Acceptability of Gynecologic e-Health Modalities by Hispanic/Latina Women of Reproductive Age.

Introduction: Electronic health (e-Health) modalities effectively address healthcare access limitations; however, there are limited data on their adoption by Hispanic/Latina women who are disproportionally affected by health disparities. Methods: We conducted a cross-sectional study by disseminating an anonymous electronic questionnaire via social media to assess the perception of Hispanic/Latina women of reproductive age regarding facilitators and barriers for using e-Health modalities, including telemedicine and mobile apps, to monitor gynecologic health. Results: The questionnaire was completed by 351 Hispanic/Latina participants with high levels (98.3%) of advanced technological expertise. Current use of a gynecologic mobile app was reported by 63.8%, primarily for menstruation (85.1%) and ovulation (46.3%) tracking. While only 17.6% of participants were offered the option of a gynecologic consultation via telemedicine, the majority (90.5%) would agree to one. Higher education and advanced technological expertise correlated with acceptance of telemedicine for gynecological consults. Being younger (<29 y/o), a student, not having a preferred gynecologist and having a lower income significantly correlated with gynecologic mobile app acceptability. Conclusions: We showed that e-Health modalities are highly acceptable for Hispanic/Latina women of reproductive age to facilitate gynecological care and documented factors that are significantly associated with e-Health acceptability. These findings are relevant to public health emergencies that cause access to care limitations disproportionally affecting this already underserved population.

App-Based Addiction Prevention at German Vocational Schools: Implementation and Reach for a Cluster-Randomized Controlled Trial.

This article examines the implementation, participation rates, and potential determinants of participation in the digital addiction prevention program "ready4life." A two-arm cluster-randomized trial recruited German vocational students via class-based strategies. Intervention group received 16 weeks of in-app coaching; the control group received health behavior information, with coaching offered after 12 months. Potential determinants of participation were analyzed based on class and individual characteristics. Out of 525 contacted schools, 35 participated, enrolling 376 classes. Implementation during the pandemic required flexible adjustments, with 49.7% of introductions conducted in person, 43.1% digitally via online streaming, and 7.2% received a video link via email. Despite challenges, 72.3% of the vocational students downloaded the app, and 46.7% gave informed consent. Participation rates were highest among (associate) professionals, vocational grammar school classes, classes introduced by females, younger individuals, members of the project team, and classes introduced face-to-face. Female gender, lower social competencies, lifetime cannabis use, higher problematic internet use, and higher perceived stress were associated with higher individual participation. The study highlights the importance of proactive outreach and personalized interventions for addiction prevention programs in vocational schools. While reached students aligned with the aims of the app, tailored recruitment strategies could enhance engagement among under-represented groups. The trial was registered in the German Clinical Trials Register (DRKS): DRKS00022328; registration date 09.10.2020.

Mobile App-Based Mindfulness Intervention for Addressing Psychological Distress Among Survivors of Hospitalization for COVID-19 Infection.

BACKGROUND: Psychological distress symptoms are present and persistent among many patients who survive a critical illness like COVID-19. RESEARCH QUESTION: Could a self-directed mobile app-delivered mindfulness intervention be feasibly and rapidly implemented within a clinical trials network to reduce distress symptoms? STUDY DESIGN AND METHODS: A randomized clinical trial was conducted between January 2021 and May 2022 at 29 US sites and included survivors of hospitalization due to COVID-19-related illness with elevated symptoms of depression at discharge. Participants were randomized to intervention or usual care control. The intervention consisted of four themed weeks of daily audio, video, and text content. All study procedures were virtual. The primary outcome was depression symptoms assessed with the Patient Health Questionnaire 9 at 3 months. Secondary outcomes included anxiety (Generalized Anxiety Disorder 7-item scale), quality of life (EQ-5D), and adherence. We used general linear models to estimate treatment arm differences in outcomes over time. RESULTS: Among 56 randomized participants (mean age ± SD, 51.0 ± 13.2 years; 38 female [67.9%]; 14 Black participants [25%]), 45 (intervention: n = 23 [79%]; control: n = 22 [81%]) were retained at 6 months. There was no difference in mean improvement between intervention and control participants at 3 months in Patient Health Questionnaire 9 (-0.5 vs 0.1), Generalized Anxiety Disorder 7-item scale (-0.3 vs 0.1), or EQ-5D (-0.03 vs 0.02) scores, respectively; 6-month results were similar. Only 15 participants (51.7%) initiated the intervention, whereas the mean number ± SD of the 56 prescribed intervention activities completed was 12.0 ± 15.2. Regulatory approvals delayed trial initiation by nearly a year. INTERPRETATION: Among survivors of COVID-19 hospitalization with elevated psychological distress symptoms, a self-directed mobile app-based mindfulness intervention had poor adherence. Future psychological distress interventions mobilized at broad scale should focus efforts on patient engagement and regulatory simplification to enhance success. TRIAL REGISTRATION: ClinicalTrials.gov; No.: NCT04581200; URL: www.clinicaltrials.gov.

