JMIR Formative Research最新文献

{"title":"A digital program for daily life management with endometriosis: Pilot study on symptoms and quality of life among participants.","authors":"Zélia Breton, Emilie Stern, Mathilde Pinault, Delphine Lhuillery, Erick Petit, Pierre Panel, Maïa Alexaline","doi":"10.2196/58262","DOIUrl":"https://doi.org/10.2196/58262","url":null,"abstract":"<p><strong>Background: </strong>After suffering for an average of 7 years before diagnosis, endometriosis patients are usually left with more questions than answers about managing their symptoms in the absence of a cure. To help women with endometriosis after their diagnosis, we developed an online support program combining user research, evidence-based medicine, and clinical expertise. Structured around CBT and the quality-of-life metrics from the EHP score, the program is designed to guide participants over a 3-month and is available in France.</p><p><strong>Objective: </strong>This cohort study was designed to measure the impact of a digital health program on the symptom and quality of life levels of women with endometriosis.</p><p><strong>Methods: </strong>Ninety-two participants were included in the pilot study, among a total of 146 program participants who volunteered and assessed for eligibility for this research. They were recruited either free of charge through employer health insurance or via individual direct access. A control group of women with endometriosis who did not follow the program was recruited (n=404) through social media and mailing campaign. Questionnaires assessing quality of life and symptom levels were sent to program participants and controls at baseline and at three months via email. The control group was sampled according to initial pain level in order to obtain a similar pain profile between controls and program participants (n=149). Descriptive statistics and statistical tests (Chi-square, Fisher's exact, Wilcoxon, Mann-Whitney U, Student t-tests) were used to analyze intra- and inter-group differences, with Cohen's D measuring effect size for significant results.</p><p><strong>Results: </strong>Over three months, global symptom burden, the general level of pain, anxiety, depression, dysmenorrhea, dysuria, chronic fatigue, neuropathic pain, and endobelly levels improved significantly among program participants. These improvements were significantly different from the control group for global symptom burden (mean±SD: participants=-0.7±1.6, controls=-0.3±1.3, P=.048, small d), anxiety (participants=-1.1±2.8, controls=0.2±2.5, P<.001, medium d) and depression levels (participants=-0.9±2.5, controls=0.0±3.1, P=.04, small d), neuropathic pain (participants=-1.0±2.7, controls=-0.1±2.6, P=.004, small d), and endobelly (participants=-0.9±2.5, controls=-0.3±2.4, P=.03, small d). Participant quality of life evolution between baseline and three months improved and significantly differed from the control group for the core part of the EHP-5 (participants=-5.9±21.0, controls=1.0±14.8, P=.03, small d) and the EQ-5D (participants=0.1±0.1, controls=-0.0±0.1, P=.001, medium d). Perceived knowledge of endometriosis was significantly greater at three months among participants than in controls (P<.001).</p><p><strong>Conclusions: </strong>The results from this pilot study suggest that a digital health program providing medical and sci","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142949103","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}

{"title":"Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods.","authors":"Lisa M Gandy, Lana V Ivanitskaya, Leeza L Bacon, Rodina Bizri-Baryak","doi":"10.2196/57395","DOIUrl":"10.2196/57395","url":null,"abstract":"<p><strong>Background: </strong>Sentiment analysis is one of the most widely used methods for mining and examining text. Social media researchers need guidance on choosing between manual and automated sentiment analysis methods.</p><p><strong>Objective: </strong>Popular sentiment analysis tools based on natural language processing (NLP; VADER [Valence Aware Dictionary for Sentiment Reasoning], TEXT2DATA [T2D], and Linguistic Inquiry and Word Count [LIWC-22]), and a large language model (ChatGPT 4.0) were compared with manually coded sentiment scores, as applied to the analysis of YouTube comments on videos discussing the opioid epidemic. Sentiment analysis methods were also examined regarding ease of programming, monetary cost, and other practical considerations.</p><p><strong>Methods: </strong>Evaluation methods included descriptive statistics, receiver operating characteristic (ROC) curve analysis, confusion matrices, Cohen κ, accuracy, specificity, precision, sensitivity (recall), F<sub>1</sub>-score harmonic mean, and the Matthews correlation coefficient. An inductive, iterative approach to content analysis of the data was used to obtain manual sentiment codes.