DIGITAL HEALTH最新文献

{"title":"Addressing health service equity through telehealth: A systematic review of reviews.","authors":"Siyu Wang, Amy von Huben, Prithivi Prakash Sivaprakash, Emily Saurman, Sarah Norris, Andrew Wilson","doi":"10.1177/20552076251326233","DOIUrl":"10.1177/20552076251326233","url":null,"abstract":"<p><strong>Objective: </strong>To synthesize existing reviews on the impact of telehealth programs on health service equity in non-urban areas, focusing on six dimensions of access: accessibility, availability, acceptability, affordability, adequacy, and awareness.</p><p><strong>Methods: </strong>We included systematic and non-systematic reviews published from 2012 to 2023 on telehealth interventions in rural or remote settings. Content was mapped to the six dimensions, and coverage within each dimension was rated based on predefined criteria.</p><p><strong>Results: </strong>A total of 42 reviews (43% systematic) were identified. Most reviews (90.5%) addressed at least one dimension, yet comprehensive coverage was rare. Acceptability had the highest number of \"good\" ratings (24%), while awareness was the least explored. Gaps included digital literacy, infrastructure challenges, and cultural barriers-factors critical to equitable telehealth access. Cost-effectiveness analyses were also limited, leaving affordability underexamined.</p><p><strong>Conclusion: </strong>Telehealth shows promise for improving healthcare access in non-urban regions. However, existing reviews often provide incomplete assessments across the six dimensions. This suggests a need for clearer, more robust evaluation frameworks to ensure more comprehensive reporting of equity impacts in telehealth research.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251326233"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use machine learning to predict bone metastasis of esophageal cancer: A population-based study.","authors":"Jun Wan, Jia Zhou","doi":"10.1177/20552076251325960","DOIUrl":"10.1177/20552076251325960","url":null,"abstract":"<p><strong>Objective: </strong>The objective of this study is to develop a machine learning (ML)-based predictive model for bone metastasis (BM) in esophageal cancer (EC) patients.</p><p><strong>Methods: </strong>This study utilized data from the Surveillance, Epidemiology, and End Results database spanning 2010 to 2020 to analyze EC patients. A total of 21,032 confirmed cases of EC were included in the study. Through univariate and multivariate logistic regression (LR) analysis, 10 indicators associated with the risk of BM were identified. These factors were incorporated into seven different ML classifiers to establish predictive models. The performance of these models was assessed and compared using various metrics including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F-score, precision, and decision curve analysis.</p><p><strong>Results: </strong>Factors such as age, gender, histological type, T stage, N stage, surgical intervention, chemotherapy, and the presence of brain, lung, and liver metastases were identified as independent risk factors for BM in EC patients. Among the seven models developed, the ML model based on LR algorithm demonstrated excellent performance in the internal validation set. The AUC, accuracy, sensitivity, and specificity of this model were 0.831, 0.721, 0.787, and 0.717, respectively.</p><p><strong>Conclusion: </strong>We have successfully developed an online calculator utilizing a LR model to assist clinicians in accurately assessing the risk of BM in patients with EC. This tool demonstrates high accuracy and specificity, thereby enhancing the development of personalized treatment plans.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251325960"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of immersive leisure-based virtual reality cognitive training on cognitive and physical function in community-based older adults: A randomized controlled trial.","authors":"I-Ching Chuang, Auwal Abdullahi, I-Chen Chen, Yih-Ru Wu, Ching-Yi Wu","doi":"10.1177/20552076251328491","DOIUrl":"10.1177/20552076251328491","url":null,"abstract":"<p><strong>Background: </strong>Older adults are at risk of developing cognitive impairments, and cognitive training is commonly used to enhance cognitive function in this population. The effectiveness of cognitive training is further optimized with the integration of leisure-based activities, such as horticultural therapy. However, to the best of our knowledge, there is a lack of studies examining the effect of integrating virtual reality (VR) with leisure-based activities to provide real-world experiences and enhance cognitive outcomes in older adults. Furthermore, while immersive VR cognitive training has demonstrated effectiveness in enhancing multiple cognitive domains, methodological limitations-such as the absence of control groups or the use of passive controls-hinder the ability to draw conclusive conclusions regarding its comparative effectiveness.