Equity implications of extended reality technologies for health and procedural anxiety: a systematic review and implementation-focused framework

OBJECTIVES

Extended reality (XR) applications are gaining support as a method of reducing anxieties about medical treatments and conditions; however, their impacts on health service inequalities remain underresearched. We therefore undertook a synthesis of evidence relating to the equity implications of these types of interventions.

MATERIALS AND METHODS

Searches of MEDLINE, Embase, APA PsycINFO, and Epistemonikos were conducted in May 2023 to identify reviews of patient-directed XR interventions for health and procedural anxiety. Equity-relevant data were extracted from records (n = 56) that met these criteria, and from individual trials (n = 63) evaluated within 5 priority reviews. Analyses deductively categorized data into salient situation- and technology-related mechanisms, which were then developed into a novel implementation-focused framework.

RESULTS

Analyses highlighted various mechanisms that impact on the availability, accessibility, and/or acceptability of services aiming to reduce patient health and procedural anxieties. On one hand, results showed that XR solutions offer unique opportunities for addressing health inequities, especially those concerning transport, cost, or mobility barriers. At the same time, however, these interventions can accelerate areas of inequity or even engender additional disparities.

DISCUSSION

Our "double jeopardy, common impact" framework outlines unique pathways through which XR could help address health disparities, but also accelerate or even generate inequity across different systems, communities, and individuals. This framework can be used to guide prospective interventions and assessments.

CONCLUSION

Despite growing positive assertions about XR's capabilities for managing patient anxieties, we emphasize the need for taking a cautious, inclusive approach to implementation in future programs.

Apple watch accuracy in monitoring health metrics: a systematic review and meta-analysis

Objective. Wearable technology like the Apple Watch is increasingly important for monitoring health metrics. Accurate measurement is crucial, as inaccuracies can impact health outcomes. Despite extensive research, findings on the Apple Watch's accuracy vary across different conditions. While previous reviews have summarized findings, few have utilized a meta-analytic approach. This study aims to quantitatively evaluate the accuracy of the Apple Watch in measuring health metrics. The accuracy of the Apple Watch was assessed in measuring energy expenditure (EE), heart rate (HR), and step counts (steps).Approach. We searched Embase, PubMed, Scopus, and SPORTDiscus for studies on adults using the Apple Watch compared to reference measures. The Bland-Altman framework was applied to assess mean bias and limits of agreement (LoA), with robust variance estimation to address within-study correlations. Heterogeneity was assessed across variables such as age, health status, device series, activity intensity, and activity type. Additionally, the mean absolute percentage error (MAPE) reported in the included studies was summarized by subgroups.Main results. This review included 56 studies, comprising 270 effect sizes on EE (71), HR (148), and steps (51). The meta-analysis showed a mean bias of 0.30 (LoA: -2.09-2.69) for EE (kcal min-1), -0.12 (LoA: -11.06-10.81) for HR (beats min-1), -1.83 (LoA: -9.08-5.41) for steps (steps min-1). The forest plots showed variability in LoA across subgroups. For MAPE, all subgroups for EE exceeded the 10% validity threshold, while none of the subgroups for HR exceeded this threshold. For steps, some subgroups exceeded 10%, highlighting variability in accuracy based on different conditions.Significance. This study demonstrates that while the Apple Watch generally provides accurate HR and step measurements, its accuracy for EE is limited. Although HR and step measurements showed acceptable accuracy, variability was observed across different user characteristics and measurement conditions. These findings highlight the importance of considering such factors when evaluating validity.

Cancer patients' acceptance of virtual reality interventions for self-emotion regulation

This study investigates the acceptability of Virtual reality (VR) technology for emotional regulation among cancer patients. Drawing from extensive literature, we enhanced external variables across user characteristics, product impact factors, and social environment influences, creating the "Theoretical Model of Cancer Patients' Acceptance of VR Intervention for Self-Emotion Regulation." Surveying 489 Chinese cancer patients validated the model's strong reliability through SPSS AMOS analysis. The acceptance of VR intervention for self-emotional regulation among cancer patients was assessed, revealing that the average scores across all 13 dimensions exceeded 3. This indicates that cancer patients hold a positive attitude toward VR-based emotional regulation interventions. Perceived usefulness, usage attitude, social norms, immersion, and personal innovation correlated positively with behavioral intention, while technological anxiety and perceived risk showed negative correlations. Findings support 15 hypotheses, offering theoretical backing for VR technology in emotional regulation for cancer patients. These insights provide medical institutions with valuable data on patient attitudes, facilitating the development of targeted treatment approaches.

The Effectiveness of Telerehabilitation in Managing Pain, Strength, and Balance in Adult Patients With Knee Osteoarthritis: Systematic Review

BACKGROUND

Knee osteoarthritis (KOA) is a chronic, degenerative joint disease characterized by pain, stiffness, and functional impairment, significantly affecting mobility and quality of life. Traditional rehabilitation, mainly through in-person physiotherapy, is widely recommended for KOA management. However, access to these services is often limited due to geographic, financial, and mobility constraints. Telerehabilitation has emerged as an alternative, providing remote rehabilitation through digital platforms. Despite its increasing adoption, its effectiveness in improving key functional parameters such as pain, strength, and balance remains uncertain. While previous studies have focused primarily on pain relief and overall functional improvement, a broader assessment of its impact on mobility and fall prevention is needed.

OBJECTIVE

This systematic review examines the effectiveness of telerehabilitation in improving pain, strength, and balance in adults with KOA compared with traditional rehabilitation or no intervention. In addition, it evaluates the impact of different telerehabilitation models, such as therapist-guided versus self-managed programs, and explores the feasibility of integrating telerehabilitation as an alternative in KOA management.

METHODS

A systematic search of 4 databases (PubMed, PEDro, Cochrane, and Scopus) was conducted to identify randomized controlled trials (RCTs) published from May 2004 to May 2024. Inclusion criteria consisted of adults with KOA, evaluation of telerehabilitation either as a stand-alone intervention or in comparison to traditional rehabilitation or no intervention, and measurement of at least one primary outcome (pain, strength, or balance). A total of 2 independent reviewers assessed the risk of bias using validated tools. Due to variations in intervention programs and assessment methods, a narrative synthesis was performed instead of a meta-analysis. The review followed established guidelines, and data extraction was conducted using appropriate software.

RESULTS

A total of 6 RCTs (N=581 participants) met the inclusion criteria. The results indicate that telerehabilitation effectively reduces pain and improves strength and balance, although the extent of benefits varies. Some studies reported similar pain reductions between telerehabilitation and traditional rehabilitation, while others highlighted greater functional improvements in telerehabilitation groups. Therapist-guided telerehabilitation was associated with higher adherence rates and better functional outcomes compared with self-managed programs. The risk of bias assessment showed that most studies were of moderate to good quality, though common issues included selection bias, performance bias, and participant attrition.

CONCLUSIONS

Telerehabilitation is a promising alternative for KOA management, especially for individuals facing barriers to in-person therapy. It is effective in reducing pain and improving strength and balance, though its success depends on patient engagement, intervention delivery, and rehabilitation protocols. Therapist-guided programs yield better outcomes than self-managed approaches. Further research is needed to standardize intervention protocols, integrate emerging technologies, and evaluate cost-effectiveness to guide clinical practice and health care policies.

TRIAL REGISTRATION

PROSPERO CRD42024564141; https://tinyurl.com/25ykvy7d.

Global landscape and hotspot analysis of meditation research in cancer: a bibliometric study

PURPOSE

Meditation is well known for its positive effects on recovery and quality of life enhancement among cancer patients. Meditation as an adjuvant therapy has received extensive attention from international scholars in relieving pain, reducing psychological pressure and improving the quality of life of cancer patients. In this study, we examine the current status of meditation in cancer research and its potential application value and future development.

METHODS

We collected 825 articles published in the Web of science Core Collection between January 1, 1976, and July 1, 2024, covering 11 cancer types. Bibliometric tools such as VOSviewer, Citespace, and Biblioshiny were used to analyze publication trends, international collaborations, author contributions, keywords, co-citations, and journal impact.

RESULTS

First, the steadily rising number of publications indicates an increasing scholarly focus on meditation's benefits for patients. Second, the USA, Australia, and China are the countries with the highest number of publications in each of the three clusters. Additionally, Carlson Linda E and eight other scholars are influential scholars in this field. Finally, through keyword co-occurrence and co-citation analysis, we identified "breast cancer," "quality of life," and "psychological intervention" as the hot topics of current research.

CONCLUSIONS

The study provides a valuable reference for scientific researchers to further explore meditation in cancer treatment.

IMPLICATIONS FOR CANCER SURVIVORS

This study highlights the growing interest in meditation as an adjuvant therapy for cancer patients, underscoring its potential to improve survivors' quality of life. Current research primarily focuses on quality of life, mindfulness-based stress reduction therapy, and clinical trials. Additionally, online, virtual reality technology, cancer survivors, fear of cancer recurrence, and qualitative research may become cutting-edge research directions in the future.

