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Remote Patient Monitoring and Digital Therapeutics Enhancing the Continuum of Care in Heart Failure: Nonrandomized Pilot Study. 远程患者监护和数字治疗增强心力衰竭患者的持续护理:非随机试点研究。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-06 DOI: 10.2196/53444
Emmanuel Marier-Tétrault, Emmanuel Bebawi, Stéphanie Béchard, Philippe Brouillard, Priccila Zuchinali, Emilie Remillard, Zoé Carrier, Loyda Jean-Charles, John Nam Kha Nguyen, Pascale Lehoux, Marie-Pascale Pomey, Paula A B Ribeiro, François Tournoux
<p><strong>Background: </strong>Heart failure (HF) is the primary cause of hospitalization among Canadian patients aged ≥65 years. Care for HF requires regular clinical follow-ups to prevent readmissions and facilitate medical therapy optimization. Multiple barriers lead to therapeutic medical inertia including limited human resources and regional inequities. Remote patient monitoring (RPM) and digital therapeutics (DTx) solutions have been developed to improve HF management, but their adoption remains limited and underexplored. The Continuum project emerged as a collaborative initiative involving a health care center, a software start-up, and an industrial partner.</p><p><strong>Objective: </strong>We aimed to develop and test the feasibility of the Continuum intervention that seamlessly combined an RPM system with a DTx solution for HF within the same software.</p><p><strong>Methods: </strong>A 3-month pre-post pilot study was conducted from October 2020 to June 2021. Patients with HF who owned a smartphone or tablet (having remote patient monitoring [RPM+]), had (1) access to a self-care app where they could enter their vital signs, weight, and HF symptoms and view educational content; (2) daily monitoring of their data by a nurse; and (3) a DTx module with automated HF medication suggestions based on national guidelines, made available to their treating medical team. Bluetooth devices were offered to facilitate data recording. Nurses on RPM monitoring could call patients and arrange appointments with their medical team. Patients without a mobile device or unable to use the app were followed in another group (without remote patient monitoring [RPM-]).</p><p><strong>Results: </strong>In total, 52 patients were enrolled in this study (32 RPM+ and 20 RPM-). Among patients owning a mobile device, only 14% (5/37) could not use the app. In the RPM+ group, 47% (15/32) of the patients used the app for more than 80% (67 days) of the 12-week study period. The use of our digital solution was integrated into the regular nursing workday and only 34 calls had to be made by the nurse during the study period. Only 6% (2/32) of the patients in the RPM+ group experienced at least 1 all-cause hospitalization versus 35% (7/20) of the RPM- ones during the follow-up (6%, 2/32 vs 25%, 5/20 for HF hospitalization) and patients were more likely to have their HF therapy optimized if the DTx solution was available. Quality of life improved in patients compliant with the use of the mobile app (mean score variation +10.6, SD 14.7).</p><p><strong>Conclusions: </strong>This pilot study demonstrated the feasibility of implementing our digital solution, within the specific context of HF. The seamless integration of Continuum into nursing workflow, mobile app accessibility, and adoption by patients, were the 3 main key learning points of this study. Further investigation is required to assess the potential impacts on hospitalizations, drug optimization, and quality of life.</p
背景:心力衰竭(HF)是加拿大 65 岁以上患者住院治疗的主要原因。心力衰竭的治疗需要定期进行临床随访,以防止再次入院并促进医疗优化。多种障碍导致了治疗上的医疗惰性,包括人力资源有限和地区不平等。目前已开发出患者远程监护(RPM)和数字治疗(DTx)解决方案,以改善心房颤动的管理,但其应用仍然有限,且尚未得到充分探索。Continuum项目是由一家医疗保健中心、一家软件初创公司和一家工业合作伙伴共同参与的合作项目:我们的目标是开发并测试 Continuum 干预措施的可行性,该措施将 RPM 系统与 DTx 解决方案无缝结合到同一软件中:从 2020 年 10 月到 2021 年 6 月,我们进行了为期 3 个月的事前-事后试点研究。拥有智能手机或平板电脑(具有远程患者监护[RPM+]功能)的高血压患者可以:(1)访问自我护理应用程序,输入生命体征、体重和高血压症状并查看教育内容;(2)由护士对其数据进行日常监测;(3)向其治疗医疗团队提供基于国家指南的自动高血压用药建议的 DTx 模块。提供蓝牙设备以方便数据记录。负责 RPM 监测的护士可以给患者打电话,并安排与医疗团队的预约。没有移动设备或无法使用应用程序的患者则在另一组(无远程患者监测[RPM-])中接受随访:共有 52 名患者参与了这项研究(32 名 RPM+ 和 20 名 RPM-)。在拥有移动设备的患者中,只有 14% 的患者(5/37)无法使用该应用程序。在 RPM+ 组中,47%(15/32)的患者在为期 12 周的研究中使用该应用程序的时间超过了 80%(67 天)。我们将数字解决方案的使用纳入了常规护理工作日,在研究期间,护士只需拨打 34 次电话。在随访期间,RPM+ 组仅有 6% 的患者(2/32)经历了至少一次全因住院,而 RPM- 组则有 35% 的患者(7/20)经历了至少一次全因住院(6%,2/32 vs 25%,5/20 为高血压住院)。使用手机应用后,患者的生活质量有所改善(平均得分+10.6,标准差14.7):这项试点研究证明了在高血压的特殊情况下实施我们的数字解决方案的可行性。Continuum与护理工作流程的无缝整合、移动应用程序的易用性以及患者的采用是本研究的三大关键点。还需要进一步调查,以评估对住院、药物优化和生活质量的潜在影响:试验注册:ClinicalTrials.gov NCT05377190;https://clinicaltrials.gov/study/NCT05377190(试验研究 #21.403)。
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引用次数: 0
Insights From the Development of a Dynamic Consent Platform for the Australians Together Health Initiative (ATHENA) Program: Interview and Survey Study. 为澳大利亚人共同健康倡议(ATHENA)计划开发动态同意平台的启示:访谈与调查研究。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-06 DOI: 10.2196/57165
Eddy Xiong, Carissa Bonner, Amanda King, Zoltan Maxwell Bourne, Mark Morgan, Ximena Tolosa, Tony Stanton, Kim Greaves

Background: Dynamic consent has the potential to address many of the issues facing traditional paper-based or electronic consent, including enrolling informed and engaged participants in the decision-making process. The Australians Together Health Initiative (ATHENA) program aims to connect participants across Queensland, Australia, with new research opportunities. At its core is dynamic consent, an interactive and participant-centric digital platform that enables users to view ongoing research activities, update consent preferences, and have ongoing engagement with researchers.

