Version Of App Research Articles

The Classification Method is Used for Sentiment Analysis in My Telkomsel

User reviews significantly impact how mobile apps are perceived and provide developers with valuable insights into improving the functionality and quality of their products. Sentiment analysis of these evaluations helps identify the main issues faced by consumers, such as technical difficulties, costs, and service levels. The main objective of this study is to classify user sentiment into positive and negative categories, focusing on the MyTelkomsel app. With the use of Google Play Scraper, 39,493 reviews on various app versions and user experiences were collected. This data was analyzed using multiple machine learning models, including Support Vector Machines (SVM), Naive Bayes, Random Forest, and Gradient Boosting, alongside the Natural Language Processing (NLP) approach. The results show that 39.2% of the reviews are positive, while 60.8% reflect negative sentiment. Among the models, SVM showed the highest accuracy in sentiment classification with a value of (0.854792), while Naive Bayes (0.775541), Random Forest (0.829725), and Gradient Boosting (0.819344) also performed well in sentiment classification. These findings suggest that developers can leverage the insights gained from this analysis to proactively improve the performance and user experience of the MyTelkomsel app, by addressing technical and service-related issues identified in user reviews.

Assessing the efficacy and safety of STOP (successful treatment for paranoia)—an app-based cognitive bias modification therapy for paranoia: a randomised clinical trial protocol

BackgroundParanoia, the belief that you are at risk of significant physical or emotional harm from others, is a common difficulty, which causes significant distress and impairment to daily functioning, including in psychosis-spectrum disorders. According to cognitive models of psychosis, paranoia may be partly maintained by cognitive processes, including interpretation biases. Cognitive bias modification for paranoia (CBM-pa) is an intervention targeting the bias towards interpreting ambiguous social scenarios in a way that is personally threatening. This study aims to test the efficacy and safety of a mobile app version of CBM-pa, called STOP (successful treatment of paranoia).MethodsThe STOP study is a double-blind, superiority, three-arm randomised controlled trial (RCT). People are eligible for the trial if they experience persistent, distressing paranoia, as assessed by the Positive and Negative Syndrome Scales, and show evidence of an interpretation bias towards threat on standardised assessments. Participants are randomised to either STOP (two groups: 6- or 12-session dose) or text-reading control (12 sessions). Treatment as usual will continue for all participants. Sessions are completed weekly and last around 40 min. STOP is completely self-administered with no therapist assistance. STOP involves reading ambiguous social scenarios, all of which could be interpreted in a paranoid way. In each scenario, participants are prompted to consider more helpful alternatives by completing a word and answering a question. Participants are assessed at baseline, after each session, and at 6, 12, 18 and 24 weeks post-randomisation. The primary outcome is the self-reported severity of paranoid symptoms at 24 weeks, measured using the Paranoia Scale. The target sample size is 273 which is powered to detect a 15% symptom reduction on the primary outcome. The secondary outcomes are standardized measures of depression, anxiety and recovery and measures of interpretation bias. Safety is a primary outcome and measured by the Negative Effects Questionnaire and a checklist of adverse events completed fortnightly with researchers. The trial is conducted with the help of a Lived Experience Advisory Panel.DiscussionThis study will assess STOP’s efficacy and safety. STOP has the potential to be an accessible intervention to complement other treatments for any conditions that involve significant paranoia.Trial registrationISRCTN registry, ISRCTN17754650. Registered on 03/08/2021. https://doi.org/10.1186/ISRCTN17754650.

Fishing Vessel Risk and Safety Analysis: A Bibliometric Analysis, Clusters Review and Future Research Directions

This study conducts a bibliometric analysis to evaluate the current research landscape and identify future directions in fishing vessel safety. Using the Scopus database, 285 relevant documents were collected and analyzed using the Biblioshiny app version 4.1 in the bibliometrix R package and VOSviewer version 1.6.20. The analysis generated an authors’ collaboration network, a three-field plot, and a keyword Thematic map, which were used for citation analysis, while VOSviewer was utilized to build networks between journals, articles, affiliations, countries, and keywords, enabling bibliographic coupling. The results identified four primary research clusters: Accident/Risk Analysis, Human Errors, Stability, and Operation. A detailed review of selected studies within these clusters was conducted, focusing on accident and risk factors, analytical methods, their strengths and weaknesses, and potential improvements. Based on these findings, a discussion was provided on future research directions in fishing vessel safety. The results suggest that future research should prioritize the integration of advanced technologies, enhancement of real-time monitoring capabilities, and promotion of international collaboration to ensure the safety of fishermen in hazardous environments.

