Pub Date : 2024-07-02DOI: 10.1038/s41746-024-01156-y
Rebecca Mathias, Peter McCulloch, Anastasia Chalkidou, Stephen Gilbert
Individual digital health devices are increasingly being bundled together as interacting, multicomponent suites, to deliver clinical services (e.g., teleconsultation and ‘hospital-at-home services’). In the first article of this two-article series we described the challenges in implementation and the current limitations in frameworks for the regulation, health technology assessment, and reimbursement of these device suites and linked novel care pathways. A flexible and fit-for-purpose evaluation framework that can analyze the strengths and weaknesses of digital technology suites is needed. In this second article we describe adaptations that could enable this new technological paradigm while maintaining patient safety and fair value.
{"title":"How can regulation and reimbursement better accommodate flexible suites of digital health technologies?","authors":"Rebecca Mathias, Peter McCulloch, Anastasia Chalkidou, Stephen Gilbert","doi":"10.1038/s41746-024-01156-y","DOIUrl":"10.1038/s41746-024-01156-y","url":null,"abstract":"Individual digital health devices are increasingly being bundled together as interacting, multicomponent suites, to deliver clinical services (e.g., teleconsultation and ‘hospital-at-home services’). In the first article of this two-article series we described the challenges in implementation and the current limitations in frameworks for the regulation, health technology assessment, and reimbursement of these device suites and linked novel care pathways. A flexible and fit-for-purpose evaluation framework that can analyze the strengths and weaknesses of digital technology suites is needed. In this second article we describe adaptations that could enable this new technological paradigm while maintaining patient safety and fair value.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01156-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.1038/s41746-024-01165-x
Donnchadh O’Sullivan, Scott Anjewierden, Grace Greason, Itzhak Zachi Attia, Francisco Lopez-Jimenez, Paul A. Friedman, Peter Noseworthy, Jason Anderson, Anthony Kashou, Samuel J. Asirvatham, Benjamin W. Eidem, Jonathan N. Johnson, Talha Niaz, Malini Madhavan
AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model’s discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.
{"title":"Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development","authors":"Donnchadh O’Sullivan, Scott Anjewierden, Grace Greason, Itzhak Zachi Attia, Francisco Lopez-Jimenez, Paul A. Friedman, Peter Noseworthy, Jason Anderson, Anthony Kashou, Samuel J. Asirvatham, Benjamin W. Eidem, Jonathan N. Johnson, Talha Niaz, Malini Madhavan","doi":"10.1038/s41746-024-01165-x","DOIUrl":"10.1038/s41746-024-01165-x","url":null,"abstract":"AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model’s discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01165-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1038/s41746-024-01148-y
Hang Yuan, Tatiana Plekhanova, Rosemary Walmsley, Amy C. Reynolds, Kathleen J. Maddison, Maja Bucan, Philip Gehrman, Alex Rowlands, David W. Ray, Derrick Bennett, Joanne McVeigh, Leon Straker, Peter Eastwood, Simon D. Kyle, Aiden Doherty
{"title":"Author Correction: Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality","authors":"Hang Yuan, Tatiana Plekhanova, Rosemary Walmsley, Amy C. Reynolds, Kathleen J. Maddison, Maja Bucan, Philip Gehrman, Alex Rowlands, David W. Ray, Derrick Bennett, Joanne McVeigh, Leon Straker, Peter Eastwood, Simon D. Kyle, Aiden Doherty","doi":"10.1038/s41746-024-01148-y","DOIUrl":"10.1038/s41746-024-01148-y","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11217470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-29DOI: 10.1038/s41746-024-01169-7
Claire Garnett, Larisa-Maria Dinu, Melissa Oldham, Olga Perski, Gemma Loebenberg, Emma Beard, Colin Angus, Robyn Burton, Matt Field, Felix Greaves, Matthew Hickman, Eileen Kaner, Susan Michie, Marcus Munafò, Elena Pizzo, Jamie Brown
This is a process evaluation of a large UK-based randomised controlled trial (RCT) (n = 5602) evaluating the effectiveness of recommending an alcohol reduction app, Drink Less, compared with usual digital care in reducing alcohol consumption in increasing and higher risk drinkers. The aim was to understand whether participants’ engagement (‘self-reported adherence’) and behavioural characteristics were mechanisms of action underpinning the effectiveness of Drink Less. Self-reported adherence with both digital tools was over 70% (Drink Less: 78.0%, 95% CI = 77.6–78.4; usual digital care: 71.5%, 95% CI = 71.0–71.9). Self-reported adherence to the intervention (average causal mediation effect [ACME] = −0.250, 95% CI = −0.42, −0.11) and self-monitoring behaviour (ACME = −0.235, 95% CI = −0.44, −0.03) both partially mediated the effect of the intervention (versus comparator) on alcohol reduction. Following the recommendation (self-reported adherence) and the tracking (self-monitoring behaviour) feature of the Drink Less app appear to be important mechanisms of action for alcohol reduction among increasing and higher risk drinkers.