Telehealth Strategies in Arthritis Chronic Pain Management: Bibliometric Analysis of Two Decades of Research and Innovations

Background: Arthritis, characterized by joint inflammation, pain, and impaired daily activities, has seen a rapid increase globally. Telehealth has emerged as a transformative approach in managing chronic diseases, including arthritis, by overcoming barriers such as geographic limitations and high costs. Objectives: The primary objectives of this study were to conduct a comprehensive bibliometric analysis of telehealth in arthritis pain management over the past two decades, examine publication trends, citation patterns, and keyword co-occurrences related to telehealth strategies in arthritis management, identify key research areas, influential works, and emerging themes within the field. Methods: A comprehensive search was conducted in the Scopus database for articles related to telehealth in arthritis. A systematic screening process, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, was adopted. Bibliometric analysis was used for keyword analysis, citation analysis, and research trends. Results: The bibliometric analysis revealed significant trends in telehealth research for arthritis pain management. A sharp increase in publications was observed from 2020 onwards, coinciding with advancements in digital health technologies and the COVID-19 pandemic. Frequently occurring keywords included "telemedicine," "telehealth," "digital health," "m-health," and "telerehabilitation." The top cited articles primarily explored the efficacy of telerehabilitation in managing postsurgical recovery and chronic knee pain. Emerging themes indicated an increased focus on mobile applications, digital health solutions, and patient-centered care. Conclusion: Telehealth has evolved from a novel concept to a mainstream solution in managing arthritis, driven by technological advancements and the necessity for accessible and cost-effective care.

A prospective controlled study of a wearable rhythmic vibrotactile device for tremor in Parkinson's disease

OBJECTIVE

Tremor in Parkinson's disease (PD) does not always respond to dopaminergic medication, therefore new treatment strategies are needed. Preliminary evidence has suggested that manipulation of peripheral afferents may reduce tremor amplitude, but existing research is inconclusive and has not been properly controlled. Here, we explored the effects of peripheral vibrotactile stimulation (ViS) on PD tremor using a within-subjects controlled design.

METHODS

Thirty PD patients with clear tremor were included. ViS (open-loop) was applied to the most affected wrist. Four stimulation conditions were compared: tremor frequency (TF), 1.5*TF, 80 Hz stimulation, and sham. We tested the effect of these stimulation conditions on tremor power (measured with accelerometry) during three contexts: rest tremor, rest tremor during cognitive load, and postural tremor. Entrainment between tremor and stimulation was tested using complex phase-locking value (PLV).

RESULTS

There were no significant effects on tremor power when ViS was applied. Stimulation effects did not depend on the context in which tremor occurred. PLVs showed that tremor phase was not influenced by ViS.

CONCLUSIONS

Open-loop ViS does not modulate PD tremor.

SIGNIFICANCE

This study is one of the first controlled large sample studies to investigate how ViS may influence the objective measures of tremor in PD.

The future of artificial intelligence in cardiovascular monitoring

PURPOSE OF REVIEW

Cardiovascular monitoring is essential for managing hemodynamic instability and preventing complications in critically ill patients. Conventional monitoring approaches are limited by predefined thresholds, dependence on clinician expertise, and a lack of adaptability to individual patients. The aim of this review is to explore recent findings about the use of artificial intelligence (AI) in cardiovascular monitoring.

RECENT FINDINGS

AI has the potential to transform monitoring in critical care through the automated real-time analysis of extensive, high-resolution datasets, and can facilitate early detection of patient deterioration, minimize false alarms, and support patient clustering for tailored therapeutic strategies. These innovations facilitate a shift toward precision medicine, tailoring treatments based on physiological and temporal data patterns. Moreover, wearable devices can further enhance real-time patient surveillance and risk stratification, extending intensivist monitoring beyond the ICU. Despite advantages, challenges persist, including algorithm generalizability, issues with patient consent and data privacy, and the current lack of external validation. Overcoming these barriers is essential for realizing the full potential of AI in critical care and hemodynamic monitoring.

SUMMARY

The integration of continuous high-resolution monitoring with AI real-time applications has the potential to transform hemodynamic assessment, enhance clinical decision-making, and improve safety and clinical outcomes.

Proceedings from The Consortium for Technology & Innovation in Pediatrics (CTIP) 2024 Annual Pediatric Device Innovation Symposium

On August 9, 2024, the CTIP symposium brought together various stakeholders in pediatric medical device (PMD) innovation to discuss the current state of pediatric medical devices (PMDs) and action steps that can collectively be taken to further drive PMD innovation. Meeting topics included 1) the Future of Pediatric Innovation, 2) Engaging Patients and Their Families in PMD Development, 3) Partnership Opportunities to Support PMD Research and Development (R&D), 4) Leveraging Real-World Evidence to Enhance PMDs, and 5) Fundraising and Investing in Pediatrics. This paper provides a comprehensive summary of the symposium proceedings, highlighting the critical needs, challenges, and opportunities in the PMD sector, and outlines potential areas for collaboration among stakeholders to drive progress in PMD development.

Wrist-Worn and Arm-Worn Wearables for Monitoring Heart Rate During Sedentary and Light-to-Vigorous Physical Activities: Device Validation Study

Background

Heart rate (HR) is a vital physiological parameter, serving as an indicator of homeostasis and a key metric for monitoring cardiovascular health and physiological responses. Wearable devices using photoplethysmography (PPG) technology provide noninvasive HR monitoring in real-life settings, but their performance may vary due to factors such as wearing position, blood flow, motion, and device updates. Therefore, ongoing validation of their accuracy and reliability across different activities is essential.

objectives

This study aimed to assess the accuracy and reliability of the HR measurement from the PPG-based Polar Verity Sense and the Polar Vantage V2 devices across a range of physical activities and intensities as well as wearing positions (ie, upper arm, forearm, and both wrists).

Methods

Sixteen healthy participants were recruited to participate in this study protocol, which involved 9 activities of varying intensities, ranging from lying down to high-intensity interval training, each repeated twice. The HR measurements from the Verity Sense and Vantage V2 were compared with the criterion measure Polar H10 electrocardiogram (ECG) chest strap. The data were processed to eliminate artifacts and outliers. Accuracy and reliability were assessed using multiple statistical methods, including systematic bias (mean of differences), mean absolute error (MAE) and mean absolute percentage error (MAPE), Pearson product moment correlation coefficient (r), Lin concordance correlation coefficient (CCC), and within-subject coefficient of variation (WSCV).

Results

All 16 participants (female=7; male=9; mean 27.4, SD 5.8 years) completed the study. The Verity Sense, worn on the upper arm, demonstrated excellent accuracy across most activities, with a systematic bias of -0.05 bpm, MAE of 1.43 bpm, MAPE of 1.35%, r=1.00, and CCC=1.00. It also demonstrated high reliability across all activities with a WSCV of 2.57% and no significant differences between the 2 sessions. The wrist-worn Vantage V2 demonstrated moderate accuracy with a slight overestimation compared with the ECG and considerable variation in accuracy depending on the activity. For the nondominant wrist, it demonstrated a systematic bias of 2.56 bpm, MAE of 6.41 bpm, MAPE 6.82%, r=0.93, and CCC=0.92. Reliability varied considerably, ranging from a WSCV of 3.64% during postexercise sitting to 23.03% during lying down.

Conclusions

The Verity Sense was found to be highly accurate and reliable, outperforming many other wearable HR devices and establishing itself as a strong alternative to ECG-based chest straps, especially when worn on the upper arm. The Vantage V2 was found to have moderate accuracy, with performance highly dependent on activity type and intensity. While it exhibited greater variability and limitations at lower HR, it performed better at higher intensities and outperformed several wrist-worn devices from previous research, particularly during vigorous activities. These findings highlight the importance of device selection and wearing position to ensure the highest possible accuracy in the intended context.

Inferring forced expiratory volume in 1 second (FEV1) from mobile ECG signals collected during quiet breathing

Objective.Forced expiratory volume in one second (FEV1) is an important metric for patients to track at home for their self-management of asthma and chronic obstructive pulmonary disease (COPD). Unfortunately, the state-of-the-art for measuring FEV1 at home either depends on the patient's physical effort and motivation, or relies on bulky wearable devices that are impractical for long-term monitoring. This paper explores the feasibility of using a machine learning model to infer FEV1 from 270 seconds of a single-lead electrocardiogram (ECG) signal measured on the fingers with a mobile device.Methods.We evaluated the model's inferred FEV1 values against the ground truth of hospital-grade spirometry tests, which were performed by twenty-five patients with obstructive respiratory disease.Results.The model-inferred FEV1 compared to the spirometry-measured FEV1 with a correlation coefficient ofr = 0.73, a mean absolute percentage error of 23% and a bias of -0.08.Conclusions.These results suggest that the ECG signal contains useful information about FEV1, although a larger, richer dataset might be necessary to train a machine learning model that can extract this information with better accuracy.Significance.The benefit of a mobile ECG-based solution for measuring FEV1 is that it would require minimal effort, thus encouraging patient adherence and promoting successful self-management of asthma and COPD.

Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms

In recent years, diabetes has become a global public health problem, and it is reported that the migrant Indians have more prevalence rate of Type-II diabetes. Also, the type-II diabetes in Indians are increased to a large extent due to modern lifestyle, food habits etc. In this work, an ElectroGastroGram (EGG) based non-invasive assessment for early prediction of type-II diabetes is proposed. Furthermore, the EGG signals are acquired from normal individuals and people with an age group between 50 and 65 who are suffering from Type-II diabetes using three electrode EGG acquisition devices. Also, the Explainable Artificial Intelligence (XAI) especially SHapley Additive exPlanations (SHAP) and Meta-Heuristics based feature selection methods are utilized to determine the prominent EGG signal features. A framework is devised using Meta-Heuristic based Hybrid Extreme Gradient (MH-XGB) Boost Classifier for an efficient classification of normal EGG signals and diabetic EGG signals. The proposed MH-XGB classifier is compared with the benchmark models namely Random Forest (RF) classifier and conventional Extreme Gradient Boosting (XGBoost) classifier by using performance metrics. Results demonstrate that the proposed MH-XGB classifier exhibits accuracy, sensitivity, specificity of 95.8%, 100%, and 92.3% respectively which is superior to other benchmark models. Additionally, it is demonstrated that the AUC, F1 Score and False Positive Rate (FPR) of the proposed MH-XGB classifier is 0.9545, 0.96 and 0.077 respectively. The proposed method is highly useful for early prediction of real-time societal disease (diabetes-Type-II) in an effective manner.

Sleep-wake variations of electrodermal activity in bipolar disorder

BACKGROUND

Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities.

METHODS

We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates.

RESULTS

Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not.

CONCLUSION

EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Technological interventions for the suppression of hand tremors: A literature review

A tremor is a neurological disorder that results in trembling or shaking in one or more body parts. A thorough literature review was conducted to investigate the methods for suppressing tremors. We looked for articles published between 1995 and 2024 in the databases CINAHL (Cumulative Index to Nursing and Allied Health Literature), PubMed, Medline, Embase, Scopus, and Cochrane. Two thousand two hundred fifty distinct items were discovered after an extensive search. Based only on the title, 250 were included. Two hundred papers were deemed ineligible after the abstracts were assessed. The remaining 26 articles were shortlisted after screening titles and abstracts and categorized based on treatment methods for hand tremors. According to the study's findings, deep brain stimulation (DBS) and electrical stimulation both reduced tremors considerably. It was also evident that attenuation systems and passive devices lessen the effects of tremors; target tracking tasks can lessen physiological tremors in postural posture; ET may have better hand functions after cold water treatment than warm water or at baseline; and targeted ultrasound thalamotomy is an effective treatment for ET, as it improved quality of life (QoL) significantly. Additionally, the design, development, and evaluation of wearable devices and pharmaceutical interventions for tremor suppression were investigated in detail. The main objective was to perform a comparative analysis of the merits and demerits of both treatment methodologies in terms of functional outcomes, users' comfort, and side effects. The review highlights wearable devices as a beneficial option for tremor suppression, offering comfort, safety, and advanced technology over pharmaceutical intervention methodologies.

Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children

The analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child's well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods. This highlights the necessity for automated tools to identify anomalous repetitive hand movements and behavioral transition patterns in children. Our study aimed to develop an automated model for the early identification of anomalous repetitive hand movements and the detection of unique behavioral patterns. Utilizing autoencoders, self-similarity matrices, and unsupervised clustering algorithms, we analyzed skeleton and image-based features, repetition count, and frequency of repetitive child hand movements. This approach aimed to distinguish between typical and atypical repetitive hand movements of varying speeds, addressing data limitations through dimension reduction. Additionally, we aimed to categorize behaviors into clusters beyond binary classification. Through experimentation on three datasets (Hand Movements in Wild, Updated Self-Stimulatory Behaviours, Autism Spectrum Disorder), our model effectively differentiated between typical and atypical hand movements, providing insights into behavioral transitional patterns. This aids the medical community in understanding the evolving behaviors in children. In conclusion, our research addresses the need for early detection of atypical behaviors through an automated model capable of discerning repetitive hand movement patterns. This innovation contributes to early intervention strategies for neurological conditions.

Next-Generation Cognitive-Behavioral Therapy for Depression: Integrating Digital Tools, Teletherapy, and Personalization for Enhanced Mental Health Outcomes

Background and Objectives: This systematic review aims to present the latest developments in next-generation CBT interventions of digital support tools, teletherapies, and personalized treatment modules in enhancing accessibility, improving treatment adherence, and optimizing therapeutic outcomes for depression. Materials and Methods: This review analyzed 81 PRISMA-guided studies on the efficacy, feasibility, and applicability of NG-CBT approaches. Other important innovations include web-based interventions, AI-operated chatbots, and teletherapy platforms, each of which serves as a critical challenge in delivering mental health care. Key messages have emerged regarding technological readiness, patient engagement, and the changing role of therapists within the digital context of care. Results: Findings indicate that NG-CBT interventions improve treatment accessibility and engagement while maintaining clinical effectiveness. Personalized digital tools enhance adherence, and teletherapy platforms provide scalable and cost-effective alternatives to traditional therapy. Conclusions: Such developments promise great avenues for decreasing the global burden of depression and enhancing the quality of life through novel, accessible, and high-quality therapeutic approaches.

Reward Feedback Mechanism in Virtual Reality Serious Games in Interventions for Children With Attention Deficits: Pre- and Posttest Experimental Control Group Study

BACKGROUND

Virtual reality (VR) serious games, due to their high level of freedom and realism, influence the rehabilitation training of inhibitory control abilities in children with attention-deficit/hyperactivity disorder (ADHD). Although reward feedback has a motivating effect on improving inhibitory control, the effectiveness and differences between various forms of rewards lack empirical research.

OBJECTIVE

This study aimed to investigate the effectiveness of different forms of reward feedback on the inhibitory control abilities of children with attention deficits in a VR serious game environment.

METHODS

This study focuses on children who meet the diagnostic criteria for ADHD tendencies, using a 2 (material rewards: coin reward and token reward) × 2 (psychological rewards: verbal encouragement and badge reward) factorial between-subject design (N=84), with a control group (n=15) for pre- and posttest experiments. The experimental group received VR feedback reinforcement training, while the control group underwent conventional VR training without feedback. The training period lasted 0.5 months, with each intervention session lasting 25 minutes, occurring twice daily with an interval of at least 5 hours for 28 sessions. Before and after training, the Swanson, Nolan, and Pelham, Version IV Scale (SNAP-IV) Scale, stop signal task, inhibition conflict task, and Simon task were administered to assess the hyperactivity index and the 3 components of inhibitory control ability. The pretest included the SNAP-IV Scale and 3 task tests to obtain baseline data; the posttest involved repeating the above tests after completing all training. Data were entered and analyzed using SPSS (IBM) software. Independent sample t tests were performed on the experimental and control groups' pre- and posttest task results to determine whether significant differences existed between group means. Paired sample t tests were also conducted on the SNAP-IV Scale's pre- and posttest results to assess the intervention effect's significance.

RESULTS

Reward feedback was more effective than no reward feedback in improving behaviors related to attention deficits in children. Material rewards showed significant effects in the Stop-Signal Task (F1=13.04, P=.001), Inhibition Conflict Task (F1=7.34, P=.008), and SNAP-IV test (F1=69.23, P<.001); mental rewards showed significant effects in the Stop-Signal Task (F1=38.54, P<.001) and SNAP-IV test (F1=70.78, P<.001); the interaction between the 2 showed significant effects in the Stop-Signal Task (F1=4.47, P=.04) and SNAP-IV test (F1=23.85, P<.001).

CONCLUSIONS

Combining material and psychological rewards within a VR platform can effectively improve attention-deficit behaviors in children with ADHD, enhancing their inhibitory control abilities. Among these, coin rewards are more effective than token rewards, and verbal encouragement outperforms badge rewards. The combined feedback of coin rewards and verbal encouragement yields the most significant improvement in inhibitory control abilities.

A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life

In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device-skin coupling conditions without positioning or skin preparation requirements. A printed textile-based strain sensor array responds to strain from 0.1 to 10% with a gauge factor as high as 100 and shows independence to extrinsic motion artifacts via strain-isolating printed pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled to achieve batch-to-batch performance variation <10%. Coupled with deep learning, explainable AI, and transfer learning data processing, the smart garment is capable of classifying six sleep states with an accuracy of 98.6%, maintaining excellent explainability (classification with low bias) and generalization (95% accuracy on new users with few-shot learning less than 15 samples per class) in practical applications, paving the way for next-generation daily sleep healthcare management.

Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders

This paper explores the progressive era of sleep monitoring, focusing on wearable and remote devices contributing to advances in the concept of home polysomnography. We begin by exploring the basic physiology of sleep, establishing a theoretical basis for understanding sleep stages and associated changes in physiological variables. The review then moves on to an analysis of specific cutting-edge devices and technologies, with an emphasis on their practical applications, user comfort, and accuracy. Attention is also given to the ability of these devices to predict neurological disorders, particularly Alzheimer's and Parkinson's disease. The paper highlights the integration of hardware innovations, targeted sleep parameters, and partially advanced algorithms, illustrating how these elements converge to provide reliable sleep health information. By bridging the gap between clinical diagnosis and real-world applicability, this review aims to elucidate the role of modern sleep monitoring tools in improving personalised healthcare and proactive disease management.

Explainable machine learning model for predicting decline in platelet count after interventional closure in children with patent ductus arteriosus

Background

This study aimed to apply four machine learning algorithms to develop the optimal model to predict decline in platelet count (DPC) after interventional closure in children with patent ductus arteriosus (PDA).

Methods

Data from children with PDA who underwent successful transcatheter closure at the Second Affiliated Hospital of Wenzhou Medical University and Yuying Children's Hospital from January 2016, to December 2022, were collected. The cohort data were split into training and testing sets. DPC following the intervention is defined as a percentage DPC ≥25% [(baseline platelet count-nadir platelet count)/baseline platelet count]. The extra tree algorithm was used for feature selection and four ML algorithms [random forest (RF), adaptive boosting, extreme gradient boosting, and logistic regression] were established. Moreover, SHapley Additive exPlanation (SHAP) to explain the importance of features and the ML models.

Results

This study included 330 children who underwent successful transcatheter closure of PDA, of which 113 (34.2%) experienced DPC. After 62 clinical features were considered, the extra tree algorithm selected six clinical features to build the ML models. Amongst the four ML algorithms, the RF model achieved the greatest AUC. SHAP analysis revealed that pulmonary artery systolic pressure, size of defect and weight were the top three most important clinical features in the RF model. Furthermore, clinical descriptions of two children with PDA, with accurate predictions, and explanations of the prediction results were provided.

Conclusion

In this study, an ML model (RF) capable of predicting post-intervention DPC in children with PDA undergoing transcatheter closure was established.

The Efficacy and Patient Experience of Virtual Reality in Labor: An Integrative Review of Pain and Anxiety Management

BACKGROUND

Labor pain and anxiety are significant challenges in maternal healthcare, often managed through pharmacological interventions. Virtual Reality (VR), as a non- pharmacological method, has emerged as a potential tool for pain and anxiety relief in labor. This integrative review aims to synthesize evidence from randomized controlled trials (RCTs), qualitative studies, and mixed-methods research to evaluate the effectiveness of VR in labor pain and anxiety management and to understand patient experiences.

METHODS

Adhering to the PRISMA guidelines, a structured literature search was conducted across databases, including PsycINFO, CINAHL, and PubMed, yielding 1,227 studies. Following a meticulous screening and selection process by authors, 13 studies (10 RCTs, 2 qualitative, and 1 mixed methods) met the inclusion criteria. Data extraction focused on study design, population characteristics, VR interventions, outcomes measured, and key findings, with a content analysis approach employed for thematic synthesis.

RESULTS

The RCTs consistently showed VR's efficacy in reducing labor pain and, to some extent, anxiety. Qualitative studies highlighted VR's role in enhancing patient experiences, offering distraction, relaxation, and improved self-efficacy in pain management. The integration of findings from quantitative and qualitative studies provided a comprehensive understanding of VR's effectiveness and acceptability in labor. Notable themes included the importance of VR's immersive nature and its potential to reduce reliance on pharmacological interventions.

CONCLUSION

VR emerges as a promising tool for managing labor pain and anxiety, offering a non-invasive and patient-friendly alternative to traditional pain relief methods. Its implementation in clinical practice could enhance patient satisfaction and overall birthing experiences. However, further research is needed to standardize VR interventions, assess long-term effects, and determine cost-effectiveness. The findings encourage the consideration of VR as part of holistic maternal care, emphasizing the need to integrate patient-centered healthcare technologies.

Impact of Virtual Reality, Augmented Reality, and Sensor Technology in Knee Osteoarthritis Rehabilitation: A Systematic Review

Knee osteoarthritis (KOA) is a progressive degenerative joint disorder that significantly impacts mobility, pain levels, and overall quality of life. Conventional rehabilitation methods, while effective, often suffer from limitations related to patient adherence, accessibility, and cost. This systematic review examines the role of virtual reality (VR), augmented reality (AR), and sensor-based technologies in KOA rehabilitation, evaluating their effectiveness in pain reduction, functional improvement, and patient engagement. A comprehensive literature search identified four randomized controlled trials (RCTs) comprising 405 participants, with an average Physiotherapy Evidence Database (PEDro) score of 6/10, indicating moderate to high methodological quality. Findings suggest that VR and AR interventions enhance rehabilitation adherence and engagement, while sensor-based systems provide real-time biofeedback, enabling personalized therapeutic adjustments. These technologies demonstrated significant improvements in pain management, muscle strength, and functional mobility. However, challenges such as high costs, limited accessibility, and the absence of standardized treatment protocols remain barriers to widespread clinical adoption. Further research should focus on long-term efficacy, cost-effectiveness, and the integration of these innovations into routine clinical practice.

Cartilage Tissue Engineering in Multilayer Tissue Regeneration

The functional and structural integrity of the tissue/organ can be compromised in multilayer reconstructive applications involving cartilage tissue. Therefore, multilayer structures are needed for cartilage applications. In this review, we have examined multilayer scaffolds for use in the treatment of damage to organs such as the trachea, joint, nose, and ear, including the multilayer cartilage structure, but we have generally seen that they have potential applications in trachea and joint regeneration. In conclusion, when the existing studies are examined, the results are promising for the trachea and joint connections, but are still limited for the nasal and ear. It may have promising implications in the future in terms of reducing the invasiveness of existing grafting techniques used in the reconstruction of tissues with multilayered layers.

Wearable Smartphone-Based Multisensory Feedback System for Torso Posture Correction: Iterative Design and Within-Subjects Study

BACKGROUND

The prevalence of stroke is high in both males and females, and it rises with age. Stroke often leads to sensor and motor issues, such as hemiparesis affecting one side of the body. Poststroke patients require torso stabilization exercises, but maintaining proper posture can be challenging due to their condition.

OBJECTIVE

Our goal was to develop the Postural SmartVest, an affordable wearable technology that leverages a smartphone's built-in accelerometer to monitor sagittal and frontal plane changes while providing visual, tactile, and auditory feedback to guide patients in achieving their best-at-the-time posture during rehabilitation.

METHODS

To design the Postural SmartVest, we conducted brainstorming sessions, therapist interviews, gathered requirements, and developed the first prototype. We used this initial prototype in a feasibility study with individuals without hemiparesis (n=40, average age 28.4). They used the prototype during 1-hour seated sessions. Their feedback led to a second prototype, which we used in a pilot study with a poststroke patient. After adjustments and a kinematic assessment using the Vicon Gait Plug-in system, the third version became the Postural SmartVest. We assessed the Postural SmartVest in a within-subject experiment with poststroke patients (n=40, average age 57.1) and therapists (n=20, average age 31.3) during rehabilitation sessions. Participants engaged in daily activities, including walking and upper limb exercises, without and with app feedback.

RESULTS

The Postural SmartVest comprises a modified off-the-shelf athletic lightweight compression tank top with a transparent pocket designed to hold a smartphone running a customizable Android app securely. This app continuously monitors sagittal and frontal plane changes using the built-in accelerometer sensor, providing multisensory feedback through audio, vibration, and color changes. Patients reported high ratings for weight, comfort, dimensions, effectiveness, ease of use, stability, durability, and ease of adjustment. Therapists noted a positive impact on rehabilitation sessions and expressed their willingness to recommend it. A 2-tailed t-test showed a significant difference (P<.001) between the number of the best-at-the-time posture positions patients could maintain in 2 stages, without feedback (mean 13.1, SD 7.12) and with feedback (mean 4.2, SD 3.97), demonstrating the effectiveness of the solution in improving posture awareness.

CONCLUSIONS

The Postural SmartVest aids therapists during poststroke rehabilitation sessions and assists patients in improving their posture during these sessions.

Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit

PURPOSE OF REVIEW

Artificial Intelligence (AI) technology will significantly alter critical care cardiology, from our understanding of diseases to the way in which we communicate with patients and colleagues. We summarize the potential applications of AI in the cardiac intensive care unit (CICU) by reviewing current evidence, future developments and possible challenges.

RECENT FINDINGS

Machine Learning (ML) methods have been leveraged to improve interpretation and discover novel uses for diagnostic tests such as the ECG and echocardiograms. ML-based dynamic risk stratification and prognostication may help optimize triaging and CICU discharge procedures. Latent class analysis and K-means clustering may reveal underlying disease sub-phenotypes within heterogeneous conditions such as cardiogenic shock and decompensated heart failure. AI technology may help enhance routine clinical care, facilitate medical education and training, and unlock individualized therapies for patients in the CICU. However, robust regulation and improved clinician understanding of AI is essential to overcome important practical and ethical challenges.