A Novel Web App for Dietary Weight Management: Development, Implementation, and Usability Study.

Online weight loss programs have ambiguous efficacy. There is a growing body of evidence that weight loss programs when combined with apps have better outcomes; however, many apps lack an evidence-based approach to dietary changes for weight loss and do not rely on a theoretical framework for behavior change. This study aimed to describe the development and the preliminary usability and acceptability testing of a web app that uses behavior change techniques (BCTs) to support users of a comprehensive online weight loss program. The weight loss program intervention components were nutrient and weight tracking charts that needed a remotely accessible and online format. The app was designed by nutrition researchers and developers in a collaborative effort. A review of BCTs in weight loss and web apps was performed as well as an assessment of user needs to inform the initial prototype. A preliminary app prototype, version 1.0, was provided to participants of a weight loss trial (N=30) to assess for feasibility of its use. A full app prototype, version 2.0, was feasibility and acceptability tested by trial participants (n=11) with formal feedback by Likert-scale survey and open-ended questions. In the final round of testing, a user group of scientists and developers (n=11) was selected to provide a structured 3-month review through which the group met weekly for collective feedback sessions. The process resulted in a fully developed web app, MealPlot, by the Applied Research Institute, for meal planning and weight tracking that can be used by weight loss users and health professionals to track their patients. MealPlot includes a weight chart, a protein-fiber chart, and a chat feature. In addition, MealPlot has 2 distinct platforms, 1 for weight loss users and 1 for health professionals. Selected BCTs for incorporation into the app were goal setting, feedback, problem-solving, self-monitoring, and social support. Version 1.0 was used successfully to provide a functioning, online weight chart over the course of a 1-year trial. Version 2.0 provided a functional weight chart and meal planning page, but 8 out of 11 participants indicated MealPlot was difficult to use. Version 3.0 was developed based on feedback and strategies provided from user group testing. The web app, MealPlot, was developed to improve outcomes and functionality of an online weight loss program by providing a remote method of tracking weight, food intake, and connecting users to health professionals for consistent guidance that is not otherwise available in a traditional in-person health care setting. The final version 3.0 of the web app will be refined based on findings of a review study gathering feedback from health professionals and from actual weight loss users who are part of a clinical weight loss trial.

Optimizing the Measurement of Information on the Context of Alcohol Consumption Within the Drink Less App Among People Drinking at Increasing and Higher Risk Levels: Mixed-Methods Usability Study.

There is a growing public health evidence base focused on understanding the links between drinking contexts and alcohol consumption. However, the potential value of developing context-based interventions to help people drinking at increasing and higher risk levels to cut down remains underexplored. Digital interventions, such as apps, offer significant potential for delivering context-based interventions as they can collect contextual information and flexibly deliver personalized interventions while addressing barriers associated with face-to-face interventions, such as time constraints. This early phase study aimed to identify the best method for collecting information on the contexts of alcohol consumption among users of an alcohol reduction app by comparing 2 alternative drinking diaries in terms of user engagement, data quality, usability, and acceptability. Participants were recruited using the online platform Prolific and were randomly assigned to use 1 of the 2 adapted versions of the Drink Less app for 14 days. Tags (n=31) included tags for location, motivation, and company that participants added to drink records. Occasion type (n=31) included a list of occasion types that participants selected from when adding drink records. We assessed engagement and data quality with app data, usability with a validated questionnaire, and acceptability with semistructured interviews. Quantitative findings on engagement, data quality, and app usability were good overall, with participants using the app on most days (tags: mean 12.23, SD 2.46 days; occasion type: mean 12.39, SD 2.12 days). However, around 40% of drinking records in tags did not include company and motivation tags. Mean usability scores were similar across app versions (tags: mean 72.39, SD 8.10; occasion type: mean 74.23, SD 6.76). Qualitative analysis found that both versions were acceptable to users and were relevant to their drinking occasions, and participants reported increased awareness of their drinking contexts. Several participants reported that the diary helped them to reduce alcohol consumption in some contexts (eg, home or lone drinking) more than others (eg, social drinking) and suggested that they felt less negative affect recording social drinking contexts out of their home. Participants also suggested the inclusion of "work drinks" in both versions and "habit" as a motivation in the tags version. There was no clearly better method for collecting data on alcohol consumption as both methods had good user engagement, usability, acceptability, and data quality. Participants recorded sufficient data on their drinking contexts to suggest that an adapted version of Drink Less could be used as the basis for context-specific interventions. The occasion type version may be preferable owing to lower participant burden. A more general consideration is to ensure that context-specific interventions are designed to minimize the risk of unintended positive reinforcement of drinking occasions that are seen as sociable by users.