这是对英国一项大型随机对照试验（RCT）（n = 5602）进行的过程评估，该试验评估了推荐一款名为 "少喝点 "的减酒应用程序与常规数字护理相比，在减少日益饮酒者和高风险饮酒者的饮酒量方面的效果。目的是了解参与者的参与度（"自我报告的依从性"）和行为特征是否是支持 "少喝点 "有效性的作用机制。两种数字工具的自报坚持率均超过70%（少喝一点：78.0%，95% CI = 77.6-78.4；常规数字护理：71.5%，95% CI = 71.0-71.9）。自我报告的干预坚持率（平均因果中介效应[ACME] = -0.250，95% CI = -0.42，-0.11）和自我监测行为（ACME = -0.235，95% CI = -0.44，-0.03）均部分中介了干预（相对于比较者）对减少饮酒的影响。少饮应用程序的推荐（自我报告的遵守情况）和跟踪（自我监控行为）功能似乎是对饮酒量增加和饮酒风险较高者进行减酒的重要作用机制。
{"title":"Do engagement and behavioural mechanisms underpin the effectiveness of the Drink Less app?","authors":"Claire Garnett, Larisa-Maria Dinu, Melissa Oldham, Olga Perski, Gemma Loebenberg, Emma Beard, Colin Angus, Robyn Burton, Matt Field, Felix Greaves, Matthew Hickman, Eileen Kaner, Susan Michie, Marcus Munafò, Elena Pizzo, Jamie Brown","doi":"10.1038/s41746-024-01169-7","DOIUrl":"10.1038/s41746-024-01169-7","url":null,"abstract":"This is a process evaluation of a large UK-based randomised controlled trial (RCT) (n = 5602) evaluating the effectiveness of recommending an alcohol reduction app, Drink Less, compared with usual digital care in reducing alcohol consumption in increasing and higher risk drinkers. The aim was to understand whether participants’ engagement (‘self-reported adherence’) and behavioural characteristics were mechanisms of action underpinning the effectiveness of Drink Less. Self-reported adherence with both digital tools was over 70% (Drink Less: 78.0%, 95% CI = 77.6–78.4; usual digital care: 71.5%, 95% CI = 71.0–71.9). Self-reported adherence to the intervention (average causal mediation effect [ACME] = −0.250, 95% CI = −0.42, −0.11) and self-monitoring behaviour (ACME = −0.235, 95% CI = −0.44, −0.03) both partially mediated the effect of the intervention (versus comparator) on alcohol reduction. Following the recommendation (self-reported adherence) and the tracking (self-monitoring behaviour) feature of the Drink Less app appear to be important mechanisms of action for alcohol reduction among increasing and higher risk drinkers.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01169-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-29DOI: 10.1038/s41746-024-01177-7
Sarah Wilson, Clare Tolley, Ríona Mc Ardle, Lauren Lawson, Emily Beswick, Nehal Hassan, Robert Slight, Sarah Slight
The World Health Organisation advocates Digital Health Technologies (DHTs) for advancing population health, yet concerns about inequitable outcomes persist. Differences in access and use of DHTs across different demographic groups can contribute to inequities. Academics and policy makers have acknowledged this issue and called for inclusive digital health strategies. This systematic review synthesizes literature on these strategies and assesses facilitators and barriers to their implementation. We searched four large databases for qualitative studies using terms relevant to digital technology, health inequities, and socio-demographic factors associated with digital exclusion summarised by the CLEARS framework (Culture, Limiting conditions, Education, Age, Residence, Socioeconomic status). Following the PRISMA guidelines, 10,401 articles were screened independently by two reviewers, with ten articles meeting our inclusion criteria. Strategies were grouped into either outreach programmes or co-design approaches. Narrative synthesis of these strategies highlighted three key themes: firstly, using user-friendly designs, which included software and website interfaces that were easy to navigate and compatible with existing devices, culturally appropriate content, and engaging features. Secondly, providing supportive infrastructure to users, which included devices, free connectivity, and non-digital options to help access healthcare. Thirdly, providing educational support from family, friends, or professionals to help individuals develop their digital literacy skills to support the use of DHTs. Recommendations for advancing digital health equity include adopting a collaborative working approach to meet users’ needs, and using effective advertising to raise awareness of the available support. Further research is needed to assess the feasibility and impact of these recommendations in practice.
