Zoltan Andre Torok, Larisa Gavrilova, Amish Patel, Matthew Jason Zawadzki
Background: Improving sleep is critical for optimizing short-term and long-term health. Although in-person meditation training has been shown to impact sleep positively, there is a gap in our understanding of whether apps that teach self-guided meditation are also effective.
Objective: This study aims to test whether Headspace (Headspace, Inc) improves sleep quality, tiredness, sleep duration, and sleep efficiency.
Methods: Staff employees (N=135; mean age 38.1, SD 10.9; 75.0% female; 59.3% non-Hispanic White; 27.1% Hispanic) from a university in California's San Joaquin Valley participated in the study. Participants were randomized to complete 10 minutes of daily meditation via the Headspace app for 8 weeks or waitlist control. Sleep assessments were taken for 4 consecutive days at baseline, and then for 4-day bursts at 2, 5, and 8 weeks after randomization. Sleep quality and subjective sleep duration were assessed each morning with a sleep diary, tiredness was assessed throughout the day using ecological momentary assessment, and objective sleep duration and efficiency were measured using a Fitbit Charge 2.
Results: Both subjective and objective sleep outcomes improved. For subjective sleep outcomes, multilevel modeling revealed that those in the Headspace condition, compared to the control group, reported better sleep quality at sessions 2 (β=0.48, SE=0.12; P<.001), 5 (β=0.91, SE=0.13; P<.001), and 8 (β=0.69, SE=0.15; P<.001) compared to baseline, and a decrease in tiredness at session 5 (β=-0.58, SE=0.19; P=.001) compared to baseline, but not at sessions 2 or 8. For objective sleep outcomes, those in the Headspace condition compared to the control group had longer sleep durations at session 5 (β=23.96, SE=12.19; P=.04) compared to baseline, but not at sessions 2 or 8. There were no significant effects for sleep efficiency.
Conclusions: This study continues adding to the ever-developing field of mobile health apps by demonstrating that Headspace can positively impact sleep quality, tiredness, and duration.
{"title":"The Effectiveness of the Headspace App for Improving Sleep: Randomized Controlled Trial.","authors":"Zoltan Andre Torok, Larisa Gavrilova, Amish Patel, Matthew Jason Zawadzki","doi":"10.2196/56287","DOIUrl":"10.2196/56287","url":null,"abstract":"<p><strong>Background: </strong>Improving sleep is critical for optimizing short-term and long-term health. Although in-person meditation training has been shown to impact sleep positively, there is a gap in our understanding of whether apps that teach self-guided meditation are also effective.</p><p><strong>Objective: </strong>This study aims to test whether Headspace (Headspace, Inc) improves sleep quality, tiredness, sleep duration, and sleep efficiency.</p><p><strong>Methods: </strong>Staff employees (N=135; mean age 38.1, SD 10.9; 75.0% female; 59.3% non-Hispanic White; 27.1% Hispanic) from a university in California's San Joaquin Valley participated in the study. Participants were randomized to complete 10 minutes of daily meditation via the Headspace app for 8 weeks or waitlist control. Sleep assessments were taken for 4 consecutive days at baseline, and then for 4-day bursts at 2, 5, and 8 weeks after randomization. Sleep quality and subjective sleep duration were assessed each morning with a sleep diary, tiredness was assessed throughout the day using ecological momentary assessment, and objective sleep duration and efficiency were measured using a Fitbit Charge 2.</p><p><strong>Results: </strong>Both subjective and objective sleep outcomes improved. For subjective sleep outcomes, multilevel modeling revealed that those in the Headspace condition, compared to the control group, reported better sleep quality at sessions 2 (β=0.48, SE=0.12; P<.001), 5 (β=0.91, SE=0.13; P<.001), and 8 (β=0.69, SE=0.15; P<.001) compared to baseline, and a decrease in tiredness at session 5 (β=-0.58, SE=0.19; P=.001) compared to baseline, but not at sessions 2 or 8. For objective sleep outcomes, those in the Headspace condition compared to the control group had longer sleep durations at session 5 (β=23.96, SE=12.19; P=.04) compared to baseline, but not at sessions 2 or 8. There were no significant effects for sleep efficiency.</p><p><strong>Conclusions: </strong>This study continues adding to the ever-developing field of mobile health apps by demonstrating that Headspace can positively impact sleep quality, tiredness, and duration.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e56287"},"PeriodicalIF":6.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12872213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146119333","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}
Isabelle Symes, Alexandra Burton, Daniela Mercado, Feifei Bu
Background: Multimorbidity, the coexistence of 2 or more chronic conditions, is associated with poor well-being. Health coaching apps offer cost-effective and accessible support. However, there is a lack of evidence of the impact of health coaching apps on individuals with multimorbidity.
Objective: This study aimed to assess the impact and acceptability of a health coaching app (the Holly Health [HH] app) on the subjective well-being (SWB) of adults with multimorbidity.
Methods: This study used an explanatory-sequential mixed methods design, with quantitative secondary data analysis in the first phase and qualitative interviews in the second phase. In the quantitative phase (n=565), pre- and post-SWB (Office for National Statistics' 4 personal well-being questions [ONS4]) scores from existing app users with multimorbidity were analyzed using Bayesian growth curve modeling to assess the impact of HH. In the qualitative phase (n=22), data were collected via semistructured interviews and analyzed using reflexive thematic analysis. Mechanisms of action that supported SWB were categorized using the Multi-Level Leisure Mechanisms Framework.
Results: There was a significant increase in life satisfaction (Coef.=0.71, 95% highest density interval [HDI] 0.52-0.89), worthwhileness (Coef.=0.62, 95% HDI 0.43-0.81), and happiness (Coef.=0.74, 95% HDI 0.54-0.92) and a decrease in anxiety (Coef.=-0.50, 95% HDI -0.74 to -0.25) before and after using the HH app. Overall, 8 acceptable app features activated 5 mechanisms of action, including behavioral, psychological, and social mechanisms. Three additional factors influenced the acceptability of the health coaching app: type of chronic condition, availability of time, and the use of other support tools.
Conclusions: The study demonstrates that health coaching apps could be effective and acceptable support tools for individuals with multimorbidity. This study contributes to understanding why health coaching apps support SWB and could be used to inform the development of future digital health interventions in multimorbidity.