Comparison of a non-invasive point-of-care measurement of anemia to conventionally used HemoCue devices in Gambella refugee camp, Ethiopia, 2022

Annual surveys of refugees in Gambella, Ethiopia suggest that anemia is a persistent public health problem among non-pregnant women of reproductive age (NP-WRA, 15-49 years). Measurement of anemia in most refugee camp settings is conducted using an invasive HemoCue 301. We assessed the accuracy and precision of a non-invasive, pulse CO-oximeter in measuring anemia among NP-WRA in four Gambella refugee camps. We conducted a population-representative household survey between November 7 and December 4, 2022. Hemoglobin (Hb) concentration was measured by HemoCue 301, using capillary blood, and Rad-67, a novel non-invasive device. We collected four measurements per participant: two per device. We calculated Rad-67 bias and precision of Hb measurements and sensitivity and specificity of detecting anemia. Of the 812 NP-WRAs selected, 807 (99%) participated in the study. Anemia was detected in 39% of NP-WRA as classified by the Rad-67 compared with 47% of NP-WRA as classified by the HemoCue 301. Average bias of Rad-67 measurements was 1.1 ± 1.0 SD g/dL, using HemoCue 301 as a comparator. Absolute mean difference between the first and second measurements was 0.9 g/dL (95% CI 0.8, 0.9) using the Rad-67, compared with 0.6 g/dL (95% CI 0.5, 0.6) using the HemoCue 301. The Rad-67 had 49% sensitivity and 70% specificity for detecting anemia, compared with the HemoCue 301. The Rad-67 can be a useful tool for anemia screening; however, lower accuracy and precision, and poor sensitivity suggest it cannot immediately replace the HemoCue 301 in the study area.

Optimising early detection of degenerative cervical myelopathy: a systematic review of quantitative screening tools for primary care

Background

Early diagnosis of degenerative cervical myelopathy (DCM) is often challenging due to subtle, non-specific symptoms, limited disease awareness and a lack of definitive diagnostic criteria. As primary care physicians are typically the first to encounter patients with early DCM, equipping them with effective screening tools is crucial for reducing diagnostic delays and improving patient outcomes. This systematic review evaluates the efficacy of quantitative screening methods for DCM that can be implemented in primary care settings.

Methods

A systematic search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted across PubMed, Embase and Cochrane Library up to July 2024 using keywords relevant to DCM screening. Studies were included if they evaluated the sensitivity and specificity of DCM screening tools applicable to primary care settings. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

Results

The search identified 14 studies evaluating 18 screening methods for DCM. Questionnaires consistently showed high diagnostic accuracy, with Youden indices exceeding 0.60, while only three out of nine conventional physical performance tests met the same threshold. Sensor-assisted tests, particularly those using advanced technology like finger-wearable gyro sensors, exhibited the highest diagnostic accuracy but present challenges related to accessibility and learning curves.

Conclusion

This review highlights the potential of quantitative screening methods for early DCM detection in primary care. While questionnaires and conventional tests are effective and accessible, sensor-assisted tests offer greater accuracy but face implementation challenges. A tailored, multifaceted approach is crucial for improving outcomes. Future research should focus on validating these tools in diverse populations and standardising diagnostic criteria.

Examination of the requirements for powered air-purifying respirator (PAPR) utilization as an alternative to lockdown

With the emergence of COVID-19 variants and new viruses, it remains uncertain when the next pandemic will occur. A lockdown is considered the last resort to halt the spread of infection; however, it causes significant economic and social damage. Therefore, exploring less harmful alternatives during such scenarios is crucial. This study explores the feasibility of using a powered air-purifying respirator (PAPR) as an alternative to lockdowns and as a strategy for infection control. Specifically, the study examines the potential impact of the PAPR wearing rate and PAPR aerosol shielding performance on the controllability of the spread of infections. The study investigated the necessary PAPR wearing rate and aerosol shielding performance to control infections as an alternative to the lockdown, using a simple simulation under limited conditions. When using a PAPR with 100% aerosol shielding capability in air supply, only 55% of the population needs to consistently wear the PAPR to reduce the effective reproduction number from a critical level (Rt = 2) to a target level (Rt = 0.9). Furthermore, if everyone consistently wears PAPR, only 55% the reduction ratio in the probability of becoming infected by PAPR supplying air Ir_in is enough for reducing the effective reproduction number from a critical level (Rt = 2) to a target level (Rt = 0.9). This study demonstrates the potential for utilizing PAPR as an alternative to lockdowns.

Surgical decision-making in the digital age: the role of telemedicine - a narrative review

This narrative review delves into the transformative role of telemedicine in the realm of surgical decision-making. Telemedicine, a significant innovation in healthcare services, leverages electronic information and communication technologies to provide healthcare services when distance separates the participants. It addresses the challenges of increased healthcare demands, an aging population, and budget constraints. Telemedicine technologies are employed for pre- and postoperative consultations, monitoring, and international surgical teleconferencing and education. They enhance healthcare access, particularly in remote areas, and facilitate knowledge sharing among healthcare professionals. The review also provides a historical context and discusses the technological advancements in telemedicine, including the rise of digital health technologies and the integration of artificial intelligence and machine learning in healthcare. It delves into the details of telemedicine technologies such as telesurgery, telerobotics, telepathology, teleimaging, remote patient monitoring, and virtual and augmented reality. Despite the numerous benefits, the implementation of telemedicine is often hindered by various complex and diverse ethical and legal concerns, including privacy and data security. The review highlights the need for further evidence on health outcomes and cost savings, bridging the digital divide, and enacting policies to support telemedicine reimbursement. It also emphasizes the need for incorporating telemedicine modules in medical education. It recommends that policy-making bodies consider utilizing telemedicine to address healthcare coverage gaps, particularly in rural areas.

KeyGAN: Synthetic keystroke data generation in the context of digital phenotyping

OBJECTIVE

This paper aims to introduce and assess KeyGAN, a generative modeling-based keystroke data synthesizer. The synthesizer is designed to generate realistic synthetic keystroke data capturing the nuances of fine motor control and cognitive processes that govern finger-keyboard kinematics, thereby paving the way to support biomarker development for psychomotor impairment due to neurodegeneration.

METHODS

KeyGAN is designed with two primary objectives: (i) to ensure high realism in the synthetic distributions of the keystroke features and (ii) to analyze its ability to replicate the subtleties of natural typing for enhancing biomarker development. The quality of synthetic keystroke data produced by KeyGAN is evaluated against two keystroke-based applications, TypeNet and nQiMechPD, employed as'referee' controls. The performance of KeyGAN is compared with a reference random Gaussian generator, testing its ability to fool the biometric authentication method TypeNet, and its ability to characterize fine motor impairment in Parkinson's Disease using nQiMechPD.

RESULTS

KeyGAN outperformed the reference comparator in fooling the biometric authentication method TypeNet. It also exhibited a superior approximation to real data than the reference comparator when using nQiMechPD, showcasing its adaptability and versatility in mimicking early signs of Parkinson's Disease in natural typing. KeyGAN's synthetic data demonstrated that almost 20% of real PD samples could be replaced in the training set without a decline in classification performance on the real test set. Low Fréchet Distance (<0.03) and Kullback-Leibler Divergence (<700) between KeyGAN outputs and real data distributions underline the high performance of KeyGAN.

CONCLUSION

KeyGAN presents strong potential as a realistic keystroke data synthesizer, displaying impressive capability to reproduce complex typing patterns relevant to biomarkers for neurological disorders, like Parkinson's Disease. The ability of its synthetic data to effectively supplement real data for training algorithms without affecting performance implies significant promise for advancing research in digital biomarkers for neurodegenerative and psychomotor disorders.

Development and clinical application of bioelectrical impedance analysis method for body composition assessment

Obesity, which is characterized by excessive body fat, increases the risk of chronic diseases, such as type 2 diabetes, cardiovascular diseases, and certain cancers. Sarcopenia, a decline in muscle mass, is also associated with many chronic disorders and is therefore a major concern in aging populations. Body composition analysis is important in the evaluation of obesity and sarcopenia because it provides information about the distribution of body fat and muscle mass. It is also useful for monitoring nutritional status, disease severity, and the effectiveness of interventions, such as exercise, diet, and drugs, and thus helps assess overall health and longevity. Computed tomography, magnetic resonance imaging, and dual-energy X-ray absorptiometry are commonly used for this purpose. However, they have limitations, such as high cost, long measurement time, and radiation exposure. Instead, bioelectrical impedance analysis (BIA), which was introduced several decades ago and has undergone significant technological advancements, can be used. It is easily accessible, affordable, and importantly, poses no radiation risk, making it suitable for use in hospitals, fitness centers, and even at home. Herein, we review the recent technological developments and clinical applications of BIA to provide an updated understanding of BIA technology and its strengths and limitations.

