Sara Browne, Anya Umlauf, David J Moore, Constance A Benson, Florin Vaida
Background: The success or failure of a digital health technology depends upon how it is received by the user. Objective: We conducted a detailed evaluation of user experience (UX) amongst persons who utilized an FDA-approved digital health feedback system (DHFS) incorporating ingestible sensors (IS) to capture medication adherence, after they were prescribed oral pre-exposure prophylaxis (PrEP) to prevent HIV infection. We then performed an association analysis with individual participant characteristics captured at baseline, to see if ‘personas’ associated with positive or negative UX emerged. Methods: UX data was collected upon exit from a prospective intervention study of HIV-negative adults prescribed oral PrEP who used the DHFS with ingestible sensor-enabled tenofovir disoproxil fumarate plus emtricitabine (IS-Truvada®). Baseline demographics, urine toxicology, and self-report questionnaires evaluating sleep (PSQI), self-efficacy, habitual self-control, HIV risk perception (PRHS 8-item), and depressive symptoms (PHQ-8) were obtained. Participants with ≥ 28 days on study completed a Likert-scale UX questionnaire of 27 questions grouped into 4 domain categories: Overall Experience, Ease of Use, Intention of Future Use, and Perceived Utility. Averages and interquartile range (IQR) were computed for participant total and domain sub-scores, and mixed-effects logistic regression modeled baseline participant characteristics associated with UX responses. Demographic characteristics of participants who responded to the questionnaire versus non-responders were compared using Fisher’s exact test. Results: Seventy-one participants enrolled with a mean age of 37.6 years (range 18-69), 90.1% male, 77.5% white, 33.8% Hispanic, 95.8% housed and 74.6% employed. No difference in demographics were observed in the 63 participants who persisted on the intervention for ≥28 days. Participants completing the detailed exit UX questionnaire (n= 53) were housed (98.1% vs 80%; p=0.063) and less likely to have a positive urine toxicology (35.3% vs 70%; p=0.075), particularly with methamphetamine (7.8% vs 40%; p=0.020), than non-completers (n=10). Based on IQR values 75% of participants had favorable UX with Total Score (IQR 3.17-4.20), mean(SD) 3.74 (0.70); Overall Experience (IQR 3.50-4.50), mean (SD) 3.89 (0.87); Ease of Use (IQR 3.33-4.22), mean (SD)=3.74 (0.65); and Perceived Utility (IQR 3.22-4.25), mean (SD)=3.73 (0.76). At least 50% of participants expressed Intention of Future Use (median=3.80, IQR 2.80-4.40). Following multi-predictor modeling self-efficacy was significantly associated with the total score 0.822 (0.405. 1.240) p<0.001, and all sub-scores. Persons with more depressive symptoms reported better perceived utility, worse PHQ-8 score natural cubic spline with 3 knots, overall p=0.013. Poor sleep was associated with a worse overall experience, per point PSQI -0.07 (-0.133, -0.006) P<0.032. Conclusions: User Experience amongst persons using ingestible
{"title":"User Experience of Persons Using Ingestible Sensor–Enabled Pre-Exposure Prophylaxis to Prevent HIV Infection: Cross-Sectional Survey Study","authors":"Sara Browne, Anya Umlauf, David J Moore, Constance A Benson, Florin Vaida","doi":"10.2196/53596","DOIUrl":"https://doi.org/10.2196/53596","url":null,"abstract":"Background: The success or failure of a digital health technology depends upon how it is received by the user. Objective: We conducted a detailed evaluation of user experience (UX) amongst persons who utilized an FDA-approved digital health feedback system (DHFS) incorporating ingestible sensors (IS) to capture medication adherence, after they were prescribed oral pre-exposure prophylaxis (PrEP) to prevent HIV infection. We then performed an association analysis with individual participant characteristics captured at baseline, to see if ‘personas’ associated with positive or negative UX emerged. Methods: UX data was collected upon exit from a prospective intervention study of HIV-negative adults prescribed oral PrEP who used the DHFS with ingestible sensor-enabled tenofovir disoproxil fumarate plus emtricitabine (IS-Truvada®). Baseline demographics, urine toxicology, and self-report questionnaires evaluating sleep (PSQI), self-efficacy, habitual self-control, HIV risk perception (PRHS 8-item), and depressive symptoms (PHQ-8) were obtained. Participants with ≥ 28 days on study completed a Likert-scale UX questionnaire of 27 questions grouped into 4 domain categories: Overall Experience, Ease of Use, Intention of Future Use, and Perceived Utility. Averages and interquartile range (IQR) were computed for participant total and domain sub-scores, and mixed-effects logistic regression modeled baseline participant characteristics associated with UX responses. Demographic characteristics of participants who responded to the questionnaire versus non-responders were compared using Fisher’s exact test. Results: Seventy-one participants enrolled with a mean age of 37.6 years (range 18-69), 90.1% male, 77.5% white, 33.8% Hispanic, 95.8% housed and 74.6% employed. No difference in demographics were observed in the 63 participants who persisted on the intervention for ≥28 days. Participants completing the detailed exit UX questionnaire (n= 53) were housed (98.1% vs 80%; p=0.063) and less likely to have a positive urine toxicology (35.3% vs 70%; p=0.075), particularly with methamphetamine (7.8% vs 40%; p=0.020), than non-completers (n=10). Based on IQR values 75% of participants had favorable UX with Total Score (IQR 3.17-4.20), mean(SD) 3.74 (0.70); Overall Experience (IQR 3.50-4.50), mean (SD) 3.89 (0.87); Ease of Use (IQR 3.33-4.22), mean (SD)=3.74 (0.65); and Perceived Utility (IQR 3.22-4.25), mean (SD)=3.73 (0.76). At least 50% of participants expressed Intention of Future Use (median=3.80, IQR 2.80-4.40). Following multi-predictor modeling self-efficacy was significantly associated with the total score 0.822 (0.405. 1.240) p<0.001, and all sub-scores. Persons with more depressive symptoms reported better perceived utility, worse PHQ-8 score natural cubic spline with 3 knots, overall p=0.013. Poor sleep was associated with a worse overall experience, per point PSQI -0.07 (-0.133, -0.006) P<0.032. Conclusions: User Experience amongst persons using ingestible","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"53 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140839069","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}
Adam C Cunningham, Carley Prentice, Kimberly Peven, Aidan Wickham, Ryan Bamford, Tara Radovic, Anna Klepchukova, Maria Fomina, Katja Cunningham, Sarah Hill, Liisa Hantsoo, Jennifer Payne, Liudmila Zhaunova, Sonia Ponzo
<strong>Background:</strong> Reproductive health literacy and menstrual health awareness play a crucial role in ensuring the health and well-being of women and people who menstruate. Further, awareness of one’s own menstrual cycle patterns and associated symptoms can help individuals identify and manage conditions of the menstrual cycle such as premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD). Digital health products, and specifically menstrual health apps, have the potential to effect positive change due to their scalability and ease of access. <strong>Objective:</strong> The primary aim of this study was to measure the efficacy of a menstrual and reproductive health app, Flo, in improving health literacy and health and well-being outcomes in menstruating individuals with and without PMS and PMDD. Further, we explored the possibility that the use of the Flo app could positively influence feelings around reproductive health management and communication about health, menstrual cycle stigma, unplanned pregnancies, quality of life, work productivity, absenteeism, and body image. <strong>Methods:</strong> We conducted 2 pilot, 3-month, unblinded, 2-armed, remote randomized controlled trials on the effects of using the Flo app in a sample of US-based (1) individuals who track their cycles (n=321) or (2) individuals who track their cycles and are affected by PMS or PMDD (n=117). <strong>Results:</strong> The findings revealed significant improvements at the end of the study period