Technology-Based Strategy to Improve Medication Compliance Among Patients With Schizophrenia Spectrum Disorders.

INTRODUCTION:  Non-compliance to medications remains a challenging problem in schizophrenia. Newer strategies with high feasibility and acceptability are always being researched. This study aimed to assess the effectiveness of technology-based intervention in improving medication compliance in individuals with schizophrenia. METHOD: This was a prospective intervention study where participants were required to use the SuperMD smartphone application (Digital-Health Technologies Pte Ltd, Kuala Lumpur, Malaysia) for a month. A change in the Medication Adherence Rating Scale-Malay Translation (MARS-M) and Malay Translation of Drug Adherence Inventory-9 (MDAI-9) scores indicated a change in compliance and attitude to medication. Positive and Negative Syndrome Scale (PANSS) was used to assess change in symptoms and insight. Medication compliance was also obtained from the SuperMD application. Paired T-test was used to evaluate the significance of changes in mean scores of research variables over the study period. Wilcoxon signed-rank test was used to analyze the subscale of MDAI-9 and the change in PANSS score. The Kruskal-Wallis test was used to determine the effect of the change of insight on the level of compliance with medication. RESULTS: There were 36 participants in this study. The results showed statistically significant improvement in compliance (0.65, p ≤ 0.01) but not in attitude towards medication (0.78, p = 0.065). There was also an improvement in PANNS score (-2.58, P ≤ 0.01). There was no significant change in insight (χ2(2) = 3.802, p = 0.15).  Conclusion:The use of technology-based strategies like SuperMD is effective in improving medication compliance for individuals with schizophrenia.

Raising Awareness of Mistreatment Policies and Reporting Procedures Using a Novel Medical Student Mobile Application.

BACKGROUND: Reports of mistreatment are an important first step to improving medical students' learning environment. Students may not report mistreatment due to a lack of awareness of institutional policies, reporting procedures, or for fear of reprisal. AIM: We sought to determine if a medical school cross-platform mobile application (app) could be used to improve students' awareness of mistreatment policies and procedures. SETTING AND PARTICIPANTS: Participants in this intervention included Drexel University College of Medicine (DUCOM) medical students, faculty, and Student Affairs Deans. PROGRAM DESCRIPTION: We created the DUCOMpass© app to make mistreatment policies and procedures more readily available and to ease mistreatment reporting for medical students. PROGRAM EVALUATION: To determine the efficacy of the app at raising mistreatment awareness, we analyzed our institutional Graduation Questionnaire data before and after the introduction of the app (from 2016 to 2023) as compared with the national average. We verified our students' self-reported data with app usage data. DISCUSSION: To our knowledge, this is the first instance of a medical school mobile app being implemented to successfully address medical student mistreatment awareness and reporting. We found that reaching students in a familiar and easily accessible mode(s) of communication is a catalyst for lasting change. NIH TRIAL REGISTRY: Not applicable.

Algorithmic Spaced Retrieval Enhances Long-Term Memory in Alzheimer Disease: Case-Control Pilot Study.