{"title":"Recommendations to advance digital health equity: a systematic review of qualitative studies","authors":"Sarah Wilson, Clare Tolley, Ríona Mc Ardle, Lauren Lawson, Emily Beswick, Nehal Hassan, Robert Slight, Sarah Slight","doi":"10.1038/s41746-024-01177-7","DOIUrl":"10.1038/s41746-024-01177-7","url":null,"abstract":"The World Health Organisation advocates Digital Health Technologies (DHTs) for advancing population health, yet concerns about inequitable outcomes persist. Differences in access and use of DHTs across different demographic groups can contribute to inequities. Academics and policy makers have acknowledged this issue and called for inclusive digital health strategies. This systematic review synthesizes literature on these strategies and assesses facilitators and barriers to their implementation. We searched four large databases for qualitative studies using terms relevant to digital technology, health inequities, and socio-demographic factors associated with digital exclusion summarised by the CLEARS framework (Culture, Limiting conditions, Education, Age, Residence, Socioeconomic status). Following the PRISMA guidelines, 10,401 articles were screened independently by two reviewers, with ten articles meeting our inclusion criteria. Strategies were grouped into either outreach programmes or co-design approaches. Narrative synthesis of these strategies highlighted three key themes: firstly, using user-friendly designs, which included software and website interfaces that were easy to navigate and compatible with existing devices, culturally appropriate content, and engaging features. Secondly, providing supportive infrastructure to users, which included devices, free connectivity, and non-digital options to help access healthcare. Thirdly, providing educational support from family, friends, or professionals to help individuals develop their digital literacy skills to support the use of DHTs. Recommendations for advancing digital health equity include adopting a collaborative working approach to meet users’ needs, and using effective advertising to raise awareness of the available support. Further research is needed to assess the feasibility and impact of these recommendations in practice.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01177-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-27DOI: 10.1038/s41746-024-01164-y
Abrar Majeedi, Ryan M. McAdams, Ravneet Kaur, Shubham Gupta, Harpreet Singh, Yin Li
Early-life exposure to stress results in significantly increased risk of neurodevelopmental impairments with potential long-term effects into childhood and even adulthood. As a crucial step towards monitoring neonatal stress in neonatal intensive care units (NICUs), our study aims to quantify the duration, frequency, and physiological responses of care manipulation activities, based on bedside videos and physiological signals. Leveraging 289 h of video recordings and physiological data within 330 sessions collected from 27 neonates in 2 NICUs, we develop and evaluate a deep learning method to detect manipulation activities from the video, to estimate their duration and frequency, and to further integrate physiological signals for assessing their responses. With a 13.8% relative error tolerance for activity duration and frequency, our results were statistically equivalent to human annotations. Further, our method proved effective for estimating short-term physiological responses, for detecting activities with marked physiological deviations, and for quantifying the neonatal infant stressor scale scores.