</p><p><strong>Objective: </strong>This study conducted immersive leisure-based VR cognitive training in community-dwelling older adults to investigate its effectiveness on cognitive and physical functions. We employed an active control group in which participants received well-arranged leisure activities without focusing on cognitive components.</p><p><strong>Methods: </strong>This cluster randomized controlled trial was conducted in the community facilities in northern Taiwan between 2022 and 2023. The VR cognitive training group received simulated gardening activities, such as planting, fertilizing, and harvesting, and tasks involving cognitive challenges, such as producing plant essential oils, for 60 min daily, 2 days per week, for 8 weeks. The control group received non-cognitive training. The outcomes evaluated were cognitive function assessed by Montreal Cognitive Assessment (MoCA), immediate memory assessed by Wechsler Memory Scale (WMS)-Word List, working memory and mental flexibility assessed by WMS-Digit Span Forward, WMS-Digit Span Backward, and WMG-Digit Span Sequencing (DSS), and physical function assessed by the Timed Up and Go (TUG) test.</p><p><strong>Results: </strong>The study recruited 137 older adults. After VR cognitive training, higher significant improvements were seen in MoCA (<i>p</i> < 0.001), WMS-DSS (<i>p</i> = 0.015), and TUG (0.008*) compared with the control group.</p><p><strong>Conclusions: </strong>This study is the first to examine the effects of fully immersive, leisure-based VR cognitive training on cognitive and physical function in community-dwelling older adults, highlighting its potential as a promising tool for promoting health compared to the non-cognitive training commonly used in community facilities.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251328491"},"PeriodicalIF":2.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11960153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of connected health on the psychological wellbeing and quality of life of people with multiple sclerosis and their caregivers: A systematic review.","authors":"Joan Alaboson, Laura Coffey, Sowmya Shrivastava, Adeola Ade-Alao, Rebecca Maguire","doi":"10.1177/20552076251326230","DOIUrl":"10.1177/20552076251326230","url":null,"abstract":"<p><strong>Background: </strong>Connected health (CH) interventions may improve psychological wellbeing and quality of life (QoL) in caregivers and people with multiple sclerosis (MS); however, this impact has not been rigorously evaluated. This systematic review aims to synthesize the literature assessing CH technology's impact on psychological wellbeing and/or QoL of people with MS (PwMS) and/or their caregivers.</p><p><strong>Methods: </strong>This systematic review's protocol is registered with International Prospective Register of Systematic Reviews (PROSPERO) with identification number CRD42023402434. CINAHL, Web of Science, PubMed, Embase, and PsycINFO databases were searched with terms relating to (a) CH; (b) MS; (c) psychological wellbeing/QoL; and (d) caregivers/people with MS. Of 2821 screened articles, 47 met the eligibility criteria, with just three including MS caregivers.</p><p><strong>Results: </strong>Heterogenous interventions supporting self-management (<i>n</i> = 20 studies), education (<i>n</i> = 17 studies), psychological (<i>n</i> = 14 studies) or physical (<i>n</i> = 9 studies) rehabilitation and peer support (<i>n</i> = 5 studies) were found. CH technologies had mixed effectiveness in improving psychological and QoL outcomes, with results potentially impacted by technology type, intervention and target group. The study's findings have limited generalizability to improve access across sub-national locations, with no studies disaggregating between urban and rural residence and the majority conducted in the USA and Western Europe.</p><p><strong>Conclusion: </strong>CH technologies show promise in improving psychological wellbeing and QoL among PwMS and their caregivers. However, this necessitates further study comparing connected health and MS subtypes to improve reproducibility and effectiveness.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251326230"},"PeriodicalIF":2.