BACKGROUND: Spaced retrieval is a learning technique that involves engaging in repeated memory testing after increasingly lengthy intervals of time. Spaced retrieval has been shown to improve long-term memory in Alzheimer disease (AD), but it has historically been difficult to implement in the everyday lives of individuals with AD. OBJECTIVE: This research aims to determine, in people with mild cognitive impairment (MCI) due to AD, the efficacy and feasibility of a mobile app that combines spaced retrieval with a machine learning algorithm to enhance memory retention. Specifically, the app prompts users to answer questions during brief daily sessions, and a machine learning algorithm tracks each user's rate of forgetting to determine the optimal spacing schedule to prevent anticipated forgetting. METHODS: In this pilot study, 61 participants (young adults: n=21, 34%; healthy older adults: n=20, 33%; people with MCI due to AD: n=20, 33%) used the app for 4 weeks to learn new facts and relearn forgotten name-face associations. Participation during the 4-week period was characterized by using the app once per day to answer 15 questions about the facts and names. After the 4-week learning phase, participants completed 2 recognition memory tests approximately 1 week apart, which tested memory for information they had studied using the app as well as information they had not studied. RESULTS: After using the mobile app for 1 month, every person with MCI due to AD demonstrated improvements in memory for new facts that they had studied via the app compared to baseline (P<.001). All but one person with MCI due to AD (19/20, 95%) showed improvements of more than 10 percentage points, comparable to the improvements shown by young adults and healthy older adults. Memory for name-face associations was similarly improved for all participant groups after using the app but to a lesser degree. Furthermore, for both new facts and name-face associations, we found no memory decay for any participant group after they took a break of approximately 1 week from using the app at the end of the study. Regarding usability, of the 20 people with MCI due to AD, 16 (80%) self-adhered to the app's automated practice schedule, and half of them (n=10, 50%) expressed an interest in continuing to use it. CONCLUSIONS: These results demonstrate early evidence that spaced retrieval mobile apps are both feasible for people with early-stage AD to use in their everyday lives and effective for supporting memory retention of recently learned facts and name-face associations.

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis.

BACKGROUND: Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders and malignancies highlight the increasing need for AI-aided diagnosis. Although AI-based applications for the identification of dermatological conditions are widely available, research assessing their reliability and accuracy is lacking. OBJECTIVE: The aim of this study was to analyze the efficacy of the Aysa AI app as a preliminary diagnostic tool for various dermatological conditions in a semiurban town in India. METHODS: This observational cross-sectional study included patients over the age of 2 years who visited the dermatology clinic. Images of lesions from individuals with various skin disorders were uploaded to the app after obtaining informed consent. The app was used to make a patient profile, identify lesion morphology, plot the location on a human model, and answer questions regarding duration and symptoms. The app presented eight differential diagnoses, which were compared with the clinical diagnosis. The model's performance was evaluated using sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1-score. Comparison of categorical variables was performed with the χ2 test and statistical significance was considered at P<.05. RESULTS: A total of 700 patients were part of the study. A wide variety of skin conditions were grouped into 12 categories. The AI model had a mean top-1 sensitivity of 71% (95% CI 61.5%-74.3%), top-3 sensitivity of 86.1% (95% CI 83.4%-88.6%), and all-8 sensitivity of 95.1% (95% CI 93.3%-96.6%). The top-1 sensitivities for diagnosis of skin infestations, disorders of keratinization, other inflammatory conditions, and bacterial infections were 85.7%, 85.7%, 82.7%, and 81.8%, respectively. In the case of photodermatoses and malignant tumors, the top-1 sensitivities were 33.3% and 10%, respectively. Each category had a strong correlation between the clinical diagnosis and the probable diagnoses (P<.001). CONCLUSIONS: The Aysa app showed promising results in identifying most dermatoses.

Impact of mobile application and outpatient follow-up on renal endpoints and physiological indices in patients with chronic kidney disease: a retrospective cohort study in Southwest China.