compared to baseline for our primary outcomes of health literacy (cycle tracking: D̄=1.11; <i>t</i><sub>311</sub>=5.73, <i>P</i><.001; PMS or PMDD: D̄=1.20; <i>t</i><sub>115</sub>=3.76, <i>P</i><.001) and menstrual health awareness (D̄=3.97; <i>t</i><sub>311</sub>=7.71, <i>P</i><.001), health and well-being (D̄=3.44; <i>t</i><sub>311</sub>=5.94, <i>P</i><.001), and PMS or PMDD symptoms burden (D̄=–7.08; <i>t</i><sub>115</sub>=–5.44, <i>P</i><.001). Improvements were also observed for our secondary outcomes of feelings of control and management over health (D̄=1.01; <i>t</i><sub>311</sub>=5.08, <i>P</i><.001), communication about health (D̄=0.93; <i>t</i><sub>311</sub>=2.41, <i>P</i>=.002), menstrual cycle stigma (D̄=–0.61; <i>t</i><sub>311</sub>=–2.73, <i>P</i>=.007), and fear of unplanned pregnancies (D̄=–0.22; <i>t</i><sub>311</sub>=–2.11, <i>P</i>=.04) for those who track their cycles, as well as absenteeism from work and education due to PMS or PMDD (D̄=–1.67; <i>t</i><sub>144</sub>=–2.49, <i>P</i>=.01). <strong>Conclusions:</strong> These pilot randomized controlled trials demonstrate that the use of the Flo app improves menstrual health literacy and awareness, general health and well-being, and PMS or PMDD symptom burden. Considering the widespread use and affordability of the Flo app, these findings show promise for filling important gaps in current health care provisioning such as improving menstrual knowledge and health. <stron
{"title":"Efficacy of the Flo App in Improving Health Literacy, Menstrual and General Health, and Well-Being in Women: Pilot Randomized Controlled Trial","authors":"Adam C Cunningham, Carley Prentice, Kimberly Peven, Aidan Wickham, Ryan Bamford, Tara Radovic, Anna Klepchukova, Maria Fomina, Katja Cunningham, Sarah Hill, Liisa Hantsoo, Jennifer Payne, Liudmila Zhaunova, Sonia Ponzo","doi":"10.2196/54124","DOIUrl":"https://doi.org/10.2196/54124","url":null,"abstract":"<strong>Background:</strong> Reproductive health literacy and menstrual health awareness play a crucial role in ensuring the health and well-being of women and people who menstruate. Further, awareness of one’s own menstrual cycle patterns and associated symptoms can help individuals identify and manage conditions of the menstrual cycle such as premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD). Digital health products, and specifically menstrual health apps, have the potential to effect positive change due to their scalability and ease of access. <strong>Objective:</strong> The primary aim of this study was to measure the efficacy of a menstrual and reproductive health app, Flo, in improving health literacy and health and well-being outcomes in menstruating individuals with and without PMS and PMDD. Further, we explored the possibility that the use of the Flo app could positively influence feelings around reproductive health management and communication about health, menstrual cycle stigma, unplanned pregnancies, quality of life, work productivity, absenteeism, and body image. <strong>Methods:</strong> We conducted 2 pilot, 3-month, unblinded, 2-armed, remote randomized controlled trials on the effects of using the Flo app in a sample of US-based (1) individuals who track their cycles (n=321) or (2) individuals who track their cycles and are affected by PMS or PMDD (n=117). <strong>Results:</strong> The findings revealed significant improvements at the end of the study period compared to baseline for our primary outcomes of health literacy (cycle tracking: D̄=1.11; <i>t</i><sub>311</sub>=5.73, <i>P</i><.001; PMS or PMDD: D̄=1.20; <i>t</i><sub>115</sub>=3.76, <i>P</i><.001) and menstrual health awareness (D̄=3.97; <i>t</i><sub>311</sub>=7.71, <i>P</i><.001), health and well-being (D̄=3.44; <i>t</i><sub>311</sub>=5.94, <i>P</i><.001), and PMS or PMDD symptoms burden (D̄=–7.08; <i>t</i><sub>115</sub>=–5.44, <i>P</i><.001). Improvements were also observed for our secondary outcomes of feelings of control and management over health (D̄=1.01; <i>t</i><sub>311</sub>=5.08, <i>P</i><.001), communication about health (D̄=0.93; <i>t</i><sub>311</sub>=2.41, <i>P</i>=.002), menstrual cycle stigma (D̄=–0.61; <i>t</i><sub>311</sub>=–2.73, <i>P</i>=.007), and fear of unplanned pregnancies (D̄=–0.22; <i>t</i><sub>311</sub>=–2.11, <i>P</i>=.04) for those who track their cycles, as well as absenteeism from work and education due to PMS or PMDD (D̄=–1.67; <i>t</i><sub>144</sub>=–2.49, <i>P</i>=.01). <strong>Conclusions:</strong> These pilot randomized controlled trials demonstrate that the use of the Flo app improves menstrual health literacy and awareness, general health and well-being, and PMS or PMDD symptom burden. Considering the widespread use and affordability of the Flo app, these findings show promise for filling important gaps in current health care provisioning such as improving menstrual knowledge and health. <stron","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"1 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838720","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}
<strong>Background:</strong> The development of digital applications based on behavioral therapies to support patients with knee osteoarthritis (KOA) has attracted increasing attention in the field of rehabilitation. This paper presents a systematic review of research on digital applications based on behavioral therapies for people with KOA. <strong>Objective:</strong> This review aims to describe the characteristics of relevant digital applications, with a special focus on the current state of behavioral therapies, digital interaction technologies, and user participation in design. The secondary aim is to summarize intervention outcomes and user evaluations of digital applications. <strong>Methods:</strong> A systematic literature search was conducted using the keywords “Knee Osteoarthritis,” “Behavior Therapy,” and “Digitization” in the following databases (from January 2013 to July 2023): Web of Science, Embase, Science Direct, Ovid, and PubMed. The Mixed Methods Assessment Tool (MMAT) was used to assess the quality of evidence. Two researchers independently screened and extracted the data. <strong>Results:</strong> A total of 36 studies met the inclusion criteria and were further analyzed. Behavioral change techniques (BCTs) and cognitive behavioral therapy (CBT) were frequently combined when developing digital applications. The most prevalent areas were goals and planning (n=31) and repetition and substitution (n=27), which were frequently used to develop physical activity (PA) goals and adherence. The most prevalent combination strategy was app/website plus SMS text message/telephone/email (n=12), which has tremendous potential. This area of application design offers notable advantages, primarily manifesting in pain mitigation (n=24), reduction of physical dysfunction (n=21), and augmentation of PA levels (n=12). Additionally, when formulating design strategies, it is imperative to consider the perspectives of stakeholders, especially in response to the identified shortcomings in application design elucidated within the study. <strong>Conclusions:</strong> The results demonstrate that “goals and planning” and “repetition and substitution” are frequently used to develop PA goals and PA behavior adherence. The most prevalent combination strategy was app/website plus SMS text message/telephone/email, which has tremendous potential. Moreover, incorporating several stakeholders in the design and development stages might enhance user experience, considering the distinct variations in their requirements. To improve the efficacy and availability of digital applications, we have several proposals. First, comprehensive care for patients should be ensured by integrating multiple behavioral therapies that encompass various aspects of the rehabilitation process, such as rehabilitation exercises and status monitoring. Second, therapists could benefit from more precise recommendations by incorporating additional intelligent algorithms to analyze patient d
