Journal of Medical Internet Research最新文献

{"title":"Integrating the Situational Theory of Problem Solving and Technology Acceptance Model to Predict Intention to Practice Health Protective Behavior for Influenza-Like Illness Among TikTok Users: Cross-Sectional Study.","authors":"Can Li, Jen-Sern Tham, Ghazali Akmar Hayati Ahmad, Norliana Hashim, Jeong-Nam Kim","doi":"10.2196/73677","DOIUrl":"10.2196/73677","url":null,"abstract":"<p><strong>Background: </strong>Outbreaks of influenza-like illness (ILI) pose ongoing public health challenges, prompting widespread demand for timely and accessible health information. TikTok, a leading short video platform, has emerged as an overarching channel for disseminating health-related content, particularly in mainland China. While previous studies have examined health communication on social media, few have integrated complementary theoretical frameworks to understand how user perceptions and motivations jointly influence health behaviors.</p><p><strong>Objective: </strong>This study integrates the situational theory of problem solving (STOPS) and the technology acceptance model (TAM) to examine the communicative actions and intentions of Chinese TikTok users to adopt health protective behaviors in response to ILI.</p><p><strong>Methods: </strong>A cross-sectional web-based survey was conducted in China between June and July 2023 using convenience and snowball sampling. A total of 1109 valid responses were analyzed using partial least squares structural equation modeling. Constructs from STOPS (problem recognition, constraint recognition, involvement recognition, situational motivation, and communicative action in problem solving) and TAM (perceived usefulness, perceived ease of use, and attitude) were measured alongside risk perception and intention to engage in protective behaviors.</p><p><strong>Results: </strong>Perceived usefulness (β=.344; P<.001) and ease of use (β=.359; P<.001) positively influenced the attitude toward using TikTok. Risk perception (β=.050, P=.02) had a small but significant impact on attitude. Situational motivation was positively predicted by risk perception (β=.154; P<.001), problem recognition (β=.153; P<.001), and involvement recognition (β=.248; P<.001) but negatively predicted by constraint recognition (β=-.265; P<.001). Both attitude (β=.390; P<.001) and situational motivation (β=.471; P<.001) significantly influenced communicative action, which in turn predicted intention to practice protective behaviors (β=.570; P<.001). Mediation analyses confirmed the partial mediating roles of attitude and situational motivation.</p><p><strong>Conclusions: </strong>TikTok is an effective platform for public health communication in China, particularly for ILI-related content. Integrating the STOPS and TAM provides a robust framework for explaining how user perceptions and motivations translate into digital engagement and health protective intentions. These findings suggest that interventions should not only enhance technological usability and credibility but also tailor content to elevate perceived personal relevance and reduce psychological or contextual constraints. Future public health campaigns can benefit from engaging influencers, using participatory content formats, and targeting specific motivational cues to increase user involvement in health communication and behavioral change. Caution is warranted in gen","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73677"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perinatal Women's Perception of Maternal Health Information Quality on Digital Media: Scoping Review.","authors":"Bowen Li, Ningning Jin, Yingli Wang, Xiaoni Hou, Jing Meng, Yihong Zhang","doi":"10.2196/67620","DOIUrl":"10.2196/67620","url":null,"abstract":"<p><strong>Background: </strong>Perinatal women are increasingly turning to digital media for maternal health information; however, concerns regarding the quality of this information persist. Understanding perinatal women's perceptions of information quality is essential for enhancing the effectiveness of information services.</p><p><strong>Objective: </strong>This review aims to (1) identify the key features that perinatal women focus on when perceiving the quality of maternal health information on digital media and (2) summarize the quality issues with maternal health information on digital media that perinatal women have reported.</p><p><strong>Methods: </strong>A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines using PubMed, Web of Science, Embase, Scopus, and ScienceDirect databases (2000-2024). The search strategy combined the following four conceptual clusters using Boolean operators: (1) perinatal population terms (\"pregnant women,\" \"expectant mothers,\" and \"perinatal\"), (2) information-related terms (\"information,\" \"education,\" and \"resource\"), (3) perception-related terms (\"perception,\" \"experience,\" and \"expectation\"), and (4) digital media terms (\"online,\" \"social media,\" and \"app\"). Thematic analysis was used for data synthesis.