JMIR biomedical engineering最新文献

{"title":"Home Automated Telemanagement System for Individualized Exercise Programs: Design and Usability Evaluation.","authors":"Aref Smiley, Joseph Finkelstein","doi":"10.2196/65734","DOIUrl":"10.2196/65734","url":null,"abstract":"<p><strong>Background: </strong>Exercise is essential for physical rehabilitation, helping to improve functional performance and manage chronic conditions. Telerehabilitation offers an innovative way to deliver personalized exercise programs remotely, enhancing patient adherence and clinical outcomes. The Home Automated Telemanagement (HAT) System, integrated with the interactive bike (iBikE) system, was designed to support home-based rehabilitation by providing patients with individualized exercise programs that can be monitored remotely by a clinical rehabilitation team.</p><p><strong>Objective: </strong>This study aims to evaluate the design, usability, and efficacy of the iBikE system within the HAT platform. We assessed the system's ability to enhance patient adherence to prescribed exercise regimens while minimizing patient and clinician burden in carrying out the rehabilitation program.</p><p><strong>Methods: </strong>We conducted a quasi-experimental study with 5 participants using a pre- and posttest design. Usability testing included 2 primary tasks that participants performed with the iBikE system. Task completion times, adherence to exercise protocols, and user satisfaction were measured. A System Usability Scale (SUS) was also used to evaluate participants' overall experience. After an initial introduction, users performed the tasks independently following a 1-week break to assess retention of the system's operation skills and its functionality.</p><p><strong>Results: </strong>Task completion times improved substantially from the pretest to the posttest: execution time for task 1 reduced from a mean of 8.6 (SD 4.7) seconds to a mean of 1.8 (SD 0.8) seconds, and the time for task 2 decreased from a mean of 315 (SD 6.9) seconds to a mean of 303.4 (SD 1.1) seconds. Adherence to the prescribed cycling speed also improved, with deviations from the prescribed speed reduced from a mean of 6.26 (SD 1.00) rpm (revolutions per minute) to a mean of 4.02 (SD 0.82) rpm (t=3.305, n=5, P=.03). SUS scores increased from a mean of 92 (SD 8.6) to a mean of 97 (SD 3.3), indicating high user satisfaction and confidence in system usability. All participants successfully completed both tasks without any additional assistance during the posttest phase, demonstrating the system's ease of use and effectiveness in supporting independent exercise.</p><p><strong>Conclusions: </strong>The iBikE system, integrated into the HAT platform, effectively supports home-based telerehabilitation by enabling patients to follow personalized exercise prescriptions with minimal need for further training or supervision. The significant improvements in task performance and exercise adherence suggest that the system is well-suited for use in home-based rehabilitation programs, promoting sustained patient engagement and adherence to exercise regimens. Further studies with larger sample sizes are recommended to validate these findings and explore the long-term benefits of the syst","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":"e65734"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808775","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":"Pump-Free Microfluidics for Cell Concentration Analysis on Smartphones in Clinical Settings (SmartFlow): Design, Development, and Evaluation.","authors":"Sixuan Wu, Kefan Song, Jason Cobb, Alexander T Adams","doi":"10.2196/62770","DOIUrl":"10.2196/62770","url":null,"abstract":"<p><strong>Background: </strong>Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility. Microfluidic systems, although cheaper than flow cytometers, still require high-speed cameras and syringe pumps to drive the flow and ensure video quality. In this paper, we present SmartFlow, a low-cost solution for cell concentration estimation using smartphone-based computer vision on 3D-printed, pump-free microfluidic platforms.</p><p><strong>Objective: </strong>The objective was to design and fabricate microfluidic chips, coupled with clinical utilities, for cell counting and concentration analysis. We answered the following research questions (RQs): RQ1, Can gravity drive the flow within the microfluidic chips, eliminating the need for external pumps? RQ2, How does the microfluidic chip design impact video quality for cell analysis? RQ3, Can smartphone-captured videos be used to estimate cell count and concentration in microfluidic chips?</p><p><strong>Methods: </strong>To answer the 3 RQs, 2 experiments were conducted. In the cell flow velocity experiment, diluted sheep blood flowed through the microfluidic chips with and without a bottleneck design to answer RQ1 and RQ2, respectively. In the cell concentration analysis experiment, sheep blood diluted into 13 concentrations flowed through the microfluidic chips while videos were recorded by smartphones for the concentration measurement.