Objective: This study aimed to describe the development of the ATHENA dynamic consent platform within the framework of the ATHENA program, including how the platform was designed, its utilization by participants, and the insights gained.

Methods: One-on-one interviews were undertaken with consumers, followed by a workshop with health care staff to gain insights into the dynamic consent concept. Five problem statements were developed, and solutions were posed, from which a dynamic consent platform was constructed, tested, and used for implementation in a clinical trial. Potential users were randomly recruited from a pre-existing pool of 615 participants in the ATHENA program. Feedback on user platform experience was gained from a survey hosted on the platform.

Results: In the 13 consumer interviews undertaken, participants were positive about dynamic consent, valuing privacy, ease of use, and adequate communication. Motivators for registration were feedback on data usage and its broader community benefits. Problem statements were security, trust and governance, ease of use, communication, control, and need for a scalable platform. Using the newly constructed dynamic consent platform, 99 potential participants were selected, of whom 67 (68%) were successfully recontacted. Of these, 59 (88%) agreed to be sent the platform, 44 (74%) logged on (indicating use), and 22 (57%) registered for the clinical trial. Survey feedback was favorable, with an average positive rating of 78% across all questions, reflecting satisfaction with the clarity, brevity, and flexibility of the platform. Barriers to implementation included technological and health literacy.

Conclusions: This study describes the successful development and testing of a dynamic consent platform that was well-accepted, with users recognizing its advantages over traditional methods of consent regarding flexibility, ease of communication, and participant satisfaction. This information may be useful to other researchers who plan to use dynamic consent in health care research.

背景:动态同意书有可能解决传统纸质或电子同意书所面临的许多问题,包括在决策过程中招募知情和参与的参与者。澳大利亚人共同健康倡议(ATHENA)计划旨在将澳大利亚昆士兰州的参与者与新的研究机会联系起来。其核心是动态同意,这是一个以参与者为中心的互动式数字平台,使用户能够查看正在进行的研究活动、更新同意偏好,并与研究人员持续互动:本研究旨在描述在 ATHENA 计划框架内开发 ATHENA 动态同意平台的过程,包括平台的设计方式、参与者对平台的使用情况以及获得的启示:方法:先对消费者进行一对一访谈,然后与医护人员召开研讨会,深入了解动态同意概念。在此基础上构建、测试了动态同意平台,并将其用于临床试验。潜在用户是从 ATHENA 计划的 615 名参与者中随机招募的。通过在平台上进行调查,获得用户对平台体验的反馈:在进行的 13 次消费者访谈中,参与者对动态同意、重视隐私、易用性和充分沟通持肯定态度。注册的动机是对数据使用的反馈及其更广泛的社区利益。问题陈述包括安全性、信任和管理、易用性、沟通、控制以及对可扩展平台的需求。利用新构建的动态同意平台,选出了 99 名潜在参与者,其中 67 人(68%)成功地与他们取得了联系。其中,59 人(88%)同意发送平台,44 人(74%)登录(表示使用),22 人(57%)注册了临床试验。调查反馈良好,所有问题的平均好评率为 78%,反映出对平台的清晰度、简洁性和灵活性的满意度。实施障碍包括技术和健康知识:本研究介绍了动态同意书平台的成功开发和测试,该平台得到了广泛认可,用户认识到其在灵活性、沟通便利性和参与者满意度方面比传统同意书方法更具优势。这些信息可能对其他计划在医疗保健研究中使用动态同意的研究人员有用。
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引用次数: 0
A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study. 利用超声造影图像预测乳房植入物纹理类型的深度学习模型:可行性开发研究。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-05 DOI: 10.2196/58776
Ho Heon Kim, Won Chan Jeong, Kyungran Pi, Angela Soeun Lee, Min Soo Kim, Hye Jin Kim, Jae Hong Kim

Background: Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured type, which is one of the shell texture types of breast implants, has been identified as a possible etiologic factor for lymphoma, specifically breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Identifying the shell texture type of the implant is critical to diagnosing BIA-ALCL. However, distinguishing the shell texture type can be difficult due to the loss of human memory and medical history. An alternative approach is to use ultrasonography, but this method also has limitations in quantitative assessment.

Objective: This study aims to determine the feasibility of using a deep learning model to classify the shell texture type of breast implants and make robust predictions from ultrasonography images from heterogeneous sources.

Methods: A total of 19,502 breast implant images were retrospectively collected from heterogeneous sources, including images captured from both Canon and GE devices, images of ruptured implants, and images without implants, as well as publicly available images. The Canon images were trained using ResNet-50. The model's performance on the Canon dataset was evaluated using stratified 5-fold cross-validation. Additionally, external validation was conducted using the GE and publicly available datasets. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PRAUC) were calculated based on the contribution of the pixels with Gradient-weighted Class Activation Mapping (Grad-CAM). To identify the significant pixels for classification, we masked the pixels that contributed less than 10%, up to a maximum of 100%. To assess the model's robustness to uncertainty, Shannon entropy was calculated for 4 image groups: Canon, GE, ruptured implants, and without implants.