{"title":"Rehabilitation Applications Based on Behavioral Therapy for People With Knee Osteoarthritis: Systematic Review","authors":"Dian Zhu, Jianan Zhao, Mingxuan Wang, Bochen Cao, Wenhui Zhang, Yunlong Li, Chenqi Zhang, Ting Han","doi":"10.2196/53798","DOIUrl":"https://doi.org/10.2196/53798","url":null,"abstract":"<strong>Background:</strong> The development of digital applications based on behavioral therapies to support patients with knee osteoarthritis (KOA) has attracted increasing attention in the field of rehabilitation. This paper presents a systematic review of research on digital applications based on behavioral therapies for people with KOA. <strong>Objective:</strong> This review aims to describe the characteristics of relevant digital applications, with a special focus on the current state of behavioral therapies, digital interaction technologies, and user participation in design. The secondary aim is to summarize intervention outcomes and user evaluations of digital applications. <strong>Methods:</strong> A systematic literature search was conducted using the keywords “Knee Osteoarthritis,” “Behavior Therapy,” and “Digitization” in the following databases (from January 2013 to July 2023): Web of Science, Embase, Science Direct, Ovid, and PubMed. The Mixed Methods Assessment Tool (MMAT) was used to assess the quality of evidence. Two researchers independently screened and extracted the data. <strong>Results:</strong> A total of 36 studies met the inclusion criteria and were further analyzed. Behavioral change techniques (BCTs) and cognitive behavioral therapy (CBT) were frequently combined when developing digital applications. The most prevalent areas were goals and planning (n=31) and repetition and substitution (n=27), which were frequently used to develop physical activity (PA) goals and adherence. The most prevalent combination strategy was app/website plus SMS text message/telephone/email (n=12), which has tremendous potential. This area of application design offers notable advantages, primarily manifesting in pain mitigation (n=24), reduction of physical dysfunction (n=21), and augmentation of PA levels (n=12). Additionally, when formulating design strategies, it is imperative to consider the perspectives of stakeholders, especially in response to the identified shortcomings in application design elucidated within the study. <strong>Conclusions:</strong> The results demonstrate that “goals and planning” and “repetition and substitution” are frequently used to develop PA goals and PA behavior adherence. The most prevalent combination strategy was app/website plus SMS text message/telephone/email, which has tremendous potential. Moreover, incorporating several stakeholders in the design and development stages might enhance user experience, considering the distinct variations in their requirements. To improve the efficacy and availability of digital applications, we have several proposals. First, comprehensive care for patients should be ensured by integrating multiple behavioral therapies that encompass various aspects of the rehabilitation process, such as rehabilitation exercises and status monitoring. Second, therapists could benefit from more precise recommendations by incorporating additional intelligent algorithms to analyze patient d","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"29 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838881","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}
Eric Hurwitz, Zachary Butzin-Dozier, Hiral Master, Shawn T O'Neil, Anita Walden, Michelle Holko, Rena C Patel, Melissa A Haendel
<strong>Background:</strong> Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. <strong>Objective:</strong> The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. <strong>Methods:</strong> Using the <i>All of Us</i> Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and <i>F</i><sub>1</sub>-score. <strong>Results:</strong> Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method’s specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. <strong>Conclusions:</strong> This research establishes consumer wearables as a promising to
背景：产后抑郁症（PPD）是孕产妇健康的一大挑战。目前检测 PPD 的方法依赖于产后亲自探访，这导致了诊断不足。此外，识别 PPD 症状也是一项挑战。因此，我们探索了使用消费类可穿戴设备中的数字生物标志物来识别 PPD 的可能性。研究目的本研究的主要目的是展示利用机器学习（ML）和消费级可穿戴设备中与心率、体力活动和能量消耗相关的数字生物标记识别 PPD 的可行性。研究方法利用 "我们所有人研究计划 "注册层 v6 数据集，我们对分娩后患有和未患有 PPD 的妇女进行了计算表型分析。利用 Fitbit 的数字生物标记开发了个体内 ML 模型，以区分孕前、孕期、产后无抑郁期和产后抑郁期（即 PPD 诊断期）。我们使用广义线性模型、随机森林、支持向量机和 k 近邻算法建立了模型，并使用 κ 统计量和多类接收器工作特征曲线下面积 (mAUC) 进行了评估，以确定性能最佳的算法。我们的个性化 ML 方法的特异性在一组未经历过 PPD 的产妇中得到了证实。此外，我们还评估了既往抑郁症病史对模型性能的影响。我们使用沙普利加法解释确定了预测 PPD 期的变量重要性，并使用排列组合方法确认了结果。最后，我们将个性化 ML 方法与用于 PPD 识别的传统基于队列的 ML 模型进行了比较，并使用灵敏度、特异性、精确度、召回率和 F1 分数对模型性能进行了比较。结果拥有有效 Fitbit 数据的产妇队列包括 20 名患有 PPD 的产妇和 39 名未患有 PPD 的产妇。我们的研究结果表明，使用数字生物标记物的个体内模型可以区分孕前、孕期、产后无抑郁期和产后抑郁期（即 PPD 诊断期），其中随机森林模型（mAUC=0.85；κ=0.80）优于广义线性模型（mAUC=0.82；κ=0.74）、支持向量机（mAUC=0.75；κ=0.72）和 k 近邻模型（mAUC=0.74；κ=0.62）。模型在无 PPD 的妇女中性能下降，这说明了该方法的特异性。既往抑郁症病史并不影响模型识别 PPD 的效果。此外，我们还发现最能预测 PPD 的生物标志物是基础代谢率消耗的卡路里。最后，个性化模型在 PPD 检测方面的表现超过了基于队列的传统模型。结论：这项研究将消费类可穿戴设备作为 PPD 识别的一种有前途的工具，并强调了个性化 ML 方法，这种方法可以改变早期疾病检测策略。
{"title":"Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study","authors":"Eric Hurwitz, Zachary Butzin-Dozier, Hiral Master, Shawn T O'Neil, Anita Walden, Michelle Holko, Rena C Patel, Melissa A Haendel","doi":"10.2196/54622","DOIUrl":"https://doi.org/10.2196/54622","url":null,"abstract":"<strong>Background:</strong> Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. <strong>Objective:</strong> The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. <strong>Methods:</strong> Using the <i>All of Us</i> Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and <i>F</i><sub>1</sub>-score. <strong>Results:</strong> Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method’s specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. <strong>Conclusions:</strong> This research establishes consumer wearables as a promising to","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"17 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838888","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}
Background: Wearables measuring vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart t-shirt with embedded sensors. Initially, the smart t-shirts were designed to aid athletes in their performance analysis but with an ambition to be a supportive tool in health care. In general, the knowledge of the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are sparse. Objective: The aim of this study was to evaluate the adherence and patient experience of using a smart t-shirt for home monitoring of biometric sensor data in adolescents and young adults (AYA) undergoing cancer treatment during a two-week period. Methods Methods: The study was a prospective, single-cohort, mixed-method feasibility study. Inclusion criteria were patients ≥18-39 years receiving treatment at Copenhagen University Hospital, Rigshospitalet, D.K. Consenting patients were asked to wear the ChronolifeTM smart t-shirt for a period of two weeks. The smart t-shirt has multiple sensors and electrodes, which engender six measurements: ECG, thoracic and abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary endpoint was adherence, defined as wear time > 8 hours/day. The patient experience was investigated in individual semi-structured telephone interviews and a paper questionnaire. Results: Ten patients were included. Wear time >8 h/d during the study period (14 days) varied between 0-6 days, mean 2 days. Three patients had a mean wear time >8 h/d during their days with data registration. Days with any data registration were 0-10, mean 6.4 days. The thematic analysis of interviews pointed to three main themes: 1) The smart t-shirt is cool but does not fit cancer patients; 2) The technology limits the use of the smart t-shirt; and 3) Monitoring of data increases the safety feeling. Results from the questionnaire showed that the patients generally had confidence in the device. Conclusions: Although the primary endpoint was not reached, the patients’ experience using the smart t-shirt led to the knowledge that patients acknowledged the need for new technologies for improved supportive care in cancer. The patients were positive when asked to wear the smart t-shirt. However, technical and practical challenges in using the device led to low adherence. Although wearables might have a potential for home monitoring, the present technology is immature for clinical use.
