JMIR mHealth and uHealth最新文献

{"title":"Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture.","authors":"I Wayan Pulantara, Yuhan Wang, Lora E Burke, Susan M Sereika, Zhadyra Bizhanova, Jacob K Kariuki, Jessica Cheng, Britney Beatrice, India Loar, Maribel Cedillo, Molly B Conroy, Bambang Parmanto","doi":"10.2196/50043","DOIUrl":"10.2196/50043","url":null,"abstract":"<p><strong>Unlabelled: </strong>The integration of health and activity data from various wearable devices into research studies presents technical and operational challenges. The Awesome Data Acquisition Method (ADAM) is a versatile, web-based system that was designed for integrating data from various sources and managing a large-scale multiphase research study. As a data collecting system, ADAM allows real-time data collection from wearable devices through the device's application programmable interface and the mobile app's adaptive real-time questionnaires. As a clinical trial management system, ADAM integrates clinical trial management processes and efficiently supports recruitment, screening, randomization, data tracking, data reporting, and data analysis during the entire research study process. We used a behavioral weight-loss intervention study (SMARTER trial) as a test case to evaluate the ADAM system. SMARTER was a randomized controlled trial that screened 1741 participants and enrolled 502 adults. As a result, the ADAM system was efficiently and successfully deployed to organize and manage the SMARTER trial. Moreover, with its versatile integration capability, the ADAM system made the necessary switch to fully remote assessments and tracking that are performed seamlessly and promptly when the COVID-19 pandemic ceased in-person contact. The remote-native features afforded by the ADAM system minimized the effects of the COVID-19 lockdown on the SMARTER trial. The success of SMARTER proved the comprehensiveness and efficiency of the ADAM system. Moreover, ADAM was designed to be generalizable and scalable to fit other studies with minimal editing, redevelopment, and customization. The ADAM system can benefit various behavioral interventions and different populations.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e50043"},"PeriodicalIF":5.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141901822","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":"Correction: The Effect of an mHealth Self-Monitoring Intervention (MI-BP) on Blood Pressure Among Black Individuals With Uncontrolled Hypertension: Randomized Controlled Trial.","authors":"Lorraine R Buis, Junhan Kim, Ananda Sen, Dongru Chen, Katee Dawood, Reema Kadri, Rachelle Muladore, Melissa Plegue, Caroline R Richardson, Zora Djuric, Candace McNaughton, David Hutton, Lionel P Robert, Sun Young Park, Phillip Levy","doi":"10.2196/64632","DOIUrl":"10.2196/64632","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/57863.].</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e64632"},"PeriodicalIF":5.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11336500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897461","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":"Using Wearables to Study Biopsychosocial Dynamics in Couples Who Cope With a Chronic Health Condition: Ambulatory Assessment Study.","authors":"Theresa Pauly, Janina Lüscher, Lea Olivia Wilhelm, Melanie Alexandra Amrein, George Boateng, Tobias Kowatsch, Elgar Fleisch, Guy Bodenmann, Urte Scholz","doi":"10.2196/49576","DOIUrl":"10.2196/49576","url":null,"abstract":"<p><strong>Background: </strong>Technology has become an integral part of our everyday life, and its use to manage and study health is no exception. Romantic partners play a critical role in managing chronic health conditions as they tend to be a primary source of support.</p><p><strong>Objective: </strong>This study tests the feasibility of using commercial wearables to monitor couples' unique way of communicating and supporting each other and documents the physiological correlates of interpersonal dynamics (ie, heart rate linkage).</p><p><strong>Methods: </strong>We analyzed 617 audio recordings of 5-minute duration (384 with concurrent heart rate data) and 527 brief self-reports collected from 11 couples in which 1 partner had type II diabetes during the course of their typical daily lives. Audio data were coded by trained raters for social support. The extent to which heart rate fluctuations were linked among couples was quantified using cross-correlations. Random-intercept multilevel models explored whether cross-correlations might differ by social contexts and exchanges.</p><p><strong>Results: </strong>Sixty percent of audio recordings captured speech between partners and partners reported personal contact with each other in 75% of self-reports. Based on the coding, social support was found in 6% of recordings, whereas at least 1 partner self-reported social support about half the time (53%). Couples, on average, showed small to moderate interconnections in their heart rate fluctuations (r=0.04-0.22). Couples also varied in the extent to which there was lagged linkage, that is, meaning that changes in one partner's heart rate tended to precede changes in the other partner's heart rate. Exploratory analyses showed that heart rate linkage was stronger (1) in rater-coded partner conversations (vs moments of no rater-coded partner conversations: r_diff=0.13; P=.03), (2) when partners self-reported interpersonal contact (vs moments of no self-reported interpersonal contact: r_diff=0.20; P<.001), and (3) when partners self-reported social support exchanges (vs moments of no self-reported social support exchange: r_diff=0.15; P=.004).</p><p><strong>Conclusions: </strong>Our study provides initial evidence for the utility of using wearables to collect biopsychosocial data in couples managing a chronic health condition in daily life. Specifically, heart rate linkage might play a role in fostering chronic disease management as a couple. Insights from collecting such data could inform future technology interventions to promote healthy lifestyle engagement and adaptive chronic disease management.