</p><p><strong>Results: </strong>From 5290 records identified, 30 (0.57%) articles were selected for inclusion in this review. The perceived quality features of information can be categorized into four distinct aspects: (1) information providers, which encompasses 2 features, transparency and authority; (2) information content, consisting of 9 features, trustworthiness, evidence based, timeliness, comprehensiveness, need-based relevance, practicality, motivational simulation, emotional supportiveness, and cultural sensitivity; (3) information presentation, which includes 3 features, understandability, attractiveness, and conciseness; and (4) information platforms, comprising 3 features, user-friendly navigation, proactive delivery, and interactivity. Furthermore, several perceived quality issues associated with these aspects were noteworthy. Specifically, (1) quality issues regarding information providers primarily pertained to their lack of credibility; (2) quality issues related to information content encompassed an overwhelming volume of information, inaccuracies, lack of scientific evidence, prevalence of contradictory information, insufficient breadth and depth, a mismatch between content and the needs of women, and information that induces negative emotions; (3) presentation issues manifested as difficulties in understanding the information; and (4) quality issues regarding information platforms included poor usability and the commercialization of these platforms.</p><p><strong>Conclusions: </strong>Our review identifies 17 key quality features across various dimensions that are valued by pe","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e67620"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study.","authors":"Michael S Deiner, Russell Y Deiner, Cherie Fathy, Natalie A Deiner, Vagelis Hristidis, Stephen D McLeod, Thomas J Bukowski, Thuy Doan, Gerami D Seitzman, Thomas M Lietman, Travis C Porco","doi":"10.2196/65226","DOIUrl":"10.2196/65226","url":null,"abstract":"<p><strong>Background: </strong>The use of web-based search and social media can help identify epidemics, potentially earlier than clinical methods or even potentially identifying unreported outbreaks. Monitoring for eye-related epidemics, such as conjunctivitis outbreaks, can facilitate early public health intervention to reduce transmission and ocular comorbidities. However, monitoring social media content for conjunctivitis outbreaks is costly and laborious. Large language models (LLMs) could overcome these barriers by assessing the likelihood that real-world outbreaks are being described. However, public health actions for likely outbreaks could benefit more by knowing additional epidemiological characteristics, such as outbreak type, size, and severity.</p><p><strong>Objective: </strong>We aimed to assess whether and how well LLMs can classify epidemiological features from social media posts beyond conjunctivitis outbreak probability, including outbreak type, size, severity, etiology, and community setting. We used a validation framework comparing LLM classifications to those of other LLMs and human experts.</p><p><strong>Methods: </strong>We wrote code to generate synthetic conjunctivitis outbreak social media posts, embedded with specific preclassified epidemiological features to simulate various infectious eye disease outbreak and control scenarios. We used these posts to develop effective LLM prompts and test the capabilities of multiple LLMs. For top-performing LLMs, we gauged their practical utility in real-world epidemiological surveillance by comparing their assessments of Twitter/X, forum, and YouTube conjunctivitis posts. Finally, human raters also classified the posts, and we compared their classifications to those of a leading LLM for validation. Comparisons entailed correlation or sensitivity and specificity statistics.</p><p><strong>Results: </strong>We assessed 7 LLMs for effectively classifying epidemiological data from 1152 synthetic posts, 370 Twitter/X posts, 290 forum posts, and 956 YouTube posts. Despite some discrepancies, the LLMs demonstrated a reliable capacity for nuanced epidemiological analysis across various data sources and compared to humans or between LLMs. Notably, GPT-4 and Mixtral 8x22b exhibited high performance, predicting conjunctivitis outbreak characteristics such as probability (GPT-4: correlation=0.73), size (Mixtral 8x22b: correlation=0.82), and type (infectious, allergic, or environmentally caused); however, there were notable exceptions. Assessing synthetic and real-world posts for etiological factors, infectious eye disease specialist validations revealed that GPT-4 had high specificity (0.83-1.00) but variable sensitivity (0.32-0.71). Interrater reliability analyses showed that LLM-expert agreement exceeded expert-expert agreement for severity assessment (intraclass correlation coefficient=0.69 vs 0.38), while agreement varied by condition type (κ=0.37-0.94).