Lancet Digital Health最新文献

{"title":"Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies","authors":"Peter J Cho BA , Iredia M Olaye PhD , Md Mobashir Hasan Shandhi PhD , Eric J Daza DrPH , Luca Foschini PhD , Prof Jessilyn P Dunn PhD","doi":"10.1016/S2589-7500(24)00219-X","DOIUrl":"10.1016/S2589-7500(24)00219-X","url":null,"abstract":"<div><h3>Background</h3><div>Longitudinal digital health studies combine passively collected information from digital devices, such as commercial wearable devices, and actively contributed data, such as surveys, from participants. Although the use of smartphones and access to the internet supports the development of these studies, challenges exist in collecting representative data due to low adherence and retention. We aimed to identify key factors related to adherence and retention in digital health studies and develop a methodology to identify factors that are associated with and might affect study participant engagement.</div></div><div><h3>Methods</h3><div>In this exploratory secondary analysis, we used data from two separate prospective longitudinal digital health studies, conducted among adult participants (age ≥18 years) during the COVID-19 pandemic by the BIG IDEAs Laboratory (BIL) at Duke University (Durham, NC, USA; April 2, 2020 to May 25, 2021) and Evidation Health (San Mateo, CA, USA; April 4 to Aug 31, 2020). Prospective daily or weekly surveys were administered for up to 15 months in the BIL study and daily surveys were administered for 5 months in the Evidation Health study. We defined metrics related to adherence to assess how participants engage with longitudinal digital health studies and developed models to infer how demographic factors and the day of survey delivery might be associated with these metrics. We defined retention as the time until a participant drops out of the study. For the purpose of clustering analysis, we defined three metrics of survey adherence: (1) total number of surveys completed, (2) participation regularity (ie, frequency of filling out surveys consecutively), and (3) time of activity (ie, engagement pattern relative to enrolment time). We assessed these metrics and explored differences by age, sex, race, and day of survey delivery. We analysed the data by unsupervised clustering, survival analysis, and recurrent event analysis with multistate modelling, with analyses restricted to individuals who provided data on age, sex, and race.</div></div><div><h3>Findings</h3><div>In the BIL study, 5784 unique participants with the required demographic data completed 388 600 unique daily surveys (mean 67 [SD 90] surveys per participant). In the Evidation Health study, 89 479 unique participants with the required demographic data completed 2 080 992 unique daily surveys (23 [32] surveys per participant). Participants were grouped into adherence clusters based on the three metrics of adherence, and we identified statistically discernible differences in age, race, and sex between clusters. Most of the individuals aged 18–29 years were observed in the clusters with low or medium adherence, whereas the oldest age group (≥60 years) was generally more represented in clusters with high adherence than younger age groups. For retention, survival analysis indicated that 18–29 years was the age group with the highest risk ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e23-e34"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11725373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899342","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}

{"title":"Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations","authors":"Joseph E Alderman MBChB ,&nbsp;Joanne Palmer PhD ,&nbsp;Elinor Laws MBBCh ,&nbsp;Melissa D McCradden PhD ,&nbsp;Johan Ordish MA ,&nbsp;Marzyeh Ghassemi PhD ,&nbsp;Stephen R Pfohl PhD ,&nbsp;Negar Rostamzadeh PhD ,&nbsp;Heather Cole-Lewis PhD ,&nbsp;Prof Ben Glocker PhD ,&nbsp;Prof Melanie Calvert PhD ,&nbsp;Tom J Pollard PhD ,&nbsp;Jaspret Gill MSc ,&nbsp;Jacqui Gath MBCS ,&nbsp;Adewale Adebajo MBE ,&nbsp;Jude Beng BSc ,&nbsp;Cassandra H Leung ,&nbsp;Stephanie Kuku MD ,&nbsp;Lesley-Anne Farmer BSc ,&nbsp;Rubeta N Matin PhD ,&nbsp;Xiaoxuan Liu PhD","doi":"10.1016/S2589-7500(24)00224-3","DOIUrl":"10.1016/S2589-7500(24)00224-3","url":null,"abstract":"<div><div>Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. Draft recommendation items were informed by a systematic review and stakeholder survey. The recommendations were developed using a Delphi approach, supplemented by a public consultation and international interview study. Overall, more than 350 representatives from 58 countries provided input into this initiative. 194 Delphi participants from 25 countries voted and provided comments on 32 candidate items across three electronic survey rounds and one in-person consensus meeting. The 29 STANDING Together consensus recommendations are presented here in two parts. Recommendations for Documentation of Health Datasets provide guidance for dataset curators to enable transparency around data composition and limitations. Recommendations for Use of Health Datasets aim to enable identification and mitigation of algorithmic biases that might exacerbate health inequalities. These recommendations are intended to prompt proactive inquiry rather than acting as a checklist. We hope to raise awareness that no dataset is free of limitations, so transparent communication of data limitations should be perceived as valuable, and absence of this information as a limitation. We hope that adoption of the STANDING Together recommendations by stakeholders across the AI health technology lifecycle will enable everyone in society to benefit from technologies which are safe and effective.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e64-e88"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865660","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}

