JMIR mHealth and uHealth最新文献

Background: Mobile health (mHealth) interventions show promise in supporting tobacco cessation. However, Black adults who use tobacco products are not well represented in mHealth studies for tobacco cessation, and their preferred features of mHealth apps are not well known. Identifying types of mHealth app features for tobacco cessation preferred by Black adults is critical to developing a culturally adapted app, with increased uptake by the target population.

Objective: The goal of this study was to explore culturally relevant preferences for features of smoking cessation mHealth apps among Black adults who use tobacco products.

Methods: A comprehensive list of features of mHealth apps for tobacco cessation was developed based on previous research and a review of existing mHealth literature. Through a content analysis, this list was divided into subgroups and used to develop a focus group guide. We recruited participants from Instagram, a social media platform. Eligible focus group participants included people who reported current use of a tobacco product, identified as being African American or Black, were 21 years old or older, and had access to Wi-Fi or the internet. Participants had to indicate interest in the use of an mHealth app for tobacco cessation. Participants discussed their opinions about different app features, including what features they felt would increase the use of an app by Black adults. Recordings from the focus groups were transcribed and coded deductively and inductively. We conducted a thematic content analysis of the resulting transcripts.

Results: Forty adults aged 21-69 (mean 43, SD 13.6) years participated in 8 focus groups. Fifty-seven percent were female, and 88% endorsed current cigarette use. Four central themes that represented app features emerged. (1) Participants wanted representation and inclusivity through personalization and featuring people with similar lived experiences, including representative images and relevant health information. (2) Participants desired the app to feature a diversity of experiences such as testimonials from individuals from different backgrounds rather than solely focusing on racial identity or excessive targeting of the Black community. (3) Participants desired accountability through trusted connections with health care professionals and other support groups within the app, as well as app tracking capability. (4) Encouragement and motivation were more salient incentives than monetary rewards.

Conclusions: Black adults who use tobacco products prefer a tobacco cessation app with features that are inclusive, relatable, supportive, and motivating. These findings can serve as the groundwork for the development of an mHealth app that will appeal to Black adults, potentially leading to increased app use, successful cessation, and health equity.

Background: The use of mobile health (mHealth) apps can assist with the management of gestational diabetes (GDM). Although a number of studies have demonstrated their efficacy in improving maternal-fetal outcomes, opinions differ regarding their usability and overall quality. Poorly designed apps, with ill-conceived features or inappropriate content, may pose a threat to patient safety. Nevertheless, very few studies provide in-depth evaluations of app design quality, and the diversity of features and techniques used remains insufficiently explored.

Objective: We aimed to evaluate the quality and multifunctionality of commercially available mHealth apps for GDM.

Methods: This is a systematic app review guided by the TECH (target user, evaluation focus, connectedness, and health domain) framework and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist. Searches were conducted on the Apple App Store and Google Play. Apps were screened by name, description, and full navigation to identify inclusions. The quality of the apps was evaluated using the Mobile App Rating Scale and IMS Institute for Healthcare Informatics Functionality Score. Multifunctionality of the apps was evaluated using the GDM-adapted features and techniques list developed from the App Behavior Change Scale, NICE (National Institute for Health and Care Excellence) 2015 guidelines, and previous studies. The general features list, which contains instruction, data security, customization, and technical issues, was derived from previous studies.

Results: The search (June 2024) identified 23 commercially available apps from UK app stores. The overall app quality was evaluated to be satisfactory (Mobile App Rating Scale: mean 4.0, SD 0.36; IMS Institute for Healthcare Informatics Functionality Score: mean 5.83, SD 3.03). The multifunctionality evaluation found that the apps had a mean of 17.95 and SD of 7.31 across all 45 items. Overall, our findings suggested that mHealth apps for GDM achieved a certain level of multifunctionality. However, their feature types and supporting digital techniques are relatively basic. The apps focused on education and managing blood glucose control rather than integrating other self-monitoring data and pregnancy-relevant management into their design. The digital techniques used to achieve these features included text and manual operation, rather than other automated features.