The evolution of bioimpedance analysis: From traditional methods to wearable technology

Body composition assessments are essential for understanding health and nutritional status. Traditional methods like deuterium oxide dilution, while accurate, are impractical due to cost and complexity. Bioimpedance analysis (BIA) has emerged as a preferred clinical and research technique. BIA measures total body water and, by extension, fat mass and fat-free mass, based on constant hydration assumptions. Wearable BIA technology provides real-time body composition data, enhancing at-home monitoring. Although these devices show promise in measuring parameters like body fat percentage and skeletal muscle mass, accuracy discrepancies compared to methods like dual-energy X-ray absorptiometry and the 4-compartment model require further validation. Addressing user adherence and environmental limitations is essential for reliable results. This narrative review examines the current landscape of wearable BIA technology. Despite challenges, wearable BIA devices offer significant benefits, emphasizing ongoing innovation and validation.

A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition

This study addresses the growing importance of hand gesture recognition across diverse fields, such as industry, education, and healthcare, targeting the often-neglected needs of the deaf and dumb community. The primary objective is to improve communication between individuals, thereby enhancing the overall quality of life, particularly in the context of advanced healthcare. This paper presents a novel approach for real-time hand gesture recognition using bio-impedance techniques. The developed device, powered by a Raspberry Pi and connected to electrodes for impedance data acquisition, employs an impedance chip for data collection. To categorize hand gestures, Convolutional Neuron Network (CNN), XGBoost, and Random Forest were used. The model successfully recognized up to nine distinct gestures, achieving an average accuracy of 97.24% across ten subjects using a subject-dependent strategy, showcasing the efficacy of the bioimpedance-based system in hand gesture recognition. The promising results lay a foundation for future developments in nonverbal communication between humans and machines as it contributes to the advancement of technology for the benefit of individuals with hearing impairments, addressing a critical social need.

Dynamic Prediction of Physical Exertion: Leveraging AI Models and Wearable Sensor Data During Cycling Exercise

Background/Objectives: This study aimed to explore machine learning approaches for predicting physical exertion using physiological signals collected from wearable devices. Methods: Both traditional machine learning and deep learning methods for classification and regression were assessed. The research involved 27 healthy participants engaged in controlled cycling exercises. Physiological data, including ECG, heart rate, oxygen saturation, and pedal speed (RPM), were collected during these sessions, which were divided into eight two-minute segments. Heart rate variability (HRV) was also calculated to serve as a predictive indicator. We employed two feature selection algorithms to identify the most relevant features for model training: Minimum Redundancy Maximum Relevance (MRMR) for both classification and regression, and Univariate Feature Ranking for Classification. A total of 34 traditional models were developed using MATLAB's Classification Learner App, utilizing 20% of the data for testing. In addition, Long Short-Term Memory (LSTM) networks were trained on the top features selected by the MRMR and Univariate Feature Ranking algorithms to enhance model performance. Finally, the MRMR-selected features were used for regression to train the LSTM model for predicting continuous outcomes. Results: The LSTM model for regression demonstrated robust predictive capabilities, achieving a mean squared error (MSE) of 0.8493 and an R-squared value of 0.7757. The classification models also showed promising results, with the highest testing accuracy reaching 89.2% and an F1 score of 91.7%. Conclusions: These results underscore the effectiveness of combining feature selection algorithms with advanced machine learning (ML) and deep learning techniques for predicting physical exertion levels using wearable sensor data.

TMS-evoked potential propagation reflects effective brain connectivity

Objective.Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region. However, the TEP is confounded by multiple factors and there is a need for further investigation of the TEP as a measure of effective connectivity and to compare it to existing statistical measures of effective connectivity.Approach.To this end, we used a pre-existing experimental dataset to compare TEPs between a motor control task with and without visual feedback. We then used the results to compare our TEP-based measures of effective connectivity to established statistical measures of effective connectivity provided by multivariate auto-regressive modelling.Main results.Our results reveal significantly more negative TEPs when feedback is not presented from 40 ms to 100 ms post-TMS over frontal and central channels. We also see significantly more positive later TEPs from 280-400 ms on the contra-lateral hemisphere motor and parietal channels when no feedback is presented. These results suggest differences in effective connectivity are induced by visual feedback of movement. We further find that the variation in one of these early TEPs (the N40) is reliably related to directed coherence.Significance.Taken together, these results indicate components of the TEPs serve as a measure of effective connectivity. Furthermore, our results also support the idea that effective connectivity is a dynamic process and, importantly, support the further use of TEPs in delineating region-to-region maps of changes in effective connectivity as a result of motor control feedback.

Resting Heart Rate and Associations With Clinical Measures From the Project Baseline Health Study: Observational Study

BACKGROUND

Though widely used, resting heart rate (RHR), as measured by a wearable device, has not been previously evaluated in a large cohort against a variety of important baseline characteristics.

OBJECTIVE

This study aimed to assess the validity of the RHR measured by a wearable device compared against the gold standard of ECG (electrocardiography), and assess the relationships between device-measured RHR and a broad range of clinical characteristics.

METHODS

The Project Baseline Health Study (PHBS) captured detailed demographic, occupational, social, lifestyle, and clinical data to generate a deeply phenotyped cohort. We selected an analysis cohort within it, which included participants who had RHR determined by both ECG and the Verily Study Watch (VSW). We examined the correlation between these simultaneous RHR measures and assessed the relationship between VSW RHR and a range of baseline characteristics, including demographic, clinical, laboratory, and functional assessments.

RESULTS

From the overall PBHS cohort (N=2502), 875 (35%) participants entered the analysis cohort (mean age 50.9, SD 16.5 years; n=519, 59% female and n=356, 41% male). The mean and SD of VSW RHR was 66.6 (SD 11.2) beats per minute (bpm) for female participants and 64.4 (SD 12.3) bpm for male participants. There was excellent reliability between the two measures of RHR (ECG and VSW) with an intraclass correlation coefficient of 0.946. On univariate analyses, female and male participants had similar baseline characteristics that trended with higher VSW RHR: lack of health care insurance (both P<.05), higher BMI (both P<.001), higher C-reactive protein (both P<.001), presence of type 2 diabetes mellitus (both P<.001) and higher World Health Organization Disability Assessment Schedule (WHODAS) 2.0 score (both P<.001) were associated with higher RHR. On regression analyses, within each domain of baseline characteristics (demographics and socioeconomic status, medical conditions, vitals, physical function, laboratory assessments, and patient-reported outcomes), different characteristics were associated with VSW RHR in female and male participants.

CONCLUSIONS

RHR determined by the VSW had an excellent correlation with that determined by ECG. Participants with higher VSW RHR had similar trends in socioeconomic status, medical conditions, vitals, laboratory assessments, physical function, and patient-reported outcomes irrespective of sex. However, within each domain of baseline characteristics, different characteristics were most associated with VSW RHR in female and male participants.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03154346; https://clinicaltrials.gov/study/NCT03154346.

Effectiveness of Machine Learning-Based Adjustments to an eHealth Intervention Targeting Mild Alcohol Use

INTRODUCTION

This study aimed to evaluate effects of three machine learning based adjustments made to an eHealth intervention for mild alcohol use disorder, regarding (a) early dropout, (b) participation duration, and (c) success in reaching personal alcohol use goals. Additionally, we aimed to replicate earlier machine learning analyses.

METHODS

We used three cohorts of observational log data from the Jellinek Digital Self-help intervention. First, a cohort before implementation of adjustments (T0; n = 320); second, a cohort after implementing two adjustments (i.e., sending daily emails in the first week and nudging participants towards a "no alcohol use" goal; T1; n = 146); third, a cohort comprising the prior adjustments complemented with eliminated time constraints to reaching further in the intervention (T2; n = 236).

RESULTS

We found an increase in participants reaching further in the intervention, yet an increase in early dropout after implementing all adjustments. Moreover, we found that more participants aimed for a quit goal, whilst participation duration declined at T2. Intervention success increased, yet not significantly. Lastly, machine learning demonstrated reliability for outcome prediction in smaller datasets of an eHealth intervention.

CONCLUSION

Strong correlates as indicated by machine learning analyses were found to affect goal setting and use of an eHealth program for alcohol use problems.

Quantifying Similarities Between MediaPipe and a Known Standard to Address Issues in Tracking 2D Upper Limb Trajectories: Proof of Concept Study

Background

Markerless motion tracking methods have promise for use in a range of domains, including clinical settings where traditional marker-based systems for human pose estimation are not feasible. Artificial intelligence (AI)-based systems can offer a markerless, lightweight approach to motion capture. However, the accuracy of such systems, such as MediaPipe, for tracking fine upper limb movements involving the hand has not been explored.

Objective

The aim of this study is to evaluate the 2D accuracy of MediaPipe against a known standard.