{"title":"The Impact of a Health Coaching App on the Subjective Well-Being of Individuals With Multimorbidity: Mixed Methods Study.","authors":"Isabelle Symes, Alexandra Burton, Daniela Mercado, Feifei Bu","doi":"10.2196/78738","DOIUrl":"10.2196/78738","url":null,"abstract":"<p><strong>Background: </strong>Multimorbidity, the coexistence of 2 or more chronic conditions, is associated with poor well-being. Health coaching apps offer cost-effective and accessible support. However, there is a lack of evidence of the impact of health coaching apps on individuals with multimorbidity.</p><p><strong>Objective: </strong>This study aimed to assess the impact and acceptability of a health coaching app (the Holly Health [HH] app) on the subjective well-being (SWB) of adults with multimorbidity.</p><p><strong>Methods: </strong>This study used an explanatory-sequential mixed methods design, with quantitative secondary data analysis in the first phase and qualitative interviews in the second phase. In the quantitative phase (n=565), pre- and post-SWB (Office for National Statistics' 4 personal well-being questions [ONS4]) scores from existing app users with multimorbidity were analyzed using Bayesian growth curve modeling to assess the impact of HH. In the qualitative phase (n=22), data were collected via semistructured interviews and analyzed using reflexive thematic analysis. Mechanisms of action that supported SWB were categorized using the Multi-Level Leisure Mechanisms Framework.</p><p><strong>Results: </strong>There was a significant increase in life satisfaction (Coef.=0.71, 95% highest density interval [HDI] 0.52-0.89), worthwhileness (Coef.=0.62, 95% HDI 0.43-0.81), and happiness (Coef.=0.74, 95% HDI 0.54-0.92) and a decrease in anxiety (Coef.=-0.50, 95% HDI -0.74 to -0.25) before and after using the HH app. Overall, 8 acceptable app features activated 5 mechanisms of action, including behavioral, psychological, and social mechanisms. Three additional factors influenced the acceptability of the health coaching app: type of chronic condition, availability of time, and the use of other support tools.</p><p><strong>Conclusions: </strong>The study demonstrates that health coaching apps could be effective and acceptable support tools for individuals with multimorbidity. This study contributes to understanding why health coaching apps support SWB and could be used to inform the development of future digital health interventions in multimorbidity.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e78738"},"PeriodicalIF":6.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12871578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146119342","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}
Yiran Zhu, Wenwen Peng, Die Hu, Edmond Pui Hang Choi, Maritta Anneli Välimäki, Ci Zhang, Xianhong Li
<p><strong>Background: </strong>Youth aged 15-24 years carry a disproportionate HIV/sexually transmitted infections (STIs) burden. In recent years, different modalities of digital health interventions (DHIs) have been explored to promote safer sex behaviors among youth, but their comparative effectiveness across modalities and relative to nondigital interventions (NDIs) remains unclear.</p><p><strong>Objective: </strong>This study aimed to compare DHI modalities on safer sex behaviors and HIV/STI incidence, rank modalities using Bayesian network meta-analysis (NMA), and position their effectiveness relative to NDIs.</p><p><strong>Methods: </strong>A systematic review and Bayesian NMA of randomized controlled trials were conducted by comprehensively searching PubMed, EMBASE, Web of Science, and Cochrane Library (inception to November 2025). Eligible studies were those that enrolled youth aged 15-24 years and evaluated mobile app-based intervention, telecommunication-based intervention (TCI), static web-based intervention (SWI), or interactive online-based intervention (IOI)-with an NDI or another DHI. Primary outcomes were condom use at last sexual contact, consistent condom use, and proportion of condom use. Secondary outcomes included condom use self-efficacy, number of sexual partners, and STI incidence (including HIV). Risk of bias was assessed with the Cochrane Risk of Bias 2 tool, and certainty of evidence with GRADE/CINeMA (Confidence in NMA). Bayesian random-effects NMAs estimated odds ratios (ORs) with 95% credible intervals (CrIs), and complementary frequentist NMAs provided 95% CIs and 95% prediction intervals.</p><p><strong>Results: </strong>Twenty-four randomized controlled trials (20,134 participants) were included, forming treatment networks across 5 intervention types. TCI was the only intervention that significantly improved condom use at last sex compared with NDI (OR 1.13, 95% CrI 1.02-1.26). For consistent condom use, SWI and IOI outperformed TCI (SWI vs TCI: OR 1.77, 95% CrI 1.03-3.06; IOI vs TCI: OR 1.68, 95% CrI 1.02-2.76). For the proportion of condom use, IOI outperformed SWI (OR 1.34, 95% CrI 1.01-1.80), and mobile app-based intervention ranked highest in probability rankings, though estimates lacked precision. For STI incidence, NDI was associated with fewer STIs than SWI (OR 0.61, 95% CrI 0.46-0.82).</p><p><strong>Conclusions: </strong>This is the first NMA to compare the effectiveness of DHIs on condom use and HIV/STI outcomes among youth populations. It demonstrates that the impact of DHIs on HIV prevention varies substantially by intervention modality and outcome type. While TCI demonstrates the most consistent improvement in condom use at last sex, SWI and IOI may be more effective for promoting consistent condom use, though estimates remain imprecise. However, wide prediction intervals and low-certainty evidence suggest that self-reported behavioral changes may not translate into reductions in HIV/STI incidents wit