BACKGROUND: Chronic kidney disease (CKD) is a significant public health concern, and patient self-management is an effective approach to manage the condition. Mobile applications have been used as tools to assist in improving patient self-management, but their effectiveness in long-term outpatient follow-up management of patients with CKD remains to be validated. This study aimed to investigate whether using a mobile application combined with traditional outpatient follow-up can improve health outcomes of patients with CKD . METHODS: This retrospective cohort study recruited CKD patients with stage 1-5 who were not receiving renal replacement therapy from a CKD management center. Two groups were established: the APP + outpatient follow-up group and the traditional outpatient follow-up group. Baseline data was collected from January 2015 to December 2019, followed by a three-year long-term follow-up until December 2022. Laboratory data, all-cause mortality, and renal replacement treatment were then collected and compared between the two groups. RESULTS: 5326 patients were included in the study, including 2492 in the APP + outpatient group and 2834 in the traditional outpatient group. After IPTW virtualization matching, the final matched the APP + outpatient group consisted of 2489 cases (IQR, 33-55) and 2850 (IQR, 33-55) in the traditional outpatient group. By the end of the study, it was observed that the laboratory data of Phosphorus, Sodium, Triglyceride, Hemoglobin showed significant improvements, Furthermore the APP + outpatient group demonstrated superior results compared to the traditional outpatient group (P < .05). And it was observed that there were 34 deaths (1.4%) in the APP + outpatient group and 46 deaths (1.6%) in the traditional outpatient group(P = .49). After matching for renal replacement therapy outcomes, the two groups were found to be comparable (95% CI [0.72-1.08], P = .23), with no significant difference. However, it was noted that the traditional outpatient group had a lower incidence of using temporary catheters during initial hemodialysis (95% CI [8.4-29.8%], P < .001). CONCLUSION: The development and application of an app combined with outpatient follow-up management can improve patient health outcomes. However, to ensure optimal preparation for kidney replacement therapy, patients in CKD stages 4-5 may require more frequent traditional outpatient follow-ups, and further develop an information-based decision-making support tool for renal replacement therapy.

Development of "DiabetesSutra" a mobile application for lifestyle management of Type 2 Diabetes in India.

Background: Multiple mhealth (mobile health) interventions and mobile applications have been developed to support diabetes self-management. However, most of the apps are developed without the need for assessment and evaluation by experts in the field. This study aimed to design and develop a mobile application (app) supporting diabetes self-management for people with Type 2 Diabetes Mellitus (T2D) using a systematic approach. Methods: In this study mixed method design was used to develop the mobile application. The mhealth intervention was designed and developed in five steps: i) Extensive literature search, ii) Needs assessment of patients with T2D with the help of healthcare providers and patients (Interviews with 15 healthcare providers like clinicians, dietitians, and diabetes educators, and 2 focus group discussions with patients) iii) Ideation and content development of app based on outcomes of needs assessment; iv) content validation (by 10 healthcare providers) and v) App development on a hybrid platform. Evaluation of the app by users i.e., type 2 diabetes patients was done using the users' Mobile App rating scale (uMARS). The app was evaluated by 40 patients and rated on the uMARS questionnaire. Results: A patient-centric mobile app was developed for the nutritional management of diabetes with three modules: The patient module, the Evaluation module, and the Healthcare provider module. The patient module was the app that was provided to the patients with features like diet, physical activity, blood glucose log, education, etc., in addition to, a symptom checker, Stress meter blog, and FAQ. The evaluation module was integrated with the app it works when a user enters any log, it evaluates the entry against the standard cutoffs and flash prompts on the screen. The Healthcare provider module interacts with the server to provide them with patient data, comments, and feedback. Conclusions: The users found the app to be satisfactory. Incorporating additional features to enhance the user interface and streamline navigation could potentially enhance user engagement, thereby aiding in the management of T2D.

Parents' Perceptions of Their Parenting Journeys and a Mobile App Intervention (Parentbot-A Digital Healthcare Assistant): Qualitative Process Evaluation.

BACKGROUND: Parents experience many challenges during the perinatal period. Mobile app-based interventions and chatbots show promise in delivering health care support for parents during the perinatal period. OBJECTIVE: This descriptive qualitative process evaluation study aims to explore the perinatal experiences of parents in Singapore, as well as examine the user experiences of the mobile app-based intervention with an in-built chatbot titled Parentbot-a Digital Healthcare Assistant (PDA). METHODS: A total of 20 heterosexual English-speaking parents were recruited via purposive sampling from a single tertiary hospital in Singapore. The parents (control group: 10/20, 50%; intervention group: 10/20, 50%) were also part of an ongoing randomized trial between November 2022 and August 2023 that aimed to evaluate the effectiveness of the PDA in improving parenting outcomes. Semistructured one-to-one interviews were conducted via Zoom from February to June 2023. All interviews were conducted in English, audio recorded, and transcribed verbatim. Data analysis was guided by the thematic analysis framework. The COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist was used to guide the reporting of data. RESULTS: Three themes with 10 subthemes describing parents' perceptions of their parenting journeys and their experiences with the PDA were identified. The main themes were (1) new babies, new troubles, and new wonders; (2) support system for the parents; and (3) reshaping perinatal support for future parents. CONCLUSIONS: Overall, the PDA provided parents with informational, socioemotional, and psychological support and could be used to supplement the perinatal care provided for future parents. To optimize users' experience with the PDA, the intervention could be equipped with a more sophisticated chatbot, equipped with more gamification features, and programmed to deliver personalized care to parents. Researchers and health care providers could also strive to promote more peer-to-peer interactions among users. The provision of continuous, holistic, and family-centered care by health care professionals could also be emphasized. Moreover, policy changes regarding maternity and paternity leaves, availability of infant care centers, and flexible work arrangements could be further explored to promote healthy work-family balance for parents.