Conclusions: This is the first app review to consider the relationship between app features and usability for women with GDM. Future app development should integrate a wide range of pregnancy-relevant information and more automated features and use advanced digital techniques to enable a holistic digital solution for women with GDM.

Background: Mobile apps are being increasingly used to foster healthy lifestyles. There is a growing need for clear, standardized guidelines to help users select safe and effective health apps.

Objective: Our study aimed to highlight the importance of establishing a structured framework for quality evaluation in mobile health (mHealth) through a case study of mobile apps promoting healthy eating.

Methods: We conducted a systematic review of apps promoting healthy eating that had already been evaluated by one or more of 28 recognized health app certification bodies. Three rounds of app evaluations were conducted by experts in nutrition and behavior change. The first two rounds focused on the quality of the content of the recommendations and were performed pairwise using the Quality Evaluation Scoring Tool (QUEST), which has not been previously used by the certification bodies. In addition, in the second and third rounds, each reviewer answered the question "How probable is it that you would recommend this app?" using a subjective scale score from 0 to 10. In the third round, this score was weighed by usability (30%), content quality (40%), and promotion of behavior change (30%). Discussions were held to resolve scoring discrepancies and to identify the top-quality apps. We also assessed correlations among QUEST, Google Play Store, and certification body scores.

Results: Of the 41 apps identified by five certification bodies, 19 (46.3%) met the inclusion criteria and were examined. Only 16 (84.2%) of these remained accessible for the second round. Eight of these surpassed 20 points (out of a maximum of 28) on the QUEST scale and were evaluated by all six experts in the third round, and the top 5 (62.5%) apps were selected. No correlations were found among QUEST, Google Play Store, and certification body scores.

Conclusions: Despite numerous evaluations by various certification bodies, only 5 (12.2%) of the 41 apps met the quality standards set by our experts. Our results mark the importance of rigorous, transparent, and standardized app evaluation processes to guide users toward making informed decisions about health apps. Guidelines for developers for the design of evidence-based, unbiased, high-quality apps, as well as technological solutions for real-time monitoring of the health apps, would address these challenges and improve reliability.

Background: Enhanced recovery after surgery (ERAS) guidelines recommend early postoperative mobilization to reduce complications, but adherence is often suboptimal, highlighting the need for effective tools to monitor and encourage movement. The Mindray enhanced patient monitoring (ePM)/electrophysiology (ep) pod, capable of tracking activity, vital signs, sleep, and pain, offers high-precision postoperative monitoring and is well-suited for research on activity feedback.

Objective: The study aims to assess whether wearable device-based (ePM/ep pod) activity feedback could reduce postoperative complications within 30 days of colorectal cancer (CRC) surgery.

Methods: We conducted an open-label, evaluator-blind, randomized controlled trial involving patients aged ≥18 years scheduled for CRC surgery. Patients were randomized to a feedback group or a control group. Both groups were set the same target activity time postoperatively based on ERAS guidelines. The feedback group received real-time visual feedback of movement time daily through the ePM/ep pod device, while the control group did not receive feedback. The primary outcome was the comprehensive complication index (CCI) within postoperative 30 days. Secondary outcomes included daily activity time, pain Numeric Rating Scale scores for rest and movement during the first 3 postoperative days, length of stay, percentage of reaching the scheduled mobilization target, 30-day postoperative mortality rate, and the times of first exhaust and defecation.

Results: Two hundred thirty-nine patients were recruited between February 2023 and September 2023, with 216 randomized (n=108 for each group). There was no significant difference in CCI within 30 postoperative days between the control group (median CCI 0, range 0-20.90) and the activity feedback group (median CCI 0, range 0-12.20). The estimated mean difference was -0.59 (95% CI -3.56 to 2.38; P=.66). Sensitivity analysis excluding patients with low device compliance did not alter these findings. No significant differences between groups were found in daily activity time, length of hospital stay, or pain scores. Post hoc analysis revealed significant negative correlations between 30-day CCI and activity on the second day after operation (r=-0.166) and the third day after operation (POD3) (r=-0.264; P<.05 for both). Linear regression indicated that POD3 activity significantly reduced CCI (β=-.025; 95% CI -0.045 to -0.006; P=.01), with peak CCI reduction at 215 minutes of activity.