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.2196/13685.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e49576"},"PeriodicalIF":5.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893453","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":"Physical Activity, Body Composition, and Fitness Variables in Adolescents After Periods of Mandatory, Promoted or Nonmandatory, Nonpromoted Use of Step Tracker Mobile Apps: Randomized Controlled Trial.","authors":"Adrián Mateo-Orcajada, Raquel Vaquero-Cristóbal, Jorge Mota, Lucía Abenza-Cano","doi":"10.2196/51206","DOIUrl":"10.2196/51206","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;It is not known whether an intervention made mandatory as a physical education (PE) class assignment and aimed at promoting physical activity (PA) in adolescents can create a healthy walking habit, which would allow further improvements to be achieved after the mandatory and promoted intervention has been completed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aims of this study were to (1) investigate whether, after a period of using a step tracker mobile app made mandatory and promoted as a PE class assignment, adolescents continue to use it when its use is no longer mandatory and promoted; (2) determine whether there are changes in the PA level, body composition, and fitness of adolescents when the use of the app is mandatory and promoted and when it is neither mandatory nor promoted; and (3) analyze whether the covariates maturity status, gender, and specific app used can have an influence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A total of 357 students in compulsory secondary education (age: mean 13.92, SD 1.91 y) participated in the study. A randomized controlled trial was conducted consisting of 2 consecutive 10-week interventions. Participants' PA level, body composition, and fitness were measured at baseline (T1), after 10 weeks of mandatory and promoted app use (T2), and after 10 weeks of nonmandatory and nonpromoted app use (T3). Each participant in the experimental group (EG) used 1 of 4 selected step tracker mobile apps after school hours.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The results showed that when the use of the apps was neither mandatory nor promoted as a PE class assignment, only a few adolescents (18/216, 8.3%) continued the walking practice. After the mandatory and promoted intervention period (T1 vs T2), a decrease in the sum of 3 skinfolds (mean difference [MD] 1.679; P=.02) as well as improvements in the PA level (MD -0.170; P&lt;.001), maximal oxygen uptake (MD -1.006; P&lt;.001), countermovement jump test (MD -1.337; P=.04), curl-up test (MD -3.791; P&lt;.001), and push-up test (MD -1.920; P&lt;.001) in the EG were recorded. However, the changes between T1 and T2 were significantly greater in the EG than in the control group only in the PA level and curl-up test. Thus, when comparing the measurements taken between T1 and T3, no significant changes in body composition (P=.07) or fitness (P=.84) were observed between the EG and the control group. The covariates maturity status, gender, and specific app used showed a significant effect in most of the analyses performed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;A period of mandatory and promoted use of step tracker mobile apps benefited the variables of body composition and fitness in adolescents but did not create a healthy walking habit in this population; therefore, when the use of these apps ceased to be mandatory and promoted, the effects obtained disappeared.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial registration: &lt;/strong&gt;ClinicalTrials.gov NCT06164041; https://clinicaltrials.gov/study/NCT06","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e51206"},"PeriodicalIF":5.4,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141855552","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":"Efficacy of a Mobile Health App (eMOTIVA) Regarding Compliance With Cardiac Rehabilitation Guidelines in Patients With Coronary Artery Disease: Randomized Controlled Clinical Trial.","authors":"Celia Cruz-Cobo, María Ángeles Bernal-Jiménez, Germán Calle, Livia Luciana Gheorghe, Alejandro Gutiérrez-Barrios, Dolores Cañadas, Josep A Tur, Rafael Vázquez-García, María José Santi-Cano","doi":"10.2196/55421","DOIUrl":"10.2196/55421","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cardiac rehabilitation is fundamental among patients recovering from a coronary event, and mHealth technology may constitute a useful tool that provides guidelines based on scientific evidence in an entertaining, attractive, and user-friendly format.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to compare the efficacy of an mHealth intervention involving the eMOTIVA app and that of usual care regarding compliance with cardiac rehabilitation guidelines in terms of lifestyle, cardiovascular risk factors, and satisfaction among patients with acute coronary syndrome.