背景：测量生命参数的可穿戴设备是癌症治疗期间在家监测病人的潜在工具。其中一种可穿戴设备是带有嵌入式传感器的智能 T 恤。智能 T 恤最初是为帮助运动员进行成绩分析而设计的，但其目标是成为医疗保健的辅助工具。总体而言，人们对可穿戴设备在癌症治疗期间用于症状监测的了解有限，对遵从性或依从性的共识和认识也很少。研究目的本研究旨在评估正在接受癌症治疗的青少年使用智能 T 恤进行为期两周的家庭生物测量传感器数据监测的依从性和患者体验。方法该研究是一项前瞻性、单一队列、混合方法的可行性研究。纳入标准是年龄≥18-39 岁在哥本哈根大学医院（Rigshospitalet）接受治疗的患者。智能 T 恤上有多个传感器和电极，可进行六项测量：心电图、胸部和腹部呼吸、胸部阻抗、体力活动（步数）和皮肤温度。主要终点是坚持佩戴，即佩戴时间大于 8 小时/天。通过个人半结构化电话访谈和纸质问卷调查了患者的体验。结果共纳入 10 名患者。在研究期间（14 天），佩戴时间大于 8 小时/天的患者为 0-6 天，平均为 2 天。三名患者在有数据登记的日子里，平均佩戴时间大于 8 小时/天。有数据登记的天数为 0-10 天，平均 6.4 天。对访谈进行的主题分析表明了三大主题：1）智能 T 恤很酷，但不适合癌症患者；2）技术限制了智能 T 恤的使用；3）数据监控增加了安全感。问卷调查结果显示，患者普遍对该设备有信心。结论虽然没有达到主要终点，但通过患者使用智能 T 恤的体验，我们了解到患者认识到需要新技术来改善癌症支持性护理。患者在被要求穿上智能 T 恤时表现积极。然而，由于在使用该设备时遇到了技术和实际挑战，导致患者的依从性不高。虽然可穿戴设备在家庭监测方面可能具有潜力，但目前的技术在临床应用方面还不成熟。
{"title":"Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study)","authors":"Emma Balch Steen-Olsen, Helle Pappot, Maiken Hjerming, Signe Hanghoej, Cecilie Holländer-Mieritz","doi":"10.2196/50620","DOIUrl":"https://doi.org/10.2196/50620","url":null,"abstract":"Background: Wearables measuring vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart t-shirt with embedded sensors. Initially, the smart t-shirts were designed to aid athletes in their performance analysis but with an ambition to be a supportive tool in health care. In general, the knowledge of the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are sparse. Objective: The aim of this study was to evaluate the adherence and patient experience of using a smart t-shirt for home monitoring of biometric sensor data in adolescents and young adults (AYA) undergoing cancer treatment during a two-week period. Methods Methods: The study was a prospective, single-cohort, mixed-method feasibility study. Inclusion criteria were patients ≥18-39 years receiving treatment at Copenhagen University Hospital, Rigshospitalet, D.K. Consenting patients were asked to wear the ChronolifeTM smart t-shirt for a period of two weeks. The smart t-shirt has multiple sensors and electrodes, which engender six measurements: ECG, thoracic and abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary endpoint was adherence, defined as wear time > 8 hours/day. The patient experience was investigated in individual semi-structured telephone interviews and a paper questionnaire. Results: Ten patients were included. Wear time >8 h/d during the study period (14 days) varied between 0-6 days, mean 2 days. Three patients had a mean wear time >8 h/d during their days with data registration. Days with any data registration were 0-10, mean 6.4 days. The thematic analysis of interviews pointed to three main themes: 1) The smart t-shirt is cool but does not fit cancer patients; 2) The technology limits the use of the smart t-shirt; and 3) Monitoring of data increases the safety feeling. Results from the questionnaire showed that the patients generally had confidence in the device. Conclusions: Although the primary endpoint was not reached, the patients’ experience using the smart t-shirt led to the knowledge that patients acknowledged the need for new technologies for improved supportive care in cancer. The patients were positive when asked to wear the smart t-shirt. However, technical and practical challenges in using the device led to low adherence. Although wearables might have a potential for home monitoring, the present technology is immature for clinical use.","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"47 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838721","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}
Cyd K Eaton, Emma McWilliams, Dana Yablon, Irem Kesim, Renee Ge, Karissa Mirus, Takeera Sconiers, Alfred Donkoh, Melanie Lawrence, Cynthia George, Mary Leigh Morrison, Emily Muther, Gabriela R Oates, Meghana Sathe, Gregory S Sawicki, Carolyn Snell, Kristin Riekert
Background: Mobile health (mHealth) interventions have immense potential to support disease self-management for people with complex medical conditions following treatment regimens that involve taking medicine and other self-management activities. However, there is no consensus on what discrete behavior change techniques should be used in an effective adherence and self-management promoting mHealth solution for any chronic illness. Reviewing the extant literature to identify effective, cross-cutting behavior change techniques in mHealth interventions for adherence and self-management promotion could help accelerate the development, evaluation, and dissemination of behavior change interventions with potential generalizability across complex medical conditions. Objective: To identify cross-cutting mHealth-based behavior change techniques to incorporate in effective mHealth adherence and self-management interventions for people with complex medical conditions by systematically reviewing the literature across chronic medical conditions with similar adherence and self-management demands. Methods: A registered systematic review was conducted to identify published evaluations of mHealth adherence and self-management interventions for chronic medical conditions with complex adherence and self-management demands. Methodological characteristics and behavior change techniques in each study were extracted using a standard data collection form. Results: 122 studies were reviewed; the majority involved people with type 2 diabetes (n=28/122, 23%), asthma (n=27/122, 22%), and type 1 diabetes (n=19/122, 16%). mHealth interventions rated as having a positive outcome on adherence/self-management used more behavior change techniques (M=4.95, SD=2.56) compared to interventions with no impact on outcomes (M=3.57, SD=1.95) or used >1 outcome measure or analytic approach (M=3.90, SD=1.93; P=.02). The following behavior change techniques were associated with positive outcomes: Self-monitoring outcomes of behavior (39/59, 66%), feedback on outcomes of behavior (34/59, 58%), self-monitoring of behavior (34/59, 58%), feedback on behavior (29/59, 49%), credible source (24/59, 41%), and goal setting (behavior; 14/59, 24%). In adult-only samples, prompts/cues were associated with positive outcomes (34/45, 76%). In adolescent/young adult samples, information about health consequences (1/4, 25%), problem-solving (1/4, 25%), and material reward-behavior (2/4, 50%) were associated with positive outcomes. In interventions explicitly targeting taking medicine, prompts/cues (25/33, 76%) and credible source (13/33, 39%) were associated with positive outcomes. In interventions focused on self-management and other adherence targets, instruction on how to perform the behavior (8/26, 31%), goal setting (behavior; 8/26, 31%)), and action planning (5/26, 19%) were associated with positive outcomes. Conclusions: To support adherence and self-management in people with complex medical condition