A Culturally Sensitive Mobile App (DiaFriend) to Improve Self-Care in Patients With Type 2 Diabetes: Development Study.

Mobile apps designed with cultural sensitivity have demonstrated higher user acceptability and greater effectiveness in enhancing self-care skills. However, a significant gap exists in developing such apps for specific populations, such as Portuguese Americans living in southern Massachusetts, home to the second-largest Portuguese community in the United States. This group possesses unique cultural traditions, particularly in dietary practices, including a tendency toward high carbohydrate intake. Tailoring diabetes self-care apps to address these specific cultural requirements could substantially improve diabetes management within this population. The aim of this app development project was to develop a prototype diabetes management app for Portuguese Americans with type 2 diabetes mellitus using the design thinking methodology, incorporating user-centered design principles and cultural sensitivity. This paper describes the phase-2 results, focusing on app design and development. Phase 2 of this app development project adhered to the design thinking methodology delineated by the Hasso Plattner Institute of Design at Stanford University, focusing on 2 critical steps: ideation and prototyping. This phase started in March 2022 and continued until April 2024. The project was driven by a multidisciplinary team consisting of 2 nurse educators; an app development specialist; and 2 graduate research assistants from the university's Computer and Information Sciences Department, both well-versed in mobile app development. Data collected during phase 1, which will be published separately, informed the app design and development process. The prototype of the DiaFriend app (version 1) was designed and developed. The app comprises five features: (1) blood glucose monitoring, (2) weight tracking, (3) carbohydrate tracking, (4) exercise log, and (5) medication reminder. The carbohydrate tracking feature was explicitly tailored to correspond to Portuguese food culture. This paper presents the front-end interface flowchart, demonstrating how the user navigates through each screen. It also discusses the challenges faced during the backend development, such as data not being able to be stored and retrieved. The DiaFriend app (version 1) distinguishes itself from conventional diabetes self-care apps through its emphasis on cultural sensitivity. The development of this app underscores the importance of cultural considerations in health informatics. It establishes a foundation for future research in developing and evaluating culturally sensitive mobile health apps. The adaptation of such technologies has the potential to enhance self-care practices among Portuguese Americans with type 2 diabetes mellitus, with improved glycated hemoglobin levels as a potential outcome. The last step of the design thinking methodology, testing the app, will be conducted in phase 3 and the results will be published elsewhere.

Analysis of the Validity and Reliability of the Photo Finish® Smartphone App to Measure Sprint Time

In athletic training and research, the evaluation of sprint speed is widely used, and its accurate measurement is especially demanding. High-cost photocells are the gold-standard system for sprint time assessment, although low-cost smartphone applications can be a suitable option. This study assesses the validity and reliability of an application to measure sprint time compared to photocells. Five physically active subjects completed six sprints of 10 m and 20 m at maximal speed and a 5 m go and return sprint to evaluate the validity of the Photo Finish® app (Version 2.30). To assess reliability, six trials of 5 m go and return sprints were measured by two smartphones. The validity results showed a mean bias of 0.012 s (95% CL: 0.000, 0.024) between the application and the photocells for the 10 m sprint, 0.007 s (95% CL: −0.007, 0.022) for the 20 m sprint and 0.005 s (95% CL: −0.005, 0.017) for the 5 m go and return test. The results also found R2 between both systems (R2= 0.9863, 0.990 and 0.958) for each distance (10 m, 20 m and 5 m go and return, respectively). As for reliability, the application showed outstanding consistency between two smartphones operating simultaneously (ICC 0.999; R2: 0.999). This study shows that the Photo Finish® app is an accurate and reliable tool to measure sprint time with an error of 0.09 s.