</p><p><strong>Results: </strong>A subset of comments were analyzed by a second coder, producing good agreement between the 2 coders' judgments (κ=0.734). YouTube social media about the opioid crisis had many more negative comments (4286/4871, 88%) than positive comments (79/662, 12%), making it possible to evaluate the performance of sentiment analysis models in an unbalanced dataset. The tone summary measure from LIWC-22 performed better than other tools for estimating the prevalence of negative versus positive sentiment. According to the ROC curve analysis, VADER was best at classifying manually coded negative comments. A comparison of Cohen κ values indicated that NLP tools (VADER, followed by LIWC's tone and T2D) showed only fair agreement with manual coding. In contrast, ChatGPT 4.0 had poor agreement and failed to generate binary sentiment scores in 2 out of 3 attempts. Variations in accuracy, specificity, precision, sensitivity, F<sub>1</sub>-score, and MCC did not reveal a single superior model. F<sub>1</sub>-score harmonic means were 0.34-0.38 (SD 0.02) for NLP tools and very low (0.13) for ChatGPT 4.0. None of the MCCs reached a strong correlation level.</p><p><strong>Conclusions: </strong>Researchers studying negative emotions, public worries, or dissatisfaction with social media face unique challenges in selecting models suitable for unbalanced datasets. We recommend VADER, the only cost-free tool we evaluated, due to its excellent discrimination, which can be further improved when the comments are at least 100 characters long. If estimating the prevalence of negative comments in an unbalanced dataset is important, we recommend the tone summary measure from LIWC-22. Researchers using T2D must know that it may only score some data and, compared with other methods, be more ti","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e57395"},"PeriodicalIF":2.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11784633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142948961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing the Recruitment Frequency, Implementation Fidelity, and Feasibility of Outcomes of the Heart Failure Activity Coach Study (HEALTHY): Pilot Randomized Controlled Trial.","authors":"Andreas Blomqvist, Maria Bäck, Leonie Klompstra, Anna Strömberg, Tiny Jaarsma","doi":"10.2196/62910","DOIUrl":"10.2196/62910","url":null,"abstract":"<p><strong>Background: </strong>Heart failure (HF) is a common and deadly disease, precipitated by physical inactivity and sedentary behavior. Although the 1-year survival rate after the first diagnosis is high, physical inactivity and sedentary behavior are associated with increased mortality and negatively impact the health-related quality of life (HR-QoL).</p><p><strong>Objective: </strong>We tested the recruitment frequency, implementation fidelity, and feasibility of outcomes of the Activity Coach app that was developed using an existing mobile health (mHealth) tool, Optilogg, to support older adults with HF to be more physically active and less sedentary.</p><p><strong>Methods: </strong>In this pilot clinical randomized controlled trial (RCT), patients with HF who were already using Optilogg to enhance self-care behavior were recruited from 5 primary care health centers in Sweden. Participants were randomized to either have their mHealth tool updated with the Activity Coach app (intervention group) or a sham version (control group). The intervention duration was 12 weeks, and in weeks 1 and 12, the participants wore an accelerometer daily to objectively measure their physical activity. The HR-QoL was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ), and subjective goal attainment was assessed using goal attainment scaling. Baseline data were collected from the participants' electronic health records (EHRs).</p><p><strong>Results: </strong>We found 67 eligible people using the mHealth tool, of which 30 (45%) initially agreed to participate, with 20 (30%) successfully enrolled and randomized to the control and intervention groups in a ratio of 1:1. The participants' daily adherence to registering physical activity in the Activity Coach app was 69% (range 24%-97%), and their weekly adherence was 88% (range 58%-100%). The mean goal attainment score was -1.0 (SD 1.1) for the control group versus 0.6 (SD 0.6) for the intervention group (P=.001). The mean change in the overall HR-QoL summary score was -9 (SD 10) for the control group versus 3 (SD 13) in the intervention group (P=.027). There was a significant difference in the physical limitation scores between the control (mean 45, SD 27) and intervention (mean 71, SD 20) groups (P=.04). The average length of sedentary bouts increased by 27 minutes to 458 (SD 84) in the control group minutes and decreased by 0.70 minutes to 391 (SD 117) in the intervention group (P=.22). There was a nonsignificant increase in the mean light physical activity (LPA): 146 (SD 46) versus 207 (SD 80) minutes in the control and intervention groups, respectively (P=.07).