Healthcare Technology Letters最新文献

{"title":"Comparative Process Mining for Identifying the Critical Activities in Sepsis Trajectories","authors":"Mohsen Mohammadi","doi":"10.1049/htl2.70010","DOIUrl":"https://doi.org/10.1049/htl2.70010","url":null,"abstract":"<p>Sepsis, a life-threatening condition with high mortality and readmission rates, demands precise and timely management to improve patient outcomes. Despite advancements, identifying the critical activities within sepsis treatment pathways remains a challenge, limiting the effectiveness of interventions. This study addresses this issue by utilising comparative process mining techniques to analyse sepsis trajectories, focusing on key performance metrics—sojourn time, arrival rate and finish rate—across distinct patient clusters. The analysis is based on real-life event logs from a hospital's sepsis cases, using K-means clustering to segment patients by age, severity and key clinical indicators. The study reveals critical activities such as ‘Return ER’, ‘Admission IC’, and ‘Release C’, which consistently exhibit high sojourn times and influence patient outcomes significantly. These activities emerge as bottlenecks in the patient care process, particularly in cases of severe sepsis, where delays can lead to increased complications and mortality. By identifying these critical points, the study provides actionable insights for healthcare providers to optimize resource allocation, reduce delays and enhance the overall efficiency of sepsis management. The findings underscore the importance of targeted interventions in these key areas, offering a path toward improved clinical outcomes and reduced sepsis-related mortality and readmission rates. This research contributes to the growing field of process mining in healthcare, highlighting its potential to transform complex clinical pathways into more efficient and effective treatment processes.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid deep learning method to identify key genes in autism spectrum disorder","authors":"Naveen Kumar Singh,&nbsp;Asmita Patel,&nbsp;Nidhi Verma,&nbsp;R. K. Brojen Singh,&nbsp;Saurabh Kumar Sharma","doi":"10.1049/htl2.12104","DOIUrl":"https://doi.org/10.1049/htl2.12104","url":null,"abstract":"<p>Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic component. This research aims to identify key genes associated with autism spectrum disorder using a hybrid deep learning approach. To achieve this, a protein–protein interaction network is constructedand analyzed through a graph convolutional network, which extracts features based on gene interactions. Logistic regression is then employed to predict potential key regulatorgenes using probability scores derived from these features. To evaluate the infection ability of these potential key regulator genes, a susceptible–infected (SI) model, is performed, which reveals the higher infection ability for the genes identified by the proposed method, highlighting its effectiveness in pinpointing key genetic factors associated with ASD. The performance of the proposed method is compared with centrality methods, showing significantly improved results. Identified key genes are further compared with the SFARI gene database and the Evaluation of Autism Gene Link Evidence (EAGLE) framework, revealing commongenes that are strongly associated with ASD. This reinforces the validity of the method in identifying key regulator genes. The proposed method aligns with advancements in therapeutic systems, diagnostics, and neural engineering, providing a robust framework for ASD research and other neurodevelopmental disorders.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Bionics Bus for Neurology and Neuropsychiatry: Concept Development and Validation","authors":"Christopher James,&nbsp;Sarang Shankar,&nbsp;Samuel J. Tromans,&nbsp;Richard Laugharne,&nbsp;Paraskevi Triantafyllopoulou,&nbsp;Maria Richards,&nbsp;Rohit Shankar","doi":"10.1049/htl2.70008","DOIUrl":"https://doi.org/10.1049/htl2.70008","url":null,"abstract":"<p>Healthcare delivery in the United Kingdom is increasingly becoming a challenging issue where demand is regularly outstripping availability. This is particularly a challenge in neurology and neuropsychiatry, where delays in diagnosis and treatment are leading to worse health and social outcomes. The Darzi report, which focused on three key tenants, has been hailed as the future blueprint for National Health Service (NHS) sustainability and high-quality care delivery. These three tenants are moving from analogue to digital approaches, focusing on prevention and wellbeing, and supporting diagnosis and treatment in communities instead of hospitals. Technological interventions are relevant at