“No miracle cure” interface evaluation of a UK suicide prevention app in a Norwegian context

Background 600 Norwegians die by suicide annually. Self-help apps may potentially reach and support suicidal individuals, next of kin, bereaved and professionals with educational information, access to suicide emergency help, hotlines, and coping tools. Knowledge regarding feasible app interface for suicide prevention self-help apps in a Norwegian context is however lacking. Aim Evaluate user-friendliness and user interface of the Norwegian version of the British Stay Alive suicide prevention app and assess whether the app could be useful in suicide prevention. Materials and Methods Eight Norwegian Fountain house members and twelve university students evaluated the app interface, and their experiences were discussed through individual- or focus-group interviews. Results Participants perceived app tools as relevant and useful for suicide prevention, however they raised concerns on privacy, evidence base, interface complexity, unintended symbolic value of app colours and content, and suitability in suicidal states. Conclusion The current Norwegian Stay Alive version has the potential to be user friendly, however improvements of interface are suggested. Significance Stay Alive interface must be adjusted and subjected to reiterative evaluations and feasibility assessments in collaboration with additional stakeholder populations to remedy interface concerns, and identify optimal Norwegian interface design, prior to implementation.

Clust&See3.0 : clustering, module exploration and annotation.

Cytoscape is an open-source software to visualize and analyze networks. However, large networks, such as protein interaction networks, are still difficult to analyze as a whole. Here, we propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. It is now enriched with functionalities allowing custom annotations of nodes and computation of their statistical enrichments. As the wealth of multi-omics data is growing, such functionalities are highly valuable for a better understanding of biological module composition, as illustrated by the presented use case. In summary, the originality of Clust&See3.0 lies in providing users with a complete tool for network clusters analyses: from cluster identification, visualization, node and cluster annotations to annotation statistical analyses.

Automating the Extraction of Words and Topics in Indonesian Using the Term Frequency-Inverse Document Frequency Algorithm and Latent Dirichlet Allocation

Keyword extraction and topic modeling in the analysis of Gojek user reviews in Indonesian are very important. By understanding user preferences and needs through keyword extraction, as well as grouping user reviews into different topics through topic modeling, stakeholders can use the information to further improve services. This research uses TF-IDF and LDA approaches to analyze text data from Gojek user reviews and feedback. The data spans from Nov 5, 2021, to Jan 2, 2024, totaling 225,002 rows. Each row includes username, content, time, and app version. The focus is on content reviews. The average length is 8 words, with a maximum of 104 and a minimum of a few words. The variability indicates a non-normal distribution. Preprocessing is conducted to maintain topic analysis accuracy. The TF-IDF method is used to extract relevant keywords, while the LDA approach is used to model the topics in user reviews. The topic analysis reveals patterns in Gojek user reviews. The first topic discusses experience, services, and affordable pricing. The second emphasizes app usability and benefits. The third relates to promos, discounts, and vouchers. The fourth reflects positive evaluations of service quality. However, the fifth topic highlights high costs and app issues. The sixth underscores overall user satisfaction and service convenience. Testing on the topic model yielded a coherence level of 0.509, indicating that the model's topics demonstrate a good level of consistency in finding relevant topics from Gojek user review data. The use of a combination of TF-IDF and LDA in Indonesian text analysis, particularly in the context of Gojek user reviews, is an important step in enhancing understanding and utilization of text data to improve overall user experience.

Evaluating a New Digital App-Based Program for Heart Health: Feasibility and Acceptability Pilot Study.