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11960181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart insole-based abnormal gait identification: Deep sequential networks and feature ablation study.","authors":"Beomjoon Park, Minhye Kim, Dawoon Jung, Jinwook Kim, Kyung-Ryoul Mun","doi":"10.1177/20552076251332999","DOIUrl":"10.1177/20552076251332999","url":null,"abstract":"<p><strong>Objective: </strong>Gait analysis plays a pivotal role in evaluating walking abilities, with recent advancements in digital health stressing the importance of efficient data collection methods. This study aims to classify nine gait types including one normal and eight abnormal gaits, using sequential network-based models and diverse feature combinations obtained from insole sensors.</p><p><strong>Methods: </strong>The dataset was collected using insole sensors from subjects performing 15 m walking with designated gait types. The sensors incorporated pressure sensors and inertial measurement units (IMUs), along with the center of pressure engineered from the pressure readings. A number of deep learning architectures were evaluated for their ability to classify the gait types, focusing on feature sets including temporal parameters, statistical features of pressure signals, center of pressure data, and IMU data. Ablation studies were also conducted to assess the impact of combining features from different modalities.</p><p><strong>Results: </strong>Our results demonstrate that models incorporating IMU features outperform those using different combinations of modalities including individual feature sets, with the top-performing models achieving F1-scores of up to 90% in sample-wise classification and 92% in subject-wise classification. Additionally, an ablation study reveals the importance of considering diverse feature modalities, including temporal parameters, statistical features from pressure signals, center of pressure data, and IMU data, for comprehensive gait classification.</p><p><strong>Conclusion: </strong>Overall, this study successfully developed deep sequential models that effectively classify nine different gait types, with the ablation study underscoring the potential for integrating features from diverse domains to enhance clinical applications, such as intervention for gait-related disorders.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251332999"},"PeriodicalIF":2.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11960168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 telehealth by US adults with depression or anxiety disorder: Results from 2022 Health Information National Trends Survey.","authors":"Pu Bai, Emily Brignone, Bibo Jiang, Casey Pinto, Li Wang","doi":"10.1177/20552076251321999","DOIUrl":"10.1177/20552076251321999","url":null,"abstract":"<p><strong>Background: </strong>Telehealth use has significantly increased recently. However, little is known about its use by individuals with depression or anxiety disorders. This study aims to explore the patterns of telehealth use among those individuals.</p><p><strong>Methods: </strong>Data used were from the 2022 Health Information National Trends Survey (HINTS) cycle 6. Weighted logistic regression was performed to test the association between depression/anxiety disorder and telehealth use, and to explore reasons for using/not using telehealth among those with depression/anxiety, compared to those without.</p><p><strong>Results: </strong>Out of the 4952 study participants, 2887 (weighted percentage: 39.36%) had used telehealth in the past 12 months. Those with depression/anxiety disorder had significantly higher telehealth use, compared to those without (57% vs. 32%; OR = 2.65; 95% CI: (2.04, 3.43)). Factors affecting telehealth use could differ by depression/anxiety disorder status. Among those with depression/anxiety disorder, being woman or married was not associated with telehealth use, whereas they were significant factors among those without depression/anxiety disorder. Among those with depression/anxiety, non-Hispanic Black participants (OR = 0.51; CI: (0.78, 0.94)) were less likely to use telehealth, compared to non-Hispanic White participants; additionally, higher income was associated with telehealth use. Regarding reasons for using telehealth, convenience (OR = 1.80; CI: (1.21, 2.68)) and avoiding COVID infection (OR = 1.40; CI: (1.06, 1.86)) were more likely considered by those with depression/anxiety disorder.</p><p><strong>Conclusion: </strong>Individuals with depression/anxiety disorder were more likely to use telehealth and to do so for reasons of convenience and avoiding infection. Promoting telehealth to those with depression/anxiety disorder should consider their unique utilization patterns.