Development of an Artificial Intelligence-Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial.

BACKGROUND: Nurse burnout leads to an increase in turnover, which is a serious problem in the health care system. Although there is ample evidence of nurse burnout, interventions developed in previous studies were general and did not consider specific burnout dimensions and individual characteristics. OBJECTIVE: The objectives of this study were to develop and optimize the first tailored mobile intervention for nurse burnout, which recommends programs based on artificial intelligence (AI) algorithms, and to test its usability, effectiveness, and satisfaction. METHODS: In this study, an AI-based mobile intervention, Nurse Healing Space, was developed to provide tailored programs for nurse burnout. The 4-week program included mindfulness meditation, laughter therapy, storytelling, reflective writing, and acceptance and commitment therapy. The AI algorithm recommended one of these programs to participants by calculating similarity through a pretest consisting of participants' demographics, research variables, and burnout dimension scores measured with the Copenhagen Burnout Inventory. After completing a 4-week program, burnout, job stress, stress response using the Stress Response Inventory Modified Form, the usability of the app, coping strategy by the coping strategy indicator, and program satisfaction (1: very dissatisfied; 5: very satisfied) were measured. The AI recognized the recommended program as effective if the user's burnout score reduced after the 2-week program and updated the algorithm accordingly. After a pilot test (n=10), AI optimization was performed (n=300). A paired 2-tailed t test, ANOVA, and the Spearman correlation were used to test the effect of the intervention and algorithm optimization. RESULTS: Nurse Healing Space was implemented as a mobile app equipped with a system that recommended 1 program out of 4 based on similarity between users through AI. The AI algorithm worked well in matching the program recommended to participants who were most similar using valid data. Users were satisfied with the convenience and visual quality but were dissatisfied with the absence of notifications and inability to customize the program. The overall usability score of the app was 3.4 out of 5 points. Nurses' burnout scores decreased significantly after the completion of the first 2-week program (t=7.012; P<.001) and reduced further after the second 2-week program (t=2.811; P=.01). After completing the Nurse Healing Space program, job stress (t=6.765; P<.001) and stress responses (t=5.864; P<.001) decreased significantly. During the second 2-week program, the burnout level reduced in the order of participation (r=-0.138; P=.04). User satisfaction increased for both the first (F=3.493; P=.03) and second programs (F=3.911; P=.02). CONCLUSIONS: This program effectively reduced burnout, job stress, and stress responses. Nurse managers were able to prevent nurses from resigning and maintain the quality of medical services using this AI-based program to provide tailored interventions for nurse burnout. Thus, this app could improve qualitative health care, increase employee satisfaction, reduce costs, and ultimately improve the efficiency of the health care system.

Mobile-UI-Repair: a deep learning based UI smell detection technique for mobile user interface.

The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated testing. However, it is time-consuming and inefficient due to the need for testers to perform actions repeatedly and the possibility of missing some functionalities. Although several approaches have been proposed, they require significant performance improvement. In addition, the key challenges of these approaches are incorporating the design guidelines and rules necessary to follow during app development and combine the syntactical and semantic information available on the development forums. In this study, we proposed a UI bug identification and localization approach called Mobile-UI-Repair (M-UI-R). M-UI-R is capable of recognizing graphical user interfaces (GUIs) display issues and accurately identifying the specific location of the bug within the GUI. M-UI-R is trained and tested on the history data and also validated on real-time data. The evaluation shows that the average precision is 87.7% and the average recall is 86.5% achieved in the detection of UI display issues. M-UI-R also achieved an average precision of 71.5% and an average recall of 70.7% in the localization of UI design smell. Moreover, a survey involving eight developers demonstrates that the proposed approach provides valuable support for enhancing the user interface of mobile applications. This aids developers in their efforts to fix bugs.

GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat.

Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.

mHealth Intervention for Elevated Blood Pressure Among College Students: Single-Arm Intervention Study.

BACKGROUND: Current evidence reveals a growing pattern of hypertension among young adults, significantly increasing their risk for cardiovascular disease later in life. Young adults, particularly those of college age, often develop risk factors related to lifestyle choices in diet, exercise, and alcohol consumption. Developing useful interventions that can assist with screening and possible behavioral modifications that are suitable and appealing to college-aged young adults could help with early identification and intervention for hypertension. Recent studies indicate mobile health (mHealth) apps are acceptable and effective for communication and message delivery among this population. OBJECTIVE: The purpose of this study was to examine the feasibility of using a mobile smartphone delivery system that provides tailored messages based on participant self-measured blood pressure (BP) with college-aged young adults. METHODS: Using a single-arm intervention, pilot study design, the mHealth to Optimize BP Improvement (MOBILE) intervention was implemented with college students aged 18 years to 39 years who had systolic BP >120 mm Hg and diastolic BP ≥80 mm Hg. Participants were required to measure their BP daily for 28 days, submit the readings to the app, and receive preset educational text messages tailored to their BP value and related to encouraging healthy lifestyle modifications. Changes in a participant's BP was evaluated using a mixed regression model, and a postintervention survey evaluated their perspectives on the mHealth intervention. RESULTS: The participants' (N=9) mean age was 22.64 (SD 4.54) years; 56% (5/9) were overweight, and 11% (1/9) were obese. The average daily participation rate was 86%. Of the 9 participants, 8 completed the survey, and all indicated the intervention was easy to use, found it increased awareness of their individual BP levels, indicated the text messages were helpful, and reported making lifestyle changes based on the study intervention. They also provided suggestions for future implementation of the intervention and program. Overall, no significant changes were noted in BP over the 28 days. CONCLUSIONS: The mHealth-supported MOBILE intervention for BP monitoring and tailored text messaging was feasible to implement, as our study indicated high rates of participation and acceptability. These encouraging findings support further development and testing in a larger sample over a longer time frame and hold the potential for early identification and intervention among college-aged adults, filling a gap in current research.

Understanding and Involving the Perspective of Pregnant Women as Users When Designing the Framework of e-Health and Exercise Interventions during Pregnancy: Preliminary Study.

Health and exercise technology may promote a healthy lifestyle during pregnancy. The objective of this cross-sectional study was to understand and involve the perspective of pregnant women as users in the design of a framework for future e-health and exercise interventions during pregnancy. Pregnant women replied to a questionnaire aimed at understanding their physical activity patterns, needs, and preferences regarding the use of mobile applications (apps). The main results showed that one-third of the women did not practice any type of exercise during pregnancy. Women preferred to exercise in a gym, outdoors, or at home. The majority already had or were currently using a fitness app, but never used any pregnancy-specific app. Most women agreed that it was important to have a specific app for pregnancy to improve knowledge about recommendations on lifestyle, have direct contact with health and exercise professionals, have social interaction with other mothers, and have guidance on preparation for childbirth and postpartum recovery. Understanding and involving the perspective of pregnant women as users will allow researchers to improve the design of a pregnancy-specific app and future e-health and exercise interventions during pregnancy. These preliminary results will lead to the development of the "active pregnancy app" focused on the promotion of an active and healthy lifestyle during pregnancy and postpartum.

Effects of the "AI-TA" Mobile App With Intelligent Design on Psychological and Related Symptoms of Young Survivors of Breast Cancer: Randomized Controlled Trial.