Cardiovascular disease (CVD) is the leading cause of death in the United States, affecting a significant proportion of adults. Digital health lifestyle change programs have emerged as a promising method of CVD prevention, offering benefits such as on-demand support, lower cost, and increased scalability. Prior research has shown the effectiveness of digital health interventions in reducing negative CVD outcomes. This pilot study focuses on the Lark Heart Health program, a fully digital artificial intelligence (AI)-powered smartphone app, providing synchronous CVD risk counseling, educational content, and personalized coaching. This pilot study evaluated the feasibility and acceptability of a fully digital AI-powered lifestyle change program called Lark Heart Health. Primary analyses assessed (1) participant satisfaction, (2) engagement with the program, and (3) the submission of health screeners. Secondary analyses were conducted to evaluate weight loss outcomes, given that a major focus of the Heart Health program is weight management. This study enrolled 509 participants in the 90-day real-world single-arm pilot study of the Heart Health app. Participants engaged with the app by participating in coaching conversations, logging meals, tracking weight, and completing educational lessons. The study outcomes included participant satisfaction, app engagement, the completion of screeners, and weight loss. On average, Heart Health study participants were aged 60.9 (SD 10.3; range 40-75) years, with average BMI indicating class I obesity. Of the 509 participants, 489 (96.1%) stayed enrolled until the end of the study (dropout rate: 3.9%). Study retention, based on providing a weight measurement during month 3, was 80% (407/509; 95% CI 76.2%-83.4%). Participant satisfaction scores indicated high satisfaction with the overall app experience, with an average score of ≥4 out of 5 for all satisfaction indicators. Participants also showed high engagement with the app, with 83.4% (408/489; 95% CI 80.1%-86.7%) of the sample engaging in ≥5 coaching conversations in month 3. The results indicated that participants were successfully able to submit health screeners within the app, with 90% (440/489; 95% CI 87%-92.5%) submitting all 3 screeners measured in the study. Finally, secondary analyses showed that participants lost weight during the program, with analyses showing an average weight nadir of 3.8% (SD 2.9%; 95% CI 3.5%-4.1%). The study results indicate that participants in this study were satisfied with their experience using the Heart Health app, highly engaged with the app features, and willing and able to complete health screening surveys in the app. These acceptability and feasibility results provide a key first step in the process of evidence generation for a new AI-powered digital program for heart health. Future work can expand these results to test outcomes with a commercial version of the Heart Health app in a diverse real-world sample.

"An app is just available at all times"-the process and outcomes of converting the Georgia Tuberculosis Reference Guide into a mobile application.

The physical, paper-based Georgia TB Reference Guide has served as the clinical reference handbook on tuberculosis (TB) diagnostic and treatment guidelines for the state of Georgia in the United States. Supported by the Georgia Department of Public Health, the production of the 112-page palm-sized booklet was previously led by a team of Georgia-based TB experts at Emory University and printed every three-five years with updates to clinical management guidelines and TB consult contact information. However, the costs associated with editorial printing combined with delays in updating a static printed booklet with revised guidance hampered the utility of the tool. Considering the barriers with paper-based production and based on the beneficial use of apps to support the dissemination of clinical management guidance in other settings, the booklet was converted into a mobile application. This paper describes the process of developing a mobile app version of the Georgia TB Reference Guide in an easy-to-update and readily available format. We employed a user-centered design approach to develop the app, including a series of qualitative interviews and quantitative surveys. Participants included a mix of state officials and local TB experts. First, initial foundational interviews were conducted to conceptualize current utilization practices of both the paper and PDF versions of the tool. Second, the findings from the initial interviews were organized thematically and informed the design of the app, which was then beta tested by a round of previously unsampled TB experts as well as a re-sample from the initial interviews. Third, the designs were coded into developmental phases and beta tested among users of the current Georgia TB Reference Guide. Fourth, the app was published and downloaded by a pre-selected group of local users who provided answers to a follow-up survey after using the app for one month. Fifth, user growth, self-reported demographics, and app usage between February and July 2022 were recorded through automatic data metrics built into the app. The paper copy Georgia TB Reference Guide usage themes included commonly referenced content, navigation paths, and desired features and content. The themes were converted into features and designs such as prioritizing commonly reviewed topics and guide customization with bookmarks and notes. Iterations of the designs were driven by feedback from TB experts and included home page featured content, improving content readability, and improving the search feature. The follow-up survey revealed a 90% preference for the app over the paper version of the guide. In the six months following the app's release, the app was downloaded by 281 individuals in the United States. The majority of downloads were in Georgia and the app also expanded organically to 19 other states. The experience of converting the Georgia TB Reference Guide offers specific and effective steps to converting a medical reference guide into a mobile application tool that is readily available, easy to use, and easy to update. The organic dissemination of the app beyond the state of Georgia's borders within the first six months of app launch underscores desire among TB healthcare professionals for high-quality digital reference content outside the state. This experience offers clear outlines for replication in other contexts and demonstrates the utility of similar mobile medical reference tools.