</p><p><strong>Results: </strong>In the cell flow velocity experiment, we designed and fabricated 2 versions of microfluidic chips. The ANOVA test (Straight: F<sub>6, 99</sub>=6144.45, P<.001; Bottleneck: F<sub>6, 99</sub>=3475.78, P<.001) showed the height difference had a significant impact on the cell velocity, which implied gravity could drive the flow. The video sharpness analysis demonstrated that video quality followed an exponential decay with increasing height differences (video quality=100e<sup>-k×Height</sup>) and a bottleneck design could effectively preserve video quality (Straight: R<sup>2</sup>=0.95, k=4.33; Bottleneck: R<sup>2</sup>=0.91, k=0.59). Samples from the 13 cell concentrations were used for cell counting and cell concentration estimation analysis. The accuracy of cell counting (n=35, 60-second samples, R<sup>2</sup>=0.96, mean absolute error=1.10, mean squared error=2.24, root mean squared error=1.50) and cell concentration regression (n=39, 150-second samples, R<sup>2</sup>=0.99, mean absolute error=0.24, mean squared error=0.11, root mean squared error=0.33 on a logarithmic scale, mean average percentage error=0.25) were evaluated using 5-fold cross-validation by comparing the algorithmic estimation to ground truth.</p><p><strong>Conclusions: </strong>In conclusion, we demonstrated the i","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e62770"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883776","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":"Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.","authors":"Shreeram Athreya, Ashwath Radhachandran, Vedrana Ivezić, Vivek R Sant, Corey W Arnold, William Speier","doi":"10.2196/58911","DOIUrl":"10.2196/58911","url":null,"abstract":"<p><strong>Background: </strong>Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices. The challenge remains to develop a universal framework capable of improving image quality across different devices and conditions, independent of registration or specific organ characteristics.</p><p><strong>Objective: </strong>This study aims to develop a robust framework that enhances the quality of ultrasound images, particularly those captured with compact, portable devices, which are often constrained by low quality due to hardware limitations. The framework is designed to effectively process nonregistered ultrasound image pairs, a common challenge in medical imaging, across various clinical settings and device types. By addressing these challenges, the research seeks to provide a more generalized and adaptable solution that can be widely applied across diverse medical scenarios, improving the accessibility and quality of diagnostic imaging.</p><p><strong>Methods: </strong>A retrospective analysis was conducted by using a cycle-consistent generative adversarial network (CycleGAN) framework enhanced with perceptual loss to improve the quality of ultrasound images, focusing on nonregistered image pairs from various organ systems. The perceptual loss was integrated to preserve anatomical integrity by comparing deep features extracted from pretrained neural networks. The model's performance was evaluated against corresponding high-resolution images, ensuring that the enhanced outputs closely mimic those from high-end ultrasound devices. The model was trained and validated using a publicly available, diverse dataset to ensure robustness and generalizability across different imaging scenarios.</p><p><strong>Results: </strong>The advanced CycleGAN framework, enhanced with perceptual loss, significantly outperformed the previous state-of-the-art, stable CycleGAN, in multiple evaluation metrics. Specifically, our method achieved a structural similarity index of 0.2889 versus 0.2502 (P<.001), a peak signal-to-noise ratio of 15.8935 versus 14.9430 (P<.001), and a learned perceptual image patch similarity score of 0.4490 versus 0.5005 (P<.001). These results demonstrate the model's superior ability to enhance image quality while preserving critical anatomical details, thereby improving diagnostic usefulness.</p><p><strong>Conclusions: </strong>This study presents a significant advancement in ultrasound imaging by leveraging a CycleGAN model enhanced with perceptual loss to bridge the quality gap ","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e58911"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848586","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":"Validation of a Wearable Sensor Prototype for Measuring Heart Rate to Prescribe Physical Activity: Cross-Sectional Exploratory Study.","authors":"Fernanda Laís Loro, Riane Martins, Janaína Barcellos Ferreira, Cintia Laura Pereira de Araujo, Lucio Rene Prade, Cristiano Bonato Both, Jéferson Campos Nobre Nobre, Mariane Borba Monteiro, Pedro Dal Lago","doi":"10.2196/57373","DOIUrl":"10.2196/57373","url":null,"abstract":"<p><strong>Background: </strong>Wearable sensors are rapidly evolving, particularly in health care, due to their ability to facilitate continuous or on-demand physiological monitoring.</p><p><strong>Objective: </strong>This study aimed to design and validate a wearable sensor prototype incorporating photoplethysmography (PPG) and long-range wide area network technology for heart rate (HR) measurement during a functional test.</p><p><strong>Methods: </strong>We conducted a transversal exploratory study involving 20 healthy participants aged between 20 and 30 years without contraindications for physical exercise. Initially, our laboratory developed a pulse wearable sensor prototype for HR monitoring. Following this, the participants were instructed to perform the Incremental Shuttle Walk Test while wearing the Polar H10 HR chest strap sensor (the reference for HR measurement) and the wearable sensor. This test allowed for real-time comparison of HR responses between the 2 devices. Agreement between these measurements was determined using the intraclass correlation coefficient (ICC_3.1) and Lin concordance correlation coefficient. The mean absolute percentage error was calculated to evaluate reliability or validity. Cohen d was used to calculate the agreement's effect size.