all stages of these care pathways. There is an opportunity to identify an easy to use community-based mobile resource to help screen, triage and refer suspect neurology and neuropsychiatric presentations to the right support. The potential benefits to patients, clinicians, organisations and communities could be significant. To enable this vision, the concept of Bionic Bus (https://bionicsbus.org/) was developed. This study looked to understand the acceptability, utility and scope of the Bionics Bus concept among the public using mixed-methods research techniques. Results suggest high acceptability, utility and wide scope. This study gives a template for similar evidence-based innovation to be applied for other health conditions.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential analysis of brain functional network parameters in MHE patients","authors":"Li Song,&nbsp;Yiting Zhang,&nbsp;Xiaoyan Wang,&nbsp;Xucai Ji","doi":"10.1049/htl2.70004","DOIUrl":"https://doi.org/10.1049/htl2.70004","url":null,"abstract":"<p>Resting-state functional magnetic resonance imaging, using blood-oxygen-level-dependence signal data and graph theory, was employed to explore brain functional network parameter changes in 32 MHE patients and 21 healthy controls. The Gretna software package and spm8 are used to preprocess and process the data in matlab2012b to calculate the global efficiency (Eg), local efficiency (El), nodal degree (nodal De), nodal clustering coefficient (nodal Cp), nodal shortest path length (nodal Lp), and nodal betweenness (nodal Be) as brain functional network characteristic parameters. The BrainNet View soft is used to draw network maps and present surface-based data. Within the sparsity range of the selected network, A double-sample t-test revealed significant differences about the characteristic parameters in the following brain regions: the Nodal Cp in AAL62, AAL26, AAL43, and AAL47; the De in AAL66, AAL68, AAL47, and AAL74; the nodal Lp in AAL28, the El in AAL62, AAL31, and AAL47; the Eg in AAL28, AAL32, and AAL51, and the nodal Be in AAL28, AAL32, AAL76, and AAL82. These changes in brain network nodes may signal early brain damage in MHE, helping to characterize MHE and predict mental decline in cirrhosis patients.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Feasibility of Ambulatory Heart Rate Variability Monitoring in Non-Suicidal Self-Injury","authors":"Alje van Hoorn,&nbsp;Anna Mankee-Williams,&nbsp;Gareth Lewis,&nbsp;Rafaella Mellili,&nbsp;Jessica Eccles,&nbsp;Cristina Ottaviani,&nbsp;Richard Laugharne,&nbsp;Rohit Shankar","doi":"10.1049/htl2.70007","DOIUrl":"https://doi.org/10.1049/htl2.70007","url":null,"abstract":"<p>The polyvagal theory proposes that the autonomic nervous system influences affective systems and top-down emotional regulation. Vagal tone, as indexed by heart rate variability (HRV), is a measure of emotion regulation capacity. It is possible that non-suicidal self-injury (NSSI) occurs at times of low vagal tone and that NSSI may increase it. Little is known about the feasibility of collecting ambulatory HRV data in the context of NSSI. This prospective observational study examined the feasibility of ambulatory HRV monitoring during NSSI. Ten participants wore a chest-based heart rate monitor and used a diary app for 1 week. Baseline characteristics were collected. Heart rate monitoring duration, diary app entries, distress scores, and NSSI occurrences were recorded. Participant experience was assessed in a post-study questionnaire. At baseline, six had a history of NSSI, in two of whom it was current. Ten participants wore the monitor for an average of 137 h. Nine participants successfully used the diary app, making an average of 14 entries over a week. Although no NSSI occurred during the study, the overall experience of participation was positive. It is feasible to monitor HRV and collect app-based distress scores for a week, including in those with NSSI history.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Signal-quality-aware multisensor fusion for atrial fibrillation detection","authors":"Shane Malone,&nbsp;Barry Cardiff,&nbsp;Deepu John,&nbsp;Arlene John","doi":"10.1049/htl2.12121","DOIUrl":"https://doi.org/10.1049/htl2.12121","url":null,"abstract":"<p>This letter introduces a novel method to enhance atrial fibrillation detection accuracy in healthcare monitoring. Wearable devices often face inconsistent signal quality due to noise. To address this, a multimodal data fusion technique that improves signal reliability during continuous monitoring is proposed. The method improves the precision of detecting R–R intervals by integrating wavelet coefficients from electrocardiogram, photoplethysmogram, and arterial blood pressure signals, weighted according to the quality of each signal. Furthermore, a bi-directional long short-term memory network is developed to accurately detect AF based on the derived heartrate or R–R intervals. Unlike prior studies, this work uniquely evaluates the system’s performance under noisy conditions, demonstrating significant accuracy improvements over single-channel methods. The system's generalizability is confirmed by evaluating the classifier's performance as the number of sensor inputs increases. At a signal-to-noise ratio of −10 dB, the accuracy improves by 4.51% with two sensor inputs and by 10.92% with three inputs, compared to using a single input.