背景：15-24岁的青年背负着不成比例的艾滋病毒/性传播感染负担。近年来，人们探索了不同模式的数字健康干预（DHIs）来促进青少年更安全的性行为，但它们在不同模式下的相对有效性以及相对于非数字干预（ndi）的有效性仍不清楚。目的：本研究旨在比较DHI模式对安全性行为和HIV/STI发病率的影响，使用贝叶斯网络荟萃分析（NMA）对模式进行排名，并相对于ndi对其有效性进行定位。方法：综合检索PubMed、EMBASE、Web of Science、Cochrane Library（建库至2025年11月），对随机对照试验进行系统评价和贝叶斯NMA分析。符合条件的研究招募了15-24岁的青少年，并评估了基于移动应用程序的干预、基于电信的干预（TCI）、静态基于网络的干预（SWI）或交互式基于网络的干预（IOI）——采用NDI或另一种DHI。主要结局是最后一次性接触时使用避孕套、持续使用避孕套和使用避孕套的比例。次要结果包括避孕套使用的自我效能、性伴侣数量和性传播感染发生率（包括HIV）。采用Cochrane Risk of bias 2工具评估偏倚风险，并采用GRADE/CINeMA （NMA置信度）评估证据的确定性。贝叶斯随机效应nma估计的比值比（or）具有95%可信区间（CrIs），互补频率nma提供95% ci和95%预测区间。结果：纳入24项随机对照试验（20134名受试者），形成5种干预类型的治疗网络。与NDI相比，TCI是唯一能显著改善末次性行为中安全套使用的干预措施（OR 1.13, 95% CrI 1.02-1.26）。对于持续使用避孕套，SWI和IOI优于TCI （SWI vs TCI: OR 1.77, 95% CrI 1.03-3.06; IOI vs TCI: OR 1.68, 95% CrI 1.02-2.76）。对于避孕套使用的比例，IOI优于SWI (OR 1.34, 95% CrI 1.01-1.80)，基于移动应用程序的干预在概率排名中排名最高，尽管估计缺乏准确性。在性传播感染发生率方面，与SWI相比，NDI与较少的性传播感染相关（OR 0.61, 95% CrI 0.46-0.82）。结论：这是第一个比较DHIs在青年人群中避孕套使用和艾滋病毒/性传播感染结果有效性的NMA。研究表明，DHIs对艾滋病毒预防的影响因干预方式和结果类型而有很大差异。虽然TCI显示了在最后性行为中避孕套使用的最一致的改善，但SWI和IOI可能更有效地促进了避孕套的持续使用，尽管估计仍然不精确。然而，广泛的预测间隔和低确定性证据表明，如果不结合线下服务和更广泛的结构支持，自我报告的行为改变可能不会转化为艾滋病毒/性传播感染事件的减少。未来的试验可能考虑纳入标准化的结果指标和更长时间的随访，以更准确地估计DHIs的有效性，并指导以青年为中心的数字化艾滋病毒/性传播感染预防的推广。
{"title":"Effects of Digital Health Interventions to Promote Safer Sex Behaviors Among Youth: Systematic Review and Bayesian Network Meta-Analysis.","authors":"Yiran Zhu, Wenwen Peng, Die Hu, Edmond Pui Hang Choi, Maritta Anneli Välimäki, Ci Zhang, Xianhong Li","doi":"10.2196/87071","DOIUrl":"10.2196/87071","url":null,"abstract":"<p><strong>Background: </strong>Youth aged 15-24 years carry a disproportionate HIV/sexually transmitted infections (STIs) burden. In recent years, different modalities of digital health interventions (DHIs) have been explored to promote safer sex behaviors among youth, but their comparative effectiveness across modalities and relative to nondigital interventions (NDIs) remains unclear.</p><p><strong>Objective: </strong>This study aimed to compare DHI modalities on safer sex behaviors and HIV/STI incidence, rank modalities using Bayesian network meta-analysis (NMA), and position their effectiveness relative to NDIs.</p><p><strong>Methods: </strong>A systematic review and Bayesian NMA of randomized controlled trials were conducted by comprehensively searching PubMed, EMBASE, Web of Science, and Cochrane Library (inception to November 2025). Eligible studies were those that enrolled youth aged 15-24 years and evaluated mobile app-based intervention, telecommunication-based intervention (TCI), static web-based intervention (SWI), or interactive online-based intervention (IOI)-with an NDI or another DHI. Primary outcomes were condom use at last sexual contact, consistent condom use, and proportion of condom use. Secondary outcomes included condom use self-efficacy, number of sexual partners, and STI incidence (including HIV). Risk of bias was assessed with the Cochrane Risk of Bias 2 tool, and certainty of evidence with GRADE/CINeMA (Confidence in NMA). Bayesian random-effects NMAs estimated odds ratios (ORs) with 95% credible intervals (CrIs), and complementary frequentist NMAs provided 95% CIs and 95% prediction intervals.</p><p><strong>Results: </strong>Twenty-four randomized controlled trials (20,134 participants) were included, forming treatment networks across 5 intervention types. TCI was the only intervention that significantly improved condom use at last sex compared with NDI (OR 1.13, 95% CrI 1.02-1.26). For consistent condom use, SWI and IOI outperformed TCI (SWI vs TCI: OR 1.77, 95% CrI 1.03-3.06; IOI vs TCI: OR 1.68, 95% CrI 1.02-2.76). For the proportion of condom use, IOI outperformed SWI (OR 1.34, 95% CrI 1.01-1.80), and mobile app-based intervention ranked highest in probability rankings, though estimates lacked precision. For STI incidence, NDI was associated with fewer STIs than SWI (OR 0.61, 95% CrI 0.46-0.82).</p><p><strong>Conclusions: </strong>This is the first NMA to compare the effectiveness of DHIs on condom use and HIV/STI outcomes among youth populations. It demonstrates that the impact of DHIs on HIV prevention varies substantially by intervention modality and outcome type. While TCI demonstrates the most consistent improvement in condom use at last sex, SWI and IOI may be more effective for promoting consistent condom use, though estimates remain imprecise. However, wide prediction intervals and low-certainty evidence suggest that self-reported behavioral changes may not translate into reductions in HIV/STI incidents wit","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e87071"},"PeriodicalIF":6.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12871581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146119323","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: Culturally Adapted Guided Internet-Based Cognitive Behavioral Therapy for Hong Kong People With Depressive Symptoms: Randomized Controlled Trial.","authors":"Jia-Yan Pan, Jonas Rafi","doi":"10.2196/88495","DOIUrl":"https://doi.org/10.2196/88495","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/64303.].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e88495"},"PeriodicalIF":6.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113435","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}
<p><strong>Background: </strong>Both internet gaming disorder (IGD) and internet addiction (IA) have been associated with diverse psychopathological symptoms. However, how the 2 conditions relate to each other and which is more strongly associated with psychopathology remain unclear.</p><p><strong>Objective: </strong>This study aimed to examine the association between IGD and IA and compare the strength of their associations with various types of psychopathological symptoms.</p><p><strong>Methods: </strong>This cross-sectional study surveyed 3 independent samples of Chinese adolescents: the first sample (S1) comprised 8194 first-year undergraduates at a comprehensive university in Chengdu, the second sample (S2) comprised 1720 students from a high school in Hangzhou, and the third sample (S3) comprised 551 inpatients aged 13 to 19 years recruited from 2 tertiary psychiatric hospitals in Hangzhou and Chengdu. IGD was defined as a score of 22 or more on the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), whereas IA was defined as a score of 50 or more on Young's 20-item Internet Addiction Test (IAT-20). Symptoms of depression, anxiety, psychoticism, paranoid ideation, and attention-deficit or hyperactivity were assessed using internationally validated scales including 9-item the Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, psychoticism and paranoid ideation subscales of the Symptom Checklist 90 (absent for S2), and Adult ADHD Self-Report Scale (absent for S1), through online surveys in S1 (October 2020) and S3 (January 2022 to February 2025) and via an offline survey in S2 (March 2024).