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251321999"},"PeriodicalIF":2.9,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration of digital health literacy among community members and healthcare teams in the deep south: A quasi-experimental study.","authors":"Gabrielle B Rocque, Nicole L Henderson, Keyonsis Hildreth, Noon Eltoum, Omari Whitlow, Loretta Herring, Stacey Ingram, Daniel I Chu, Connie C Shao, Claudia Hardy, Timiya S Nolan, Chelsea McGowan, Jennifer Young Pierce, Courtney P Williams","doi":"10.1177/20552076251325581","DOIUrl":"10.1177/20552076251325581","url":null,"abstract":"<p><strong>Background: </strong>Given increasing technology reliance, there is a need for a deeper understanding of individual and community-level comfort with technology as it pertains to basic and more complex healthcare-related skills.</p><p><strong>Methods: </strong>The objective of this quasi-experimental study was to engage participants in conversations about digital health literacy to facilitate awareness and to compare digital health literacy for community members, healthcare providers, non-clinical navigators, and community health advisors and county coordinators (henceforth referred to as CHAs) in the Deep South (AL, MS, FL). Interactive community conversations on digital health literacy were given in community (n = 16) and clinical (n = 5) settings. Participants completed pre- and post- surveys assessing personal comfort performing technological tasks on a 5-point scale. Mixed models estimated both within- and between-role changes in self-reported comfort.</p><p><strong>Results: </strong>Of 248 participants, 56% were community members, 18% healthcare providers, 17% CHAs, and 8% non-clinical navigators. Community members had the lowest personal comfort performing every task assessed (all p < .05). In the pre-test, the largest differences in reported personal comfort performing tasks were seen for basic skills including scanning QR codes (mean comfort score: community members 2.7 [SD 1.5] vs. non-clinical navigator 4.5 [1.0], p < .001) and sharing a website (mean comfort score: community members 2.9 [SD 1.6] vs. non-clinical navigator 4.5 [1.0], p < .001). Pre- vs. post-community conversation, community members experienced significant increases in their personal comfort scanning QR codes (β=0.8, 95% CI 0.5-1.0), creating an online account (general use) (β=0.4, 95% CI 0.2-0.6), and using a smartphone (β=0.3, 95% CI 0.1-0.5).</p><p><strong>Conclusions: </strong>As technological advances continue to be implemented, gaps in digital health literacy must be addressed. Non-clinical navigators may play a future role in teaching patients technology skills.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251325581"},"PeriodicalIF":2.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11952038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven prediction of hospital admissions using gradient boosting and GPT-2.","authors":"Xingyu Zhang, Hairong Wang, Guan Yu, Wenbin Zhang","doi":"10.1177/20552076251331319","DOIUrl":"10.1177/20552076251331319","url":null,"abstract":"<p><strong>Background: </strong>Accurately predicting hospital admissions from the emergency department (ED) is essential for improving patient care and resource allocation. This study aimed to predict hospital admissions by integrating both structured clinical data and unstructured text data using machine learning models.</p><p><strong>Methods: </strong>Data were obtained from the 2021 National Hospital Ambulatory Medical Care Survey-Emergency Department (NHAMCS-ED), including adult patients aged 18 years and older. Structured data included demographics, visit characteristics, vital signs, and medical history, while unstructured data consisted of free-text chief complaints and injury descriptions. A Gradient Boosting Classifier (GBC) was applied to structured data, while a fine-tuned GPT-2 model processed the unstructured text. A combined model was created by averaging the outputs of both models. Model performance was evaluated using 5-fold cross-validation, assessing accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).</p><p><strong>Results: </strong>Among the 13,115 patients, 2264 (17.3%) were admitted to the hospital. The combined model outperformed the individual structured and unstructured models, achieving an accuracy of 75.8%, precision of 39.5%, sensitivity of 75.8%, and specificity of 75.8%. In comparison, the structured data model achieved 73.8% accuracy, while the unstructured model reached 64.6%. The combined model had the highest AUC, indicating superior performance.</p><p><strong>Conclusions: </strong>Combining structured and unstructured data using machine learning significantly improves the prediction of hospital admissions from the ED. This integrated approach can enhance decision-making and optimize ED operations.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251331319"},"PeriodicalIF":2.