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep regression 2D-3D ultrasound registration for liver motion correction in focal tumour thermal ablation","authors":"Shuwei Xing,&nbsp;Derek W. Cool,&nbsp;David Tessier,&nbsp;Elvis C. S. Chen,&nbsp;Terry M. Peters,&nbsp;Aaron Fenster","doi":"10.1049/htl2.12117","DOIUrl":"https://doi.org/10.1049/htl2.12117","url":null,"abstract":"&lt;p&gt;Liver tumour ablation procedures require accurate placement of the needle applicator at the tumour centroid. The lower-cost and real-time nature of ultrasound (US) has advantages over computed tomography for applicator guidance, however, in some patients, liver tumours may be occult on US and tumour mimics can make lesion identification challenging. Image registration techniques can aid in interpreting anatomical details and identifying tumours, but their clinical application has been hindered by the tradeoff between alignment accuracy and runtime performance, particularly when compensating for liver motion due to patient breathing or movement. Therefore, we propose a 2D–3D US registration approach to enable intra-procedural alignment that mitigates errors caused by liver motion. Specifically, our approach can correlate imbalanced 2D and 3D US image features and use continuous 6D rotation representations to enhance the model's training stability. The dataset was divided into 2388, 196, and 193 image pairs for training, validation and testing, respectively. Our approach achieved a mean Euclidean distance error of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2.28&lt;/mn&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$2.28 ,mathrm{m}mathrm{m}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;annotation&gt;$pm$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1.81&lt;/mn&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$1.81 ,mathrm{m}mathrm{m}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and a mean geodesic angular error of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2.99&lt;/mn&gt;\u0000 &lt;msup&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$2.99 ,mathrm{^{circ }}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;annotation&gt;$pm$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1.95&lt;/mn&gt;\u0000 &lt;msup&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$1.95 ,mathrm{^{circ }}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, with a runtime of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Writing the Signs: An Explainable Machine Learning Approach for Alzheimer's Disease Classification from Handwriting","authors":"Ngoc Truc Ngan Ho,&nbsp;Paulina Gonzalez,&nbsp;Gideon K. Gogovi","doi":"10.1049/htl2.70006","DOIUrl":"https://doi.org/10.1049/htl2.70006","url":null,"abstract":"&lt;p&gt;Alzheimer's disease is a global health challenge, emphasizing the need for early detection to enable timely intervention and improve outcomes. This study analyzes handwriting data from individuals with and without Alzheimer's to identify predictive features across copying, graphic and memory-based tasks. Machine learning models, including Random Forest, Bootstrap Aggregating (Bagging), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost) and Gradient Boosting, were applied to classify patients, with SHapley Additive exPlanations (SHAP) enhancing model interpretability. Time-related features were crucial in copying and graphic tasks, reflecting cognitive processing speed, while pressure-related features were significant in memory tasks, indicating recall confidence. Simpler graphic tasks showed strong discriminatory power, aiding early detection. Performance metrics demonstrated model effectiveness: For memory tasks, Random Forest achieved the highest accuracy (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0.840&lt;/mn&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mn&gt;0.038&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$0.840 pm 0.038$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;), while Bagged SVC was the lowest (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0.617&lt;/mn&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mn&gt;0.046&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$0.617 pm 0.046$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). Copying tasks recorded a peak accuracy of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0.804&lt;/mn&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mn&gt;0.075&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$0.804 pm 0.075$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; with Gradient Boost and a low of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0.566&lt;/mn&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mn&gt;0.032&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$0.566 pm 0.032$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for Bagged SVC. Graphic tasks reached &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0.799&lt;/mn&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mn&gt;0.041&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$0.799 pm 0.041$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; with Gradient Boost and 0.643 ± 0.071 with AdaBoost. For all tasks combined, Random Forest excelled (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0.854&lt;/mn&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mn&gt;0.033&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotati","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying factors shaping the behavioural intention of Nepalese youths to adopt digital health tools","authors":"Sujal Mani Timsina,&nbsp;Ujjwal Bhattarai","doi":"10.1049/htl2.70005","DOIUrl":"https://doi.org/10.1049/htl2.70005","url":null,"abstract":"<p>The digitalization of healthcare has gained global importance, especially post-COVID-19, yet remains a challenge in developing countries due to the slow adoption of digital health tools. This study aims to identify major predictors impacting the behavioural intention of Nepalese youths to adopt digital health tools by utilizing the framework based on the extended unified theory of acceptance and use of technology (UTAUT-2). The cross-sectional data from 280 respondents was collected from youths (i.e., aged 16-40) in the Kathmandu Valley and were analyzed through PLS-SEM. Most of the respondents were using smartwatches followed by blood pressure monitors and pulse oximeters. The findings revealed hedonic motivation as the strongest predictor of behavioural intention to use digital health tools followed by facilitating conditions, social influence, habit, and performance expectancy. The behavioural intention significantly influenced actual usage behaviour. Additionally, behavioural intention mediated the relationship between the above-mentioned five constructs and usage behaviour, except for effort expectancy and price value. The study emphasizes the role of major predictors such as facilitating conditions in shaping the intention of youths to adopt digital health tools providing insights for government, hospitals, and developers to understand consumer perceptions and motivations.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability and validity of computer vision-based markerless human pose estimation for measuring hip and knee range of motion","authors":"Thomas Hellstén,&nbsp;Jari Arokoski,&nbsp;Jonny Karlsson,&nbsp;Leena Ristolainen,&nbsp;Jyrki Kettunen","doi":"10.1049/htl2.70002","DOIUrl":"10.1049/htl2.70002","url":null,"abstract":"<p>Telerehabilitation requires accurate joint range of motion (ROM) measurement methods. The aim of this study was to evaluate the reliability and validity of a computer vision (CV)-based markerless human pose estimation (HPE) application measuring active hip and knee ROMs. For this study, the joint ROM of 30 healthy young adults (10 females, 20 males) aged 20–33 years (mean: 22.9 years) was measured, and test–retests were assessed for reliability. For validity evaluation, the CV-based markerless HPE application used in this study was compared with an identical reference picture frame. The intraclass correlation coefficient (ICC) for the CV-based markerless HPE application was 0.93 for active hip inner rotation, 0.83 for outer rotation, 0.82 for flexion, 0.82 for extension, and 0.74 for knee flexion. Correlations (<i>r</i>) of the two measurement methods were 0.99 for hip-active inner rotation, 0.98 for outer rotation, 0.87 for flexion, 0.85 for extension, and 0.90 for knee flexion. This study highlights the potential of a CV-based markerless HPE application as a reliable and valid tool for measuring hip and knee joint ROM. It could offer an accessible solution for telerehabilitation, enabling ROM monitoring.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}