</p><p><strong>Results: </strong>The prevalence estimates (95% CI) of IGD were 4.8% (4.3%-5.2%) in S1, 15.8% (14.0%-17.5%) in S2, and 32.3% (28.4%-36.2%) in S3, whereas prevalence estimates (95% CI) of IA were consistently higher across samples, ranging from 7.3% (6.8%-7.9%) in S1 and 18.8% (17.0%-20.6%) in S2 to 45.9% (41.8%-50.1%) in S3. The IGDS9-SF and the IAT-20 were moderately correlated (Pearson r=0.51-0.57; all P<.001) and were associated with the severity of most psychopathological symptom domains, with consistently stronger associations observed for IAT-20 scores. In multivariate models including all psychopathological symptoms as independent variables, the coefficients of determination (R², 95% CIs) were consistently higher for the IAT-20 than for the IGDS9-SF in S1 (0.33, 0.30-0.35 vs 0.13, 0.11-0.16) and S2 (0.44, 0.39-0.49 vs 0.23, 0.18-0.27), with a similar but nonsignificant pattern observed in S3 (0.13, 0.06-0.26 vs 0.06, 0.03-0.16). Post hoc analyses indicated that psychopathological symptoms were generally more severe in individuals with IA, either alone or comorbid with IGD, than in those with IGD only.</p><p><strong>Conclusions: </strong>This study provides additional evidence that IGD and IA are distinct yet interrelated constructs, and further demonstrates that IA consistently exhibits stronger associations with the
{"title":"Comparing the Associations of Internet Addiction and Internet Gaming Disorder With Psychopathological Symptoms: Cross-Sectional Study of Three Independent Adolescent Samples.","authors":"Ying-Ying Li, A-Qian Hu, Ling-Li Yi, Zi-Xin Mao, Qiu-Yue Lü, Juan Wang, Wei Wei, Yue-Qi Huang, Shu Huang, Wen-Jing Dai, Meng-Xuan Qiao, Jia-Jun Xu, Qiang Wang, Xiao-Jing Li, Fu-Gang Luo, Wei Deng, Yu-Zheng Hu, Tao Li, Wan-Jun Guo","doi":"10.2196/82414","DOIUrl":"10.2196/82414","url":null,"abstract":"<p><strong>Background: </strong>Both internet gaming disorder (IGD) and internet addiction (IA) have been associated with diverse psychopathological symptoms. However, how the 2 conditions relate to each other and which is more strongly associated with psychopathology remain unclear.</p><p><strong>Objective: </strong>This study aimed to examine the association between IGD and IA and compare the strength of their associations with various types of psychopathological symptoms.</p><p><strong>Methods: </strong>This cross-sectional study surveyed 3 independent samples of Chinese adolescents: the first sample (S1) comprised 8194 first-year undergraduates at a comprehensive university in Chengdu, the second sample (S2) comprised 1720 students from a high school in Hangzhou, and the third sample (S3) comprised 551 inpatients aged 13 to 19 years recruited from 2 tertiary psychiatric hospitals in Hangzhou and Chengdu. IGD was defined as a score of 22 or more on the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), whereas IA was defined as a score of 50 or more on Young's 20-item Internet Addiction Test (IAT-20). Symptoms of depression, anxiety, psychoticism, paranoid ideation, and attention-deficit or hyperactivity were assessed using internationally validated scales including 9-item the Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, psychoticism and paranoid ideation subscales of the Symptom Checklist 90 (absent for S2), and Adult ADHD Self-Report Scale (absent for S1), through online surveys in S1 (October 2020) and S3 (January 2022 to February 2025) and via an offline survey in S2 (March 2024).</p><p><strong>Results: </strong>The prevalence estimates (95% CI) of IGD were 4.8% (4.3%-5.2%) in S1, 15.8% (14.0%-17.5%) in S2, and 32.3% (28.4%-36.2%) in S3, whereas prevalence estimates (95% CI) of IA were consistently higher across samples, ranging from 7.3% (6.8%-7.9%) in S1 and 18.8% (17.0%-20.6%) in S2 to 45.9% (41.8%-50.1%) in S3. The IGDS9-SF and the IAT-20 were moderately correlated (Pearson r=0.51-0.57; all P<.001) and were associated with the severity of most psychopathological symptom domains, with consistently stronger associations observed for IAT-20 scores. In multivariate models including all psychopathological symptoms as independent variables, the coefficients of determination (R², 95% CIs) were consistently higher for the IAT-20 than for the IGDS9-SF in S1 (0.33, 0.30-0.35 vs 0.13, 0.11-0.16) and S2 (0.44, 0.39-0.49 vs 0.23, 0.18-0.27), with a similar but nonsignificant pattern observed in S3 (0.13, 0.06-0.26 vs 0.06, 0.03-0.16). Post hoc analyses indicated that psychopathological symptoms were generally more severe in individuals with IA, either alone or comorbid with IGD, than in those with IGD only.</p><p><strong>Conclusions: </strong>This study provides additional evidence that IGD and IA are distinct yet interrelated constructs, and further demonstrates that IA consistently exhibits stronger associations with the ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e82414"},"PeriodicalIF":6.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12867465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113369","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}
Jingjing Chen, Jianjuan Zhao, Huiyu Qiu, Yanhui Liu, Yunqi Zhang, Qicheng Sun, Yan Yi, Hongying Tang, Jing Zhao, Bin Xu, Qiong Zhang, Ge Yang, Hui Li, Junjie Liu, Zhongzhou Yang, Shaolin Liang, Yanping Li, Jing Fu
Background: Accurately predicting ovarian response and determining the optimal starting dose of follicle-stimulating hormone (FSH) remain critical yet challenging for effective ovarian stimulation. Currently, there is a lack of a comprehensive model capable of simultaneously forecasting the number of oocytes retrieved (NOR) and assessing the risk of early-onset moderate-to-severe ovarian hyperstimulation syndrome (OHSS).