{"title":"Cross-Cutting mHealth Behavior Change Techniques to Support Treatment Adherence and Self-Management of Complex Medical Conditions: Systematic Review","authors":"Cyd K Eaton, Emma McWilliams, Dana Yablon, Irem Kesim, Renee Ge, Karissa Mirus, Takeera Sconiers, Alfred Donkoh, Melanie Lawrence, Cynthia George, Mary Leigh Morrison, Emily Muther, Gabriela R Oates, Meghana Sathe, Gregory S Sawicki, Carolyn Snell, Kristin Riekert","doi":"10.2196/49024","DOIUrl":"https://doi.org/10.2196/49024","url":null,"abstract":"Background: Mobile health (mHealth) interventions have immense potential to support disease self-management for people with complex medical conditions following treatment regimens that involve taking medicine and other self-management activities. However, there is no consensus on what discrete behavior change techniques should be used in an effective adherence and self-management promoting mHealth solution for any chronic illness. Reviewing the extant literature to identify effective, cross-cutting behavior change techniques in mHealth interventions for adherence and self-management promotion could help accelerate the development, evaluation, and dissemination of behavior change interventions with potential generalizability across complex medical conditions. Objective: To identify cross-cutting mHealth-based behavior change techniques to incorporate in effective mHealth adherence and self-management interventions for people with complex medical conditions by systematically reviewing the literature across chronic medical conditions with similar adherence and self-management demands. Methods: A registered systematic review was conducted to identify published evaluations of mHealth adherence and self-management interventions for chronic medical conditions with complex adherence and self-management demands. Methodological characteristics and behavior change techniques in each study were extracted using a standard data collection form. Results: 122 studies were reviewed; the majority involved people with type 2 diabetes (n=28/122, 23%), asthma (n=27/122, 22%), and type 1 diabetes (n=19/122, 16%). mHealth interventions rated as having a positive outcome on adherence/self-management used more behavior change techniques (M=4.95, SD=2.56) compared to interventions with no impact on outcomes (M=3.57, SD=1.95) or used >1 outcome measure or analytic approach (M=3.90, SD=1.93; P=.02). The following behavior change techniques were associated with positive outcomes: Self-monitoring outcomes of behavior (39/59, 66%), feedback on outcomes of behavior (34/59, 58%), self-monitoring of behavior (34/59, 58%), feedback on behavior (29/59, 49%), credible source (24/59, 41%), and goal setting (behavior; 14/59, 24%). In adult-only samples, prompts/cues were associated with positive outcomes (34/45, 76%). In adolescent/young adult samples, information about health consequences (1/4, 25%), problem-solving (1/4, 25%), and material reward-behavior (2/4, 50%) were associated with positive outcomes. In interventions explicitly targeting taking medicine, prompts/cues (25/33, 76%) and credible source (13/33, 39%) were associated with positive outcomes. In interventions focused on self-management and other adherence targets, instruction on how to perform the behavior (8/26, 31%), goal setting (behavior; 8/26, 31%)), and action planning (5/26, 19%) were associated with positive outcomes. Conclusions: To support adherence and self-management in people with complex medical condition","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"103 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838722","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}
<strong>Background:</strong> The COVID-19 pandemic has significantly reduced physical activity (PA) levels and increased sedentary behavior (SB), which can lead to worsening physical fitness (PF). Children and adolescents may benefit from mobile health (mHealth) apps to increase PA and improve PF. However, the effectiveness of mHealth app–based interventions and potential moderators in this population are not yet fully understood. <strong>Objective:</strong> This study aims to review and analyze the effectiveness of mHealth app–based interventions in promoting PA and improving PF and identify potential moderators of the efficacy of mHealth app–based interventions in children and adolescents. <strong>Methods:</strong> We searched for randomized controlled trials (RCTs) published in the PubMed, Web of Science, EBSCO, and Cochrane Library databases until December 25, 2023, to conduct this meta-analysis. We included articles with intervention groups that investigated the effects of mHealth-based apps on PA and PF among children and adolescents. Due to high heterogeneity, a meta-analysis was conducted using a random effects model. The Cochrane Risk of Bias Assessment Tool was used to evaluate the risk of bias. Subgroup analysis and meta-regression analyses were performed to identify potential influences impacting effect sizes. <strong>Results:</strong> We included 28 RCTs with a total of 5643 participants. In general, the risk of bias of included studies was low. Our findings showed that mHealth app–based interventions significantly increased total PA (TPA; standardized mean difference [SMD] 0.29, 95% CI 0.13-0.45; <i>P</i><.001), reduced SB (SMD –0.97, 95% CI –1.67 to –0.28; <i>P</i>=.006) and BMI (weighted mean difference –0.31 kg/m<sup>2</sup>, 95% CI –0.60 to –0.01 kg/m<sup>2</sup>; <i>P</i>=.12), and improved muscle strength (SMD 1.97, 