JMIR mHealth and uHealth最新文献

{"title":"Conversational Chatbot for Cigarette Smoking Cessation: Results From the 11-Step User-Centered Design Development Process and Randomized Controlled Trial.","authors":"Jonathan B Bricker, Brianna Sullivan, Kristin Mull, Margarita Santiago-Torres, Juan M Lavista Ferres","doi":"10.2196/57318","DOIUrl":"10.2196/57318","url":null,"abstract":"<p><strong>Background: </strong>Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot.</p><p><strong>Objective: </strong>We aim to report results of the user-centered design development process and randomized controlled trial for a novel and comprehensive quit smoking conversational chatbot called QuitBot.</p><p><strong>Methods: </strong>The 4 years of formative research for developing QuitBot followed an 11-step process: (1) specifying a conceptual model; (2) conducting content analysis of existing interventions (63 hours of intervention transcripts); (3) assessing user needs; (4) developing the chat's persona (\"personality\"); (5) prototyping content and persona; (6) developing full functionality; (7) programming the QuitBot; (8) conducting a diary study; (9) conducting a pilot randomized controlled trial (RCT); (10) reviewing results of the RCT; and (11) adding a free-form question and answer (QnA) function, based on user feedback from pilot RCT results. The process of adding a QnA function itself involved a three-step process: (1) generating QnA pairs, (2) fine-tuning large language models (LLMs) on QnA pairs, and (3) evaluating the LLM outputs.</p><p><strong>Results: </strong>We developed a quit smoking program spanning 42 days of 2- to 3-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing Food and Drug Administration-approved cessation medications, coping with triggers, and recovering from lapses and relapses. In a pilot RCT with 96% three-month outcome data retention, QuitBot demonstrated high user engagement and promising cessation rates compared to the National Cancer Institute's SmokefreeTXT text messaging program, particularly among those who viewed all 42 days of program content: 30-day, complete-case, point prevalence abstinence rates at 3-month follow-up were 63% (39/62) for QuitBot versus 38.5% (45/117) for SmokefreeTXT (odds ratio 2.58, 95% CI 1.34-4.99; P=.005). However, Facebook Messenger intermittently blocked participants' access to QuitBot, so we transitioned from Facebook Messenger to a stand-alone smartphone app as the communication channel. Participants' frustration with QuitBot's inability to answer their open-ended questions led to us develop a core conversational feature, enabling users to ask open-ended questions about quitting cigarette smoking and for the QuitBot to respond with accurate and professional answers. To support this functionality, we developed a library of 11,000 QnA pairs on topics associated with quitting cigarette smoking. Model testing results showed that Microsoft's Azure-based QnA maker effectively handled questions that matched our library of 11,000 QnA pairs. A fine-tuned, contextualized GPT-3.5 (OpenAI) responds to questions that are not within our library of QnA pairs.</p><p><strong>Conclusions: </strong>The development process yielded t","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":" ","pages":"e57318"},"PeriodicalIF":5.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11303891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141446198","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":"Feasibility and Preliminary Effects of a Social Media-Based Peer-Group Mobile Messaging Smoking Cessation Intervention Among Chinese Immigrants who Smoke: Pilot Randomized Controlled Trial.","authors":"Nan Jiang, Ariel Zhao, Erin S Rogers, Ana Paula Cupertino, Xiaoquan Zhao, Francisco Cartujano-Barrera, Katherine Siu, Scott E Sherman","doi":"10.2196/59496","DOIUrl":"10.2196/59496","url":null,"abstract":"<p><strong>Background: </strong>Chinese immigrants experience significant disparities in tobacco use. Culturally adapted tobacco treatments targeting this population are sparse and the use is low. The low use of these treatment programs is attributed to their exclusive focus on individuals who are ready to quit and the wide range of barriers that Chinese immigrants face to access these programs. To support Chinese immigrant smokers at all levels of readiness to quit and address their access barriers, we developed the WeChat Quit Coach, a culturally and linguistically appropriate WeChat (Tencent Holdings Limited)-based peer group mobile messaging smoking cessation intervention.</p><p><strong>Objective: </strong>This study aims to assess the feasibility, acceptability, and preliminary effects of WeChat Quit Coach.</p><p><strong>Methods: </strong>We enrolled a total of 60 Chinese immigrant smokers in 2022 in New York City for a pilot randomized controlled trial (RCT) and a single-arm pilot test. The first 40 participants were randomized to either the intervention arm (WeChat Quit Coach) or the control arm (self-help print material) using 1:1 block randomization stratified by sex. WeChat Quit Coach lasted 6 weeks, featuring small peer groups moderated by a coach, daily text messages with text questions, and chat-based instant messaging support from the coach in response to peer questions. The next 20 participants were enrolled in the single-arm pilot test to further assess intervention feasibility and acceptability. All 60 participants were offered a 4-week supply of complimentary nicotine replacement therapy. Surveys were administered at baseline and 6 weeks, with participants in the pilot RCT completing an additional survey at 6 months and biochemical verification of abstinence at both follow-ups.