Results: The deep learning model achieved an average AUROC of 0.98 and a PRAUC of 0.88 in the Canon dataset. The model achieved an AUROC of 0.985 and a PRAUC of 0.748 for images captured with GE devices. Additionally, the model predicted an AUROC of 0.909 and a PRAUC of 0.958 for the publicly available dataset. This model maintained the PRAUC values for quantitative validation when masking up to 90% of the least-contributing pixels and the remnant pixels in breast shell layers. Furthermore, the prediction uncertainty increased in the following order: Canon (0.066), GE (0072), ruptured implants (0.371), and no implants (0.777).

Conclusions: We have demonstrated the feasibility of using deep learning to predict the shell texture type of breast implants. This approach quantifies the shell texture types of breast implants, supporting the first step in the diagnosis of BIA-ALCL.

背景:乳房植入物(包括纹理型)已被广泛应用于乳房美容和整形术中。然而,作为乳房假体外壳纹理类型之一的纹理型假体已被确定为淋巴瘤(特别是乳房假体相关性无细胞大细胞淋巴瘤(BIA-ALCL))的可能致病因素。确定植入物的外壳质地类型是诊断 BIA-ALCL 的关键。然而,由于人类记忆和病史的缺失,区分外壳质地类型可能很困难。另一种方法是使用超声波检查,但这种方法在定量评估方面也有局限性:本研究旨在确定使用深度学习模型对乳房植入物的外壳纹理类型进行分类的可行性,并从异构来源的超声波图像中进行稳健预测:共回顾性收集了19502张不同来源的乳房植入物图像,包括佳能和通用电气设备采集的图像、破裂植入物图像、无植入物图像以及公开可用的图像。佳能图像使用 ResNet-50 进行训练。模型在佳能数据集上的性能使用分层 5 倍交叉验证进行评估。此外,还使用通用电气和公开数据集进行了外部验证。根据像素对梯度加权类激活映射(Grad-CAM)的贡献,计算了接收者操作特征曲线下面积(AUROC)和精度-召回曲线下面积(PRAUC)。为了识别对分类有重要意义的像素,我们屏蔽了贡献率低于 10% 的像素,最高可达 100%。为了评估模型对不确定性的稳健性,我们计算了 4 组图像的香农熵:结果:在佳能数据集中,深度学习模型的平均 AUROC 为 0.98,PRAUC 为 0.88。在使用通用电气设备拍摄的图像中,该模型的 AUROC 为 0.985,PRAUC 为 0.748。此外,该模型预测公开数据集的 AUROC 为 0.909,PRAUC 为 0.958。该模型在屏蔽了多达 90% 的贡献最小像素和乳房外壳层中的残余像素后,仍能保持定量验证的 PRAUC 值。此外,预测不确定性按以下顺序增加:佳能(0.066)、通用电气(0072)、植入物破裂(0.371)和无植入物(0.777):我们证明了使用深度学习预测乳房植入物外壳纹理类型的可行性。这种方法可以量化乳房植入物的外壳纹理类型,为 BIA-ALCL 的第一步诊断提供支持。
{"title":"A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.","authors":"Ho Heon Kim, Won Chan Jeong, Kyungran Pi, Angela Soeun Lee, Min Soo Kim, Hye Jin Kim, Jae Hong Kim","doi":"10.2196/58776","DOIUrl":"https://doi.org/10.2196/58776","url":null,"abstract":"<p><strong>Background: </strong>Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured type, which is one of the shell texture types of breast implants, has been identified as a possible etiologic factor for lymphoma, specifically breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Identifying the shell texture type of the implant is critical to diagnosing BIA-ALCL. However, distinguishing the shell texture type can be difficult due to the loss of human memory and medical history. An alternative approach is to use ultrasonography, but this method also has limitations in quantitative assessment.</p><p><strong>Objective: </strong>This study aims to determine the feasibility of using a deep learning model to classify the shell texture type of breast implants and make robust predictions from ultrasonography images from heterogeneous sources.</p><p><strong>Methods: </strong>A total of 19,502 breast implant images were retrospectively collected from heterogeneous sources, including images captured from both Canon and GE devices, images of ruptured implants, and images without implants, as well as publicly available images. The Canon images were trained using ResNet-50. The model's performance on the Canon dataset was evaluated using stratified 5-fold cross-validation. Additionally, external validation was conducted using the GE and publicly available datasets. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PRAUC) were calculated based on the contribution of the pixels with Gradient-weighted Class Activation Mapping (Grad-CAM). To identify the significant pixels for classification, we masked the pixels that contributed less than 10%, up to a maximum of 100%. To assess the model's robustness to uncertainty, Shannon entropy was calculated for 4 image groups: Canon, GE, ruptured implants, and without implants.</p><p><strong>Results: </strong>The deep learning model achieved an average AUROC of 0.98 and a PRAUC of 0.88 in the Canon dataset. The model achieved an AUROC of 0.985 and a PRAUC of 0.748 for images captured with GE devices. Additionally, the model predicted an AUROC of 0.909 and a PRAUC of 0.958 for the publicly available dataset. This model maintained the PRAUC values for quantitative validation when masking up to 90% of the least-contributing pixels and the remnant pixels in breast shell layers. Furthermore, the prediction uncertainty increased in the following order: Canon (0.066), GE (0072), ruptured implants (0.371), and no implants (0.777).</p><p><strong>Conclusions: </strong>We have demonstrated the feasibility of using deep learning to predict the shell texture type of breast implants. This approach quantifies the shell texture types of breast implants, supporting the first step in the diagnosis of BIA-ALCL.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Codesigning a Digital Type 2 Diabetes Risk Communication Tool in Singapore: Qualitative Participatory Action Research Approach. 新加坡 2 型糖尿病风险交流数字工具的代码设计:定性参与式行动研究方法。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-05 DOI: 10.2196/50456
Jumana Hashim, Lidia Luna Puerta, Pin Sym Foong, E Shyong Tai, Huso Yi, Helen Elizabeth Smith
<p><strong>Background: </strong>Diabetes is a serious public health concern worldwide. Despite public health efforts encouraging early screening and improving knowledge of effective interventions for those at increased risk of type 2 diabetes (T2D), the incorporation of preventative behaviors into an individual's daily life remains suboptimal. Successfully and accurately increasing risk perception has been demonstrated to increase behavioral intention.</p><p><strong>Objective: </strong>The study aims to codesign a T2D risk communication tool by engaging public participants to (1) identify key characteristics that contribute to an effective risk communication tool and (2) test and iterate to develop a culturally sensitive and meaningful risk communication tool that can motivate T2D preventative behaviors.</p><p><strong>Methods: </strong>We adopted a novel methodology, "Patient and Public Involvement (PPI) Hawkers," where we approached patrons at hawker centers and public eateries frequented by all local residents to evaluate and test 3 prototypes for the tool. The three prototypes were (1) "Diabetes Onset"-estimated age of diabetes onset of T2D based on one's risk factors, (2) "Relative Risk"-the relative risk of T2D is presented in a 1-10 scale indicating where one's risk score lie in relation to others, and (3) "Metabolic Age"-the median age of the risk category based on one's risk factors, presented to be compared against their chronological age. We gathered reactions and feedback through rapid testing and iteration to understand which risk result presentation would be received the best. All the collected data were revisited and analyzed using an inductive thematic analysis to identify the key characteristics contributing to an effective risk communication tool.