</p><p><strong>Results: </strong>The mean differences between the Polar H10 and the wearable sensor during the test were -2.6 (95% CI -3.5 to -1.8) for rest HR, -4.1 (95% CI -5.3 to -3) for maximum HR, -2.4 (95% CI -3.5 to -1.4) for mean test HR, and -2.5 (95% CI -3.6 to -1.5) for mean recovery HR. The mean absolute percentage errors were -3% for rest HR, -2.2% for maximum HR, -1.8% for mean test HR, and -1.6% for recovery HR. Excellent agreement was observed between the Polar H10 and the wearable sensor for rest HR (ICC_3.1=0.96), mean test HR (ICC_3.1=0.92), and mean recovery HR (ICC_3.1=0.96). The agreement for maximum HR (ICC_3.1=0.78) was considered good. By the Lin concordance correlation coefficient, the agreement was found to be substantial for rest HR (r_c=0.96) and recovery HR (r_c=0.96), moderate for mean test HR (r_c=0.92), and poor for maximum HR (r_c=0.78). The power of agreement between the Polar H10 and the wearable sensor prototype was large for baseline HR (Cohen d=0.97), maximum HR (Cohen d=1.18), and mean recovery HR (Cohen d=0.8) and medium for mean test HR (Cohen d= 0.76).</p><p><strong>Conclusions: </strong>The pulse-wearable sensor prototype tested in this study proves to be a valid tool for monitoring HR at rest, during functional tests, and during recovery compared with the Polar H10 reference device used in the laboratory setting.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e57373"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11669869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808673","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":"User Perceptions of Wearability of Knitted Sensor Garments for Long-Term Monitoring of Breathing Health: Thematic Analysis of Focus Groups and a Questionnaire Survey.","authors":"Kristel Fobelets, Nikita Mohanty, Mara Thielemans, Lieze Thielemans, Gillian Lake-Thompson, Meijing Liu, Kate Jopling, Kai Yang","doi":"10.2196/58166","DOIUrl":"10.2196/58166","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e58166"},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808517","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":"Trends in South Korean Medical Device Development for Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder: Narrative Review.","authors":"Yunah Cho, Sharon L Talboys","doi":"10.2196/60399","DOIUrl":"10.2196/60399","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are among the most prevalent mental disorders among school-aged youth in South Korea and may play a role in the increasing pressures on teachers and school-based special education programming. A lack of support for special education; tensions between teachers, students, and parents; and limited backup for teacher absences are common complaints among Korean educators. New innovations in technology to screen and treat ADHD and ASD may offer relief to students, parents, and teachers through earlier and efficient diagnosis; access to treatment options; and ultimately, better-managed care and expectations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This narrative literature review provides an account of medical device use and development in South Korea for the diagnosis and management of ADHD and ASD and highlights research gaps.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A narrative review was conducted across 4 databases (PubMed, Korean National Assembly Library, Scopus, and PsycINFO). Journal articles, dissertations, and government research and development reports were included if they discussed medical devices for ADHD and ASD. Only Korean or English papers were included. Resources were excluded if they did not correspond to the research objective or did not discuss at least 1 topic about medical devices for ADHD and ASD. Journal articles were excluded if they were not peer reviewed. Resources were limited to publications between 2013 and July 22, 2024.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 1794 records about trends in Korean medical device development were categorized into 2 major groups: digital therapeutics and traditional therapy. Digital therapeutics resulted in 5 subgroups: virtual reality and artificial intelligence, machine learning and robot, gaming and visual contents, eye-feedback and movement intervention, and electroencephalography and neurofeedback. Traditional therapy resulted in 3 subgroups: cognitive behavioral therapy and working memory; diagnosis and rating scale; and musical, literary therapy, and mindfulness-based stress reduction. Digital therapeutics using artificial intelligence, machine learning, and electroencephalography technologies account for the biggest portions of development in South Korea, rather than traditional therapies. Most resources, 94.15% (1689/1794), were from the Korean National Assembly Library.