</p><p><strong>Results: </strong>Of 74 individuals screened, 68 (92%) were eligible and 60 (88%) were enrolled. The majority of participants, with a mean age of 42.5 (SD 13.8) years, were male (49/60, 82%) and not ready to quit, with 70% (42/60) in the precontemplation or contemplation stage at the time of enrollment. The pilot RCT had follow-up rates of 98% (39/40) at 6 weeks and 93% (37/40) at 6 months, while the single-arm test achieved 100% follow-up at 6 weeks. On average, participants responded to daily text questions for 25.1 days over the 42-day intervention period and 23% (9/40) used the chat-based instant messaging support. Most participants were satisfied with WeChat Quit Coach (36/39, 92%) and would recommend it to others (32/39, 82%). At 6 months, self-reported 7-day point prevalence abstinence rates were 25% (5/20) in the intervention arm and 15% (3/20) in the control arm, with biochemically verified abstinence rates of 25% (5/20) and 5% (1/20), respectively.</p><p><strong>Conclusions: </strong>WeChat Quit Coach was feasible and well-received by Chinese immigrants who smoke and produced promising effects on abstinence. Large trials are warran","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e59496"},"PeriodicalIF":5.4,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11318363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141734202","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":"Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis.","authors":"Claire L Little, David M Schultz, Thomas House, William G Dixon, John McBeth","doi":"10.2196/48582","DOIUrl":"10.2196/48582","url":null,"abstract":"<p><strong>Background: </strong>People with chronic pain experience variability in their trajectories of pain severity. Previous studies have explored pain trajectories by clustering sparse data; however, to understand daily pain variability, there is a need to identify clusters of weekly trajectories using daily pain data. Between-week variability can be explored by quantifying the week-to-week movement between these clusters. We propose that future work can use clusters of pain severity in a forecasting model for short-term (eg, daily fluctuations) and longer-term (eg, weekly patterns) variability. Specifically, future work can use clusters of weekly trajectories to predict between-cluster movement and within-cluster variability in pain severity.</p><p><strong>Objective: </strong>This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model.</p><p><strong>Methods: </strong>Data from a population-based mobile health study were used to compile weekly pain trajectories (n=21,919) that were then clustered using a k-medoids algorithm. Sensitivity analyses tested the impact of assumptions related to the ordinal and longitudinal structure of the data. The characteristics of people within clusters were examined, and a transition analysis was conducted to understand the movement of people between consecutive weekly clusters.</p><p><strong>Results: </strong>Four clusters were identified representing trajectories of no or low pain (1714/21,919, 7.82%), mild pain (8246/21,919, 37.62%), moderate pain (8376/21,919, 38.21%), and severe pain (3583/21,919, 16.35%). Sensitivity analyses confirmed the 4-cluster solution, and the resulting clusters were similar to those in the main analysis, with at least 85% of the trajectories belonging to the same cluster as in the main analysis. Male participants spent longer (participant mean 7.9, 95% bootstrap CI 6%-9.9%) in the no or low pain cluster than female participants (participant mean 6.5, 95% bootstrap CI 5.7%-7.3%). Younger people (aged 17-24 y) spent longer (participant mean 28.3, 95% bootstrap CI 19.3%-38.5%) in the severe pain cluster than older people (aged 65-86 y; participant mean 9.8, 95% bootstrap CI 7.7%-12.3%). People with fibromyalgia (participant mean 31.5, 95% bootstrap CI 28.5%-34.4%) and neuropathic pain (participant mean 31.1, 95% bootstrap CI 27.3%-34.9%) spent longer in the severe pain cluster than those with other conditions, and people with rheumatoid arthritis spent longer (participant mean 7.8, 95% bootstrap CI 6.1%-9.6%) in the no or low pain cluster than those with other conditions. There were 12,267 pairs of consecutive weeks that contributed to the transition analysis. The empirical percentage remaining in the same cluster across consecutive weeks was 65.96% (8091/12,267). When movement between clusters occurred, the highest percentage of movement was to an adjacent cluster.</p><p><strong>Conclusions: </strong>The clusters of pain","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e48582"},"PeriodicalIF":5.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11297369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141723680","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":"Continuous Monitoring of Heart Rate Variability and Respiration for the Remote Diagnosis of Chronic Obstructive Pulmonary Disease: Prospective Observational Study.","authors":"Xiaolan Chen, Han Zhang, Zhiwen Li, Shuang Liu, Yuqi Zhou","doi":"10.2196/56226","DOIUrl":"10.2196/56226","url":null,"abstract":"<p><strong>Background: </strong>Conventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet.</p><p><strong>Objective: </strong>The aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD.</p><p><strong>Methods: </strong>We recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings.</p><p><strong>Results: </strong>In this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively.</p><p><strong>Conclusions: </strong>Continuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e56226"},"PeriodicalIF":5.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141723679","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":"Acceptability, Effectiveness, and Roles of mHealth Applications in Supporting Cancer Pain Self-Management: Integrative Review.","authors":"Weizi Wu, Teresa Graziano, Andrew Salner, Ming-Hui Chen, Michelle P Judge, Xiaomei Cong, Wanli Xu","doi":"10.2196/53652","DOIUrl":"10.2196/53652","url":null,"abstract":"<p><strong>Background: </strong> Cancer pain remains highly prevalent and persistent throughout survivorship, and it is crucial to investigate the potential of leveraging the advanced features of mobile health (mHealth) apps to empower individuals to self-manage their pain.</p><p><strong>Objective: </strong> This review aims to comprehensively understand the acceptability, users' experiences, and effectiveness of mHealth apps in supporting cancer pain self-management.</p><p><strong>Methods: </strong> We conducted an integrative review following Souza and Whittemore and Knafl's 6 review processes. Literature was searched in PubMed, Scopus, CINAHL Plus with Full Text, PsycINFO, and Embase, from 2013 to 2023. Keywords including \"cancer patients,\" \"pain,\" \"self-management,\" \"mHealth applications,\" and relevant synonyms were used in the search. The Johns Hopkins research evidence appraisal tool was used to evaluate the quality of eligible studies. A narrative synthesis was conducted to analyze the extracted data.</p><p><strong>Results: </strong> A total of 20 studies were included, with the overall quality rated as high (n=15) to good (n=5). Using mHealth apps to monitor and manage pain was acceptable for most patients with cancer. The internal consistency of the mHealth in measuring pain was 0.96. The reported daily assessment or engagement rate ranged from 61.9% to 76.8%. All mHealth apps were designed for multimodal interventions. Participants generally had positive experiences using pain apps, rating them as enjoyable and user-friendly. In addition, 6 studies reported significant improvements in health outcomes, including enhancement in pain remission (severity and intensity), medication adherence, and a reduced frequency of breakthrough pain. The most frequently highlighted roles of mHealth apps included pain monitoring, tracking, reminders, education facilitation, and support coordination.</p><p><strong>Conclusions: </strong> mHealth apps are effective and acceptable in supporting pain self-management. They offer a promising multi-model approach for patients to monitor, track, and manage their pain. These findings provide evidence-based insights for leveraging mHealth apps to support cancer pain self-management. More high-quality studies are needed to examine the effectiveness of digital technology-based interventions for cancer pain self-management and to identify the facilitators and barriers to their implementation in real-world practice.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e53652"},"PeriodicalIF":5.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141723678","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":"The Impact of User Engagement With Exposure Components on Posttraumatic Stress Symptoms in an mHealth Mobile App: Secondary Analysis of a Randomized Controlled Trial.","authors":"C Adrian Davis, Madeleine Miller, Carmen P McLean","doi":"10.2196/49393","DOIUrl":"10.2196/49393","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mobile mental health apps are a cost-effective option for managing mental health problems, such as posttraumatic stress disorder (PTSD). The efficacy of mobile health (mHealth) apps depends on engagement with the app, but few studies have examined how users engage with different features of mHealth apps for PTSD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to examine the relationship between app engagement indices and PTSD symptom reduction using data from an unblinded pilot randomized controlled trial of \"Renew\" (Vertical Design), an exposure-based app for PTSD with and without coaching support. Because exposure is an effective approach for treating PTSD, we expected that engagement with exposure activities would be positively related to symptom reduction, over and above overall app usage.