaming consoles, tablets, televisions, and computers, used to engage participants in recreational and leisure activities. These technologies impacted mood, cognition, functional activity, and overall well-being among older adults with cognitive impairments. Activities for socializing were the most common, leveraging technologies such as social robots and virtual companions, while relaxation methods used virtual reality and digital reminiscence therapy. However, challenges included technological complexity and potential distress during reminiscing activities, prompting recommendations for diversified research settings, and increased sample sizes to comprehensively understand technology's impact on leisure among this demographic.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings suggest that technology-enabled recreational activities, such as socializing, relaxation and self-awareness activities, music and dance, exergaming, and art, can positively impact the mood and overall well-being of older adults with cognitive impairment. Future research should embrace a more inclusive approach, integrating design, diverse settings, and a broader sample of older adults to develop technology-driven leisure activities tailore","PeriodicalId":73555,"journal":{"name":"JMIR neurotechnology","volume":"3 ","pages":"e53038"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}