{"title":"Using artificial intelligence technologies to improve skin cancer detection in primary care","authors":"Owain T Jones ,&nbsp;Rubeta N Matin ,&nbsp;Fiona M Walter","doi":"10.1016/S2589-7500(24)00216-4","DOIUrl":"10.1016/S2589-7500(24)00216-4","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e8-e10"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814623","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}

{"title":"Building robust, proportionate, and timely approaches to regulation and evaluation of digital mental health technologies","authors":"Gareth Hopkin PhD ,&nbsp;Richard Branson MA ,&nbsp;Paul Campbell FRCA ,&nbsp;Holly Coole PgDip ,&nbsp;Sophie Cooper BSc ,&nbsp;Francesca Edelmann BSc ,&nbsp;Grace Gatera BA ,&nbsp;Jamie Morgan MA ,&nbsp;Mark Salmon MBA","doi":"10.1016/S2589-7500(24)00215-2","DOIUrl":"10.1016/S2589-7500(24)00215-2","url":null,"abstract":"<div><div>Demand for mental health services exceeds available resources globally, and access to diagnosis and evidence-based treatment is affected by long delays. Digital mental health technologies present an opportunity to reimagine the delivery of mental health support by providing innovative, effective, and tailored approaches that meet people's individual preferences and goals. These technologies also present new challenges, however, and efforts must be made to ensure they are safe and effective. The UK Medicines and Healthcare products Regulatory Agency and the National Institute for Health and Care Excellence have launched a partnership, funded by Wellcome, that explores regulation and evaluation of digital mental health technologies. This Viewpoint describes a series of key challenges across the regulatory and health technology assessment pathways and aims to facilitate discussions to ensure that approaches to regulation and evaluation are informed by patients, the public, and professionals working within mental health. We invite partners from across the mental health community to engage with, collaborate with, and provide scrutiny of this project to ensure it delivers the best possible outcomes.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 1","pages":"Pages e89-e93"},"PeriodicalIF":23.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645005","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}

{"title":"The importance of microbiology reference laboratories and adequate funding for infectious disease surveillance","authors":"David Shaw MBBCh ,&nbsp;Raquel Abad Torreblanca PhD ,&nbsp;Zahin Amin-Chowdhury MSc ,&nbsp;Adriana Bautista BSc ,&nbsp;Desiree Bennett PhD ,&nbsp;Karen Broughton MSc ,&nbsp;Carlo Casanova PhD ,&nbsp;Eun Hwa Choi MD ,&nbsp;Heike Claus PhD ,&nbsp;Mary Corcoran PhD ,&nbsp;Simon Cottrell PhD ,&nbsp;Prof Robert Cunney MB ,&nbsp;Lize Cuypers PhD ,&nbsp;Tine Dalby PhD ,&nbsp;Heather Davies NZCS ,&nbsp;Linda de Gouveia NatDipMedTech ,&nbsp;Ala-Eddine Deghmane PhD ,&nbsp;Stefanie Desmet PhD ,&nbsp;Mirian Domenech PhD ,&nbsp;Prof Richard Drew MD ,&nbsp;Prof Angela B Brueggemann DPhil","doi":"10.1016/S2589-7500(24)00241-3","DOIUrl":"10.1016/S2589-7500(24)00241-3","url":null,"abstract":"<div><div>Microbiology reference laboratories perform a crucial role within public health systems. This role was especially evident during the COVID-19 pandemic. In this Viewpoint, we emphasise the importance of microbiology reference laboratories and highlight the types of digital data and expertise they provide, which benefit national and international public health. We also highlight the value of surveillance initiatives among collaborative international partners, who work together to share, analyse, and interpret data, and then disseminate their findings in a timely manner. Microbiology reference laboratories have substantial impact at regional, national, and international levels, and sustained support for these laboratories is essential for public health in both pandemic and non-pandemic times.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e275-e281"},"PeriodicalIF":23.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873161","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}