</p><p><strong>Conclusions: </strong>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e65226"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Impact of the COVID-19 Pandemic on Telepharmaceutical Service Effectiveness: Systematic Review and Meta-Analysis.","authors":"Puwen Zhang, Mengting Yang, Siyi He, Xiayan Li, Bingchen Lang, Linan Zeng, Lingli Zhang","doi":"10.2196/64073","DOIUrl":"10.2196/64073","url":null,"abstract":"<p><strong>Background: </strong>Telepharmaceutical services (TPS) led by pharmacists, an emerging telehealth service, improve access to medical services and enable patients to receive specialized services in areas with limited resources. With a lower risk of infection and no restriction of isolation measures, TPS showed great potential during the COVID-19 pandemic. However, whether the effectiveness of TPS changed before and after the outbreak of the COVID-19 pandemic remained unclear.</p><p><strong>Objective: </strong>This study aimed to evaluate the effectiveness of TPS, compare the effectiveness before and after the outbreak of the COVID-19 pandemic, and explore whether the effectiveness changed over time.</p><p><strong>Methods: </strong>We searched PubMed, Embase (Ovid), SinoMed, China National Knowledge Infrastructure, Wanfang, and VIP databases for randomized controlled trials that evaluated the effectiveness of TPS. The search covered studies published from inception to October 24, 2023. Eligible studies were conducted before May 5, 2023, when the World Health Organization (WHO) declared the end of the COVID-19 pandemic as a Public Health Emergency of International Concern. We used the random-effect model to pool the results and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to assess the certainty of evidence. To explore whether the effectiveness of TPS changed over time, we applied subgroup analyses (studies conducted before December 31, 2019, and studies conducted after January 1, 2020). Using the independent-sample z test, we compared the effectiveness of TPS between the 2 subgroups. When a significant difference arose between them, we conducted a meta-regression analysis to further evaluate the trend of effectiveness over time.</p><p><strong>Results: </strong>In addition, 40 studies were finally included. Compared with no TPS or usual care (ie, face-to-face pharmaceutical services), TPS probably increased patient medication adherence (risk difference [RD] 0.15, 95% CI 0.09-0.20, moderate certainty), and may reduce the occurrence of adverse events (RD -0.10, 95% CI -0.18 to -0.02, low certainty) and improve the proportion of patients who were satisfied with medication (RD 0.16, 95% CI 0.05-0.26, low certainty). Moderate to high evidence indicated that patients accepting TPS probably achieved superior management of diabetes and hypertension. The effectiveness of TPS was not significantly different before and after the outbreak of the COVID-19 pandemic except for medication adherence (RD 0.12, 95% CI 0.03-0.21, P=.007), which also increased over time (coefficient=0.01, 95% CI 0.01-0.02, P<.001).</p><p><strong>Conclusions: </strong>TPS probably improved patient medication adherence and may lead to better satisfaction and the incidence of adverse events. The effectiveness of TPS in general did not change after the outbreak of the COVID-19 pandemic.</p><p><strong>Trial registration: </strong>PROSPERO C","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e64073"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facial Emotion Recognition of 16 Distinct Emotions From Smartphone Videos: Comparative Study of Machine Learning and Human Performance.","authors":"Marie Keinert, Simon Pistrosch, Adria Mallol-Ragolta, Björn W Schuller, Matthias Berking","doi":"10.2196/68942","DOIUrl":"10.2196/68942","url":null,"abstract":"<p><strong>Background: </strong>The development of automatic emotion recognition models from smartphone videos is a crucial step toward the dissemination of psychotherapeutic app interventions that encourage emotional expressions. Existing models focus mainly on the 6 basic emotions while neglecting other therapeutically relevant emotions. To support this research, we introduce the novel Stress Reduction Training Through the Recognition of Emotions Wizard-of-Oz (STREs WoZ) dataset, which contains facial videos of 16 distinct, therapeutically relevant emotions.