Background: Mobile visual acuity (VA) apps have emerged as valuable tools in both clinical and home settings, particularly in the context of expanding teleophthalmology. Despite the growing number of apps available to measure visual acuity, studies evaluating their overall quality, functionality, and clinical relevance are limited.
Objective: This study aimed to systematically evaluate the quality and features of mobile VA apps available on iOS and Android platforms using the clinically validated Mobile App Rating Scale (MARS).
Methods: A comprehensive search of the Google Play Store and Apple App Store was conducted between January 2024 and March 2024 using standardized search terms. Eligible apps included free, English-language VA testing tools not requiring external devices. App characteristics and features were extracted. Each app was independently evaluated by 2 trained reviewers using MARS, which rates engagement, functionality, aesthetics, information quality, and subjective quality on a 5-point scale.
Results: Of the 725 apps initially identified, 44 met the inclusion criteria, with 23 from the Google Play Store and 21 from the Apple App Store. The most common VA test optotypes used were Tumbling E (n=21; 48%), Snellen Chart (18/44; 41%), and Landolt C (n=14; 32%). Common supplemental features included color vision testing (n=20; 46%), astigmatism tests (n=13; 30%), Amsler grid (n=13; 30%), and contrast testing (n=12; 28%). The average MARS scores were comparable across platforms: 3.04 (SD 0.80) for Android and 3.02 (SD 0.84) for iOS. Functionality received the highest ratings (mean 3.65, SD 0.75 for Android; mean 3.71, SD 0.82 for iOS), while subjective quality received the lowest (mean 2.09, SD 1.01 for Android; mean 2.21, SD 1.01 for iOS). Few apps had undergone clinical validation. Only Apple App Store apps demonstrated significant correlations between MARS scores and app store star ratings.
Conclusions: VA apps exhibited considerable heterogeneity in quality, functionality, and clinical use. Total mean MARS scores were similar between the Google Play Store and the Apple App Store, suggesting that neither platform consistently offers superior app quality. While many apps are technically sound, low subjective-quality scores and a lack of clinical validation limit their current use in professional practice. These findings underscore the need for more rigorous app development and validation standards to improve their relevance and reliability in teleophthalmology.
Background: Approximately 1 out of 5 pregnant women develops depression. Internet-based cognitive behavioral therapy (iCBT) is an effective way to treat not only depression but also mild depressive symptoms or subthreshold depression. While numerous iCBT programs have been developed and tested through randomized controlled trials for various mental health conditions and specific populations, research on their effectiveness and application in the real world remains limited.
Objective: This study aimed to examine the effectiveness of a previously developed iCBT program implemented in an existing app for improving depressive symptoms among pregnant women in a real-world setting.
Methods: The previously developed iCBT program for preventing perinatal depression was already implemented in an existing app called Luna Luna Baby by MTI Ltd. The app aims to provide information to pregnant women about pregnancy and babies, and potential users can download it from the Japanese version of the Apple App Store or Google Play Store without any fee. The program does not require any additional fees. The log data stored on the app identified iCBT program users and nonusers, allowing us to conduct this retrospective cohort study. Data from September 2022 to September 2024 were extracted from the app after anonymous processing. The primary outcome was the score on the self-reported Edinburgh Postnatal Depression Scale (EPDS), which participants answer by themselves on the app. The exposure group was defined as completers of all 6 modules of the iCBT program. The nonexposure group was defined as users who did not use any module of the program and matched the baseline characteristics of the exposure group. The change in EPDS score before and after using the program was compared using effect sizes, and repeated 2-way ANOVA was conducted to test the difference between the exposure and nonexposure groups.
Results: Data from 119 women who completed the iCBT program and 448 pair-matched controls were selected. The average EPDS scores at baseline were 7.24 (SD 5.30) in the exposure group and 7.25 (SD 5.18) in the nonexposure group. After using the iCBT program, the group mean EPDS scores changed by -0.69 (SD 4.92) and +0.99 (SD 5.56) over time in the exposure and nonexposure groups, respectively (Cohen d=0.31, 95% CI 0.11-0.51). The repeated 2-way ANOVA showed statistical significance in the interaction terms between the groups and the measurement time points (P=.04).
Conclusions: The previously developed iCBT program showed a significant effect with a modest effect size on decreasing depressive symptoms among pregnant women in a real-world setting. Future research should attempt to minimize dropouts and increase participation in the program.
Background: Latina adolescents report low levels of moderate-vigorous physical activity (MVPA) and high lifetime risk of lifestyle-related diseases. There is a lack of MVPA interventions targeted at this demographic despite documented health disparities. Given their high rates of mobile technology use, interventions delivered through mobile devices may be effective for this population.