{"title":"Addressing the challenges of integrating digital health technologies to measure patient-centred outcomes in clinical registries","authors":"Caroline Marra PhD ,&nbsp;Prof Tim Chico MD ,&nbsp;April Alexandrow PhD ,&nbsp;Prof Will G Dixon PhD ,&nbsp;Norman Briffa MD ,&nbsp;Erin Rainaldi MS ,&nbsp;Max A Little PhD ,&nbsp;Kristin Size MS ,&nbsp;Athanasios Tsanas PhD ,&nbsp;Joseph B Franklin JD ,&nbsp;Prof Ritu Kapur PhD ,&nbsp;Helen Grice ,&nbsp;Anwar Gariban ,&nbsp;Joy Ellery ,&nbsp;Cathie Sudlow FRCP ,&nbsp;Amy P Abernethy MD PhD ,&nbsp;Prof Andrew Morris MD","doi":"10.1016/S2589-7500(24)00223-1","DOIUrl":"10.1016/S2589-7500(24)00223-1","url":null,"abstract":"<div><div>Longitudinal patient registries generate important evidence for advancing clinical care and the regulatory evaluation of health-care products. Most national registries rely on data collected as part of routine clinical encounters, an approach that does not capture real-world, patient-centred outcomes, such as physical activity, fatigue, ability to do daily tasks, and other indicators of quality of life. Digital health technologies that obtain such real-world data could greatly enhance patient registries but unresolved challenges have so far prevented their broad adoption. Based on our experience implementing digital health technologies in registries and observational studies, we propose potential solutions to three practical challenges we have repeatedly encountered: determining what to measure digitally, selecting the appropriate device, and ensuring representativeness and engagement over time. We describe the example of a hypothetical patient registry for valvular heart disease, a condition for which there is substantial variation in treatment selection and postintervention outcomes, and for which patient-centred outcome data are urgently needed to inform clinical care guidelines and health-service commissioning.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e225-e231"},"PeriodicalIF":23.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822615","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}

{"title":"Challenges of AI-based pulmonary function estimation from chest x-rays – Authors' reply","authors":"Daiju Ueda ,&nbsp;Shannon L Walston ,&nbsp;Yasuhito Mitsuyama ,&nbsp;Hirotaka Takita ,&nbsp;Yukio Miki","doi":"10.1016/S2589-7500(24)00248-6","DOIUrl":"10.1016/S2589-7500(24)00248-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Page e881"},"PeriodicalIF":23.8,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722326","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}

{"title":"Correction to Lancet Digit Health 2024; 6: e791–802","authors":"","doi":"10.1016/S2589-7500(24)00252-8","DOIUrl":"10.1016/S2589-7500(24)00252-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Page e882"},"PeriodicalIF":23.8,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722327","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}

{"title":"Challenges of AI-based pulmonary function estimation from chest x-rays","authors":"Xinyu Li ,&nbsp;Jiajie Lv ,&nbsp;Jiajia Xue ,&nbsp;Ruhong Zhang ,&nbsp;Datao Li","doi":"10.1016/S2589-7500(24)00247-4","DOIUrl":"10.1016/S2589-7500(24)00247-4","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Page e880"},"PeriodicalIF":23.8,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722325","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}

{"title":"Advancing the management of maternal, fetal, and neonatal infection through harnessing digital health innovations","authors":"Damien K Ming PhD ,&nbsp;Abi Merriel PhD ,&nbsp;David M E Freeman PhD ,&nbsp;Carol Kingdon PhD ,&nbsp;Yamikani Chimwaza MPH ,&nbsp;Mohammad S Islam PhD ,&nbsp;Prof Anthony Cass DPhil ,&nbsp;Benjamin Greenfield MBChB ,&nbsp;Prof Address Malata PhD ,&nbsp;Prof Mahbubul Hoque FCPS ,&nbsp;Senjuti Saha PhD ,&nbsp;Prof Alison H Holmes MD FMedSci","doi":"10.1016/S2589-7500(24)00217-6","DOIUrl":"10.1016/S2589-7500(24)00217-6","url":null,"abstract":"<div><div>Infections occurring in the mother and neonate exert a substantial health burden worldwide. Optimising infection management is crucial for improving individual outcomes and reducing the incidence of antimicrobial resistance. Digital health technologies, through their accessibility and scalability, hold promise in improving the quality of care across diverse health-care settings. In settings with poor access to laboratory services, innovative uses of existing data, point-of-care diagnostics, and wearables could allow for better recognition of host responses during infection and antimicrobial optimisation. The linkage and connectivity of information can support the coordinated delivery of care between health-care facilities and the community. Continuous real-time monitoring of infection markers in the mother and neonate through biosensing can provide notable opportunities for intervention and improvements in care. However, the development and implementation of these interventions should be respectful, prioritise safety, and emphasise sustainable, locally derived solutions. Addressing existing gender, economic, and health-care disparities will be essential for ensuring equitable implementation.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e926-e933"},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639899","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}