</p><p><strong>Conclusions: </strong>The recruitment rate was lower than anticipated. An active recruitment process is advised if a future efficacy study is to be conducted. Adherence to the Activity Coach app was high, and it may be able to support older adults with HF in being physically active.</p><p><strong>Trial registration: </stro","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e62910"},"PeriodicalIF":2.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142949069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences in eHealth Access, Use, and Perceived Benefit Between Different Socioeconomic Groups in the Dutch Context: Secondary Cross-Sectional Study.","authors":"Lucille Standaar, Lilian van Tuyl, Anita Suijkerbuijk, Anne Brabers, Roland Friele","doi":"10.2196/49585","DOIUrl":"10.2196/49585","url":null,"abstract":"<p><strong>Background: </strong>There is a growing concern that digital health care may exacerbate existing health disparities. Digital health care or eHealth encompasses the digital apps that are used in health care. Differences in access, use, and perceived benefits of digital technology among socioeconomic groups are commonly referred to as the digital divide. Current research shows that people in lower socioeconomic positions (SEPs) use eHealth less frequently.</p><p><strong>Objective: </strong>This study aims to (1) investigate the association between SEP and eHealth access to, use of, and perceived benefit within the adult Dutch population and (2) evaluate disparities in eHealth access, use, and perceived benefit through three socioeconomic variables-education, standardized income, and the socioeconomic status of the neighborhood.</p><p><strong>Methods: </strong>A secondary analysis was conducted on data from the Nivel Dutch Health Care Consumer Panel (response rate 57%, 849/1500), to assess access to, use of, and perceived benefits from eHealth. These data were collected to monitor eHealth developments in the Netherlands. eHealth was examined through two concepts: (1) eHealth in general and (2) websites, apps, and wearables. Results were stratified into 9 SEP populations based on 3 indicators-education, standardized income, and socioeconomic status level of the neighborhood. Logistic regression analyses were performed to evaluate whether the outcomes varied significantly across different SEP groups. Age was included as a covariate to control for confounding.</p><p><strong>Results: </strong>This study confirms the association between eHealth and SEP and shows that low SEP respondents have less access (odds ratio [OR] 5.72, 95% CI 3.06-10.72) and use (OR 4.96, 95% CI 2.66-9.24) of eHealth compared to medium or high SEP respondents. Differences were most profound when stratifying for levels of education.</p><p><strong>Conclusions: </strong>The access to and use of eHealth has a socioeconomic gradient and emphasizes that SEP indicators cannot be used interchangeably to assess eHealth access and use. The results underline the importance of activities and policies aimed at improving eHealth accessibility and usage among low SEP groups to mitigate disparities in health between different socioeconomic groups.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e49585"},"PeriodicalIF":2.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142949247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Trifecta of Industry, Academic, and Health System Partnership to Improve Mental Health Care Through Smartphone-Based Remote Patient Monitoring: Development and Usability Study.","authors":"C Neill Epperson, Rachel Davis, Allison Dempsey, Heinrich C Haller, David J Kupfer, Tiffany Love, Pamela M Villarreal, Mark Matthews, Susan L Moore, Kimberly Muller, Christopher D Schneck, Jessica L Scott, Richard D Zane, Ellen Frank","doi":"10.2196/57624","DOIUrl":"10.2196/57624","url":null,"abstract":"<p><strong>Background: </strong>Mental health treatment is hindered by the limited number of mental health care providers and the infrequency of care. Digital mental health technology can help supplement treatment by remotely monitoring patient symptoms and predicting mental health crises in between clinical visits. However, the feasibility of digital mental health technologies has not yet been sufficiently explored. Rhythms, from the company Health Rhythms, is a smartphone platform that uses passively acquired smartphone data with artificial intelligence and predictive analytics to alert patients and providers to an emerging mental health crisis.