BACKGROUND: Young women often face substantial psychological challenges in the initial years following cancer diagnosis, leading to a comparatively lower quality of life than older survivors. While mobile apps have emerged as potential interventions, their effectiveness remains inconclusive due to the diversity in intervention types and variation in follow-up periods. Furthermore, there is a particular dearth of evidence regarding the efficacy of these apps' intelligent features in addressing psychological distress with these apps. OBJECTIVE: This study aims to evaluate the effectiveness of a mobile app with intelligent design called "AI-TA" on cancer-related psychological health and ongoing symptoms with a randomized controlled design. METHODS: Women aged 18 to 45 years diagnosed with breast cancer were randomly assigned to the intervention or control group. The intervention was AI-TA, which included 2-way web-based follow-up every 2 weeks. Both intention-to-treat (ITT) and per-protocol (PP) analyses employed repeated measurement analysis of variance. The participants' background features, primary outcomes (psychological distress and frequency, self-efficacy, and social support), and secondary outcomes (quality of life) were measured using multiple instruments at 3 time points (baseline, 1-month intervention, and 3-month intervention). RESULTS: A total of 124 participants were randomly allocated to the control group (n=62, 50%) or intervention group (n=62, 50%). In total, 92.7% (115/124) of the participants completed the intervention. Significant improvements in psychological symptoms (Memorial Symptom Assessment Scale-Short Form) were observed in the ITT group from baseline to 1-month intervention relative to the control group (ITT vs control: 1.17 vs 1.23; P<.001), which persisted at 3-month follow-up (ITT vs control: 0.68 vs 0.91; P<.001). Both the ITT and PP groups exhibited greater improvements in self-efficacy (Cancer Behavior Inventory-Brief Version) than the control group at 1-month (ITT vs PP vs control: 82.83 vs 77.12 vs 65.35; P<.001) and 3-month intervention (ITT vs PP vs control: 92.83 vs 89.30 vs 85.65; P<.001). However, the change in social support (Social Support Rating Scale) did not increase significantly until 3-month intervention (ITT vs control: 50.09 vs 45.10; P=.002) (PP vs control: 49.78 vs 45.10; P<.001). All groups also experienced beneficial effects on quality of life (Functional Assessment of Cancer Therapy-Breast), which persisted at 3-month follow-up (P<.001). CONCLUSIONS: The intelligent mobile app AI-TA incorporating intelligent design shows promise for reducing psychological and cancer-related symptoms among young survivors of breast cancer. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200058823; https://www.chictr.org.cn/showproj.html?proj=151195.

Gait characterization in rare bone diseases in a real-world environment - A comparative controlled study.

BACKGROUND: Rare bone diseases (RBD) cause physical and sensory disability that affects quality of life. Mobility challenges are common for people with RBDs, and travelling to gait analysis labs can be very complex. Smartphone sensors could provide remote monitoring. RESEARCH QUESTION: This study aimed to search for and identify variables that can be used to discriminate between people with RBD and healthy people by using built-in smartphone sensors in a real-world setting. METHODS: In total, 18 participants (healthy: n=9; RBD: n=9), controlled by age and sex, were included in this cross-sectional study. A freely available App (Phyphox) was used to gather data from built-in smartphone sensors (accelerometer & gyroscope) at 60 Hz during a 15-min walk on a level surface without turns or stops. Temporal gait parameters like cadence, mean stride time and, coefficient variance (CoVSt) and nonlinear analyses, as the largest Lyapunov exponent (LLE) & sample entropy (SE) in the three accelerometer axes were used to distinguish between the groups and describe gait patterns. RESULTS: The LLE (p=0.04) and the SE of the z-axis (p=0.01), which are correlated with balance control during walking and regularity of the gait, are sufficiently sensitive to distinguish between RBD and controls. SIGNIFICANCE: The use of smartphone sensors to monitor gait in people with RBD allows for the identification of subtle changes in gait patterns, which can be used to inform assessment and management strategies in larger cohorts.

Design of a Mobile App Interface That Engages Community Members in a Food System Pilot Study.

Supermarkets are scarce in many under-resourced urban communities, and small independently owned retail stores often carry few fresh or healthy items. The Baltimore Urban food Distribution (BUD) mobile application (app) was previously developed to address supply-side challenges in moving healthy foods from local suppliers to retailers. In-app opportunities for consumers to indicate demand for these foods are crucial, but remain absent. We sought to understand community members' perspectives on the overall role, function and features of a proposed consumer-engagement module (BUDConnect) to expand the BUD app. A series of initial high-fidelity wireframe mockups were developed based on formative research. In-depth interviews (n = 20) were conducted and thematically analyzed using ATLAS.ti Web. Participants revealed a desire for real-time crowd-sourced information to navigate their food environments safely and effectively, functionality to help build community and social networks among store owners and their customers, opportunities to share positive reviews and ratings of store quality and offerings, and interoperability with existing apps. Rewards and referral systems resulting in the discounted purchasing of promoted healthy items were suggested to increase adoption and sustained app use. Wireframe mockups were further refined for future development and integration into the BUD app, the program and policy implications of which are discussed.