Objective: This study aimed to establish an integrated mode capable of forecasting the NOR and assessing the risk of early-onset moderate-to-severe OHSS across varying starting doses of FSH.
Methods: This prognostic study included patients undergoing their first ovarian stimulation cycles at 2 independent in vitro fertilization clinics. Automated classifiers were used for variable selection. Machine learning models (11 for NOR and 11 for OHSS) were developed and validated using internal (n=6401) and external (n=3805) datasets. Shapley additive explanation was applied for variable interpretation. The best-performing models were incorporated into a web-based prediction tool.
Results: For NOR prediction, 17 variables were selected, with the gradient boosting regressor achieving the highest performance (internal dataset: R2=0.7978; external dataset: R2=0.7924). For OHSS prediction, 19 variables were identified, and the LightGBM model demonstrated superior performance (internal dataset: area under the receiver operating characteristic curve=0.7588; external dataset: area under the receiver operating characteristic curve=0.7287). Shapley additive explanation analysis highlighted the FSH starting dose to BMI ratio and baseline antral follicle count as key predictors for NOR and OHSS, respectively. Dose-response curves were generated to visualize predicted outcomes with varying FSH starting doses. The models were implemented in a user-friendly, research-oriented online prototype, individualized ovarian stimulation guide (InOvaSGuide).
Conclusions: This study introduces an integrated framework for predicting NOR and early-onset moderate-to-severe OHSS risk across different FSH doses. Future prospective evaluation is needed before clinical implementation.
{"title":"Integrated Prediction System for Individualized Ovarian Stimulation and Ovarian Hyperstimulation Syndrome Prevention: Algorithm Development and Validation.","authors":"Jingjing Chen, Jianjuan Zhao, Huiyu Qiu, Yanhui Liu, Yunqi Zhang, Qicheng Sun, Yan Yi, Hongying Tang, Jing Zhao, Bin Xu, Qiong Zhang, Ge Yang, Hui Li, Junjie Liu, Zhongzhou Yang, Shaolin Liang, Yanping Li, Jing Fu","doi":"10.2196/78245","DOIUrl":"https://doi.org/10.2196/78245","url":null,"abstract":"<p><strong>Background: </strong>Accurately predicting ovarian response and determining the optimal starting dose of follicle-stimulating hormone (FSH) remain critical yet challenging for effective ovarian stimulation. Currently, there is a lack of a comprehensive model capable of simultaneously forecasting the number of oocytes retrieved (NOR) and assessing the risk of early-onset moderate-to-severe ovarian hyperstimulation syndrome (OHSS).</p><p><strong>Objective: </strong>This study aimed to establish an integrated mode capable of forecasting the NOR and assessing the risk of early-onset moderate-to-severe OHSS across varying starting doses of FSH.</p><p><strong>Methods: </strong>This prognostic study included patients undergoing their first ovarian stimulation cycles at 2 independent in vitro fertilization clinics. Automated classifiers were used for variable selection. Machine learning models (11 for NOR and 11 for OHSS) were developed and validated using internal (n=6401) and external (n=3805) datasets. Shapley additive explanation was applied for variable interpretation. The best-performing models were incorporated into a web-based prediction tool.</p><p><strong>Results: </strong>For NOR prediction, 17 variables were selected, with the gradient boosting regressor achieving the highest performance (internal dataset: R<sup>2</sup>=0.7978; external dataset: R<sup>2</sup>=0.7924). For OHSS prediction, 19 variables were identified, and the LightGBM model demonstrated superior performance (internal dataset: area under the receiver operating characteristic curve=0.7588; external dataset: area under the receiver operating characteristic curve=0.7287). Shapley additive explanation analysis highlighted the FSH starting dose to BMI ratio and baseline antral follicle count as key predictors for NOR and OHSS, respectively. Dose-response curves were generated to visualize predicted outcomes with varying FSH starting doses. The models were implemented in a user-friendly, research-oriented online prototype, individualized ovarian stimulation guide (InOvaSGuide).</p><p><strong>Conclusions: </strong>This study introduces an integrated framework for predicting NOR and early-onset moderate-to-severe OHSS risk across different FSH doses. Future prospective evaluation is needed before clinical implementation.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e78245"},"PeriodicalIF":6.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113391","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}
Jun Tao, Ellie Pavlick, Amaris Grondin, Josue D Bustamante, Harrison Martin, Hannah Parent, Natalie Fenn, Alexi Almonte, Amanda Maguire-Wilkerson, Mofan Gu, Jack Rusley, Bryce K Perler, Tyler Wray, Amy S Nunn, Philip A Chan
<p><strong>Background: </strong>The HIV epidemic in the United States disproportionately impacts gay, bisexual, and other men who have sex with men (MSM). Despite the effectiveness of HIV preexposure prophylaxis (PrEP) in preventing HIV acquisition, uptake among MSM remains suboptimal. Motivational interviewing (MI) has demonstrated efficacy at increasing PrEP uptake among MSM but is resource-intensive, limiting scalability. The use of artificial intelligence, particularly large language models with conversational agents (ie, "chatbots") such as ChatGPT, may offer a scalable approach to delivering MI-based counseling for PrEP and HIV prevention.</p><p><strong>Objective: </strong>This internal usability testing aimed to evaluate the development of an artificial intelligence-based chatbot, including its ability to provide MI-aligned education about PrEP and HIV prevention and potential to support PrEP uptake.</p><p><strong>Methods: </strong>The Chatbot for HIV Prevention and Action (CHIA) was built on a GPT-4o base model embedded with a validated knowledge database on HIV and PrEP in English and Spanish. The CHIA was fine-tuned through training on a large MI dataset and prompt engineering. The use of the AutoGen multiagent framework enabled the CHIA to integrate 2 agents, the PrEP Counselor Agent and the Assistant Agent, which specialized in providing MI-based counseling and handling function calls (eg, assessment of HIV risk), respectively. During internal testing from March 10-April 28, 2025, we systematically evaluated the CHIA's performance in English and Spanish using a set of 5-point Likert scales to measure accuracy, conciseness, up-to-dateness, trustworthiness, and alignment with aspects of the MI spirit (eg, collaboration, autonomy support) and MI-consistent behaviors (eg, affirmation, open-ended questions). Descriptive statistics and mixed linear regression were used to analyze the data.