95% CI 0.09-3.86; <i>P</i>=.04) and agility (SMD –0.35, 95% CI –0.61 to –0.10; <i>P</i>=.006). However, mHealth app–based interventions insignificantly affected moderate to vigorous PA (MVPA; SMD 0.11, 95% CI –0.04 to 0.25; <i>P</i><.001), waist circumference (weighted mean difference 0.38 cm, 95% CI –1.28 to 2.04 cm; <i>P</i>=.65), muscular power (SMD 0.01, 95% CI –0.08 to 0.10; <i>P</i>=.81), cardiorespiratory fitness (SMD –0.20, 95% CI –0.45 to 0.05; <i>P</i>=.11), muscular endurance (SMD 0.47, 95% CI –0.08 to 1.02; <i>P</i>=.10), and flexibility (SMD 0.09, 95% CI –0.23 to 0.41; <i>P</i>=.58). Subgroup analyses and meta-regression showed that intervention duration was associated with TPA and MVPA, and age and types of intervention was associated with BMI. <strong>Conclusions:</strong> Our meta-analysis suggests that mHealth app–based interventions may yield small-to-large beneficial effects on TPA, SB, BMI, agility, and muscle strength in children and adolescents. Furthermore, age and intervention duration may correlate with the higher effectiveness of mHealth app–based interventions. However, due to the l
背景：COVID-19 大流行大大降低了体力活动（PA）水平，增加了久坐行为（SB），从而导致体能（PF）恶化。儿童和青少年可能会受益于移动健康（mHealth）应用程序，以增加体力活动并改善体能状况。然而，基于移动医疗应用程序的干预措施在这一人群中的有效性和潜在调节因素尚未得到充分了解。研究目的本研究旨在回顾和分析基于移动医疗应用程序的干预措施在促进儿童和青少年体育锻炼和改善体力活动方面的有效性，并确定基于移动医疗应用程序的干预措施在儿童和青少年中的潜在调节因素。研究方法我们检索了 PubMed、Web of Science、EBSCO 和 Cochrane Library 数据库中截至 2023 年 12 月 25 日发表的随机对照试验 (RCT)，以进行此次荟萃分析。我们纳入了研究基于移动医疗的应用程序对儿童和青少年的 PA 和 PF 影响的有干预组的文章。由于异质性较高，我们采用随机效应模型进行了荟萃分析。Cochrane 偏倚风险评估工具用于评估偏倚风险。还进行了分组分析和元回归分析，以确定影响效应大小的潜在影响因素。结果：我们纳入了 28 项 RCT，共有 5643 名参与者。总体而言，纳入研究的偏倚风险较低。我们的研究结果表明，基于移动医疗应用程序的干预措施能显著增加总运动量（TPA；标准化平均差 [SMD] 0.29，95% CI 0.13-0.45；P<.001）、降低 SB（SMD -0.97，95% CI -1.67 至 -0.28；P=.006）和体重指数（加权平均差-0.31 kg/m2，95% CI -0.60 to -0.01 kg/m2；P=.12），并改善了肌肉力量（SMD 1.97，95% CI 0.09-3.86；P=.04）和敏捷性（SMD -0.35，95% CI -0.61 to -0.10；P=.006）。然而，基于移动医疗应用程序的干预措施对中度至剧烈活动量（MVPA；SMD 0.11，95% CI -0.04 至 0.25；P<.001）、腰围（加权平均差 0.38 厘米，95% CI -1.28 至 2.04 厘米；P=.65）、肌肉力量（SMD 0.01，95% CI -0.08至0.10；P=.81）、心肺功能（SMD -0.20，95% CI -0.45至0.05；P=.11）、肌肉耐力（SMD 0.47，95% CI -0.08至1.02；P=.10）和柔韧性（SMD 0.09，95% CI -0.23至0.41；P=.58）。分组分析和元回归显示，干预持续时间与TPA和MVPA相关，年龄和干预类型与体重指数相关。结论我们的荟萃分析表明，基于移动医疗应用程序的干预可能会对儿童和青少年的全日活动量（TPA）、肌肉活动量（SB）、体重指数（BMI）、敏捷度和肌肉力量产生由小到大的有益影响。此外，年龄和干预持续时间可能与基于移动医疗应用程序的干预的较高有效性相关。然而，由于纳入研究的数量和质量有限，上述结论需要通过更多高质量的研究来验证。试验注册：ProCORD42023426532; https://tinyurl.com/25jm4kmf
{"title":"Effectiveness of mHealth App–Based Interventions for Increasing Physical Activity and Improving Physical Fitness in Children and Adolescents: Systematic Review and Meta-Analysis","authors":"Jun-Wei Wang, Zhicheng Zhu, Zhang Shuling, Jia Fan, Yu Jin, Zhan-Le Gao, Wan-Di Chen, Xue Li","doi":"10.2196/51478","DOIUrl":"https://doi.org/10.2196/51478","url":null,"abstract":"<strong>Background:</strong> The COVID-19 pandemic has significantly reduced physical activity (PA) levels and increased sedentary behavior (SB), which can lead to worsening physical fitness (PF). Children and adolescents may benefit from mobile health (mHealth) apps to increase PA and improve PF. However, the effectiveness of mHealth app–based interventions and potential moderators in this population are not yet fully understood. <strong>Objective:</strong> This study aims to review and analyze the effectiveness of mHealth app–based interventions in promoting PA and improving PF and identify potential moderators of the efficacy of mHealth app–based interventions in children and adolescents. <strong>Methods:</strong> We searched for randomized controlled trials (RCTs) published in the PubMed, Web of Science, EBSCO, and Cochrane Library databases until December 25, 2023, to conduct this meta-analysis. We included articles with intervention groups that investigated the effects of mHealth-based apps on PA and PF among children and adolescents. Due to high heterogeneity, a meta-analysis was conducted using a random effects model. The Cochrane Risk of Bias Assessment Tool was used to evaluate the risk of bias. Subgroup analysis and meta-regression analyses were performed to identify potential influences impacting effect sizes. <strong>Results:</strong> We included 28 RCTs with a total of 5643 participants. In general, the risk of bias of included studies was low. Our findings showed that mHealth app–based interventions significantly increased total PA (TPA; standardized mean difference [SMD] 0.29, 95% CI 0.13-0.45; <i>P</i><.001), reduced SB (SMD –0.97, 95% CI –1.67 to –0.28; <i>P</i>=.006) and BMI (weighted mean difference –0.31 kg/m<sup>2</sup>, 95% CI –0.60 to –0.01 kg/m<sup>2</sup>; <i>P</i>=.12), and improved muscle strength (SMD 1.97, 95% CI 0.09-3.86; <i>P</i>=.04) and agility (SMD –0.35, 95% CI –0.61 to –0.10; <i>P</i>=.006). However, mHealth app–based interventions insignificantly affected moderate to vigorous PA (MVPA; SMD 0.11, 95% CI –0.04 to 0.25; <i>P</i><.001), waist circumference (weighted mean difference 0.38 cm, 95% CI –1.28 to 2.04 cm; <i>P</i>=.65), muscular power (SMD 0.01, 95% CI –0.08 to 0.10; <i>P</i>=.81), cardiorespiratory fitness (SMD –0.20, 95% CI –0.45 to 0.05; <i>P</i>=.11), muscular endurance (SMD 0.47, 95% CI –0.08 to 1.02; <i>P</i>=.10), and flexibility (SMD 0.09, 95% CI –0.23 to 0.41; <i>P</i>=.58). Subgroup analyses and meta-regression showed that intervention duration was associated with TPA and MVPA, and age and types of intervention was associated with BMI. <strong>Conclusions:</strong> Our meta-analysis suggests that mHealth app–based interventions may yield small-to-large beneficial effects on TPA, SB, BMI, agility, and muscle strength in children and adolescents. Furthermore, age and intervention duration may correlate with the higher effectiveness of mHealth app–based interventions. However, due to the l","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"14 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838723","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}
Marga Ocké, Ceciel Simone Dinnissen, Coline van den Bogaard, Marja Beukers, José Drijvers, Eline Sanderman-Nawijn, Caroline van Rossum, Ido Toxopeus
[This corrects the article DOI: 10.2196/50196.].