</p><p><strong>Results: </strong>We engaged with 112 participants (female: n=59, 56%) across 6 hawker centers. The key characteristics that were important to participants emerged in four main themes: (1) appeal and user experience, in terms of format and readability; (2) trust and validity of the institution providing the tool and the accuracy of the risk result; (3) threat appraisal: salience of risk information, which influenced their risk perception; and (4) coping appraisal: facilitators for behavior change, which impacted their intention for implementing T2D preventative behaviors. The predictive nature of the prototype entitled "Diabetes Onset" was poorly received and removed after the first iteration. The Relative Risk prototype was valued for being straightforward but feared to be boring. The Metabolic Age prototype was anticipated to be more motivating for behavior change, but there were some concerns that the terminology may not be understood by everyone.</p><p><strong>Conclusions: </strong>Participants were divided on which of the 2 prototypes, "Metabolic Age" or "Relative Risk," they would favor adopting. Further testing is now required to determine which prototype will be mo
背景:糖尿病是全球严重的公共卫生问题。尽管公共卫生机构努力鼓励对 2 型糖尿病(T2D)高风险人群进行早期筛查,并提高对有效干预措施的认识,但将预防行为融入个人日常生活的效果仍不理想。成功、准确地提高风险意识已被证明能增强行为意向:本研究旨在通过公众参与者的参与,对 T2D 风险交流工具进行编码设计,以(1)确定有助于形成有效风险交流工具的关键特征;(2)进行测试和迭代,以开发出具有文化敏感性和意义的风险交流工具,从而激发 T2D 预防行为:我们采用了一种新颖的方法--"患者和公众参与(PPI)小贩",在当地居民经常光顾的小贩中心和公共食堂接触顾客,以评估和测试该工具的三个原型。这三个原型分别是:(1)"糖尿病发病年龄"--根据个人的风险因素估计 T2D 的发病年龄;(2)"相对风险"--T2D 的相对风险以 1-10 级表示,显示个人与其他人的风险分值;以及(3)"代谢年龄"--根据个人的风险因素显示风险类别的年龄中位数,并与他们的实际年龄进行比较。我们通过快速测试和迭代收集反应和反馈,以了解哪种风险结果呈现方式最受欢迎。我们采用归纳主题分析法对所有收集到的数据进行了重新审视和分析,以确定有助于形成有效风险交流工具的关键特征:我们与 6 个小贩中心的 112 名参与者(女性:59 人,占 56%)进行了接触。对参与者而言重要的关键特征分为四个主题:(1)吸引力和用户体验,包括格式和可读性;(2)提供工具的机构的信任度和有效性以及风险结果的准确性;(3)威胁评估:风险信息的显著性,这影响了他们的风险认知;以及(4)应对评估:行为改变的促进因素,这影响了他们实施 T2D 预防行为的意愿。名为 "糖尿病发病 "的原型的预测性很差,在第一次迭代后就被删除了。相对风险 "原型因其简单明了而受到重视,但又担心其枯燥乏味。新陈代谢年龄 "原型被认为更能促使人们改变行为,但也有人担心这些术语可能不是每个人都能理解:结论:在 "代谢年龄 "和 "相对风险 "这两个原型中,参与者对哪一个更倾向于采用意见不一。现在需要进行进一步测试,以确定哪种原型能更有效地促使人们改变行为。本研究对风险交流工具的设计过程和重要特征的见解将为今后开发此类干预措施提供参考。
{"title":"Codesigning a Digital Type 2 Diabetes Risk Communication Tool in Singapore: Qualitative Participatory Action Research Approach.","authors":"Jumana Hashim, Lidia Luna Puerta, Pin Sym Foong, E Shyong Tai, Huso Yi, Helen Elizabeth Smith","doi":"10.2196/50456","DOIUrl":"https://doi.org/10.2196/50456","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Diabetes is a serious public health concern worldwide. Despite public health efforts encouraging early screening and improving knowledge of effective interventions for those at increased risk of type 2 diabetes (T2D), the incorporation of preventative behaviors into an individual's daily life remains suboptimal. Successfully and accurately increasing risk perception has been demonstrated to increase behavioral intention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The study aims to codesign a T2D risk communication tool by engaging public participants to (1) identify key characteristics that contribute to an effective risk communication tool and (2) test and iterate to develop a culturally sensitive and meaningful risk communication tool that can motivate T2D preventative behaviors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We adopted a novel methodology, \"Patient and Public Involvement (PPI) Hawkers,\" where we approached patrons at hawker centers and public eateries frequented by all local residents to evaluate and test 3 prototypes for the tool. The three prototypes were (1) \"Diabetes Onset\"-estimated age of diabetes onset of T2D based on one's risk factors, (2) \"Relative Risk\"-the relative risk of T2D is presented in a 1-10 scale indicating where one's risk score lie in relation to others, and (3) \"Metabolic Age\"-the median age of the risk category based on one's risk factors, presented to be compared against their chronological age. We gathered reactions and feedback through rapid testing and iteration to understand which risk result presentation would be received the best. All the collected data were revisited and analyzed using an inductive thematic analysis to identify the key characteristics contributing to an effective risk communication tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We engaged with 112 participants (female: n=59, 56%) across 6 hawker centers. The key characteristics that were important to participants emerged in four main themes: (1) appeal and user experience, in terms of format and readability; (2) trust and validity of the institution providing the tool and the accuracy of the risk result; (3) threat appraisal: salience of risk information, which influenced their risk perception; and (4) coping appraisal: facilitators for behavior change, which impacted their intention for implementing T2D preventative behaviors. The predictive nature of the prototype entitled \"Diabetes Onset\" was poorly received and removed after the first iteration. The Relative Risk prototype was valued for being straightforward but feared to be boring. The Metabolic Age prototype was anticipated to be more motivating for behavior change, but there were some concerns that the terminology may not be understood by everyone.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Participants were divided on which of the 2 prototypes, \"Metabolic Age\" or \"Relative Risk,\" they would favor adopting. Further testing is now required to determine which prototype will be mo","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Correction: Views and Needs of Students, Parents, and Teachers on Closed-Circuit Television, Proximity Trackers, and Access Cards to Facilitate COVID-19 Contact Tracing in Schools: Thematic Analysis of Focus Groups and Interviews. 更正:学生、家长和教师对闭路电视、近距离追踪器和门禁卡的看法和需求,以促进在学校追踪 COVID-19 联系人:焦点小组和访谈的专题分析。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-05 DOI: 10.2196/67607
Sofia Chantziara, Ian J Craddock, Claire H McCallum, Amberly L C Brigden