</p><p><strong>Results: </strong>A total of 296 responses, including 145 English responses and 151 Spanish responses, were collected during the internal testing period. Overall, the CHIA demonstrated strong performance across both languages, receiving the highest combined scores in the general response quality metrics including up-to-dateness (mean 4.6, SD 0.8), trustworthiness (mean 4.5, SD 0.9), accuracy (mean 4.4, SD 0.9), and conciseness (mean 4.2, SD 1.1). The CHIA generally received higher combined scores for metrics that assessed alignment with the MI spirit (ie, empathy, evocation, autonomy support, and collaboration) and lower combined scores for MI-consistent behaviors (ie, affirmation, open-ended questions, and reflections). Spanish responses had significantly lower mean scores than English responses across nearly all MI-based metrics.</p><p><strong>Conclusions: </strong>Our internal usability testing highlights the potential of the CHIA as a viable tool for delivering MI-aligned counseling in English and Spanish to promote HIV prevention and su
{"title":"Evaluation of an Artificial Intelligence Conversational Chatbot to Enhance HIV Preexposure Prophylaxis Uptake: Development and Usability Internal Testing.","authors":"Jun Tao, Ellie Pavlick, Amaris Grondin, Josue D Bustamante, Harrison Martin, Hannah Parent, Natalie Fenn, Alexi Almonte, Amanda Maguire-Wilkerson, Mofan Gu, Jack Rusley, Bryce K Perler, Tyler Wray, Amy S Nunn, Philip A Chan","doi":"10.2196/79671","DOIUrl":"10.2196/79671","url":null,"abstract":"<p><strong>Background: </strong>The HIV epidemic in the United States disproportionately impacts gay, bisexual, and other men who have sex with men (MSM). Despite the effectiveness of HIV preexposure prophylaxis (PrEP) in preventing HIV acquisition, uptake among MSM remains suboptimal. Motivational interviewing (MI) has demonstrated efficacy at increasing PrEP uptake among MSM but is resource-intensive, limiting scalability. The use of artificial intelligence, particularly large language models with conversational agents (ie, \"chatbots\") such as ChatGPT, may offer a scalable approach to delivering MI-based counseling for PrEP and HIV prevention.</p><p><strong>Objective: </strong>This internal usability testing aimed to evaluate the development of an artificial intelligence-based chatbot, including its ability to provide MI-aligned education about PrEP and HIV prevention and potential to support PrEP uptake.</p><p><strong>Methods: </strong>The Chatbot for HIV Prevention and Action (CHIA) was built on a GPT-4o base model embedded with a validated knowledge database on HIV and PrEP in English and Spanish. The CHIA was fine-tuned through training on a large MI dataset and prompt engineering. The use of the AutoGen multiagent framework enabled the CHIA to integrate 2 agents, the PrEP Counselor Agent and the Assistant Agent, which specialized in providing MI-based counseling and handling function calls (eg, assessment of HIV risk), respectively. During internal testing from March 10-April 28, 2025, we systematically evaluated the CHIA's performance in English and Spanish using a set of 5-point Likert scales to measure accuracy, conciseness, up-to-dateness, trustworthiness, and alignment with aspects of the MI spirit (eg, collaboration, autonomy support) and MI-consistent behaviors (eg, affirmation, open-ended questions). Descriptive statistics and mixed linear regression were used to analyze the data.</p><p><strong>Results: </strong>A total of 296 responses, including 145 English responses and 151 Spanish responses, were collected during the internal testing period. Overall, the CHIA demonstrated strong performance across both languages, receiving the highest combined scores in the general response quality metrics including up-to-dateness (mean 4.6, SD 0.8), trustworthiness (mean 4.5, SD 0.9), accuracy (mean 4.4, SD 0.9), and conciseness (mean 4.2, SD 1.1). The CHIA generally received higher combined scores for metrics that assessed alignment with the MI spirit (ie, empathy, evocation, autonomy support, and collaboration) and lower combined scores for MI-consistent behaviors (ie, affirmation, open-ended questions, and reflections). Spanish responses had significantly lower mean scores than English responses across nearly all MI-based metrics.</p><p><strong>Conclusions: </strong>Our internal usability testing highlights the potential of the CHIA as a viable tool for delivering MI-aligned counseling in English and Spanish to promote HIV prevention and su","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e79671"},"PeriodicalIF":6.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12867473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113386","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}
<p><strong>Background: </strong>While artificial intelligence (AI) holds significant promise for health care, excessive trust in these tools may unintentionally delay patients from seeking professional care, particularly among patients with chronic illnesses. However, the behavioral dynamics underlying this phenomenon remain poorly understood.</p><p><strong>Objective: </strong>This study aims to quantify the influence of AI trust on health care delays through integrated survey-based mediation analysis and real-world research, and to simulate intervention efficacy using agent-based modeling (ABM).</p><p><strong>Methods: </strong>A cross-sectional online survey was conducted in China from December 2024 to May 2025. Participants were recruited via convenience sampling on social media (WeChat and QQ) and hospital portals. The survey included a 21-item questionnaire measuring AI trust (5-point Likert scale), AI usage frequency (6-point scale), chronic disease status (physician-diagnosed, binary), and self-reported health care delay (binary). Responses with completion time <90 seconds, logical inconsistencies, missing values, or duplicates were excluded. Analyses included descriptive statistics, multivariable logistic regression (α=.05), mediation analysis with nonparametric bootstrapping (500 iterations), and moderation testing. Subsequently, an ABM simulated 2460 agents within a small-world network over 14 days to model behavioral feedback and test 3 interventions: broadcast messaging, behavioral reward, and network rewiring.