</p><p><strong>Objective: </strong>This study aimed to develop deep learning-based automatic facial emotion recognition (FER) models for binary (positive vs negative) and multiclass emotion classification tasks, assess the models' performance, and validate them by comparing the models with human observers.</p><p><strong>Methods: </strong>The STREs WoZ dataset contains 14,412 facial videos of 63 individuals displaying the 16 emotions. The selfie-style videos were recorded during a stress reduction training using front-facing smartphone cameras in a nonconstrained laboratory setting. Automatic FER models using both appearance and deep-learned features for binary and multiclass emotion classification were trained on the STREs WoZ dataset. The appearance features were based on the Facial Action Coding System and extracted with OpenFace. The deep-learned features were obtained through a ResNet50 model. For our deep learning models, we used the appearance features, the deep-learned features, and their concatenation as inputs. We used 3 recurrent neural network (RNN)-based architectures: RNN-convolution, RNN-attention, and RNN-average networks. For validation, 3 human observers were also trained in binary and multiclass emotion recognition. A test set of 3018 facial emotion videos of the 16 emotions was completed by both the automatic FER model and human observers. The performance was assessed with unweighted average recall (UAR) and accuracy.</p><p><strong>Results: </strong>Models using appearance features outperformed those using deep-learned features, as well as models combining both feature types in both tasks, with the attention network using appearance features emerging as the best-performing model. The attention network achieved a UAR of 92.9% in the binary classification task, and accuracy values ranged from 59.0% to 90.0% in the multiclass classification task. Human performance was comparable to that of the automatic FER model in the binary classification task, with a UAR of 91.0%, and superior in the multiclass classification task, with accuracy values ranging from 87.4% to 99.8%.</p><p><strong>Conclusions: </strong>Future studies are needed to enhance the performance of automatic FER models for practical use in psychotherapeutic apps. Nevertheless, this study represents an important first step toward advancing emotion-focused psychotherapeutic interventions via smartphone apps.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e68942"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Token Probabilities to Mitigate Large Language Models Overconfidence in Answering Medical Questions.","authors":"Raphaël Bentegeac, Bastien Le Guellec, Grégory Kuchcinski, Philippe Amouyel, Aghiles Hamroun","doi":"10.2196/64348","DOIUrl":"https://doi.org/10.2196/64348","url":null,"abstract":"<p><strong>Background: </strong>Chatbots have demonstrated promising capabilities in medicine, scoring passing grades for board examinations across various specialties. However, their tendency to express high levels of confidence in their responses, even when incorrect, poses a limitation to their utility in clinical settings.</p><p><strong>Objective: </strong>To examine whether token probabilities outperform chatbots' expressed confidence levels in predicting the accuracy of their responses to medical questions.</p><p><strong>Methods: </strong>Nine large language models (LLMs), comprising both commercial (GPT-3.5, GPT-4 and GPT-4o) and open-source (Llama 3.1-8b, Llama 3.1-70b, Phi-3-Mini, Phi-3-Medium, Gemma 2-9b and Gemma 2-27b), were prompted to respond to a set of 2,522 questions from the US Medical Licensing Examination (MedQA database). Additionally, the models rated their confidence from 0 to 100 and the token probability of each response was extracted. The models' success rates were measured, and the predictive performances of both expressed confidence and response token probability in predicting response accuracy were evaluated using Area Under the Receiver Operating Characteristic Curves (AUROCs), Adapted Calibration Error (ACE) and Brier score. Sensitivity analyses were conducted using additional questions sourced from other databases in English (MedMCQA, n=2,797), Chinese (MedQA Main-land China, n=3,413 and Taiwan, n=2,808), and French (FrMedMCQA, n=1,079), different prompting strategies and temperature settings.</p><p><strong>Results: </strong>Overall, mean accuracy ranged from 56.5% [54.6 - 58.5] for Phi-3-Mini to 89.0% [87.7-90.2] for GPT-4o. Across the US Medical Licensing Examination questions, all chatbots consistently expressed high levels of confidence in their responses (ranging from 90[90-90] for Llama 3.1-70B to 100[100-100] for GPT-3.5). However, expressed confidence failed to predict response accuracy (AUROC ranging from 0.52[0.50-0.53] for Phi 3 Mini to 0.68[0.65-0.71] for GPT-4o). In contrast, the response token probability consistently outperformed expressed confidence for predicting response accuracy (AUROCs ranging from 0.71 [0.69 - 0.73] for Phi 3 mini to 0.87 [0.85 - 0.89] for GPT-4o, all p-values<0.001). Furthermore, all models demonstrated imperfect calibration, with a general trend towards overconfidence. These findings were consistent in sensitivity analyses.