</p><p><strong>Objective: </strong>The objective of this study was to test the feasibility and acceptability of Rhythms among patients attending an academic psychiatric outpatient clinic.</p><p><strong>Methods: </strong>Our group embedded Rhythms into the electronic health record of a large health system. Patients with a diagnosis of major depressive disorder, bipolar disorder, or other mood disorder were contacted online and enrolled for a 6-week trial of Rhythms. Participants provided data by completing electronic surveys as well as by active and passive use of Rhythms. Emergent and urgent alerts were monitored and managed according to passively collected data and patient self-ratings. A purposively sampled group of participants also participated in qualitative interviews about their experience with Rhythms at the end of the study.</p><p><strong>Results: </strong>Of the 104 participants, 89 (85.6%) completed 6 weeks of monitoring. The majority of the participants were women (72/104, 69.2%), White (84/104, 80.8%), and non-Hispanic (100/104, 96.2%) and had a diagnosis of major depressive disorder (71/104, 68.3%). Two emergent alerts and 19 urgent alerts were received and managed according to protocol over 16 weeks. More than two-thirds (63/87, 72%) of those participating continued to use Rhythms after study completion. Comments from participants indicated appreciation for greater self-awareness and provider connection, while providers reported that Rhythms provided a more nuanced understanding of patient experience between clinical visits.</p><p><strong>Conclusions: </strong>Rhythms is a user-friendly, electronic health record-adaptable, smartphone-based tool that provides patients and providers with a greater understanding of patient mental health status. Integration of Rhythms into health systems has the potential to facilitate mental health care and improve the experience of both patients and providers.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e57624"},"PeriodicalIF":2.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142949072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-Design of an Escape Room for e-Mental Health Training of Mental Health Care Professionals: Research Through Design Study.","authors":"Joyce J P A Bierbooms, Wouter R J W Sluis-Thiescheffer, Milou Anne Feijt, Inge M B Bongers","doi":"10.2196/58650","DOIUrl":"10.2196/58650","url":null,"abstract":"<p><strong>Background: </strong>Many efforts to increase the uptake of e-mental health (eMH) have failed due to a lack of knowledge and skills, particularly among professionals. To train health care professionals in technology, serious gaming concepts such as educational escape rooms are increasingly used, which could also possibly be used in mental health care. However, such serious-game concepts are scarcely available for eMH training for mental health care professionals.</p><p><strong>Objective: </strong>This study aims to co-design an escape room for training mental health care professionals' eMH skills and test the escape room's usability by exploring their experiences with this concept as a training method.</p><p><strong>Methods: </strong>This project used a research through design approach with 3 design stages. In the first stage, the purpose, expectations, and storylines for the escape room were formulated in 2 co-design sessions with mental health care professionals, game designers, innovation staff, and researchers. In the second stage, the results were translated into the first escape room, which was tested in 3 sessions, including one web version of the escape room. In the third stage, the escape room was tested with mental health care professionals outside the co-design team. First, 2 test sessions took place, followed by 3 field study sessions. In the field study sessions, a questionnaire was used in combination with focus groups to assess the usability of the escape room for eMH training in practice.</p><p><strong>Results: </strong>An escape room prototype was iteratively developed and tested by the co-design team, which delivered multiple suggestions for adaptations that were assimilated in each next version of the prototype. The field study showed that the escape room creates a positive mindset toward eMH. The suitability of the escape room to explore the possibilities of eMH was rated 4.7 out of 5 by the professionals who participated in the field study. In addition, it was found to be fun and educational at the same time, scoring 4.7 (SD 0.68) on a 5-point scale. Attention should be paid to the game's complexity, credibility, and flexibility. This is important for the usefulness of the escape room in clinical practice, which was rated an average of 3.8 (SD 0.77) on a 5-point scale. Finally, implementation challenges should be addressed, including organizational policy and stimulation of eMH training.