Testing Updated Apps by Adapting Learned Models

Although App updates are frequent and software engineers would like to verify updated features only, automated testing techniques verify entire Apps and are thus wasting resources. We present Continuous Adaptation of Learned Models (CALM) , an automated App testing approach that efficiently test App updates by adapting App models learned when automatically testing previous App versions. CALM focuses on functional testing. Since functional correctness can be mainly verified through the visual inspection of App screens, CALM minimizes the number of App screens to be visualized by software testers while maximizing the percentage of updated methods and instructions exercised. Our empirical evaluation shows that CALM exercises a significantly higher proportion of updated methods and instructions than six state-of-the-art approaches, for the same maximum number of App screens to be visually inspected. Further, in common update scenarios, where only a small fraction of methods are updated, CALM is even quicker to outperform all competing approaches in a more significant way.

Visual Water: An Integration of App and Web to Understand Chemical Elements

This paper presents Visual Water: a novel inter- active application designed to augment chemical education and discovery. This mobile app and web version collectively provides an interactive environment for users to explore and understand chemical elements. Using vast databases and visual interfaces, Visual Water provides real-time information about various chem- ical elements, leading to a range of new, engaging and accessible learning tools for students, educators and enthusiasts. This paper provides a description of the development of this system, key features and potential impact of the Visual Water system in helping users better understand chemical reactions and their application in formal and informal educational spaces.

Evaluating the World Health Organization's SkinNTDs App as a Training Tool for Skin Neglected Tropical Diseases in Ghana and Kenya: Cross-Sectional Study.

Neglected tropical diseases (NTDs) affect over 1.5 billion people worldwide, primarily impoverished populations in low- and middle-income countries. Skin NTDs, a significant subgroup, manifest primarily as skin lesions and require extensive diagnosis and treatment resources, including trained personnel and financial backing. The World Health Organization has introduced the SkinNTDs app, a mobile health tool designed to train and be used as a decision support tool for frontline health care workers. As most digital health guidelines prioritize the thorough evaluation of mobile health interventions, it is essential to conduct a rigorous and validated assessment of this app. This study aims to assess the usability and user experience of World Health Organization SkinNTDs app (version 3) as a capacity-building tool and decision-support tool for frontline health care workers. A cross-sectional study was conducted in Ghana and Kenya. Frontline health care workers dealing with skin NTDs were recruited through snowball sampling. They used the SkinNTDs app for at least 5 days before completing a web-based survey containing demographic variables and the user version of the Mobile Application Rating Scale (uMARS), a validated scale for assessing health apps. A smaller group of participants took part in semistructured interviews and one focus group. Quantitative data were analyzed using SPSS with a 95% CI and P≤.05 for statistical significance and qualitative data using ATLAS.ti to identify attributes, cluster themes, and code various dimensions that were explored. Overall, 60 participants participated in the quantitative phase and 17 in the qualitative phase. The SkinNTDs app scored highly on the uMARS questionnaire, with an app quality mean score of 4.02 (SD 0.47) of 5, a subjective quality score of 3.82 (SD 0.61) of 5, and a perceived impact of 4.47 (SD 0.56) of 5. There was no significant association between the app quality mean score and any of the categorical variables examined, according to Pearson correlation analysis; app quality mean score vs age (P=.37), sex (P=.70), type of health worker (P=.35), country (P=.94), work context (P=.17), frequency of dealing with skin NTDs (P=.09), and dermatology experience (P=.63). Qualitative results echoed the quantitative outcomes, highlighting the ease of use, the offline functionality, and the potential utility for frontline health care workers in remote and resource-constrained settings. Areas for improvement were identified, such as enhancing the signs and symptoms section. The SkinNTDs app demonstrates notable usability and user-friendliness. The results indicate that the app could play a crucial role in improving capacity building of frontline health care workers dealing with skin NTDs. It could be improved in the future by including new features such as epidemiological context and direct contact with experts. The possibility of using the app as a diagnostic tool should be considered. RR2-10.2196/39393.