</p><p><strong>Conclusions: </strong>Due to the limited capacity of chatbots to accurately evaluate their confidence when responding to medical queries, clinicians and patients should abstain from relying on their self-rated certainty. Instead, token probabilities emerge as a promising and easily accessible alternative for gauging the inner doubts of these models.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Chain Mediation Role of Self-Efficacy, Health Literacy, and Physical Exercise in the Relationship Between Internet Use and Older Adults' Health: Cross-Sectional Questionnaire Study.","authors":"Weizhen Liao, Chengyu Ma, Xiqiao Liu, Ziwei Sun","doi":"10.2196/73242","DOIUrl":"10.2196/73242","url":null,"abstract":"<p><strong>Background: </strong>With the widespread use of the internet, the number of older internet users is rapidly increasing. However, the role of individual factors such as self-efficacy, health literacy, and physical exercise in the chain of influence between internet use and the health of older adults is unclear.</p><p><strong>Objective: </strong>On the basis of the media effects theory and the health belief model, we aimed to explore the relationship between internet use and self-rated health among Chinese older adults. We also analyzed the mediating roles of self-efficacy, health literacy, and physical exercise and examined how these mediating effects varied by sex, residence, and per capita monthly household income groups. In addition, we investigated the moderating effect of education level.</p><p><strong>Methods: </strong>We included 1147 participants aged ≥60 years from the 2021 Psychology and Behavior Investigation of Chinese Residents project in this study. Using a combination of multiple linear regression and bootstrap testing, we constructed a chain mediation model and a moderated chain mediation model to examine how internet use affects older adults' health through self-efficacy, health literacy, and physical exercise. In addition, we explored the marginal effects of education level within each mediation pathway.</p><p><strong>Results: </strong>Internet use significantly improved the self-rated health of older adults (B=2.183; P<.001), and this improvement was exclusively mediated by self-efficacy (B=0.502; P<.001), health literacy (B=5.415; P<.001), and physical exercise (B=3.449; P<.001). These three factors could act as independent mediators or form sequential chain mediation pathways. In heterogeneity analysis, the total indirect effects were more pronounced among female participants (B=1.965; P<.05) and individuals with middle (B=1.971; P<.05) to high (B=2.710; P<.05) income levels. Furthermore, education level moderated the relationships between internet use and self-efficacy (B=0.452; P=.003) and between internet use and self-rated health (B=1.284; P=.01). This suggests that the positive influence of internet use on self-rated health was more pronounced among older adults with higher education.</p><p><strong>Conclusions: </strong>The findings of this study suggested that internet use can positively influence older adults' self-rated health through the chain mediated effects of self-efficacy, health literacy, and physical exercise. This chain mediated effect was more pronounced among those with higher levels of education. In the future, efforts should be made to promote internet use among older persons by developing age-friendly digital platforms and expanding digital training and health education. Moreover, older persons should be encouraged to participate in volunteer activities to increase their self-efficacy and improve their health.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73242"},"PeriodicalIF":5.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12264443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Efficacy of MamaLift Plus Digital Therapeutic Mobile App for Postpartum Depression (SuMMER): Randomized, Placebo-Controlled Pivotal Trial.","authors":"Shailja Dixit, Indira Malladi, Sidhartha Shankar, Amrik Shah","doi":"10.2196/69050","DOIUrl":"10.2196/69050","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Improvement in the Edinburgh Postnatal Depression Scale (EPDS) score is a regulatory-approved measure for symptom improvement in postpartum depression (PPD). While digital solutions have the potential to overcome common treatment barriers, few have shown clinically significant improvement in PPD symptoms, as measured by the EPDS.