{"title":"Deep learning to quantify care manipulation activities in neonatal intensive care units","authors":"Abrar Majeedi, Ryan M. McAdams, Ravneet Kaur, Shubham Gupta, Harpreet Singh, Yin Li","doi":"10.1038/s41746-024-01164-y","DOIUrl":"10.1038/s41746-024-01164-y","url":null,"abstract":"Early-life exposure to stress results in significantly increased risk of neurodevelopmental impairments with potential long-term effects into childhood and even adulthood. As a crucial step towards monitoring neonatal stress in neonatal intensive care units (NICUs), our study aims to quantify the duration, frequency, and physiological responses of care manipulation activities, based on bedside videos and physiological signals. Leveraging 289 h of video recordings and physiological data within 330 sessions collected from 27 neonates in 2 NICUs, we develop and evaluate a deep learning method to detect manipulation activities from the video, to estimate their duration and frequency, and to further integrate physiological signals for assessing their responses. With a 13.8% relative error tolerance for activity duration and frequency, our results were statistically equivalent to human annotations. Further, our method proved effective for estimating short-term physiological responses, for detecting activities with marked physiological deviations, and for quantifying the neonatal infant stressor scale scores.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01164-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-27DOI: 10.1038/s41746-024-01166-w
Lin Lawrence Guo, Jason Fries, Ethan Steinberg, Scott Lanyon Fleming, Keith Morse, Catherine Aftandilian, Jose Posada, Nigam Shah, Lillian Sung
Foundation models are transforming artificial intelligence (AI) in healthcare by providing modular components adaptable for various downstream tasks, making AI development more scalable and cost-effective. Foundation models for structured electronic health records (EHR), trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness to distribution shifts. However, questions remain on the feasibility of sharing these models across hospitals and their performance in local tasks. This multi-center study examined the adaptability of a publicly accessible structured EHR foundation model (FMSM), trained on 2.57 M patient records from Stanford Medicine. Experiments used EHR data from The Hospital for Sick Children (SickKids) and Medical Information Mart for Intensive Care (MIMIC-IV). We assessed both adaptability via continued pretraining on local data, and task adaptability compared to baselines of locally training models from scratch, including a local foundation model. Evaluations on 8 clinical prediction tasks showed that adapting the off-the-shelf FMSM matched the performance of gradient boosting machines (GBM) locally trained on all data while providing a 13% improvement in settings with few task-specific training labels. Continued pretraining on local data showed FMSM required fewer than 1% of training examples to match the fully trained GBM’s performance, and was 60 to 90% more sample-efficient than training local foundation models from scratch. Our findings demonstrate that adapting EHR foundation models across hospitals provides improved prediction performance at less cost, underscoring the utility of base foundation models as modular components to streamline the development of healthcare AI.