</p><p><strong>Results: </strong>The final sample included 2460 adults (mean age 34.46, SD 11.62 years; n=1345, 54.7% female). Higher AI trust was associated with increased odds of delays (odds ratio [OR] 1.09, 95% CI 1.00-1.18; P=.04), with usage frequency partially mediating this relationship (indirect OR 1.24, 95% CI 1.20-1.29; P<.001). Chronic disease status amplified the delay odds (OR 1.42, 95% CI 1.09-1.86; P=.01). The ABM demonstrated a bidirectional trust erosion loop, with population delay rates declining from 10.6% to 9.5% as mean AI trust decreased from 1.91 to 1.52. Interventions simulation found broadcast messaging most effective in reducing delay odds (OR 0.94, 95% CI 0.94-0.95; P<.001), whereas network rewiring increased odds (OR 1.04, 95% CI 1.04-1.05; P<.001), suggesting a "trust polarization" effect.</p><p><strong>Conclusions: </strong>This study reveals a nuanced relationship between AI trust and delayed health care-seeking. While trust in AI enhances engagement, it can also lead to delayed care, particularly among patients with chronic conditions or frequent AI users. Integrating survey data with ABM highlights how AI trust and delay behaviors can strengthen one another over time. Our findings indicate that AI health tools should prioritize calibrated decision support rather than full automation to balance autonomy, odds, and decision quality in digital health. Unlike previous studies that focus solely on sta
背景：虽然人工智能（AI）在医疗保健方面具有重大前景，但对这些工具的过度信任可能会无意中延迟患者寻求专业护理，特别是慢性病患者。然而，这种现象背后的行为动力学仍然知之甚少。目的：本研究旨在通过基于调查的中介分析与现实世界研究相结合，量化人工智能信任对医疗延误的影响，并利用基于agent的模型（ABM）模拟干预效果。方法：于2024年12月至2025年5月在中国进行横断面在线调查。参与者通过社交媒体（微信和QQ）和医院门户网站的方便抽样招募。该调查包括一份21项问卷，测量人工智能信任（5分制李克特量表）、人工智能使用频率（6分制量表）、慢性疾病状况（医生诊断，二进制）和自我报告的医疗延迟（二进制）。结果：最终样本包括2460名成年人（平均年龄34.46岁，SD 11.62岁；n=1345，女性占54.7%）。较高的人工智能信任与延迟就诊几率增加相关（比值比[OR] 1.09, 95% CI 1.00-1.18； P= 0.04），使用频率在一定程度上介导了这种关系(间接比值比[OR] 1.24, 95% CI 1.20-1.29；结论：本研究揭示了人工智能信任与延迟就医之间的微妙关系。虽然对人工智能的信任增强了参与，但它也可能导致延迟护理，特别是慢性病患者或频繁使用人工智能的患者。将调查数据与ABM相结合，凸显了人工智能信任和延迟行为如何随着时间的推移相互加强。我们的研究结果表明，人工智能健康工具应优先考虑校准决策支持，而不是完全自动化，以平衡数字健康中的自主性、几率和决策质量。与以往的研究只关注静态关联不同，本研究强调人工智能信任与延迟行为之间的动态交互。
{"title":"Behavioral Dynamics of AI Trust and Health Care Delays Among Adults: Integrated Cross-Sectional Survey and Agent-Based Modeling Study.","authors":"Xueyao Cai, Weidong Li, Wenjun Shi, Yuchen Cai, Jianda Zhou","doi":"10.2196/82170","DOIUrl":"https://doi.org/10.2196/82170","url":null,"abstract":"<p><strong>Background: </strong>While artificial intelligence (AI) holds significant promise for health care, excessive trust in these tools may unintentionally delay patients from seeking professional care, particularly among patients with chronic illnesses. However, the behavioral dynamics underlying this phenomenon remain poorly understood.</p><p><strong>Objective: </strong>This study aims to quantify the influence of AI trust on health care delays through integrated survey-based mediation analysis and real-world research, and to simulate intervention efficacy using agent-based modeling (ABM).</p><p><strong>Methods: </strong>A cross-sectional online survey was conducted in China from December 2024 to May 2025. Participants were recruited via convenience sampling on social media (WeChat and QQ) and hospital portals. The survey included a 21-item questionnaire measuring AI trust (5-point Likert scale), AI usage frequency (6-point scale), chronic disease status (physician-diagnosed, binary), and self-reported health care delay (binary). Responses with completion time <90 seconds, logical inconsistencies, missing values, or duplicates were excluded. Analyses included descriptive statistics, multivariable logistic regression (α=.05), mediation analysis with nonparametric bootstrapping (500 iterations), and moderation testing. Subsequently, an ABM simulated 2460 agents within a small-world network over 14 days to model behavioral feedback and test 3 interventions: broadcast messaging, behavioral reward, and network rewiring.</p><p><strong>Results: </strong>The final sample included 2460 adults (mean age 34.46, SD 11.62 years; n=1345, 54.7% female). Higher AI trust was associated with increased odds of delays (odds ratio [OR] 1.09, 95% CI 1.00-1.18; P=.04), with usage frequency partially mediating this relationship (indirect OR 1.24, 95% CI 1.20-1.29; P<.001). Chronic disease status amplified the delay odds (OR 1.42, 95% CI 1.09-1.86; P=.01). The ABM demonstrated a bidirectional trust erosion loop, with population delay rates declining from 10.6% to 9.5% as mean AI trust decreased from 1.91 to 1.52. Interventions simulation found broadcast messaging most effective in reducing delay odds (OR 0.94, 95% CI 0.94-0.95; P<.001), whereas network rewiring increased odds (OR 1.04, 95% CI 1.04-1.05; P<.001), suggesting a \"trust polarization\" effect.</p><p><strong>Conclusions: </strong>This study reveals a nuanced relationship between AI trust and delayed health care-seeking. While trust in AI enhances engagement, it can also lead to delayed care, particularly among patients with chronic conditions or frequent AI users. Integrating survey data with ABM highlights how AI trust and delay behaviors can strengthen one another over time. Our findings indicate that AI health tools should prioritize calibrated decision support rather than full automation to balance autonomy, odds, and decision quality in digital health. Unlike previous studies that focus solely on sta","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e82170"},"PeriodicalIF":6.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113414","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":"What Health Care Organizations Have Learned From Telecommunication Outages.","authors":"Catharine Solomon","doi":"10.2196/91456","DOIUrl":"10.2196/91456","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e91456"},"PeriodicalIF":6.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12867314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113349","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}
Julia Adams, Jonathan Davies, Prai Wattanatakulchat, Julieta Galante, Felicity Miller, Simon D'Alfonso, Nicholas T Van Dam