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the clinical efficacy of the MamaLift Plus digital therapeutic for the improvement of PPD symptoms in women who had recently given birth and had PPD, as assessed by the EPDS.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This double-blind, randomized, placebo-controlled phase 3 pivotal trial recruited participants remotely. Eligibility criteria required that participants have an EPDS score between 13 and 19 and a confirmed diagnosis of PPD. Participants were randomized to the MamaLift Plus intervention or sham control arm, with stratification based on new mom status. \"New moms\" are those who have had one live birth. MamaLift Plus is a self-guided 8-week digital therapeutic for symptomatic treatment for PPD. MamaLift Plus can be used on a mobile device. MamaLift Plus delivers digital Cognitive Behavioral Therapy, Behavioral Activation Therapy, Interpersonal Therapy, and Dialectical Behavior Therapy for PPD. The sham control mimicked the features, functionality, and user experience of the treatment. The most important difference between the 2 arms was that participants in the sham control app did not receive any Cognitive Behavioral Therapy. Primary and secondary endpoints were self-assessed. The primary endpoint was the proportion of participants whose EPDS scores improved by ≥4 points at the end of the study assessment. The intent-to-treat (ITT) analysis set included all randomized participants who started at least 1 module. The full analysis set (FAS) population included all participants who completed at least one postbaseline assessment. The trial is closed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants were recruited remotely between April 18 and May 24, 2023. Eligible participants were assessed by a licensed mental health provider to confirm a diagnosis of PPD. In addition, 95 participants were randomized to the intervention and 46 to the control groups. A total of 86.3% (82/95) of MamaLift Plus arm participants achieved an improvement of ≥4 points, compared with 23.9% (11/46) of sham control arm participants (P&lt;.0001). There were 2 adverse events each in the intervention arm 2.1% (2/95) and sham control arm 4.3% (2/46). Only 11 participants failed to provide any postbaseline assessment for the primary endpoint.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Participants who received MamaLift Plus exhibited significant and clinically meaningful improvement in depressive symptoms compared with control. Results suggest MamaLift Plus has the potential to improve treatment outcomes for women experiencing PPD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial registration: &lt;/strong&gt;C","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":" ","pages":"e69050"},"PeriodicalIF":5.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12264441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144475612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low Prevalence of Adequate eHealth Literacy and Willingness to Use Telemedicine Among Older Adults: Cross-Sectional Study From a Middle-Income Country.","authors":"Supawadee Sainimnuan, Rinrada Preedachitkul, Ponnapa Petchthai, Yuwadee Paokantarakorn, Arunotai Siriussawakul, Varalak Srinonprasert","doi":"10.2196/65380","DOIUrl":"10.2196/65380","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Currently, the rapid aging of global population, especially in low- and middle-income countries, is placing changing demands on health care systems. The preparation of the population for adequate eHealth literacy and good digital health is one of the challenges of social policy. The willingness to understand eHealth literacy and telemedicine use across different age groups of the population will help identify loopholes and bottlenecks in the implementation and help to develop appropriate solutions. Currently, studies on the status of eHealth literacy across different age ranges remain limited and scarce.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;In this study, we aimed to investigate the prevalence and factors associated with adequate eHealth literacy, including attitudes toward eHealth literacy and willingness to use telemedicine as an example of digital technology. We focused on the comparison between older people (aged ≥60 years) and younger adult groups in Thailand, a middle-income country.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a cross-sectional, observational study from January 2021 to July 2021. A total of 400 participants who visited the outpatient department of Siriraj Hospital were recruited and completed questionnaires collecting demographic information, frequency of internet use, and devices used for accessing the internet. eHealth literacy was assessed using the eHAELS (eHealth Literacy Scale) questionnaire. We also explored the participants' attitude and willingness to use telemedicine. We applied univariable logistic regression analysis to elucidate the factors associated with eHealth literacy and willingness to use telemedicine.