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Limitations include small sizes of populations to conclude findings in many articles, a lower number of articles discussing medical devices for ASD, and a majority of articles being dissertations. Emerging digital medical devices and those integrated with traditional therapies are important solutions to reducing the prevalence rates of ADHD and ASD in South Korea by promoting early diagnosis and intervention. Furthermore, their application will relieve pressures on teachers and ","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e60399"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11522664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482742","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":"Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach.","authors":"Russell Jeter, Raymond Greenfield, Stephen N Housley, Igor Belykh","doi":"10.2196/56980","DOIUrl":"10.2196/56980","url":null,"abstract":"<p><strong>Background: </strong>Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. Traditionally, stroke rehabilitation occurs in inpatient and outpatient rehabilitation facilities. However, recent literature increasingly explores moving the recovery process into the home and integrating technology-based interventions. This study advances this goal by promoting in-home, autonomous recovery for patients who experienced a stroke through robotics-assisted rehabilitation and classifying stroke residual severity using machine learning methods.</p><p><strong>Objective: </strong>Our main objective is to use kinematics data collected during in-home, self-guided therapy sessions to develop supervised machine learning methods, to address a clinician's autonomous classification of stroke residual severity-labeled data toward improving in-home, robotics-assisted stroke rehabilitation.</p><p><strong>Methods: </strong>In total, 33 patients who experienced a stroke participated in in-home therapy sessions using Motus Nova robotics rehabilitation technology to capture upper and lower body motion. During each therapy session, the Motus Hand and Motus Foot devices collected movement data, assistance data, and activity-specific data. We then synthesized, processed, and summarized these data. Next, the therapy session data were paired with clinician-informed, discrete stroke residual severity labels: \"no range of motion (ROM),\" \"low ROM,\" and \"high ROM.\" Afterward, an 80%:20% split was performed to divide the dataset into a training set and a holdout test set. We used 4 machine learning algorithms to classify stroke residual severity: light gradient boosting (LGB), extra trees classifier, deep feed-forward neural network, and classical logistic regression. We selected models based on 10-fold cross-validation and measured their performance on a holdout test dataset using F₁-score to identify which model maximizes stroke residual severity classification accuracy.</p><p><strong>Results: </strong>We demonstrated that the LGB method provides the most reliable autonomous detection of stroke severity. The trained model is a consensus model that consists of 139 decision trees with up to 115 leaves each. This LGB model boasts a 96.70% F₁-score compared to logistic regression (55.82%), extra trees classifier (94.81%), and deep feed-forward neural network (70.11%).</p><p><strong>Conclusions: </strong>We showed how objectively measured rehabilitation training paired with machine learning methods can be used to identify the residual stroke severity class, with efforts to enhance in-home self-guided, individualized stroke rehabilitation. The model we trained relies only on session summary statistics, meaning it can potentially be integrated into similar settings for real-time classification, such as outpatient rehabilitation facilities.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e56980"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Accuracy of Smartwatch-Based Estimation of Maximum Oxygen Uptake Using the Apple Watch Series 7: Validation Study.","authors":"Polona Caserman, Sungsoo Yum, Stefan Göbel, Andreas Reif, Silke Matura","doi":"10.2196/59459","DOIUrl":"10.2196/59459","url":null,"abstract":"<p><strong>Background: </strong>Determining maximum oxygen uptake (VO₂max) is essential for evaluating cardiorespiratory fitness. While laboratory-based testing is considered the gold standard, sports watches or fitness trackers offer a convenient alternative. However, despite the high number of wrist-worn devices, there is a lack of scientific validation for VO₂max estimation outside the laboratory setting.</p><p><strong>Objective: </strong>This study aims to compare the Apple Watch Series 7's performance against the gold standard in VO₂max estimation and Apple's validation findings.</p><p><strong>Methods: </strong>A total of 19 participants (7 female and 12 male), aged 18 to 63 (mean 28.42, SD 11.43) years were included in the validation study. VO₂max for all participants was determined in a controlled laboratory environment using a metabolic gas analyzer. Thereby, they completed a graded exercise test on a cycle ergometer until reaching subjective exhaustion. This value was then compared with the estimated VO₂max value from the Apple Watch, which was calculated after wearing the watch for at least 2 consecutive days and measured directly after an outdoor running test.</p><p><strong>Results: </strong>The measured VO₂max (mean 45.88, SD 9.42 mL/kg/minute) in the laboratory setting was significantly higher than the predicted VO₂max (mean 41.37, SD 6.5 mL/kg/minute) from the Apple Watch (t₁₈=2.51; P=.01) with a medium effect size (Hedges g=0.53). The Bland-Altman analysis revealed a good overall agreement between both measurements. However, the intraclass correlation coefficient ICC(2,1)=0.47 (95% CI 0.06-0.75) indicated poor reliability. The mean absolute percentage error between the predicted and the actual VO₂max was 15.79%, while the root mean square error was 8.85 mL/kg/minute. The analysis further revealed higher accuracy when focusing on participants with good fitness levels (mean absolute percentage error=14.59%; root-mean-square error=7.22 ml/kg/minute; ICC(2,1)=0.60 95% CI 0.09-0.87).