An App-Based Physical Activity Intervention in Community-Dwelling Chinese-, Tagalog-, and Vietnamese-Speaking Americans: Single-Arm Intervention Study.

BACKGROUND: Physical inactivity is associated with adverse health outcomes among Asian Americans, who exhibit the least adherence to physical activity guidelines compared with other racial and ethnic groups. Mobile app-based interventions are a promising approach to promote healthy behaviors. However, there is a lack of app-based interventions focused on improving physical activity among Asian Americans whose primary language is not English. OBJECTIVE: This pilot study aimed to assess the feasibility and acceptability of a 5-week intervention using a culturally and linguistically adapted, evidence-based mobile phone app with an accelerometer program, to promote physical activity among Chinese-, Tagalog-, or Vietnamese-speaking Americans. METHODS: Participants were recruited through collaborations with community-based organizations. The intervention was adapted from a 12-month physical activity randomized controlled trial involving the app and accelerometer for English-speaking adults. Sociodemographic characteristics, lifestyle factors, and physical measurements were collected at the baseline visit. A 7-day run-in period was conducted to screen for the participants who could wear a Fitbit One (Fitbit LLC) accelerometer and complete the app's daily step diary. During the 4-week intervention period, participants wore the accelerometer and reported their daily steps in the app. Participants also received daily messages to reinforce key contents taught during an in-person educational session, remind them to input steps, and provide tailored feedback. Feasibility measures were the percentage of eligible participants completing the run-in period and the percentage of participants who used the app diary for at least 5 out of 7 days during the intervention period. We conducted poststudy participant interviews to explore overall intervention acceptability. RESULTS: A total of 19 participants were enrolled at the beginning of the study with a mean age of 47 (SD 13.3; range 29-70) years, and 58% (n=11) of them were female. Of the participants, 26% (n=5) were Chinese, 32% (n=6) were Vietnamese, and 42% (n=8) were Filipino. All participants met the run-in criteria to proceed with the intervention. Adherence to the app diary ranged from 74% (n=14) in week 2 to 95% (n=18) in week 4. The daily average steps per week from accelerometers increased each week from 8451 (SD 3378) steps during the run-in period to 10,930 (SD 4213) steps in week 4. Participants reported positive experiences including an increased motivation to walk and the enjoyment of being able to monitor their physical activity. CONCLUSIONS: This is the first pilot study of a multicomponent intervention and evidence-based mobile phone app to promote physical activity among Asian Americans who use apps in traditional Chinese, Tagalog, or Vietnamese, which demonstrated high feasibility and acceptability. Future work focused on multilingual mobile apps to address disparities in physical inactivity among Asian Americans should be considered.

Providing and receiving support through a tailor-made mobile app: a qualitative study on experience of professionals and family caregivers to persons with dementia.

BACKGROUND: The ageing population and the rise of persons with dementia (PWD) living at home have increased the need for support by family caregivers (FC). Research suggests that most FCs are unprepared for the complex role of informal caregiving. The use of mobile applications (apps) provide a cost-effective and efficient opportunity for community-based social care professionals to provide tailor-made support to FCs. The literature indicates that there are limited mobile apps available to meet the needs of the FCs to PWD living at home. The aim of this study was to explore how social care professionals and FCs to PWD living at home experience providing and receiving support through a tailor-made mobile app named STAV. METHODS: A qualitative descriptive design was applied. Data were collected through semi-structured interviews with 11 community-based social care professionals and 19 FCs of PWD living at home. The data were analyzed inductively using thematic analysis. RESULTS: The social care professionals and the FCs' experience of providing and receiving support through a mobile app was represented by the following themes: Accessibility to support - Bridging the gap, Engaging from a distance, and Limitations of the support. CONCLUSIONS: This study highlights the need for FCs to PWD to receive support that is tailor-made to their needs as caregivers. The findings from this study can help community-based social care providers plan and organize long-distance support for FCs to PWD living at home. The findings further support the use of a mobile app as a complement to traditional means of support for FCs to PWD which can facilitate their knowledge, awareness, and self-care management.