<p><strong>Background: </strong>Meditation apps are increasingly popular, yet there is limited understanding of how much users actually engage with them. While meditation apps show promise for supporting mental health, engagement in real-world settings appears to be notably low. The patterns of app use and the factors that influence usage remain relatively unclear.</p><p><strong>Objective: </strong>This study aims to examine the extent of meditation app use and the factors associated with user engagement.</p><p><strong>Methods: </strong>We conducted a cross-sectional survey of 536 recent meditation app users across 5 English-speaking countries. Engagement data were collected via self-report and app-verified screenshots. Assessed factors included user characteristics (age, education, income, sex, country, personality, self-efficacy, readiness and expectations for change, self-compassion, and quality of life), mental health (distress, well-being, life satisfaction, anxiety, depression, support, and stress), and app-related elements (therapeutic alliance, appeal, functionality, aesthetics, information, quality, and perceived impact). The 4 outcome variables representing engagement were app-verified minutes, self-reported minutes, app-verified minutes per year (adjusted for app download date), and self-reported minutes per year (adjusted for app download date). Associations between app use and variables of interest were examined using correlations. Factors with significant associations were then included in multivariable regression models to identify those most strongly associated with engagement.</p><p><strong>Results: </strong>Age (ρ=0.13-0.15, PP<sup>FDR</sup>, where FDR is false discovery rate), expectations for sleep (ρ=0.12-0.33, P<sup>FDR</sup><.05), and expectations for thriving (ρ=0.12-0.18, P<sup>FDR</sup><.05) were associated with all outcome measures except adjusted objective minutes. Readiness to change was associated with all outcome measures (ρ=0.24-0.33, P<sup>FDR</sup><.05). Among app factors, appeal (ρ=0.18-0.23, P<sup>FDR</sup><.05) and perceived impact (ρ=0.23-0.32, P<sup>FDR</sup><.05) were associated with all outcome measures except adjusted self-report minutes, while perceived quality (r=0.28-0.51, P<sup>FDR</sup><.05) was associated with all outcome measures. Robust linear regressions showed that greater readiness to change (β=0.005-0.026, P=.006-.02), higher education level (β=0.029-0.540, P<.001), and higher openness (β=0.004-0.010, P=.008-.03) were associated with increased engagement. Additionally, greater expectations for sleep (β=0.004-0.009, P=.02-.04), greater expectation match (β=0.023, P=.03), and higher perceived app quality (β=0.008-0.042, P=.001-.01) were uniquely associated with increased engagement.</p><p><strong>Conclusions: </strong>Most individuals who download meditation apps engage minimally. Our findings suggest that users who are more educated, open to new experiences, and hold strong beliefs in the effec
{"title":"Engagement With Meditation Apps: Cross-Sectional Survey of Use and Associations.","authors":"Julia Adams, Jonathan Davies, Prai Wattanatakulchat, Julieta Galante, Felicity Miller, Simon D'Alfonso, Nicholas T Van Dam","doi":"10.2196/71960","DOIUrl":"https://doi.org/10.2196/71960","url":null,"abstract":"<p><strong>Background: </strong>Meditation apps are increasingly popular, yet there is limited understanding of how much users actually engage with them. While meditation apps show promise for supporting mental health, engagement in real-world settings appears to be notably low. The patterns of app use and the factors that influence usage remain relatively unclear.</p><p><strong>Objective: </strong>This study aims to examine the extent of meditation app use and the factors associated with user engagement.</p><p><strong>Methods: </strong>We conducted a cross-sectional survey of 536 recent meditation app users across 5 English-speaking countries. Engagement data were collected via self-report and app-verified screenshots. Assessed factors included user characteristics (age, education, income, sex, country, personality, self-efficacy, readiness and expectations for change, self-compassion, and quality of life), mental health (distress, well-being, life satisfaction, anxiety, depression, support, and stress), and app-related elements (therapeutic alliance, appeal, functionality, aesthetics, information, quality, and perceived impact). The 4 outcome variables representing engagement were app-verified minutes, self-reported minutes, app-verified minutes per year (adjusted for app download date), and self-reported minutes per year (adjusted for app download date). Associations between app use and variables of interest were examined using correlations. Factors with significant associations were then included in multivariable regression models to identify those most strongly associated with engagement.</p><p><strong>Results: </strong>Age (ρ=0.13-0.15, PP<sup>FDR</sup>, where FDR is false discovery rate), expectations for sleep (ρ=0.12-0.33, P<sup>FDR</sup><.05), and expectations for thriving (ρ=0.12-0.18, P<sup>FDR</sup><.05) were associated with all outcome measures except adjusted objective minutes. Readiness to change was associated with all outcome measures (ρ=0.24-0.33, P<sup>FDR</sup><.05). Among app factors, appeal (ρ=0.18-0.23, P<sup>FDR</sup><.05) and perceived impact (ρ=0.23-0.32, P<sup>FDR</sup><.05) were associated with all outcome measures except adjusted self-report minutes, while perceived quality (r=0.28-0.51, P<sup>FDR</sup><.05) was associated with all outcome measures. Robust linear regressions showed that greater readiness to change (β=0.005-0.026, P=.006-.02), higher education level (β=0.029-0.540, P<.001), and higher openness (β=0.004-0.010, P=.008-.03) were associated with increased engagement. Additionally, greater expectations for sleep (β=0.004-0.009, P=.02-.04), greater expectation match (β=0.023, P=.03), and higher perceived app quality (β=0.008-0.042, P=.001-.01) were uniquely associated with increased engagement.</p><p><strong>Conclusions: </strong>Most individuals who download meditation apps engage minimally. Our findings suggest that users who are more educated, open to new experiences, and hold strong beliefs in the effec","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e71960"},"PeriodicalIF":6.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146105921","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}
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