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Participants were veterans (N=69) with clinically significant PTSD symptoms who were recruited online using Facebook advertisements and invited to use the Renew app as often as they wanted over a 6-week period. Participants completed screening and assessments online but provided informed consent, toured the app, and completed feedback interviews via telephone. We assessed users' self-reported PTSD symptoms before and after a 6-week intervention period and collected app usage data using a research-instrumented dashboard. To examine overall app engagement, we used data on the total time spent in the app, the number of log-in days, and the number of points that the user gained in the app. To examine engagement with exposure components, we used data on total time spent completing exposure activities (both in vivo and imaginal), the number of in vivo exposure activities completed, and the number of characters written in response to imaginal exposure prompts. We used hierarchical regression analyses to test the effect of engagement indices on change in PTSD symptoms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Usage varied widely. Participants spent an average of 166.09 (SD 156.52) minutes using Renew, over an average of 14.7 (SD 10.71) mean log-in days. Engagement with the exposure components of the app was positively associated with PTSD symptom reduction (F6,62=2.31; P=.04). Moreover, this relationship remained significant when controlling for overall engagement with the app (ΔF3,62=4.42; P=.007). The number of characters written during imaginal exposure (β=.37; P=.009) and the amount of time spent completing exposure activities (β=.36; P=.03) were significant contributors to the model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;To our knowledge, this is the first study to show a relationship between symptom improvement and engagement with the active therapeutic components of an mHealth app (ie, exposure) for PTSD. This relationship held when controlling for overall app use, which suggests that it was engagement with exposure, specifically, that was associated with symptom change. Future work to identify ways of ","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e49393"},"PeriodicalIF":5.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11269958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141734203","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":"Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.","authors":"Filippo Corponi, Bryan M Li, Gerard Anmella, Clàudia Valenzuela-Pascual, Ariadna Mas, Isabella Pacchiarotti, Marc Valentí, Iria Grande, Antoni Benabarre, Marina Garriga, Eduard Vieta, Allan H Young, Stephen M Lawrie, Heather C Whalley, Diego Hidalgo-Mazzei, Antonio Vergari","doi":"10.2196/55094","DOIUrl":"10.2196/55094","url":null,"abstract":"<p><strong>Background: </strong>Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection.</p><p><strong>Objective: </strong>In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task.</p><p><strong>Methods: </strong>We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients.</p><p><strong>Results: </strong>SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability.</p><p><strong>Conclusions: </strong>We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e55094"},"PeriodicalIF":5.4,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11292167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141626799","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":"Deconstructing Fitbit to Specify the Effective Features in Promoting Physical Activity Among Inactive Adults: Pilot Randomized Controlled Trial.","authors":"Keisuke Takano, Takeyuki Oba, Kentaro Katahira, Kenta Kimura","doi":"10.2196/51216","DOIUrl":"10.2196/51216","url":null,"abstract":"<p><strong>Background: </strong>Wearable activity trackers have become key players in mobile health practice as they offer various behavior change techniques (BCTs) to help improve physical activity (PA). Typically, multiple BCTs are implemented simultaneously in a device, making it difficult to identify which BCTs specifically improve PA.</p><p><strong>Objective: </strong>We investigated the effects of BCTs implemented on a smartwatch, the Fitbit, to determine how each technique promoted PA.</p><p><strong>Methods: </strong>This study was a single-blind, pilot randomized controlled trial, in which 70 adults (n=44, 63% women; mean age 40.5, SD 12.56 years; closed user group) were allocated to 1 of 3 BCT conditions: self-monitoring (feedback on participants' own steps), goal setting (providing daily step goals), and social comparison (displaying daily steps achieved by peers). Each intervention lasted for 4 weeks (fully automated), during which participants wore a Fitbit and responded to day-to-day questionnaires regarding motivation. At pre- and postintervention time points (in-person sessions), levels and readiness for PA as well as different aspects of motivation were assessed.</p><p><strong>Results: </strong>Participants showed excellent adherence (mean valid-wear time of Fitbit=26.43/28 days, 94%), and no dropout was recorded. No significant changes were found in self-reported total PA (dz<0.28, P=.40 for the self-monitoring group, P=.58 for the goal setting group, and P=.19 for the social comparison group). Fitbit-assessed step count during the intervention period was slightly higher in the goal setting and social comparison groups than in the self-monitoring group, although the effects did not reach statistical significance (P=.052 and P=.06). However, more than half (27/46, 59%) of the participants in the precontemplation stage reported progress to a higher stage across the 3 conditions. Additionally, significant increases were detected for several aspects of motivation (ie, integrated and external regulation), and significant group differences were identified for the day-to-day changes in external regulation; that is, the self-monitoring group showed a significantly larger increase in the sense of pressure and tension (as part of external regulation) than the goal setting group (P=.04).