[This corrects the article DOI: 10.2196/44592.].

[This corrects the article DOI: 10.2196/44592.].
{"title":"Correction: Views and Needs of Students, Parents, and Teachers on Closed-Circuit Television, Proximity Trackers, and Access Cards to Facilitate COVID-19 Contact Tracing in Schools: Thematic Analysis of Focus Groups and Interviews.","authors":"Sofia Chantziara, Ian J Craddock, Claire H McCallum, Amberly L C Brigden","doi":"10.2196/67607","DOIUrl":"https://doi.org/10.2196/67607","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/44592.].</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study. 基于人工智能模型的食物摄入量估算系统,用于估算临床环境中医院剩余流质食物的摄入量:开发与验证研究。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-05 DOI: 10.2196/55218
Masato Tagi, Yasuhiro Hamada, Xiao Shan, Kazumi Ozaki, Masanori Kubota, Sosuke Amano, Hiroshi Sakaue, Yoshiko Suzuki, Takeshi Konishi, Jun Hirose
<p><strong>Background: </strong>Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients' food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake.</p><p><strong>Objective: </strong>This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI's estimation was compared with that of visual estimation for liquid foods served to hospitalized patients.</p><p><strong>Methods: </strong>The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R<sup>2</sup>) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method.</p><p><strong>Results: </strong>The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R<sup>2</sup> value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P<.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (ρ=0.89-0.97), protein (ρ=0.94-0.97), fat (ρ=0.91-0.94), and carbohydrate (ρ=0.89-0.97).</p><p><strong>Conclusions: </strong>The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue.<
背景:医务人员经常进行食物摄入量和营养素充足率等评估,以准确评价患者的食物消耗量。然而,目测食物摄入量的方法很难在众多患者中使用。因此,临床环境需要一种简单而准确的方法来测量饮食摄入量:本研究旨在开发一种食物摄入量估算系统,通过人工智能(AI)模型估算剩余食物。人工智能估算的准确性与目测估算的准确性进行了比较:估算由营养师通过观察食物照片进行评估(图像视觉估算),而视觉测量评估则由护士根据实际测量结果直接观察食物进行评估(直接视觉估算)。总共使用了 300 盘流质食物(稀饭 100 盘、蔬菜汤 100 盘、发酵乳 31 盘,以及桃汁、葡萄汁、橙汁和混合果汁分别为 18 盘、12 盘、13 盘和 26 盘)。均方根误差(RMSE)和判定系数(R2)被用来衡量评价过程的准确性。采用相应的 t 检验和斯皮尔曼等级相关系数来验证每种估算方法与称重方法测量结果的准确性:结果:人工智能估算方法得到的能量均方根误差为 8.12。与图像目测估算法(8.49)和直接目测估算法(4.34)相比,其均方误差分别趋于较小和较大。此外,人工智能估算的 R2 值分别比图像估算和直接视觉估算的 R2 值大和小。称重法估算的 AI 值(平均 71.7 千卡,标差 23.9 千卡,P=.82)与实际值之间没有差异。然而,图像直观估算的平均营养摄入量(平均 75.5 千卡,标差 23.2 千卡,P=.82)与称重法的实际值没有差异:通过基于人工智能模型的食物摄入量估算系统估算出的患者剩余流质食物摄入量与称重法得出的实际值具有很高的相关性。此外,其准确性也高于图像视觉估算法。人工智能估算方法的误差在称重法的可接受范围内,这表明基于人工智能的食物摄入量估算系统可应用于临床环境。然而,与直接目测法相比,其准确性较低仍是一个问题。
{"title":"A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study.","authors":"Masato Tagi, Yasuhiro Hamada, Xiao Shan, Kazumi Ozaki, Masanori Kubota, Sosuke Amano, Hiroshi Sakaue, Yoshiko Suzuki, Takeshi Konishi, Jun Hirose","doi":"10.2196/55218","DOIUrl":"https://doi.org/10.2196/55218","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients' food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI's estimation was compared with that of visual estimation for liquid foods served to hospitalized patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R&lt;sup&gt;2&lt;/sup&gt; value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P&lt;.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (ρ=0.89-0.97), protein (ρ=0.94-0.97), fat (ρ=0.91-0.94), and carbohydrate (ρ=0.89-0.97).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue.&lt;","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Claims-Based Algorithm to Identify Pre-Exposure Prophylaxis Indications for Tenofovir Disoproxil Fumarate and Emtricitabine Prescriptions (2012-2014): Validation Study. 美国2012-2014年基于索赔的算法验证,以确定替诺福韦酯/恩曲他滨处方的暴露前预防适应症:通过病历审查验证算法。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-04 DOI: 10.2196/55614
Patrick Sean Sullivan, Robertino M Mera-Giler, Staci Bush, Valentina Shvachko, Eleanor Sarkodie, Daniel O'Farrell, Stephanie Dubose, David Magnuson