</p><p><strong>Conclusions: </strong>Similar to other smartwatches, the Apple Watch also overestimates or underestimates the VO₂max in individuals with poor or excellent fitness levels, respectively. Assessing the accuracy and reliability of the Apple Watch's VO₂max estimation is crucial for determining its suitability as an alternative to laboratory testing. The findings of this study will apprise researchers, physical training professionals, and end users of wearable technology, thereby enhancing the knowledge base and practical application of such devices in assessing cardiorespiratory fitness parameters.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e59459"},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861892","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":"Agreement Between Apple Watch and Actical Step Counts in a Community Setting: Cross-Sectional Investigation From the Framingham Heart Study.","authors":"Nicole L Spartano, Yuankai Zhang, Chunyu Liu, Ariel Chernofsky, Honghuang Lin, Ludovic Trinquart, Belinda Borrelli, Chathurangi H Pathiravasan, Vik Kheterpal, Christopher Nowak, Ramachandran S Vasan, Emelia J Benjamin, David D McManus, Joanne M Murabito","doi":"10.2196/54631","DOIUrl":"10.2196/54631","url":null,"abstract":"<p><strong>Background: </strong>Step counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings.</p><p><strong>Objective: </strong>The purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting.</p><p><strong>Methods: </strong>Among Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices.</p><p><strong>Results: </strong>We studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity.</p><p><strong>Conclusions: </strong>Our investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e54631"},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11306942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763180","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":"Stroke Survivors' Interaction With Hand Rehabilitation Devices: Observational Study.","authors":"Chioma Obinuchi Wodu, Gillian Sweeney, Milena Slachetka, Andrew Kerr","doi":"10.2196/54159","DOIUrl":"10.2196/54159","url":null,"abstract":"<p><strong>Background: </strong>The hand is crucial for carrying out activities of daily living as well as social interaction. Functional use of the upper limb is affected in up to 55% to 75% of stroke survivors 3 to 6 months after stroke. Rehabilitation can help restore function, and several rehabilitation devices have been designed to improve hand function. However, access to these devices is compromised in people with more severe loss of function.</p><p><strong>Objective: </strong>In this study, we aimed to observe stroke survivors with poor hand function interacting with a range of commonly used hand rehabilitation devices.</p><p><strong>Methods: </strong>Participants were engaged in an 8-week rehabilitation intervention at a technology-enriched rehabilitation gym. The participants spent 50-60 minutes of the 2-hour session in the upper limb section at least twice a week. Each participant communicated their rehabilitation goals, and an Action Research Arm Test (ARAT) was used to measure and categorize hand function as poor (scores of 0-9), moderate (scores of 10-56), or good (score of 57). Participants were observed during their interactions with 3 hand-based rehabilitation devices that focused on hand rehabilitation: the GripAble, NeuroBall, and Semi-Circular Peg Board. Observations of device interactions were recorded for each session.</p><p><strong>Results: </strong>A total of 29 participants were included in this study, of whom 10 (34%) had poor hand function, 17 (59%) had moderate hand function, and 2 (7%) had good hand function. There were no differences in the age and years after stroke among participants with poor hand function and those with moderate (P=.06 and P=.09, respectively) and good (P=.37 and P=.99, respectively) hand function. Regarding the ability of the 10 participants with poor hand function to interact with the 3 hand-based rehabilitation devices, 2 (20%) participants with an ARAT score greater than 0 were able to interact with the devices, whereas the other 8 (80%) who had an ARAT score of 0 could not. Their inability to interact with these devices was clinically examined, and the reason was determined to be a result of either the presence of (1) muscle tone or stiffness or (2) muscle weakness.</p><p><strong>Conclusions: </strong>Not all stroke survivors with impairments in their hands can make use of currently available rehabilitation technologies. Those with an ARAT score of 0 cannot actively interact with hand rehabilitation devices, as they cannot carry out the hand movement necessary for such interaction. The design of devices for hand rehabilitation should consider the accessibility needs of those with poor hand function.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e54159"},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11237792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452412","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}