</p><p><strong>Conclusions: </strong>We can conclude that the perceived usability of an escape room for training mental health care professionals in eMH skills is promising. However, it requires additional effort to transfer the learnings into mental health care professionals' clinical practice. A straightforward implementation plan and testing the effectiveness of an escape room on skill enhancement in mental health care professionals are essential next steps to reach sustainable goals.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e58650"},"PeriodicalIF":2.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142949238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study.","authors":"Yousif Mohamed AlSerkal, Naseem Mohamed Ibrahim, Aisha Suhail Alsereidi, Mubaraka Ibrahim, Sudheer Kurakula, Sadaf Ahsan Naqvi, Yasir Khan, Neema Preman Oottumadathil","doi":"10.2196/64936","DOIUrl":"10.2196/64936","url":null,"abstract":"<p><strong>Background: </strong>Primary health care (PHC) services face operational challenges due to high patient volumes, leading to complex management needs. Patients access services through booked appointments and walk-in visits, with walk-in visits often facing longer waiting times. No-show appointments are significant contributors to inefficiency in PHC operations, which can lead to an estimated 3%-14% revenue loss, disrupt resource allocation, and negatively impact health care quality. Emirates Health Services (EHS) PHC centers handle over 140,000 visits monthly. Baseline data indicate a 21% no-show rate and an average patient wait time exceeding 16 minutes, necessitating an advanced scheduling and resource management system to enhance patient experiences and operational efficiency.</p><p><strong>Objective: </strong>The objective of this study was to evaluate the impact of an artificial intelligence (AI)-driven solution that was integrated with an interactive real-time data dashboard on reducing no-show appointments and improving patient waiting times at the EHS PHCs.</p><p><strong>Methods: </strong>This study introduced an innovative AI-based data application to enhance PHC efficiency. Leveraging our electronic health record system, we deployed an AI model with an 86% accuracy rate to predict no-shows by analyzing historical data and categorizing appointments based on no-show risk. The model was integrated with a real-time dashboard to monitor patient journeys and wait times. Clinic coordinators used the dashboard to proactively manage high-risk appointments and optimize resource allocation. The intervention was assessed through a before-and-after comparison of PHC appointment dynamics and wait times, analyzing data from 135,393 appointments (67,429 before implementation and 67,964 after implementation).</p><p><strong>Results: </strong>Implementation of the AI-powered no-show prediction model resulted in a significant 50.7% reduction in no-show rates (P<.001). The odds ratio for no-shows after implementation was 0.43 (95% CI 0.42-0.45; P<.001), indicating a 57% reduction in the likelihood of no-shows. Additionally, patient wait times decreased by an average of 5.7 minutes overall (P<.001), with some PHCs achieving up to a 50% reduction in wait times.</p><p><strong>Conclusions: </strong>This project demonstrates that integrating AI with a data analytics platform and an electronic health record systems can significantly improve operational efficiency and patient satisfaction in PHC settings. The AI model enabled daily assessments of wait times and allowed for real-time adjustments, such as reallocating patients to different clinicians, thus reducing wait times and optimizing resource use. These findings illustrate the transformative potential of AI and real-time data analytics in health care delivery.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e64936"},"PeriodicalIF":2.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of Sleep Measurements of an Actigraphy Watch: Instrument Validation Study.","authors":"Mari Waki, Ryohei Nakada, Kayo Waki, Yuki Ban, Ryo Suzuki, Toshimasa Yamauchi, Masaomi Nangaku, Kazuhiko Ohe","doi":"10.2196/63529","DOIUrl":"10.2196/63529","url":null,"abstract":"<p><strong>Background: </strong>The iAide2 (Tokai) physical activity monitoring system includes diverse measurements and wireless features useful to researchers. The iAide2's sleep measurement capabilities have not been compared to validated sleep measurement standards in any published work.</p><p><strong>Objective: </strong>We aimed to assess the iAide2's sleep duration and total sleep time (TST) measurement performance and perform calibration if needed.