Background: To monitor the use of tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) and related medicines for pre-exposure prophylaxis (PrEP) as HIV prevention using commercial pharmacy data, it is necessary to determine whether TDF/FTC prescriptions are used for PrEP or for some other clinical indication.

Objective: This study aimed to validate an algorithm to distinguish the use of TDF/FTC for HIV prevention or infectious disease treatment.

Methods: An algorithm was developed to identify whether TDF/FTC prescriptions were for PrEP or for other indications from large-scale administrative databases. The algorithm identifies TDF/FTC prescriptions and then excludes patients with International Classification of Diseases (ICD)-9 diagnostic codes, medications, or procedures that suggest indications other than for PrEP (eg, documentation of HIV infection, chronic hepatitis B, or use of TDF/FTC for postexposure prophylaxis). For evaluation, we collected data by clinician assessment of medical records for patients with TDF/FTC prescriptions and compared the assessed indication identified by the clinician review with the assessed indication identified by the algorithm. The algorithm was then applied and evaluated in a large, urban, community-based sexual health clinic.

Results: The PrEP algorithm demonstrated high sensitivity and moderate specificity (99.6% and 49.6%) in the electronic medical record database and high sensitivity and specificity (99% and 87%) in data from the urban community health clinic.

Conclusions: The PrEP algorithm classified the indication for PrEP in most patients treated with TDF/FTC with sufficient accuracy to be useful for surveillance purposes. The methods described can serve as a basis for developing a robust and evolving case definition for antiretroviral prescriptions for HIV prevention purposes.

背景:为了利用商业药房数据监测用于暴露前预防(PrEP)的替诺福韦酯/恩曲他滨(TDF/FTC)及相关药物作为艾滋病预防药物的使用情况,有必要确定 TDF/FTC 处方是用于 PrEP,还是用于其他临床适应症:目的:验证一种算法,以区分 TDF/FTC 是用于艾滋病预防还是用于传染病治疗:方法:开发了一种算法,用于从大规模行政数据库中识别 TDF/FTC 处方是用于 PrEP 还是用于其他适应症。该算法可识别 TDF/FTC 处方,然后排除具有《国际疾病分类》(ICD)-9 诊断代码、用药或治疗过程等表明非 PrEP 适应症的患者(例如,记录有 HIV 感染、慢性乙型肝炎 (CHB),或将 TDF/FTC 用于暴露后预防 (PEP))。为了进行评估,我们通过临床医生对使用 TDF/FTC 患者的医疗记录进行评估来收集数据,并将临床医生审查确定的评估适应症与算法确定的评估适应症进行比较。然后在一家大型城市社区性健康诊所应用并评估了该算法:结果:PrEP 算法在电子病历数据库中显示出较高的灵敏度和中等程度的特异性(99.6%,49.6%),在城市社区卫生诊所的数据中显示出较高的灵敏度和特异性(99%,87%):PrEP 算法对大多数接受 TDF/FTC 治疗者的 PrEP 适应症进行了分类,其准确性足以用于监测目的。所述方法可作为制定稳健且不断发展的病例定义的基础,用于预防艾滋病的抗逆转录病毒处方:临床试验:无要求。
{"title":"Claims-Based Algorithm to Identify Pre-Exposure Prophylaxis Indications for Tenofovir Disoproxil Fumarate and Emtricitabine Prescriptions (2012-2014): Validation Study.","authors":"Patrick Sean Sullivan, Robertino M Mera-Giler, Staci Bush, Valentina Shvachko, Eleanor Sarkodie, Daniel O'Farrell, Stephanie Dubose, David Magnuson","doi":"10.2196/55614","DOIUrl":"10.2196/55614","url":null,"abstract":"<p><strong>Background: </strong>To monitor the use of tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) and related medicines for pre-exposure prophylaxis (PrEP) as HIV prevention using commercial pharmacy data, it is necessary to determine whether TDF/FTC prescriptions are used for PrEP or for some other clinical indication.</p><p><strong>Objective: </strong>This study aimed to validate an algorithm to distinguish the use of TDF/FTC for HIV prevention or infectious disease treatment.</p><p><strong>Methods: </strong>An algorithm was developed to identify whether TDF/FTC prescriptions were for PrEP or for other indications from large-scale administrative databases. The algorithm identifies TDF/FTC prescriptions and then excludes patients with International Classification of Diseases (ICD)-9 diagnostic codes, medications, or procedures that suggest indications other than for PrEP (eg, documentation of HIV infection, chronic hepatitis B, or use of TDF/FTC for postexposure prophylaxis). For evaluation, we collected data by clinician assessment of medical records for patients with TDF/FTC prescriptions and compared the assessed indication identified by the clinician review with the assessed indication identified by the algorithm. The algorithm was then applied and evaluated in a large, urban, community-based sexual health clinic.</p><p><strong>Results: </strong>The PrEP algorithm demonstrated high sensitivity and moderate specificity (99.6% and 49.6%) in the electronic medical record database and high sensitivity and specificity (99% and 87%) in data from the urban community health clinic.</p><p><strong>Conclusions: </strong>The PrEP algorithm classified the indication for PrEP in most patients treated with TDF/FTC with sufficient accuracy to be useful for surveillance purposes. The methods described can serve as a basis for developing a robust and evolving case definition for antiretroviral prescriptions for HIV prevention purposes.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Using Extended Reality to Enhance Effectiveness and Group Identification in Remote Group Therapy for Anxiety Disorders: A Critical Analysis. 在焦虑症远程小组疗法中使用扩展现实增强疗效和小组认同:批判性分析。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-04 DOI: 10.2196/64494
Ayoub Bouguettaya, Elias Aboujaoude