Methods

Participants (N=10) performed a touchscreen-based shape-tracing task requiring them to trace the trajectory of a moving cursor using their index finger. Cursor trajectories created a reoccurring or random shape at 5 different speeds (500-2500 ms, in increments of 500 ms). Movement trajectories on each trial were simultaneously captured by the touchscreen and a separate video camera. Movement coordinates for each trial were extracted from the touchscreen and compared to those predicted by MediaPipe. Specifically, following resampling, normalization, and Procrustes transformations, root-mean-squared error (RMSE; primary outcome measure) was calculated between predicted coordinates and those generated by the touchscreen computer.

Results

Although there was some size distortion in the frame-by-frame estimates predicted by MediaPipe, shapes were similar between the 2 methods and transformations improved the general overlap and similarity of the shapes. The resultant mean RMSE between predicted coordinates and those generated by the touchscreen was 0.28 (SD 0.06) normalized px. Equivalence testing revealed that accuracy differed between MediaPipe and the touchscreen, but that the true difference was between 0 and 0.30 normalized px (t114=-3.02; P=.002). Additional analyses revealed no differences in resultant RMSE between methods when comparing across lower frame rates (30 and 60 frames per second [FPS]), although there was greater RMSE for 120 FPS than for 60 FPS (t35.43=-2.51; P=.03).

Conclusions

Overall, we quantified similarities between one AI-based approach to motion capture and a known standard for tracking fine upper limb movements, informing applications of such systems in domains such as clinical and research settings. Future work should address accuracy in 3 dimensions to further validate the use of AI-based systems, including MediaPipe, in such domains.

Comparative analysis of heart rate variability indices from ballistocardiogram and electrocardiogram: a study on measurement agreement

Ballistocardiogram (BCG) captures minute vibrations generated by heart movements. These vibrations are converted into heart rate variability (HRV) indices, allowing their unobtrusive monitoring over extended periods, while reducing the burden on patients or subjects. In this study, to evaluate the agreement between the HRV indices, we compared the HRV indices estimated from the BCG device with those obtained from the gold standard electrocardiogram (ECG). Twenty-five healthy volunteers (mean age: 40.6 ± 12.8 years; 14 males and 11 females) rested in the supine position on a bed with a BCG device placed under a pillow while ECG electrodes were attached. BCG and ECG measurements were simultaneously recorded for 20 min. Five min of time-series data for the JJ and RR intervals obtained from BCG and ECG were converted into HRV indices. These indices included the time-domain measures (mean inter-beat intervals [IBIs], standard deviation of normal-to-normal intervals [SDNN], root mean square of successive differences [RMSSD], and percent of difference between adjacent normal RR intervals greater than 50 ms [pNN50]) and frequency-domain measures (normalized low-frequency [LF], high-frequency power [HF], and LF/HF ratio). Of the 25 individuals, data of 22 (mean age: 38.9 ± 12.3 years; 13 males and 9 females) were used to assess the agreement between the two methods, excluding 3 (1 male and 2 females) with frequent premature ventricular contractions observed on ECG. Correlations between measurements were examined using scatter plots and Pearson's product-moment correlation coefficients; in contrast, differences between measurements were evaluated using paired t-tests. The Bland-Altman analysis was used to assess the agreement. For the mean IBIs, the correlation coefficient was 0.999 (p < 0.001), and the limits of agreement ranged from - 8.35 to 11.70, with no evidence of fixed bias (p = 0.139) or proportional bias (p = 0.402), indicating excellent agreement. In contrast, the correlation coefficients for SDNN, RMSSD, and pNN50 were 0.931 (p < 0.001), 0.923 (p < 0.001), and 0.964 (p < 0.001), respectively, showing high correlations. However, a fixed bias was observed in RMSSD (p = 0.007) and pNN50 (p = 0.010), and a proportional bias in SDNN (p = 0.002). The correlation coefficients for LF, HF, and LF/HF ratio were approximately 0.7, indicating lower agreement owing to observed fixed and proportional biases. These results indicate that, while the degree of agreement varies among HRV indices, the JJ intervals measured from BCG can be used as a suitable alternative to the RR intervals from ECG.

User Perceptions of Wearability of Knitted Sensor Garments for Long-Term Monitoring of Breathing Health: Thematic Analysis of Focus Groups and a Questionnaire Survey

BACKGROUND

Long-term unobtrusive monitoring of breathing patterns can potentially give a more realistic insight into the respiratory health of people with asthma or chronic obstructive pulmonary disease than brief tests performed in medical environments. However, it is uncertain whether users would be willing to wear these sensor garments long term.

OBJECTIVE

Our objective was to explore whether users would wear ordinary looking knitted garments with unobtrusive knitted-in breathing sensors long term to monitor their lung health and under what conditions.

METHODS

Multiple knitted breathing sensor garments, developed and fabricated by the research team, were presented during a demonstration. Participants were encouraged to touch and feel the garments and ask questions. This was followed by two semistructured, independently led focus groups with a total of 16 adults, of whom 4 had asthma. The focus group conversations were recorded and transcribed. Thematic analysis was carried out by three independent researchers in 3 phases consisting of familiarization with the data, independent coding, and overarching theme definition. Participants also completed a web-based questionnaire to probe opinion about wearability and functionality of the garments. Quantitative analysis of the sensors' performance was mapped to participants' garment preference to support the feasibility of the technology for long-term wear.

RESULTS

Key points extracted from the qualitative data were (1) garments are more likely to be worn if medically prescribed, (2) a cotton vest worn as underwear was preferred, and (3) a breathing crisis warning system was seen as a promising application. The qualitative analysis showed a preference for a loose-fitting garment style with short sleeves (13/16 participants), 11 out of 16 would also wear snug fitting garments and none of the participants would wear tight-fitting garments over a long period of time. In total, 10 out of 16 participants would wear the snug fitting knitted garment for the whole day and 13 out of 16 would be happy to wear it only during the night if not too hot. The sensitivity demands on the knitted wearable sensors can be aligned with most users' garment preferences (snug fit).

CONCLUSIONS

There is an overall positive opinion about wearing a knitted sensor garment over a long period of time for monitoring respiratory health. The knit cannot be tight but a snugly fitted vest as underwear in a breathable material is acceptable for most participants. These requirements can be fulfilled with the proposed garments. Participants with asthma supported using it as a sensor garment connected to an asthma attack alert system.

Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification

Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training; 0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection.

Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis

BACKGROUND

Despite the implementation of prevention strategies, family violence continues to be a prevalent issue worldwide. Current strategies to reduce family violence have demonstrated mixed success and innovative approaches are needed urgently to prevent the occurrence of family violence. Incorporating artificial intelligence (AI) into prevention strategies is gaining research attention, particularly the use of textual or voice signal data to detect individuals at risk of perpetrating family violence. However, no review to date has collated extant research regarding how accurate AI is at identifying individuals who are at risk of perpetrating family violence.

OBJECTIVE

The primary aim of this systematic review and meta-analysis is to assess the accuracy of AI models in differentiating between individuals at risk of engaging in family violence versus those who are not using textual or voice signal data.

METHODS

The following databases will be searched from conception to the search date: IEEE Xplore, PubMed, PsycINFO, EBSCOhost (Psychology and Behavioral Sciences collection), and Computers and Applied Sciences Complete. ProQuest Dissertations and Theses A&I will also be used to search the grey literature. Studies will be included if they report on human adults and use machine learning to differentiate between low and high risk of family violence perpetration. Studies may use voice signal data or linguistic (textual) data and must report levels of accuracy in determining risk. In the data screening and full-text review and quality analysis phases, 2 researchers will review the search results and discrepancies and decisions will be resolved through masked review of a third researcher. Results will be reported in a narrative synthesis. In addition, a random effects meta-analysis will be conducted using the area under the receiver operating curve reported in the included studies, assuming sufficient eligible studies are identified. Methodological quality of included studies will be assessed using the risk of bias tool in nonrandomized studies of interventions.

RESULTS

As of October 2024, the search has not commenced. The review will document the state of the research concerning the accuracy of AI models in detecting the risk of family violence perpetration using textual or voice signal data. Results will be presented in the form of a narrative synthesis. Results of the meta-analysis will be summarized in tabular form and using a forest plot.

CONCLUSIONS

The findings from this study will clarify the state of the literature on the accuracy of machine learning models to identify individuals who are at high risk of perpetuating family violence. Findings may be used to inform the development of AI and machine learning models that can be used to support possible prevention strategies.

TRIAL REGISTRATION

PROSPERO CRD42023481174; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481174.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54966.

Contactless and continuous monitoring of respiratory rate in a hospital ward: a clinical validation study

Introduction

Continuous monitoring of respiratory rate in hospital wards can provide early detection of clinical deterioration, thereby reducing mortality, reducing transfers to intensive care units, and reducing the hospital length of stay. Despite the advantages of continuous monitoring, manually counting every 1-12 h remains the standard of care in most hospital wards. The objective of this study was to validate continuous respiratory rate measurements from a radar-based contactless patient monitor [Vitalthings Guardian M10 (Vitalthings AS, Norway)] in a hospital ward.