The Effect of Culture and Social-Cognitive Characteristics on App Preference and Willingness to Use a Fitness App

Fitness apps are persuasive tools developed to motivate physical activity. Despite their popularity, there is little work on how social-cognitive characteristics such as culture, household size, physical activity level, perceived self-efficacy and social support influence users’ willingness to use them and preference (personal vs. social). Knowing these relationships can help developers tailor fitness apps to different socio-cultural groups. Hence, we conducted two studies to address the research gap. In the first study (n = 194) aimed at recruiting participants for the second study, we asked participants about their app preference (personal vs. social), physical activity level and key demographic variables. In the second study (n = 49), we asked participants about their social-cognitive beliefs about exercise and their willingness to use a fitness app (presented as a screenshot). The results of the first study showed that, in the collectivist group (Nigerians), people in large households were more likely to be active and use the social version of a fitness app than those in small households. However, in the individualist group (Canadians/Americans), neither the preference for the social or personal version of a fitness app nor the physical activity level depended on the household size. Moreover, in the second study, in the individualist model, perceived self-efficacy and perceived self-regulation have a significant total effect on willingness to use a fitness app. However, in the collectivist model, perceived social support and outcome expectation have a significant total effect on the target construct. Finally, we found that females in individualist cultures had higher overall social-cognitive beliefs about exercise than males in individualist cultures and females in collectivist cultures. The implications of the findings are discussed.

“Encephalapp Stroop”: Validity and reliability of a smartphone app to measure cognitive performance in physically active subjects

This study aimed to analyze the validity and reliability of a smartphone app (i.e., Encephalapp Stroop) to measure cognitive performance. Ten physically active individuals (mean age: 25.2 ± 4.3 years) participated in three laboratory visits. In a randomized sequence, subjects completed cognitive assessments using the app and a computer separately. The response times from 100 measurements for congruent and incongruent stimuli were compared between the two devices (app and computer) using the intraclass correlation coefficient, Pearson product-moment correlation coefficient (r), Cronbach’s alpha (α), and Bland-Altman plots. There was excellent agreement between the computerized Stroop and the app version for response times for congruent (ICC = 0.806, p < 0.001; Bland-Altman bias = 41.3 ± 56.4 ms, p < 0.001) and incongruent stimuli (ICC = 0.755, p < 0.001; Bland-Altman bias = 76.4 ± 75.1 ms, p < 0.001). In comparison to the computerized version, the app demonstrated a moderate correlation for response times of congruent (r = 0.678, p < 0.001) and incongruent (r = 0.623, p < 0.001) stimuli. The results of the present study indicate that the response times of congruent and incongruent stimuli can be easily, accurately, and reliably evaluated using a mobile app.

Abstract LB375: Multimodality dietary intervention for colorectal cancer prevention: The MyBestGI randomized trial