</p><p><strong>Methods: </strong>We performed free-living sleep monitoring in 6 convenience-sampled participants without known sleep disorders recruited from within the Waki DTx Laboratory at the Graduate School of Medicine, University of Tokyo. To assess free-living sleep, we validated the iAide2 against a second actigraph that was previously validated against polysomnography, the MotionWatch 8 (MW8; CamNtech Ltd). The participants wore both devices on the nondominant arm, with the MW8 closest to the hand, all day except when bathing. The MW8 and iAide2 assessments both used the MW8 EVENT-marker button to record bedtime and risetime. For the MW8, MotionWare Software (version 1.4.20; CamNtech Ltd) provided TST, and we calculated sleep duration from the sleep onset and sleep offset provided by the software. We used a similar process with the iAide2, using iAide2 software (version 7.0). We analyzed 64 nights and evaluated the agreement between the iAide2 and the MW8 for sleep duration and TST based on intraclass correlation coefficients (ICCs).</p><p><strong>Results: </strong>The absolute ICCs (2-way mixed effects, absolute agreement, single measurement) for sleep duration (0.69, 95% CI -0.07 to 0.91) and TST (0.56, 95% CI -0.07 to 0.82) were moderate. The consistency ICC (2-way mixed effects, consistency, single measurement) was excellent for sleep duration (0.91, 95% CI 0.86-0.95) and moderate for TST (0.78, 95% CI 0.67-0.86). We determined a simple calibration approach. After calibration, the ICCs improved to 0.96 (95% CI 0.94-0.98) for sleep duration and 0.82 (95% CI 0.71-0.88) for TST. The results were not sensitive to the specific participants included, with an ICC range of 0.96-0.97 for sleep duration and 0.79-0.87 for TST when applying our calibration equation to data removing one participant at a time and 0.96-0.97 for sleep duration and 0.79-0.86 for TST when recalibrating while removing one participant at a time.</p><p><strong>Conclusions: </strong>The measurement errors of the uncalibrated iAide2 for both sleep duration and TST seem too large for them to be useful as absolute measurements, though they could be useful as relative measurements. The measurement errors after calibration are low, and the calibration approach is general and robust, validating the use of iAide2's sleep measurement functions alongside its other features in physical activity research.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e63529"},"PeriodicalIF":2.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11747538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Smart Goggles Used at Bedtime on Objectively Measured Sleep and Self-Reported Anxiety, Stress, and Relaxation: Pre-Post Pilot Study.","authors":"Sharon Danoff-Burg, Elie Gottlieb, Morgan A Weaver, Kiara C Carmon, Duvia Lara Ledesma, Holly M Rus","doi":"10.2196/58461","DOIUrl":"10.2196/58461","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Insufficient sleep is a problem affecting millions. Poor sleep can trigger or worsen anxiety; conversely, anxiety can lead to or exacerbate poor sleep. Advances in innovative consumer products designed to promote relaxation and support healthy sleep are emerging, and their effectiveness can be evaluated accurately using sleep measurement technologies in the home environment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This pilot study examined the effects of smart goggles used at bedtime to deliver gentle, slow vibration to the eyes and temples. The study hypothesized that objective sleep, perceived sleep, self-reported stress, anxiety, relaxation, and sleepiness would improve after using the smart goggles.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A within-participants, pre-post study design was implemented. Healthy adults with subclinical threshold sleep problems (N=20) tracked their sleep nightly using a polysomnography-validated noncontact biomotion device and completed daily questionnaires over two phases: a 3-week baseline period and a 3-week intervention period. During the baseline period, participants followed their usual sleep routines at home. During the intervention period, participants used Therabody SmartGoggles in \"Sleep\" mode at bedtime. This mode, designed for relaxation, delivers a gentle eye and temple massage through the inflation of internal compartments to create a kneading sensation combined with vibrating motors. Each night, the participants completed questionnaires assessing relaxation, stress, anxiety, and sleepiness immediately before and after using the goggles. Daily morning questionnaires assessed perceived sleep, complementing the objective sleep data measured every night.