Conclusions: In the context of ERAS, this study found no evidence that activity stimulation based on feedback from the wearable device (ePM/ep pod) could reduce 30-day postoperative CCI in patients undergoing CRC surgery. However, the ePM/ep pod could accurately record daily activity duration, which may be negatively correlated with CCI on POD3.

Background: Adolescents from underserved communities, particularly Black and Hispanic youth, engage in lower levels of physical activity (PA), increasing their risk for chronic disease. Conventional interventions often face barriers such as limited access to safe environments. Wearable mobile health technologies offer scalable and context-sensitive solutions; however, predictors of sustained adherence in school-based settings among high-risk populations remain underexplored.

Objective: This study aims to examine the behavioral and contextual predictors of adherence to a consumer-grade wearable PA tracker among underserved high school students.

Methods: In this school-based observational study, 63 students (mean age 14.8, SD 1.17 years) enrolled in physical education received Fitbit devices. Adherence was defined as ≥21 valid days of step count data. Measures included self-reported PA behaviors, neighborhood perceptions, physical fitness (including anthropometrics), and device adherence. Group comparisons were conducted using t tests and chi-square tests. Logistic regression was used to identify predictors of adherence.

Results: Overall, 73% (46/63) of participants met the adherence threshold. Adherent students reported fewer days of moderate-to-vigorous PA (2 vs 4 days/week; P=.004), lower team sports participation (21/46, 46% vs 12/17, 71%; P=.004), and higher perceived neighborhood safety (P=.02). In adjusted models, lower PA frequency, greater perceived safety, and neighborhood walkability significantly predicted adherence (χ² 6=16.23; P=.01, Nagelkerke R²=0.61).

Conclusions: Wearable mobile health technologies show promise for engaging underserved adolescents in PA, particularly those with lower baseline activity and limited access to structured sports. Key predictors of adherence included perceived neighborhood walkability, team sports participation, and prior PA behavior. School-based deployment of wearable devices should emphasize personalized goals and autonomy-supportive strategies to foster sustained engagement and promote PA among high-risk youth.

Background: Current methods of cardiorespiratory fitness (CRF) assessment may discriminate against frail individuals who are challenged to perform a maximal cardiopulmonary exercise test. CRF estimations from free-living wearable data, captured over extended time periods, may offer a more representative assessment and increase usability in clinical settings.

Objective: This study aimed to review current evidence behind this novel concept and evaluate the performance and quality of models developed to estimate CRF from free-living, unsupervised data.

Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched 4 databases (MEDLINE, Embase, Scopus, and arXiv) for studies reporting the development of models to estimate CRF from continuous free-living wearable data. Studies conducted entirely under controlled laboratory conditions were excluded. Performance metrics were combined in a meta-correlation analysis using a random-effects model and Fisher Z transformation.

Results: Of 1848 papers screened, 18 met the eligibility criteria, with a total of 31,072 participants. The weighted mean age was 46.9 (SD 1.46) years. Multiple computational techniques were used, with 8 studies employing more advanced machine learning models. The meta-correlation analysis revealed a pooled overall estimate of 0.83 with a 95% CI 0.77-0.88. The I2 test indicated high heterogeneity at 97%. Risk of bias assessment found most concerns in the data analysis domain, with studies often lacking clarity around the data handling process.

Conclusions: A promising preliminary agreement between CRF predictions and measured values was noted. However, no definite conclusions can be drawn for clinical implementation due to high heterogeneity among the included studies and lack of external validation. Nonetheless, continuous data streams appear to be a valuable resource that could lead to a step change in how we measure and monitor CRF.