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our study revealed that the older participants had lower level of eHealth literacy compared to younger participants. Using an eHAELS score ≥26 points to define 'adequate eHealth literacy,' 74.0% (n=97) of older adults compared to 22.7% (n=61) of younger adults had inadequate eHealth literacy. Only 19.8% (n=26) of older adults, compared to 65.1% (n=175) of younger adults showed high levels of eHealth literacy defined by exploring each item using the eHEALS tool. The items with the lowest level of eHealth literacy among older adults pertained to confidence in finding and applying health information for self-care and in using information from the internet for making health decisions. In terms of attitude and interest toward telemedicine use, confidence in security, perceived convenience of telemedicine, and adequate eHealth literacy were the three strongest factors associated with willingness to use telemedicine, with odds ratios (ORs) of 5.90 (95% CI 3.43-10.15), 5.43(95% CI 3.12-9.43), and 4.45 (95% CI 2.60-7.62), respectively. Additionally, the younger adults were more likely to be interested in using telemedicine with an OR of 2.02 (95% CI 1.21-33.37).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our study addressed the low level of eHealth literacy, wi","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e65380"},"PeriodicalIF":5.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nurses' Experiences of Providing Dysphagia Services Through the Internet+Nursing Service Care Model: Qualitative Study.","authors":"Zhifang Ren, Ling Tong, Shuojin Fu, Shuai Jin, Yanling Wang, Qian Xiao","doi":"10.2196/67572","DOIUrl":"10.2196/67572","url":null,"abstract":"<p><strong>Background: </strong>With China's aging population and increasing prevalence of chronic diseases, the Internet+Nursing Service has emerged as a new care model, enabling registered nurses from medical institutions to provide home-based care through a web-based application and offline service model. This care model is particularly beneficial for vulnerable populations, such as patients with dysphagia, who face significant risks like malnutrition and aspiration pneumonia. Nurses play a critical role in delivering these services, yet their experiences, challenges, and support needs remain underexplored. Understanding these factors is essential for improving service quality and establishing standardized care guidelines.</p><p><strong>Objective: </strong>This study aims to explore the experiences and challenges of nurses providing the dysphagia-related Internet+Nursing Service, offering insights to guide the standardization and sustainable development of this innovative care model.</p><p><strong>Methods: </strong>A qualitative study was conducted with 18 nurses who had been providing the Internet+Nursing Service for patients with dysphagia for over 6 months. Purposive sampling ensured the selection of participants with relevant expertise. Semistructured interviews were used for data collection, focusing on nurses' experiences, challenges, and recommendations. Data were analyzed using conventional content analysis, following an inductive approach to identify recurring themes and patterns.</p><p><strong>Results: </strong>The analysis revealed 3 key themes: value representation and social impact; nursing resources and staffing; and safety and management support. Nurses emphasized that patient-centered nursing services enhanced their sense of professional fulfillment and helped alleviate pressure on hospital nursing resources. However, challenges such as insufficient time and energy, inadequate manpower, and underexploitation of service potential limited service effectiveness. To ensure sustainability, nurses highlighted the need for standardized service processes, regular experience exchange, and stronger hospital involvement in managing and supporting the Internet+Nursing Service.</p><p><strong>Conclusions: </strong>This study highlights both the opportunities and challenges of delivering the dysphagia-related Internet+Nursing Service. While nurses acknowledge the value of this care model, addressing staffing shortages, improving training programs, and strengthening regulatory frameworks are essential for optimizing service delivery. Policy makers and health care institutions should develop standardized guidelines and supportive policies to enhance service sustainability and accessibility.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e67572"},"PeriodicalIF":5.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12234399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}