{"title":"A multi-center study on the adaptability of a shared foundation model for electronic health records","authors":"Lin Lawrence Guo, Jason Fries, Ethan Steinberg, Scott Lanyon Fleming, Keith Morse, Catherine Aftandilian, Jose Posada, Nigam Shah, Lillian Sung","doi":"10.1038/s41746-024-01166-w","DOIUrl":"10.1038/s41746-024-01166-w","url":null,"abstract":"Foundation models are transforming artificial intelligence (AI) in healthcare by providing modular components adaptable for various downstream tasks, making AI development more scalable and cost-effective. Foundation models for structured electronic health records (EHR), trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness to distribution shifts. However, questions remain on the feasibility of sharing these models across hospitals and their performance in local tasks. This multi-center study examined the adaptability of a publicly accessible structured EHR foundation model (FMSM), trained on 2.57 M patient records from Stanford Medicine. Experiments used EHR data from The Hospital for Sick Children (SickKids) and Medical Information Mart for Intensive Care (MIMIC-IV). We assessed both adaptability via continued pretraining on local data, and task adaptability compared to baselines of locally training models from scratch, including a local foundation model. Evaluations on 8 clinical prediction tasks showed that adapting the off-the-shelf FMSM matched the performance of gradient boosting machines (GBM) locally trained on all data while providing a 13% improvement in settings with few task-specific training labels. Continued pretraining on local data showed FMSM required fewer than 1% of training examples to match the fully trained GBM’s performance, and was 60 to 90% more sample-efficient than training local foundation models from scratch. Our findings demonstrate that adapting EHR foundation models across hospitals provides improved prediction performance at less cost, underscoring the utility of base foundation models as modular components to streamline the development of healthcare AI.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01166-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-26DOI: 10.1038/s41746-024-01163-z
Weishan Zhang, Yun Ling, Zhonglue Chen, Kang Ren, Shengdi Chen, Pei Huang, Yuyan Tan
Gait impairments are among the most common and disabling symptoms of Parkinson’s disease and worsen as the disease progresses. Early detection and diagnosis of subtype-specific gait deficits, as well as progression monitoring, can help to implement effective and preventive personalized treatment for PD patients. Yet, the gait features have not been fully studied in PD and its motor subtypes. To characterize comprehensive and objective gait alterations and to identify the potential gait biomarkers for early diagnosis, subtype differentiation, and disease severity monitoring. We analyzed gait parameters related to upper/lower limbs, trunk and lumbar, and postural transitions from 24 tremor-dominant (TD) and 20 postural instability gait difficulty (PIGD) dominant PD patients who were in early stage and 39 matched healthy controls (HC) during the Timed Up and Go test using wearable sensors. Results show: (1) Both TD and PIGD groups showed restricted backswing range in bilateral lower extremities and more affected side (MAS) arm, reduced trunk and lumbar rotation range in the coronal plane, and low turning efficiency. The receiver operating characteristic (ROC) analysis revealed these objective gait features had high discriminative value in distinguishing both PD subtypes from the HC with the area under the curve (AUC) values of 0.7~0.9 (p < 0.01). (2) Subtle but measurable gait differences existed between TD and PIGD patients before the onset of clinically apparent gait impairment. (3) Specific gait parameters were significantly associated with disease severity in TD and PIGD subtypes. Objective gait biomarkers based on wearable sensors may facilitate timely and personalized gait treatments in PD subtypes through early diagnosis, subtype differentiation, and disease severity monitoring.
{"title":"Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes","authors":"Weishan Zhang, Yun Ling, Zhonglue Chen, Kang Ren, Shengdi Chen, Pei Huang, Yuyan Tan","doi":"10.1038/s41746-024-01163-z","DOIUrl":"10.1038/s41746-024-01163-z","url":null,"abstract":"Gait impairments are among the most common and disabling symptoms of Parkinson’s disease and worsen as the disease progresses. Early detection and diagnosis of subtype-specific gait deficits, as well as progression monitoring, can help to implement effective and preventive personalized treatment for PD patients. Yet, the gait features have not been fully studied in PD and its motor subtypes. To characterize comprehensive and objective gait alterations and to identify the potential gait biomarkers for early diagnosis, subtype differentiation, and disease severity monitoring. We analyzed gait parameters related to upper/lower limbs, trunk and lumbar, and postural transitions from 24 tremor-dominant (TD) and 20 postural instability gait difficulty (PIGD) dominant PD patients who were in early stage and 39 matched healthy controls (HC) during the Timed Up and Go test using wearable sensors. Results show: (1) Both TD and PIGD