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Multilevel regression analysis of 676 nights of objective sleep parameters showed improvements during nights when the goggles were used compared to the baseline period. Key findings include sleep duration (increased by 12 minutes, P=.01); duration of deep sleep (increased by 6 minutes, P=.002); proportion of deep sleep (7% relative increase, P=.02); BodyScore, an age- and gender-normalized measure of deep sleep (4% increase, P=.002); number of nighttime awakenings (7% decrease, P=.02); total time awake after sleep onset (reduced by 6 minutes, P=.047); and SleepScore, a measure of overall sleep quality (3% increase, P=.02). Questionnaire responses showed that compared to baseline, participants felt they had better sleep quality (P&lt;.001) and woke feeling more well-rested (P&lt;.001). Additionally, participants reported feeling sleepier, less stressed, less anxious, and more relaxed (all P values &lt;.05) immediately after using the goggles each night, compared to immediately before use. A standardized inventory administered before and after the 3-week intervention period indicated reduced anxiety (P=.03), confirming the nightly analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The use of smart goggles at bedtime sign","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e58461"},"PeriodicalIF":2.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Social Media on Enhancing Dental Care and Practice Among Dental Professionals: Cross-Sectional Survey Study.","authors":"Joseph Macadaeg Acosta, Palinee Detsomboonrat, Pagaporn Pantuwadee Pisarnturakit, Nipaporn Urwannachotima","doi":"10.2196/66121","DOIUrl":"10.2196/66121","url":null,"abstract":"<p><strong>Background: </strong>As digitalization continues to advance globally, the health care sector, including dental practice, increasingly recognizes social media as a vital tool for health care promotion, patient recruitment, marketing, and communication strategies.</p><p><strong>Objective: </strong>This study aimed to investigate the use of social media and assess its impact on enhancing dental care and practice among dental professionals in the Philippines.</p><p><strong>Methods: </strong>A cross-sectional survey was conducted among dental practitioners in the Philippines. The study used a 23-item questionnaire, which included 5 questions on dentists' background and demographic information and 18 questions regarding the use, frequency, and purpose of social media in patient advising and quality of care improvement. Data were analyzed using SPSS software, with frequency distributions and χ2 tests used to assess the association between social media use and demographic variables and the impact on dental practice.</p><p><strong>Results: </strong>The 265 dental practitioners in this study were predominantly female (n=204, 77%) and aged between 20-30 years (n=145, 54.7%). Most of the participants were general practitioners (n=260, 98.1%) working in a private practice (n=240, 90.6%), with 58.5% (n=155) having 0-5 years of clinical experience. Social media use was significantly higher among younger practitioners (20-30 years old) compared to older age groups (P<.001), though factors such as sex, dental specialty, and years of clinical practice did not significantly influence use. The majority (n=179, 67.5%) reported using social media in their practice, primarily for oral health promotion and education (n=191, 72.1%), connecting with patients and colleagues (n=165, 62.3%), and marketing (n=150, 56.6%). Facebook (n=179, 67.5%) and YouTube (n=163, 61.5%) were the most frequented platforms for clinical information, with Twitter (subsequently rebranded X) being the least used (n=4, 1.5%). Despite widespread social media engagement, only 8.7% (n=23) trusted the credibility of web-based information, and 63.4% (n=168) perceived a potential impact on the patient-dentist relationship due to patients seeking information on the internet. Social media was also perceived to enhance practice quality, with users reporting significant improvements in patient care (P=.001).</p><p><strong>Conclusions: </strong>The findings highlight that social media is widely used among younger dental practitioners, primarily for education, communication, and marketing purposes. While social media use is associated with perceived improvements in practice quality and patient care, trust in information on social media remains low, and concerns remain regarding its effect on patient relationships. It is recommended to establish enhanced guidelines and provide reliable web-based resources to help dental practitioners use social media effectively and responsibly.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e66121"},"PeriodicalIF":2.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11723565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}