</p><p><strong>Conclusions: </strong>Fitbit-implemented BCTs promote readiness and motivation for PA, although their effects on PA levels are marginal. The BCT-specific effects were unclear, but preliminary evidence showed that self-monitoring alone may be perceived demanding. Combining self-monitoring with another BCT (or goal setting, at least) may be important for enhancing continuous engagement in PA.</p><p><strong>Trial registration: </strong>Open Science Framework; https://osf.io/87qnb/?view_only=f7b72d48bb5044eca4b8ce729f6b403b.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e51216"},"PeriodicalIF":5.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11282379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141599825","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":"Impact of Remote Blood Pressure Monitoring Device Connectivity on Engagement Among Pregnant Individuals Enrolled in the Delfina Care Platform: Observational Study","authors":"Mia Charifson, Timothy Wen, Bonnie Zell, Priyanka Vaidya, Cynthia I Rios, C Funsho Fagbohun, Isabel Fulcher","doi":"10.2196/55617","DOIUrl":"https://doi.org/10.2196/55617","url":null,"abstract":"Background: Patient engagement with remote blood pressure monitoring during pregnancy is critical to optimize the associated benefits of blood pressure control and early detection. Objective: The goal of this study was to compare patient engagement and adherence to RBPM between connected and unconnected BP device users from a prospective pregnancy cohort. Methods: We compared patient engagement with and adherence to remote patient blood pressure monitoring between patients who received a connected and unconnected blood pressure device. Results: Patients with connected devices entered more blood pressure entries and had higher adherence to the remote monitoring protocols compared to patients with unconnected devices. Conclusions: In our study population of pregnant people, we found that “connected” blood pressure cuffs, which automatically sync measures to a monitoring platform or health record, increased adherence to remote monitoring protocols when compared to “unconnected” cuffs that require manual entry of measures.","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"42 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141608421","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":"Technology-Based Music Interventions to Reduce Anxiety and Pain Among Patients Undergoing Surgery or Procedures: Systematic Review of the Literature.","authors":"Sunghee Park, Sohye Lee, Sheri Howard, Jeeseon Yi","doi":"10.2196/48802","DOIUrl":"10.2196/48802","url":null,"abstract":"<p><strong>Background: </strong>Hospitalized patients undergoing surgery or procedures may experience negative symptoms. Music is a nonpharmacological complementary approach and is used as an intervention to reduce anxiety, stress, and pain in these patients. Recently, music has been used conveniently in clinical situations with technology devices, and the mode of providing music is an important factor in technology-based music interventions. However, many reviews have focused only on the effectiveness of music interventions.</p><p><strong>Objective: </strong>We aimed to review randomized controlled trials (RCTs) of technology-based music interventions for reducing anxiety and pain among patients undergoing surgery or procedures. We examined the clinical situation, devices used, delivery methods, and effectiveness of technology-based music interventions in primary articles.</p><p><strong>Methods: </strong>The search was performed in the following 5 electronic databases: PubMed, MEDLINE (OvidSP), CINAHL complete, PSYCINFO, and Embase. This systematic review focused on technology-based music interventions. The following articles were included: (1) RCTs, (2) studies using interactive technology (eg, smartphones, mHealth, tablets, applications, and virtual reality), (3) empirical studies reporting pain and anxiety outcomes, and (4) English articles published from 2018 to 2023 (as of January 18, 2023). The risk of bias was assessed using the Cochrane Risk of Bias tool version 2.</p><p><strong>Results: </strong>Among 292 studies identified, 21 met the inclusion criteria and were included. Of these studies, 9 reported that anxiety scores decreased after music interventions and 7 reported that pain could be decreased before, during, and after procedures. The methodology of the music intervention was important to the results on anxiety and pain in the clinical trials. More than 50% (13/21, 62%) of the studies included in this review allowed participants to select themes themselves. However, it was difficult to distinguish differences in effects depending on the device or software used for the music interventions.</p><p><strong>Conclusions: </strong>Technology-based music interventions could help reduce anxiety and pain among patients undergoing surgery or procedures. The findings of this review could help medical teams to choose a practical methodology for music interventions. Future studies should examine the effects of advanced technology-based music interventions using smart devices and software that promote interactions between medical staff and patients.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e48802"},"PeriodicalIF":5.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558785","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}