groups showed restricted backswing range in bilateral lower extremities and more affected side (MAS) arm, reduced trunk and lumbar rotation range in the coronal plane, and low turning efficiency. The receiver operating characteristic (ROC) analysis revealed these objective gait features had high discriminative value in distinguishing both PD subtypes from the HC with the area under the curve (AUC) values of 0.7~0.9 (p < 0.01). (2) Subtle but measurable gait differences existed between TD and PIGD patients before the onset of clinically apparent gait impairment. (3) Specific gait parameters were significantly associated with disease severity in TD and PIGD subtypes. Objective gait biomarkers based on wearable sensors may facilitate timely and personalized gait treatments in PD subtypes through early diagnosis, subtype differentiation, and disease severity monitoring.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01163-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-25DOI: 10.1038/s41746-024-01162-0
Fiona Tea, Adam M. R. Groh, Colleen Lacey, Afolasade Fakolade
Digital health technologies (DHTs) have become progressively more integrated into the healthcare of people with multiple sclerosis (MS). To ensure that DHTs meet end-users’ needs, it is essential to assess their usability. The objective of this study was to determine how DHTs targeting people with MS incorporate usability characteristics into their design and/or evaluation. We conducted a scoping review of DHT studies in MS published from 2010 to the present using PubMed, Web of Science, OVID Medline, CINAHL, Embase, and medRxiv. Covidence was used to facilitate the review. We included articles that focused on people with MS and/or their caregivers, studied DHTs (including mhealth, telehealth, and wearables), and employed quantitative, qualitative, or mixed methods designs. Thirty-two studies that assessed usability were included, which represents a minority of studies (26%) that assessed DHTs in MS. The most common DHT was mobile applications (n = 23, 70%). Overall, studies were highly heterogeneous with respect to what usability principles were considered and how usability was assessed. These findings suggest that there is a major gap in the application of standardized usability assessments to DHTs in MS. Improvements in the standardization of usability assessments will have implications for the future of digital health care for people with MS.
{"title":"A scoping review assessing the usability of digital health technologies targeting people with multiple sclerosis","authors":"Fiona Tea, Adam M. R. Groh, Colleen Lacey, Afolasade Fakolade","doi":"10.1038/s41746-024-01162-0","DOIUrl":"10.1038/s41746-024-01162-0","url":null,"abstract":"Digital health technologies (DHTs) have become progressively more integrated into the healthcare of people with multiple sclerosis (MS). To ensure that DHTs meet end-users’ needs, it is essential to assess their usability. The objective of this study was to determine how DHTs targeting people with MS incorporate usability characteristics into their design and/or evaluation. We conducted a scoping review of DHT studies in MS published from 2010 to the present using PubMed, Web of Science, OVID Medline, CINAHL, Embase, and medRxiv. Covidence was used to facilitate the review. We included articles that focused on people with MS and/or their caregivers, studied DHTs (including mhealth, telehealth, and wearables), and employed quantitative, qualitative, or mixed methods designs. Thirty-two studies that assessed usability were included, which represents a minority of studies (26%) that assessed DHTs in MS. The most common DHT was mobile applications (n = 23, 70%). Overall, studies were highly heterogeneous with respect to what usability principles were considered and how usability was assessed. These findings suggest that there is a major gap in the application of standardized usability assessments to DHTs in MS. Improvements in the standardization of usability assessments will have implications for the future of digital health care for people with MS.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01162-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141451075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-25DOI: 10.1038/s41746-024-01170-0
Libor Pastika, Arunashis Sau, Konstantinos Patlatzoglou, Ewa Sieliwonczyk, Antônio H. Ribeiro, Kathryn A. McGurk, Sadia Khan, Danilo Mandic, William R. Scott, James S. Ware, Nicholas S. Peters, Antonio Luiz P. Ribeiro, Daniel B. Kramer, Jonathan W. Waks, Fu Siong Ng
The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.
{"title":"Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease","authors":"Libor Pastika, Arunashis Sau, Konstantinos Patlatzoglou, Ewa Sieliwonczyk, Antônio H. Ribeiro, Kathryn A. McGurk, Sadia Khan, Danilo Mandic, William R. Scott, James S. Ware, Nicholas S. Peters, Antonio Luiz P. Ribeiro, Daniel B. Kramer, Jonathan W. Waks, Fu Siong Ng","doi":"10.1038/s41746-024-01170-0","DOIUrl":"10.1038/s41746-024-01170-0","url":null,"abstract":"The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01170-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141451076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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