[此处更正了文章 DOI：10.2196/50196]。
{"title":"Table Correction: A Smartphone Food Record App Developed for the Dutch National Food Consumption Survey: Relative Validity Study.","authors":"Marga Ocké, Ceciel Simone Dinnissen, Coline van den Bogaard, Marja Beukers, José Drijvers, Eline Sanderman-Nawijn, Caroline van Rossum, Ido Toxopeus","doi":"10.2196/59530","DOIUrl":"10.2196/59530","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/50196.].</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"12 ","pages":"e59530"},"PeriodicalIF":5.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11087853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140849507","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}
Kathrin Wunsch, Janis Fiedler, Sebastian Hubenschmid, Harald Reiterer, Britta Renner, Alexander Woll
<strong>Background:</strong> Numerous smartphone apps are targeting physical activity (PA) and healthy eating (HE), but empirical evidence on their effectiveness for the initialization and maintenance of behavior change, especially in children and adolescents, is still limited. Social settings influence individual behavior; therefore, core settings such as the family need to be considered when designing mobile health (mHealth) apps. <strong>Objective:</strong> The purpose of this study was to evaluate the effectiveness of a theory- and evidence-based mHealth intervention (called SMARTFAMILY [SF]) targeting PA and HE in a collective family–based setting. <strong>Methods:</strong> A smartphone app based on behavior change theories and techniques was developed, implemented, and evaluated with a cluster randomized controlled trial in a collective family setting. Baseline (<i>t</i><sub>0</sub>) and postintervention (<i>t</i><sub>1</sub>) measurements included PA (self-reported and accelerometry) and HE measurements (self-reported fruit and vegetable intake) as primary outcomes. Secondary outcomes (self-reported) were intrinsic motivation, behavior-specific self-efficacy, and the family health climate. Between <i>t</i><sub>0</sub> and <i>t</i><sub>1</sub>, families of the intervention group (IG) used the SF app individually and collaboratively for 3 consecutive weeks, whereas families in the control group (CG) received no treatment. Four weeks following <i>t</i><sub>1</sub>, a follow-up assessment (<i>t</i><sub>2</sub>) was completed by participants, consisting of all questionnaire items to assess the stability of the intervention effects. Multilevel analyses were implemented in R (R Foundation for Statistical Computing) to acknowledge the hierarchical structure of persons (level 1) clustered in families (level 2). <strong>Results:</strong> Overall, 48 families (CG: n=22, 46%, with 68 participants and IG: n=26, 54%, with 88 participants) were recruited for the study. Two families (CG: n=1, 2%, with 4 participants and IG: n=1, 2%, with 4 participants) chose to drop out of the study owing to personal reasons before <i>t</i><sub>0</sub>. Overall, no evidence for meaningful and statistically significant increases in PA and HE levels of the intervention were observed in our physically active study participants (all <i>P</i>>.30). <strong>Conclusions:</strong> Despite incorporating behavior change techniques rooted in family life and psychological theories, the SF intervention did not yield significant increases in PA and HE levels among the participants. The results of the study were mainly limited by the physically active participants and the large age range of children and adolescents. Enhancing intervention effectiveness may involve incorporating health literacy, just-in-time adaptive interventions, and more advanced features in future app development. Further research is needed to better understand intervention engagement and tailor mHealth intervent
背景：许多智能手机应用程序都以身体活动（PA）和健康饮食（HE）为目标，但有关这些应用程序对初始化和维持行为改变（尤其是儿童和青少年）的有效性的经验证据仍然有限。社会环境会影响个人行为；因此，在设计移动医疗（mHealth）应用程序时，需要考虑家庭等核心环境。研究目的本研究旨在评估以理论和证据为基础的移动医疗干预措施（称为 SMARTFAMILY [SF]）在家庭集体环境中针对 PA 和 HE 的有效性。研究方法根据行为改变理论和技术开发、实施了一款智能手机应用程序，并在集体家庭环境中进行了分组随机对照试验评估。基线（t0）和干预后（t1）的测量包括作为主要结果的运动量（自我报告和加速度测量）和高血压测量（自我报告的水果和蔬菜摄入量）。次要结果（自我报告）包括内在动力、特定行为自我效能和家庭健康氛围。在 t0 至 t1 期间，干预组（IG）的家庭连续 3 周单独或合作使用 SF 应用程序，而对照组（CG）的家庭则没有接受任何治疗。t1 之后的四周，参与者完成了后续评估（t2），包括所有问卷项目，以评估干预效果的稳定性。使用 R（R 统计计算基础）进行多层次分析，以确认以家庭（第二层）为单位的个人（第一层）聚类的层次结构。结果研究共招募了 48 个家庭（CG：n=22，46%，68 人参与；IG：n=26，54%，88 人参与）。有两个家庭（CG：n=1，2%，4 人参与；IG：n=1，2%，4 人参与）由于个人原因在 t0 前选择退出研究。总体而言，在我们的体力活动研究参与者中，没有观察到干预措施对 PA 和 HE 水平有有意义和统计学意义的提高（所有 P>.30）。结论：尽管采用了植根于家庭生活和心理学理论的行为改变技术，但自立干预并未显著提高参与者的运动量和运动负荷水平。研究结果主要受限于参加体育锻炼的参与者以及儿童和青少年的年龄跨度较大。要提高干预效果，可能需要在未来的应用程序开发中纳入健康知识、及时适应性干预和更先进的功能。需要进一步开展研究，以便更好地了解干预参与情况，并根据个人情况制定移动医疗干预措施，从而提高初级预防工作的有效性。试验注册：德国临床试验注册 DRKS00010415; https://drks.de/search/en/trial/DRKS00010415
{"title":"An mHealth Intervention Promoting Physical Activity and Healthy Eating in a Family Setting (SMARTFAMILY): Randomized Controlled Trial","authors":"Kathrin Wunsch, Janis Fiedler, Sebastian Hubenschmid, Harald Reiterer, Britta Renner, Alexander Woll","doi":"10.2196/51201","DOIUrl":"https://doi.org/10.2196/51201","url":null,"abstract":"<strong>Background:</strong> Numerous smartphone apps are targeting physical activity (PA) and healthy eating (HE), but empirical evidence on their effectiveness for the initialization and maintenance of behavior change, especially in children and adolescents, is still limited. Social settings influence individual behavior; therefore, core settings such as the family need to be considered when designing mobile health (mHealth) apps. <strong>Objective:</strong> The purpose of this study was to evaluate the effectiveness of a theory- and evidence-based mHealth intervention (called SMARTFAMILY [SF]) targeting PA and HE in a collective family–based setting. <strong>Methods:</strong> A smartphone app based on behavior change theories and techniques was developed, implemented, and evaluated with a cluster randomized controlled trial in a collective family setting. Baseline (<i>t</i><sub>0</sub>) and postintervention (<i>t</i><sub>1</sub>) measurements included PA (self-reported and accelerometry) and HE measurements (self-reported fruit and vegetable intake) as primary outcomes. Secondary outcomes (self-reported) were intrinsic motivation, behavior-specific self-efficacy, and the family health climate. Between <i>t</i><sub>0</sub> and <i>t</i><sub>1</sub>, families of the intervention group (IG) used the SF app individually and collaboratively for 3 consecutive weeks, whereas families in the control group (CG) received no treatment. Four weeks following <i>t</i><sub>1</sub>, a follow-up assessment (<i>t</i><sub>2</sub>) was completed by participants, consisting of all questionnaire items to assess the stability of the intervention effects. Multilevel analyses were implemented in R (R Foundation for Statistical Computing) to acknowledge the hierarchical structure of persons (level 1) clustered in families (level 2). <strong>Results:</strong> Overall, 48 families (CG: n=22, 46%, with 68 participants and IG: n=26, 54%, with 88 participants) were recruited for the study. Two families (CG: n=1, 2%, with 4 participants and IG: n=1, 2%, with 4 participants) chose to drop out of the study owing to personal reasons before <i>t</i><sub>0</sub>. Overall, no evidence for meaningful and statistically significant increases in PA and HE levels of the intervention were observed in our physically active study participants (all <i>P</i>>.30). <strong>Conclusions:</strong> Despite incorporating behavior change techniques rooted in family life and psychological theories, the SF intervention did not yield significant increases in PA and HE levels among the participants. The results of the study were mainly limited by the physically active participants and the large age range of children and adolescents. Enhancing intervention effectiveness may involve incorporating health literacy, just-in-time adaptive interventions, and more advanced features in future app development. Further research is needed to better understand intervention engagement and tailor mHealth intervent","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"130 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806686","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}