Abstract Colorectal cancer (CRC) is one of the cancers most highly affected by diet. Recommendations for reducing risk of CRC include weight management, eating plentiful plant-based high fiber foods, and limiting intakes of red meats and ultra-processed foods. In addition, an increased proportion of monounsaturated and omega-3 fats in the diet is beneficial for promoting weight management and reducing inflammation. Despite the evidence supporting the importance of diet and the demonstrated success for eliciting dietary changes in research settings, simple methods that can be easily implemented in medical settings are lacking. We developed a bespoke mHealth app (MyBestGI), incorporating an autonomy-supporting, self-regulatory approach to behavior change. The program promotes identification of the benefits of healthy eating on well-being. Two versions of the program are being tested in a 12-month, 3-arm randomized trial that seeks to recruit 240 people at increased risk for CRC. Participants are randomized to either 1) a control group that receives written information on cancer preventive diets, 2) a treatment group that receives a MyBestGI app version for logging 4 food groups associated with increased risks of CRC (red meats, processed meats, added sugars and refined grains), or 3) a treatment group that receives a MyBestGI app version for logging the same 4 food groups to limit plus 7 food groups to encourage in personalized quantities. The two treatment groups also receive a user manual, behavior-oriented, biweekly text messages, and supportive coaching calls. Logging food groups is requested at least 3 per week for 3 months, and 3 per week every other week for the next 9 months. The app displays logging results relative to individualized targets and has features that prompt reflecting on the user’s own results and planning for future eating. The primary endpoints are weight loss and improvement in a dietary cancer prevention score. Recruitment began in June 2023, and the study is enrolling 8-10 participants per month. The drop-out rate is lower than expected. Compliance with study procedures and app use is high. Responses to the end-of-day reflection questions within the app show that 92% of users are neutral, happy, or very happy with their dietary behavior for the day. For the 15 participants who had 12 weeks of logging data available, foods were logged for an average of 38 days (range 20-75) which is slightly higher than the requested logging of 36 days over 12 weeks. Review of the support calls for protocol fidelity indicates participants are enthusiastic about the MyBestGI program, making changes in their eating, and are internalizing the autonomy-supporting beliefs promoted in the program. These early results suggest users are engaging with the MyBestGI app at a very high level, making use of the provided app features, and enjoying the program. The MyBestGI approach therefore has excellent potential to support dietary change towards a cancer preventive diet in a format that is facile to implement in high-risk populations. Citation Format: Zora Djuric, Michelle Segar, Ananda Sen, Reema Kadri, Rob Adwere-Boamah, Juno Orr, Katherine Poore, Samara Rifkin, Lorraine Buis. Multimodality dietary intervention for colorectal cancer prevention: The MyBestGI randomized trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB375.

Co-creation of a self-management application for asthma: A citizen science protocol

Digital health can effectively engage patients in the self-management of their asthma. Still, it will only reach its full potential when apps are aligned with patients’ needs and are integrated into clinical practice. We aim to test a framework for the involvement of patients/carers in all phases of developing and implementing an app for asthma. This protocol was co-designed with six asthma patients/carers and includes 5 tasks. Regular interactions among patients, developers and healthcare professionals will be a priority for user requirements definition (task 1) and iterative development and testing until the app’s initial version (tasks 2-3). Patients/carers and healthcare professionals will be fully engaged in the real-life feasibility study (task 4) and management and dissemination activities (task 5). The eligibility criteria to participate in tasks 2-4 are 1) diagnosis of asthma or carer of a person with asthma; 2) ≥18 years; 3) ability to use apps and access a mobile device with Internet. We anticipate applying questionnaires such as the User Experience Questionnaire and the System Usability Scale in all app testing tasks. We expect to have a final version of the asthma app that meets patients’/carers’ needs and experiences and healthcare professionals’ concerns. This will be an attractive app to support patients in self-management of their disease, keeping their asthma in control, having fewer asthma symptoms, and enjoying life. This study will be a framework for fully involving patients, carers and citizens in the iterative co-production development and implementation of an asthma app.

Introducing qrlabelr: Fast user-friendly software for machine- and human-readable labels in agricultural research and development.

The advent of modern tools in agricultural experiments, digital data collection, and high-throughput phenotyping have necessitated field plot labels that are both machine- and human-readable. Such labels are usually made with commercial software, which are often inaccessible to under-funded research programs in developing countries. The availability of free fit-for-purpose label design software to under-funded research programs in developing countries would address one of the main roadblocks to modernizing agricultural research. The goal was to develop a new open-source software with design features well-suited for field trials and other agricultural experiments. We report here qrlabelr, a new software for creating print-ready plot labels that builds on the foundation of an existing open-source program. The qrlabelr software offers more flexibility in the label design steps, guarantees true string fidelity after QR encoding, and provides faster label generation to users. The new software is available as an R package and offers customizable functions for generating plot labels. For non-R users or beginners in R programming, the package provides an interactive Shiny app version that can be launched from R locally or accessed online at https://bit.ly/3Sud4xy. The design philosophy of this new program emphasizes the adoption of best practices in plot label design to enhance reproducibility, tracking, and accurate data curation in agricultural research and development studies.