Group therapy is a scalable and effective treatment for anxiety disorders. However, when performed online, the reduced ability to identify with group members and the reduced interactivity can limit its appeal and effectiveness. Extended reality (XR) technology, including virtual reality and augmented reality, may help address these limitations, thereby enhancing the reach of online group therapy and the benefits that can be drawn from it. To understand how the incorporation of XR technology may improve online group therapy for anxiety disorders, this viewpoint paper examines evidence related to the treatment of anxiety disorders using offline group therapy, online group therapy, and virtual reality, as well as ways to increase social identification and interactivity with the platform, the therapist, and other users. This viewpoint paper suggests ways to integrate these research streams to leverage the strengths of XR platforms and improve group therapeutic offerings.

团体治疗是一种可扩展的、有效的焦虑症治疗方法。然而,在网上进行时,与小组成员的认同感降低,互动性减弱,会限制其吸引力和有效性。扩展现实(XR)技术,包括虚拟现实和增强现实技术,可能有助于解决这些局限性,从而提高在线团体治疗的覆盖面,并从中获益。为了解 XR 技术的应用可如何改善焦虑症的线上团体治疗,本观点文件研究了使用线下团体治疗、线上团体治疗和虚拟现实技术治疗焦虑症的相关证据,以及增加社会认同和与平台、治疗师及其他用户互动的方法。本观点文件提出了整合这些研究流的方法,以利用 XR 平台的优势并改进团体治疗服务。
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引用次数: 0
Body Positivity, Physical Health, and Emotional Well-Being Discourse on Social Media: Content Analysis of Lizzo's Instagram. 社交媒体上的身体积极性、身体健康和情感幸福话语:Lizzo Instagram 的内容分析。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-04 DOI: 10.2196/60541
Stephanie L Albert, Rachel E Massar, Omni Cassidy, Kayla Fennelly, Melanie Jay, Philip M Massey, Marie A Bragg
<p><strong>Background: </strong>Weight stigma is a fundamental cause of health inequality. Body positivity may be a counterbalance to weight stigma. Social media is replete with weight-stigmatizing content and is a driver of poor mental health outcomes; however, there remains a gap in understanding its potential to mitigate the prevalence and impact of harmful messaging and to promote positive effects on a large scale.</p><p><strong>Objective: </strong>We selected musical artist Lizzo, whose brand emphasizes body positivity and empowerment, for an instrumental case study on the discourse on social media and specifically Instagram. We focused on 3 domains, including body positivity, physical health, and emotional well-being. These domains challenge social norms around weight and body size and have the potential to positively affect the physical and psychological health of people with diverse body sizes.</p><p><strong>Methods: </strong>We evaluated posts by Lizzo, comments from Instagram users, and replies to comments over a 2-month period (October 11 to December 12, 2019). Two coders rated Lizzo's posts and Instagram users' comments for their sentiments on the 3 domains. Replies to Instagram users' comments were assessed for their reactions to comments (ie, did they oppose or argue against the comment or did they support or bolster the comment). Engagement metrics, including the number of "likes," were also collected.</p><p><strong>Results: </strong>The final sample included 50 original posts by Lizzo, 250 comments from Instagram users, and 1099 replies to comments. A proportion of Lizzo's content included body positive sentiments (34%) and emotional well-being (18%); no posts dealt explicitly with physical health. A substantial amount Instagram users' comments and replies contained stigmatizing content including the use of nauseated and vomiting emojis, implications that Lizzo's body was shameful and should be hidden away, accusations that she was promoting obesity, and impeachments of Lizzo's health. In spite of the stigmatizing content, we also discovered content highlighting the beneficial nature of having positive representation of a Black woman living in a larger body who is thriving. Moreover, analysis of the discourse between users illustrated that stigmatizing expressions are being combated online, at least to some degree.</p><p><strong>Conclusions: </strong>This study demonstrates that Lizzo has exposed millions of social media users to messages about body positivity and provided more visibility for conversations about weight and shape. Future research should examine the extent to which body positive messages can lead to greater acceptance of individuals living in larger bodies. Instagram and other social media platforms should consider ways to reduce body-shaming content while finding ways to promote content that features diverse bodies. Shifting the landscape of social media could decrease stereotypes about weight and shape while incre
背景:体重成见是造成健康不平等的根本原因。身体积极性可以抵消体重鄙视。社交媒体上充斥着体重污名化的内容,是导致不良心理健康结果的一个驱动因素;然而,在了解社交媒体在减轻有害信息的流行和影响以及在大规模推广积极影响方面的潜力方面仍存在差距:我们选择了音乐艺人 Lizzo(其品牌强调身体积极性和赋权)进行一项关于社交媒体(尤其是 Instagram)言论的工具性案例研究。我们重点关注三个领域,包括身体积极性、身体健康和情感幸福。这些领域挑战了有关体重和体型的社会规范,有可能对不同体型的人的身心健康产生积极影响:我们对 Lizzo 发布的帖子、Instagram 用户的评论以及对评论的回复进行了为期两个月(2019 年 10 月 11 日至 12 月 12 日)的评估。两名编码员对 Lizzo 的帖子和 Instagram 用户的评论在 3 个领域的情感进行了评分。对Instagram用户评论的回复则评估了他们对评论的反应(即他们是反对或反驳评论,还是支持或支持评论)。此外,还收集了参与度指标,包括 "赞 "的数量:最终样本包括 Lizzo 的 50 篇原创文章、Instagram 用户的 250 条评论以及对评论的 1099 条回复。Lizzo的部分内容包括积极的身体情绪(34%)和情感健康(18%);没有帖子明确涉及身体健康。Instagram用户的大量评论和回复都含有污名化内容,包括使用恶心和呕吐的表情符号、暗示莉兹佐的身体是可耻的、应该藏起来、指责她助长肥胖症以及弹劾莉兹佐的健康状况。尽管有污名化的内容,但我们也发现了一些内容,这些内容强调了黑人女性以更大的身体茁壮成长的正面形象所带来的益处。此外,对用户之间对话的分析表明,至少在某种程度上,污名化的表达方式在网上受到了打击:本研究表明,Lizzo 让数以百万计的社交媒体用户接触到了有关身体积极性的信息,并为有关体重和体型的对话提供了更多的能见度。未来的研究应探讨积极的身体信息能在多大程度上促使人们更大程度地接受体型较大的人。Instagram 和其他社交媒体平台应考虑如何减少以身体为耻的内容,同时想方设法推广以多样化身体为特色的内容。改变社交媒体的面貌可以减少人们对体重和体型的刻板印象,同时加强对话,让人们更加接受和包容不同体型的人。
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引用次数: 0
User Views on Online Sexual Health Symptom Checker Tool: Qualitative Research. 用户对在线性健康症状检查工具的看法:定性研究。
IF 2 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-04 DOI: 10.2196/54565
Alicia Jean King, Jade Elissa Bilardi, Janet Mary Towns, Kate Maddaford, Christopher Kincaid Fairley, Eric P F Chow, Tiffany Renee Phillips