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A randomized controlled clinical trial with a parallel group design was conducted. It included 300 patients (mHealth group, 150; control group, 150) who underwent percutaneous coronary intervention for acute coronary syndrome. Both groups underwent evaluations initially (during hospitalization) and after 3 and 6 months (face-to-face consultations). The eMOTIVA app incorporates a virtual classroom providing audio and video information about a healthy lifestyle, a section for self-recording cardiovascular risk factors, and a section for feedback messages and gamification. The primary outcome variables were (1) adherence to the Mediterranean diet and the frequency of consumption of food; (2) physical activity level, sedentary time, and exercise capacity; (3) smoking cessation and nicotine dependence; (4) level of knowledge about cardiovascular risk factors; and (5) app satisfaction and usability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The study analyzed 287 patients (mHealth group, 145; control group, 142). Most participants were male (207/300, 69.0%), and the mean age was 62.53 (SD 8.65) years. Significant improvements were observed in the mHealth group compared with the control group at 6 months in terms of (1) adherence to the Mediterranean diet (mean 11.92, SD 1.70 vs 8.92, SD 2.66 points; P&lt;.001) and frequency of eating foods (red meat [≤1/week]: 141/143, 97.9% vs 96/141, 68.1%; industrial pastries [&lt;2/week]: 129/143, 89.6% vs 80/141, 56.8%; oily fish [≥2/week]: 124/143, 86.1% vs 64/141, 41.4%; vegetables [≥2/day]: 130/143, 90.3% vs 78/141, 55.3%; fruit [≥2/day]: 128/143, 88.9% vs 85/141, 60.2%; all P&lt;.001); (2) physical activity (mean 2112.66, SD 1196.67 vs 1372.60, SD 944.62 metabolic equivalents/week; P&lt;.001) and sedentary time (mean 8.38, SD 1.88 vs 9.59, SD 2.09 hours; P&lt;.001); (3) exercise capacity (distance: mean 473.49, SD 102.28 vs 447.25, SD 93.68 meters; P=.04); and (4) level of knowledge (mean 117.85, SD 3.83 vs 111.00, SD 7.11 points; P&lt;.001). App satisfaction was high (mean 42.53, SD 6.38 points), and its usability was excellent (mean 95.60, SD 4.03 points).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;With the eMOTIVA app, favorable results were obtained in the intervention group in terms of adherence to the Mediterranean diet, frequency of eating certain foods, physical activity, sedentary time, exercise capacity, kn","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e55421"},"PeriodicalIF":5.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11310647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141758764","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":"Mobile App Intervention to Reduce Substance Use, Gambling, and Digital Media Use in Vocational School Students: Exploratory Analysis of the Intervention Arm of a Randomized Controlled Trial.","authors":"Kristin Grahlher, Matthis Morgenstern, Benjamin Pietsch, Elena Gomes de Matos, Monika Rossa, Kirsten Lochbühler, Anne Daubmann, Rainer Thomasius, Nicolas Arnaud","doi":"10.2196/51307","DOIUrl":"10.2196/51307","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;During adolescence, substance use and digital media exposure usually peak and can become major health risks. Prevention activities are mainly implemented in the regular school setting, and youth outside this system are not reached. A mobile app (\"Meine Zeit ohne\") has been developed specifically for vocational students and encourages participants to voluntarily reduce or abstain from a self-chosen addictive behavior including the use of a substance, gambling, or a media-related habit such as gaming or social media use for 2 weeks. Results from a randomized study indicate a significant impact on health-promoting behavior change after using the app. This exploratory study focuses on the intervention arm of this study, focusing on acceptance and differential effectiveness.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aims of this study were (1) to examine the characteristics of participants who used the app, (2) to explore the effectiveness of the mobile intervention depending on how the app was used and depending on participants' characteristics, and (3) to study how variations in app use were related to participants' baseline characteristics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Log data from study participants in the intervention group were analyzed including the frequency of app use (in days), selection of a specific challenge, and personal relevance (ie, the user was above a predefined risk score for a certain addictive behavior) of challenge selection (\"congruent use\": eg, a smoker selected a challenge related to reducing or quitting smoking). Dichotomous outcomes (change vs no change) referred to past-month substance use, gambling, and media-related behaviors. The relationship between these variables was analyzed using binary, multilevel, mixed-effects logistic regression models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The intervention group consisted of 2367 vocational students, and 1458 (61.6%; mean age 19.0, SD 3.5 years; 830/1458, 56.9% male) of them provided full data. Of these 1458 students, 894 (61.3%) started a challenge and could be included in the analysis (mean 18.7, SD 3.5 years; 363/894, 40.6% female). Of these 894 students, 466 (52.1%) were considered frequent app users with more than 4 days of active use over the 2-week period. The challenge area most often chosen in the analyzed sample was related to social media use (332/894, 37.1%). A total of 407 (45.5%) of the 894 students selected a challenge in a behavioral domain of personal relevance. The effects of app use on outcomes were higher when the area of individual challenge choice was equal to the area of behavior change, challenge choice was related to a behavior of personal relevance, and the individual risk of engaging in different addictive behaviors was high.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The domain-specific effectiveness of the program was confirmed with no spillover between behavioral domains. Effectiveness appeared to be dependent on app use and use","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e51307"},"PeriodicalIF":5.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11303885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141748246","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":"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":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to assess the feasibility, acceptability, and preliminary effects of WeChat Quit Coach.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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}