Methods

An observational study (clinicaltrials.gov: NCT06083272) was conducted at the emergency ward of a university hospital. Adult patients were monitored during rest with Vitalthings Guardian M10 in both a stationary and mobile configuration simultaneously with a reference device [Nox T3s (Nox Medical, Alpharetta, GA, United States)]. The agreement was assessed using Bland-Altman 95% limits of agreement. The sensitivity and specificity of clinical alarms were evaluated using a Clarke Error grid modified for continuous monitoring of respiratory rate. Clinical aspects were further evaluated in terms of trend analysis and examination of gaps between valid measurements.

Results

32 patients were monitored for a median duration of 42 min [IQR (range) 35-46 (30-59 min)]. The bias was 0.1 and 0.0 breaths min-1 and the 95% limits of agreement ranged from -1.1 to 1.2 and -1.1 to 1.1 breaths min-1 for the stationary and mobile configuration, respectively. The concordances for trends were 96%. No clinical alarms were missed, and no false alarms or technical alarms were generated. No interval without a valid measurement was longer than 5 min.

Conclusion

Vitalthings Guardian M10 measured respiratory rate accurately and continuously in resting patients in a hospital ward.

Exploring the Use of a Length AI Algorithm to Estimate Children's Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study

BACKGROUND

Length measurement in young children younger than 18 months is important for monitoring growth and development. Accurate length measurement requires proper equipment, standardized methods, and trained personnel. In addition, length measurement requires young children's cooperation, making it particularly challenging during infancy and toddlerhood.

OBJECTIVE

This study aimed to develop a length artificial intelligence (LAI) algorithm to aid users in determining recumbent length conveniently from smartphone images and explore its performance and suitability for personal and clinical use.

METHODS

This proof-of-concept study in healthy children (aged 0-18 months) was performed at KK Women's and Children's Hospital, Singapore, from November 2021 to March 2022. Smartphone images were taken by parents and investigators. Standardized length-board measurements were taken by trained investigators. Performance was evaluated by comparing the tool's image-based length estimations with length-board measurements (bias [mean error, mean difference between measured and predicted length]; absolute error [magnitude of error]). Prediction performance was evaluated on an individual-image basis and participant-averaged basis. User experience was collected through questionnaires.

RESULTS

A total of 215 participants (median age 4.4, IQR 1.9-9.7 months) were included. The tool produced a length prediction for 99.4% (2211/2224) of photos analyzed. The mean absolute error was 2.47 cm for individual image predictions and 1.77 cm for participant-averaged predictions. Investigators and parents reported no difficulties in capturing the required photos for most participants (182/215, 84.7% participants and 144/200, 72% participants, respectively).

CONCLUSIONS

The LAI algorithm is an accessible and novel way of estimating children's length from smartphone images without the need for specialized equipment or trained personnel. The LAI algorithm's current performance and ease of use suggest its potential for use by parents or caregivers with an accuracy approaching what is typically achieved in general clinics or community health settings. The results show that the algorithm is acceptable for use in a personal setting, serving as a proof of concept for use in clinical settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05079776; https://clinicaltrials.gov/ct2/show/NCT05079776.

Gamified devices for stroke rehabilitation: A systematic review

BACKGROUND AND OBJECTIVE

Rehabilitation after stroke is essential to minimize permanent disability. Gamification, the integration of game elements into non-game environments, has emerged as a promising strategy for increasing motivation and rehabilitation effectiveness. This article systematically reviews the gamified devices used in stroke rehabilitation and evaluates their impact on emotional, social, and personal effects on patients, providing a comprehensive view of gamified rehabilitation.

METHODS

A comprehensive search using the PRISMA 2020 guidelines was conducted using the IEEE Xplore, PubMed, Springer Link, APA PsycInfo, and ScienceDirect databases. Empirical studies published between January 2019 and December 2023 that quantified the effects of gamification in terms of usability, motivation, engagement, and other qualitative patient responses were selected.

RESULTS

In total, 169 studies involving 6404 patients were included. Gamified devices are categorized into four types: robotic/motorized, non-motorized, virtual reality, and neuromuscular electrical stimulation. The results showed that gamified devices not only improved motor and cognitive function but also had a significant positive impact on patients' emotional, social and personal levels. Most studies have reported high levels of patient satisfaction and motivation, highlighting the effectiveness of gamification in stroke rehabilitation.

CONCLUSIONS

Gamification in stroke rehabilitation offers significant benefits beyond motor and cognitive recovery by improving patients' emotional and social well-being. This systematic review provides a comprehensive overview of the most effective gamified technologies and highlights the need for future multidisciplinary research to optimize the design and implementation of gamified solutions in stroke rehabilitation.

Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study

BACKGROUND

Data collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF).

OBJECTIVES

Evaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF.

METHODS

The analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders.

RESULTS

In the analyzable cohort (N=1265; 50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10; 95% CI:1.74-2.53) and A (OR vs stage 0=1.43; 95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0.

CONCLUSIONS

Digital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF.

Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data

Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult and time intensive. Given the high prevalence of their co-occurrence, there is a need for scalable and accessible methods for distinguishing the co-occurrence of autism and ADHD from individual diagnoses. The first step is to identify a core set of features that can serve as the basis for behavioral feature extraction. We trained machine learning models on data from the National Survey of Children's Health to identify behaviors to target as features in automated clinical decision support systems. A model trained on the binary task of distinguishing either developmental delay (autism or ADHD) vs. neither achieved sensitivity >92% and specificity >94%, while a model trained on the 4-way classification task of autism vs. ADHD vs. both vs. none demonstrated >65% sensitivity and >66% specificity. While the performance of the binary model was respectable, the relatively low performance in the differential classification of autism and ADHD highlights the challenges that persist in achieving specificity within clinical decision support tools for developmental delays. Nevertheless, this study demonstrates the potential of applying behavioral questionnaires not traditionally used for clinical purposes towards supporting digital screening assessments for pediatric developmental delays.

Enhancing ensemble classifiers utilizing gaze tracking data for autism spectrum disorder diagnosis

PROBLEM

Diagnosing Autism Spectrum Disorder (ASD) remains a significant challenge, especially in regions where access to specialists is limited. Computer-based approaches offer a promising solution to make diagnosis more accessible. Eye tracking has emerged as a valuable technique in aiding the diagnosis of ASD. Typically, individuals' gaze patterns are monitored while they view videos designed according to established paradigms. In a previous study, we developed a method to classify individuals as having ASD or Typical Development (TD) by processing eye-tracking data using Random Forest ensembles, with a focus on a paradigm known as joint attention.

AIM

This article aims to enhance our previous work by evaluating alternative algorithms and ensemble strategies, with a particular emphasis on the role of anticipation features in diagnosis.

METHODS

Utilizing stimuli based on joint attention and the concept of "floating regions of interest" from our earlier research, we identified features that indicate gaze anticipation or delay. We then tested seven class balancing strategies, applied seven dimensionality reduction algorithms, and combined them with five different classifier induction algorithms. Finally, we employed the stacking technique to construct an ensemble model.

RESULTS

Our findings showed a significant improvement, achieving an F1-score of 95.5%, compared to the 82% F1-score from our previous work, through the use of a heterogeneous stacking meta-classifier composed of diverse induction algorithms.

CONCLUSION

While there remains an opportunity to explore new algorithms and features, the approach proposed in this article has the potential to be applied in clinical practice, contributing to increased accessibility to ASD diagnosis.

Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care

Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can significantly enhance the rehabilitation process. Sleepsense, an AI-driven sleep-tracking device, provides real-time data and insights, enabling healthcare professionals to tailor interventions and improve sleep quality. This study explores the perspectives of an interdisciplinary hospital team on implementing Sleepsense in geriatric hospital care. Using the interpretive description approach, we conducted focus groups with physicians, nurses, care aides, and an activity worker. The Consolidated Framework for Implementation Research (CFIR) informed our thematic analysis to identify barriers and facilitators to implementation. Among 27 healthcare staff, predominantly female (88.89%) and Asian (74.1%) and mostly aged 30-50 years, themes emerged that Sleepsense is perceived as a timesaving and data-driven tool that enhances patient monitoring and assessment. However, barriers such as resistance to change and concerns about trusting the device for patient comfort and safety were noted, while facilitators included training and staff engagement. The CFIR framework proved useful for analyzing implementation barriers and facilitators, suggesting future research should prioritize effective strategies for interdisciplinary team support to enhance innovation adoption and patient outcomes in rehabilitation settings.

Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders

Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson's disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, and Scopus databases with a search time range from 2014 to 2024. This review has been registered on Open OSF (n) EH9PT. Results: Recent advancements in AI and ML are revolutionizing motor rehabilitation and diagnosis for conditions like stroke, SCI, and PD, offering new opportunities for personalized care and improved outcomes. These technologies enhance clinical assessments, therapy personalization, and remote monitoring, providing more precise interventions and better long-term management. Conclusions: AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders. Future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care.