Background: Delayed diagnosis and treatment of sexually transmitted infections (STIs) contributes to poorer health outcomes and onward transmission to sexual partners. Access to best-practice sexual health care may be limited by barriers such as cost, distance to care providers, sexual stigma, and trust in health care providers. Online assessments of risk offer a novel means of supporting access to evidence-based sexual health information, testing, and treatment by providing more individualized sexual health information based on user inputs.

Objective: This developmental evaluation aims to find potential users' views and experiences in relation to an online assessment of risk, called iSpySTI (Melbourne Sexual Health Center), including the likely impacts of use.

Methods: Individuals presenting with urogenital symptoms to a specialist sexual health clinic were given the opportunity to trial a web-based, Bayesian-powered tool that provides a list of 2 to 4 potential causes of their symptoms based on inputs of known STI risk factors and symptoms. Those who tried the tool were invited to participate in a once-off, semistructured research interview. Descriptive, action, and emotion coding informed the comparative analysis of individual cases.

Results: Findings from interviews with 14 people who had used the iSpySTI tool support the superiority of the online assessment of STI risk compared to existing sources of sexual health information (eg, internet search engines) in providing trusted and probabilistic information to users. Additionally, potential users reported benefits to their emotional well-being in the intervening period between noticing symptoms and being able to access care. Differences in current and imagined urgency of health care seeking and emotional impacts were found based on clinical diagnosis (eg, non-STI, curable and incurable but treatable STIs) and whether participants were born in Australia or elsewhere.

Conclusions: Online assessments of risk provide users experiencing urogenital symptoms with more individualized and evidence-based health information that can improve their health care-seeking and provide reassurance in the period before they can access care.

背景:延迟诊断和治疗性传播感染(STI)会导致较差的健康结果,并将疾病传染给性伴侣。获得最佳性健康保健可能会受到各种障碍的限制,如费用、与保健提供者的距离、性污名以及对保健提供者的信任。在线风险评估根据用户输入的信息提供更加个性化的性健康信息,为获取循证性健康信息、检测和治疗提供了一种新的支持手段:这项发展性评估旨在了解潜在用户对名为 iSpySTI(墨尔本性健康中心)的在线风险评估的看法和体验,包括使用可能产生的影响:方法:在性健康专科门诊出现泌尿生殖系统症状的人有机会试用一种基于贝叶斯的网络工具,该工具可根据输入的已知性传播感染风险因素和症状,提供 2 到 4 个可能导致其症状的原因列表。试用过该工具的人被邀请参加一次性半结构化研究访谈。通过描述性编码、行动编码和情感编码对个别案例进行比较分析:对 14 名使用过 iSpySTI 工具的人进行的访谈结果表明,与现有的性健康信息来源(如互联网搜索引擎)相比,在线性传播感染风险评估在向用户提供可信的概率信息方面更具优势。此外,潜在用户表示,从发现症状到能够获得治疗之间的这段时间里,他们的情绪健康得到了改善。根据临床诊断(如非性传播感染、可治愈和不可治愈但可治疗的性传播感染)以及参与者是在澳大利亚出生还是在其他地方出生,发现了当前和想象中寻求医疗保健的紧迫性和情绪影响的差异:结论:在线风险评估为出现泌尿生殖系统症状的用户提供了更多个性化的循证健康信息,可以改善他们的就医情况,并在他们就医前的这段时间里为他们提供保证。
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引用次数: 0
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JMIR Formative Research
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