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring implementation challenges of decentralized clinical trials: A qualitative study of policy stakeholder perspectives in Denmark.","authors":"Ida Hestbjerg, Ditte Bonde Stanek, Ulrik Bak Kirk, Christa Thomsen","doi":"10.1177/20552076251330519","DOIUrl":"10.1177/20552076251330519","url":null,"abstract":"<p><strong>Background: </strong>The implementation of decentralized clinical trials (DCTs) has received strong political interest in Denmark. Many policy stakeholders may directly or indirectly influence the implementation at a national strategic level. Diverging interests may drive the implementation process in different directions, which may result in an inefficient and unsustainable process.</p><p><strong>Objective: </strong>The purpose of this study is to explore implementation challenges of DCTs by examining stakeholder interests that emerge in their accounts of the advantages and disadvantages of DCTs.</p><p><strong>Method: </strong>This qualitative study is based on interviews with 15 participants from 12 institutions comprising patient institutions, healthcare institutions, industry institutions, and political institutions. All interviews were conducted between July and December 2023. Additionally, we included 13 policy documents. Interviews and documents were analysed twice. First, we conducted a data-driven thematic analysis. Second, we performed a second-order analysis informed by paradox theory. We used the concept of paradoxical tensions to understand the contradictions that occurred in the stakeholder accounts.</p><p><strong>Results: </strong>To make the implementation of DCTs efficient and sustainable, the interests of stakeholders need to be aligned. However, our study demonstrated that the many different stakeholder interests created a knot of paradoxical tensions, which must first be resolved.</p><p><strong>Conclusion: </strong>Policy stakeholders must collaborate to resolve the paradoxical tensions and align their different interests towards a common objective. The responsibility of the practical implementation process needs to be allocated to one stakeholder or a few stakeholders, who can guide the process.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251330519"},"PeriodicalIF":2.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perception, attitude and use of digital health platforms for mental health promotion among students in a national university in the south-western part of Nigeria: A cross-sectional study.","authors":"Racheal Oluwabukunmi Ogundipe, Oyenike Elizabeth Omotosho, Yetunde Olufisayo John-Akinola","doi":"10.1177/20552076251331841","DOIUrl":"10.1177/20552076251331841","url":null,"abstract":"<p><strong>Background: </strong>Nigeria faces significant challenges in providing access to and utilisation of professional mental health treatments. Digital health systems play an essential role in healthcare across diverse settings. This study aimed to determine the perceptions, attitudes and use of digital health platforms in mental health promotion among University of Ibadan students in Nigeria.</p><p><strong>Method: </strong>This study was a cross-sectional survey, and 400 respondents from the University of Ibadan, Nigeria, were selected using a multistage sampling procedure. The data were obtained using a semi-structured self-administered questionnaire. The data were analysed using SPSS version 25 via descriptive and inferential statistics, such as chi-square tests and multiple linear regression at the 5% significance level.</p><p><strong>Result: </strong>The respondents were 24.2 ± 4.8 years. Most respondents (99.8%) had access to at least one mobile phone. The majority (83.2%) were unaware of any digital health platform for mental health promotion. Despite the lack of awareness, most respondents (47%) positively perceived the usefulness of digital health platforms for mental health promotion. Most (74.2%) had favourable attitudes toward using digital health platforms for mental health promotion. Most (88.3%) reported never using any mental health promotion applications, and 11.7% reported using at least one application. In the mental health assessment, 46.5% reported mild-severe mental disorders. There was a significant association between students' awareness and use of these platforms (<i>r</i> = 25.429; <i>p</i> < 0.001).</p><p><strong>Conclusion: </strong>These findings revealed low awareness of mental health promotion digital platforms, but the respondents had positive perceptions, attitudes and good behavioural intentions.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251331841"},"PeriodicalIF":2.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}