Objective: This paper examines the efficacy of the Chicas Fuertes intervention in increasing MVPA across 6 months in Latina adolescents.
Methods: Participants were Latina adolescents (aged 13-18 years) in San Diego County who reported being underactive (<150 min/wk of MVPA). All participants received a wearable fitness tracker (Fitbit Inspire HR); half were randomly assigned to also receive the multimedia intervention. Intervention components included a personally tailored website, personalized texting based on Fitbit data, and social media. The primary outcome was change in minutes of weekly MVPA from baseline to 6 months, measured by ActiGraph accelerometers and the 7-Day Physical Activity Recall Interview. Changes in daily steps using Fitbit devices were also examined to test intervention efficacy.
Results: Participants (N=160) were 15.85 (SD 1.71) years old on average, and mostly second generation in the United States. For ActiGraph-measured MVPA, participants in the intervention group (n=83) increased from a median of 0 (IQR 0-24) minutes/week at baseline to 64 (IQR 19-72) minutes/week at 6 months compared to control participants, who showed increases from a median of 0 (IQR 0-26) at baseline to 41 (IQR 7-76) minutes/week at 6 months (P=.04). Self-reported MVPA increased in the intervention group from a median of 119 (IQR 62.5-185) minutes/week at baseline to 147 (IQR 96-181) minutes/week at 6 months compared to control participants, who showed increases from a median of 120 (IQR 48.8-235) at baseline to 124 (IQR 100-169) minutes/week at 6 months (P=.03). Steps also increased in both groups, with the intervention group showing significantly greater increases (P=.03).
Conclusions: This intervention was successful in using a tailored technology-based strategy to increase MVPA in Latina adolescents and provides a promising approach for addressing a key health behavior. Given the scalable technology used, future studies should focus on broad-scale dissemination to address health disparities.
Regular physical activity offers extensive health benefits, yet current consumer wearables struggle to accurately quantify these effects at an individualized level. Sensor performance often falls short due to susceptibility to interferences, nonstandardized validation, and reliance on indirect estimations. Further, sensors often cannot capture or account for disparities in measurement types, populations, and physiological or anatomical characteristics, nor can they account for how different exercise modalities affect results on a personalized scale. There is a drive for developers to refine the impact of how we measure the benefits of exercise, improving the usefulness of data through advanced optical modeling and spectroscopic applications. This review critically examines the shortcomings of prevailing noninvasive measurements and techniques used in common, commercially available fitness trackers and describes why it is difficult to quantify the effects of exercise as an individualized, quality-based metric. Next, we discuss newer sensing applications that attempt to curtail known limitations, some of which may unveil novel biometric insights through differentiated approaches, bridging gaps not only in technological advancement but also in physiological metrology. In conclusion, we believe that new sensing techniques should explore solutions beyond population-based statistics and aim to provide an individualized understanding of a person's response to exercise, while also reducing disparities in personalized health monitoring. The results could lead to a more effective understanding of exercise efficacy and its impact on performance management and clinical outcomes.
Background: Digital health tools, such as mobile apps and wearable devices, have been widely adopted to support self-management of health behaviors. However, user engagement remains inconsistent, particularly among populations with varying BMI. While digital health technologies have the potential to promote healthier behaviors, little is known about how psychological and behavioral factors interact with BMI to influence use patterns.
Objective: This study aimed to explore the relationship between BMI and digital health technology use and to examine how factors such as health awareness, self-efficacy, and health motivation contribute to technology engagement.
Methods: A cross-sectional online survey was conducted from January 2024 to April 2024. A total of 184 valid questionnaire participants were included in this study. The questionnaire was measured on a 5-point Likert scale. Descriptive statistics, chi-square tests, and multiple regression analyses were applied.
Results: Of the participants, 38.6% (71/184) had a BMI<24 kg/m2, 42.4% (78/184) had a BMI between 24 and 29.9 kg/m2, and 19% (35/184) had a BMI≥30 kg/m2. Significant BMI differences were observed based on sex (P<.001) and age (P<.001) but not based on prior digital health tool use. Use rates for Bluetooth or Wi-Fi devices, wearables, and mobile apps were 32.1% (59/184), 38.6% (71/184), and 39.1% (72/184), respectively. A negative correlation between BMI and mobile app use frequency was identified (P=.02). Multiple regression analysis indicated that health motivation significantly predicted digital health use (P<.001), whereas health awareness, lifestyle, and self-efficacy did not.
Conclusions: Individuals with higher BMI reported a lower frequency of digital health tool use, potentially due to lower health motivation in the studied population. Health motivation was the strongest predictor of digital health engagement. Integrating personalized medical records into apps may enhance health motivation, thereby improving user engagement and promoting healthier behaviors in individuals with higher BMI.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：