María Ángeles Bernal-Jiménez, German Calle, Alejandro Gutiérrez Barrios, Livia Luciana Gheorghe, Celia Cruz-Cobo, Nuria Trujillo-Garrido, Amelia Rodríguez-Martín, Josep A Tur, Rafael Vázquez-García, María José Santi-Cano
<strong>Background:</strong> Coronary heart disease is one of the leading causes of mortality worldwide. Secondary prevention is essential, as it reduces the risk of further coronary events. Mobile health (mHealth) technology could become a useful tool to improve lifestyles. <strong>Objective:</strong> This study aimed to evaluate the effect of an mHealth intervention on people with coronary heart disease who received percutaneous coronary intervention. Improvements in lifestyle regarding diet, physical activity, and smoking; level of knowledge of a healthy lifestyle and the control of cardiovascular risk factors (CVRFs); and therapeutic adherence and quality of life were analyzed. <strong>Methods:</strong> This was a randomized controlled trial with a parallel group design assigned 1:1 to either an intervention involving a smartphone app (mHealth group) or to standard health care (control group). The app was used for setting aims, the self-monitoring of lifestyle and CVRFs using measurements and records, educating people with access to information on their screens about healthy lifestyles and adhering to treatment, and giving motivation through feedback about achievements and aspects to improve. Both groups were assessed after 9 months. The primary outcome variables were adherence to the Mediterranean diet, frequency of food consumed, patient-reported physical activity, smoking, knowledge of healthy lifestyles and the control of CVRFs, adherence to treatment, quality of life, well-being, and satisfaction. <strong>Results:</strong> The study analyzed 128 patients, 67 in the mHealth group and 61 in the control group; most were male (92/128, 71.9%), with a mean age of 59.49 (SD 8.97) years. Significant improvements were observed in the mHealth group compared with the control group regarding adherence to the Mediterranean diet (mean 11.83, SD 1.74 points vs mean 10.14, SD 2.02 points; <i>P</i><.001), frequency of food consumption, patient-reported physical activity (mean 619.14, SD 318.21 min/week vs mean 471.70, SD 261.43 min/week; <i>P</i>=.007), giving up smoking (25/67, 75% vs 11/61, 42%; <i>P</i>=.01), level of knowledge of healthy lifestyles and the control of CVRFs (mean 118.70, SD 2.65 points vs mean 111.25, SD 9.05 points; <i>P</i><.001), and the physical component of the quality of life 12-item Short Form survey (SF-12; mean 45.80, SD 10.79 points vs mean 41.40, SD 10.78 points; <i>P</i>=.02). Overall satisfaction was higher in the mHealth group (mean 48.22, SD 3.89 vs mean 46.00, SD 4.82 points; <i>P</i>=.002) and app satisfaction and usability were high (mean 44.38, SD 6.18 out of 50 points and mean 95.22, SD 7.37 out of 100). <strong>Conclusions:</strong> The EVITE app was effective in improving the lifestyle of patients in terms of adherence to the Mediterranean diet, frequency of healthy food consumption, physical activity, giving up smoking, knowledge of healthy lifestyles and controlling CVRFs, quality of life, and overall sat
{"title":"Effectiveness of an Interactive mHealth App (EVITE) in Improving Lifestyle After a Coronary Event: Randomized Controlled Trial","authors":"María Ángeles Bernal-Jiménez, German Calle, Alejandro Gutiérrez Barrios, Livia Luciana Gheorghe, Celia Cruz-Cobo, Nuria Trujillo-Garrido, Amelia Rodríguez-Martín, Josep A Tur, Rafael Vázquez-García, María José Santi-Cano","doi":"10.2196/48756","DOIUrl":"https://doi.org/10.2196/48756","url":null,"abstract":"<strong>Background:</strong> Coronary heart disease is one of the leading causes of mortality worldwide. Secondary prevention is essential, as it reduces the risk of further coronary events. Mobile health (mHealth) technology could become a useful tool to improve lifestyles. <strong>Objective:</strong> This study aimed to evaluate the effect of an mHealth intervention on people with coronary heart disease who received percutaneous coronary intervention. Improvements in lifestyle regarding diet, physical activity, and smoking; level of knowledge of a healthy lifestyle and the control of cardiovascular risk factors (CVRFs); and therapeutic adherence and quality of life were analyzed. <strong>Methods:</strong> This was a randomized controlled trial with a parallel group design assigned 1:1 to either an intervention involving a smartphone app (mHealth group) or to standard health care (control group). The app was used for setting aims, the self-monitoring of lifestyle and CVRFs using measurements and records, educating people with access to information on their screens about healthy lifestyles and adhering to treatment, and giving motivation through feedback about achievements and aspects to improve. Both groups were assessed after 9 months. The primary outcome variables were adherence to the Mediterranean diet, frequency of food consumed, patient-reported physical activity, smoking, knowledge of healthy lifestyles and the control of CVRFs, adherence to treatment, quality of life, well-being, and satisfaction. <strong>Results:</strong> The study analyzed 128 patients, 67 in the mHealth group and 61 in the control group; most were male (92/128, 71.9%), with a mean age of 59.49 (SD 8.97) years. Significant improvements were observed in the mHealth group compared with the control group regarding adherence to the Mediterranean diet (mean 11.83, SD 1.74 points vs mean 10.14, SD 2.02 points; <i>P</i><.001), frequency of food consumption, patient-reported physical activity (mean 619.14, SD 318.21 min/week vs mean 471.70, SD 261.43 min/week; <i>P</i>=.007), giving up smoking (25/67, 75% vs 11/61, 42%; <i>P</i>=.01), level of knowledge of healthy lifestyles and the control of CVRFs (mean 118.70, SD 2.65 points vs mean 111.25, SD 9.05 points; <i>P</i><.001), and the physical component of the quality of life 12-item Short Form survey (SF-12; mean 45.80, SD 10.79 points vs mean 41.40, SD 10.78 points; <i>P</i>=.02). Overall satisfaction was higher in the mHealth group (mean 48.22, SD 3.89 vs mean 46.00, SD 4.82 points; <i>P</i>=.002) and app satisfaction and usability were high (mean 44.38, SD 6.18 out of 50 points and mean 95.22, SD 7.37 out of 100). <strong>Conclusions:</strong> The EVITE app was effective in improving the lifestyle of patients in terms of adherence to the Mediterranean diet, frequency of healthy food consumption, physical activity, giving up smoking, knowledge of healthy lifestyles and controlling CVRFs, quality of life, and overall sat","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"24 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140636416","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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