App Features Research Articles

RehabApp to promote patient participation in the rehabilitation process after HIP replacement: Development and usability study

ObjectiveThis study aims to present the developmental stages of a Mobile App Prototype designed to enhance patient participation in the rehabilitation process after hip replacement. MethodsTo ensure effective interaction between the system and the end user, a User-Centered Design methodology was followed, encompassing three phases: Requirements gathering, Prototyping, and Evaluation. Usability tests were conducted to assess the usability of the developed system. ResultsThe RehabApp for mobile devices was created, and the testing results were positive. Users expressed satisfaction with the outcome, deeming it a valuable tool for their recovery. This outcome demonstrates the high receptiveness of these technologies in the healthcare sector, making it a project that can readily be expanded into other areas of rehabilitation. ConclusionThis study demonstrated the potential of the RehabApp in the rehabilitation process after hip replacement surgery. This accomplishment was realized by ensuring the active participation of patients, potential users, and healthcare professionals throughout the app's development. InnovationThe RehabApp is a mobile application to provide users with all the necessary knowledge, enabling them to undergo a smoother and safer rehabilitation. Feedback from both patients and healthcare professionals played a crucial role in refining the app's features and addressing usability concerns.

Selecting the Best Radiology Workflow Efficiency Applications.

In the rapidly evolving digital radiology landscape, a surge in solutions has emerged including more than 500 artificial intelligence applications that have received 510k clearance by the FDA. Moreover, there is an extensive number of non-regulated applications, specifically designed to enhance workflow efficiency within radiology departments. These efficiency applications offer tremendous opportunities to resolve operational pain points and improve efficiency for radiology practices worldwide. However, selecting the most effective workflow efficiency applications presents a major challenge due to the multitude of available solutions and unclear evaluation criteria. In this article, we share our perspective on how to structure the broad field of workflow efficiency applications and how to objectively assess individual solutions. Along the different stages of the radiology workflow, we highlight 31 key operational pain points that radiology practices face and match them with features of workflow efficiency apps aiming to address them. A framework to guide practices in assessing and curating workflow efficiency applications is introduced, addressing key dimensions, including a solution's pain point coverage, efficiency claim strength, evidence and credibility, ease of integration, and usability. We apply this framework in a large-scale analysis of workflow efficiency applications in the market, differentiating comprehensive workflow efficiency ecosystems seeking to address a multitude of pain points through a unified solution from workflow efficiency niche apps following a targeted approach to address individual pain points. Furthermore, we propose an approach to quantify the financial benefits generated by different types of applications that can be leveraged for return-on-investment calculations.

Effectiveness of mHealth apps on adherence and symptoms to oral anticancer medications: a systematic review and meta-analysis.

Despite the rapid expansion of mHealth apps, their adoption has not always been based on evidence of effectiveness on patient outcomes. This systematic review aimed to determine the effect of mHealth apps on adherence and symptom to oral anticancer medications (OAMs) and identify the app design that led to such effects. Pubmed, Cochrane Central, PsycINFO, EMBASE, and WoS were searched from inception to April 2023. Randomised controlled trials (RCTs) that evaluated effects of mHealth apps on primary outcomes OAM adherence and symptom burden were included. Two reviewers independently assessed risk-of-bias using Cochrane Risk-of-Bias version 2 and extracted the data. Quality of evidence was assessed using GRADE. The protocol was registered in PROSPERO (CRD42023406024). Four RCTs involving 806 patients with cancer met the eligibility criteria. mHealth apps features included a combinations of symptom reporting, medication reminder, automated alert to care team, OAM and side effect information, one study implemented structured follow-up by a nurse. The intervention group showed no significant difference in OAM adherence (relative ratio 1.20; 95% CI 1.00 to 1.43), but significantly improved symptoms to OAMs with a lower standardised mean symptom burden score of 0.49 (SMD - 0.49; 95% CI - 0.93 to - 0.06), and a 25% lower risk of grade 3 or 4 toxicity (risk ratio 0.75; 95% CI 0.58 to 0.95) compared to usual care. These findings suggest a potential role for mHealth app in managing OAM side effect. Further research should explore the role of AI-guided algorithmic pathways on the interactive features of mHealth apps.

Reinvestigating repurchase intentions for travel apps: a comparison of China's various tiers of cities

ABSTRACT The intense competition amongst travel apps, regardless of their popularity, has made repurchase intention a critical financial challenge for travel providers. By combining the Stimulus-Organism-Response (S-O-R) framework and the New Economic Geography (NEG) theory, this research examined how city tier disparities influence the experiential features of travel apps and user repurchase intentions. Survey data was acquired from 739 travel app users in China. The findings indicate that a set of context-based experiential features significantly influences utilitarian and hedonic values, and consequently leading to a notable increase in repurchase intentions. In addition, the outcomes of a multigroup analysis revealed that China’s users located in tier 1 city (i.e. Beijing) and tier 3 city (i.e. Kunming) had distinct effects on the hypothesised relationships. Theoretically, the research provides extensive implications to information systems and tourism literatures; whilst offering some actionable insights to travel practitioners.

From engagement to retention: Unveiling factors driving user engagement and continued usage of mobile trading apps

The popularity of online mobile trading has led to an increase in the development of mobile stock trading applications. Despite this increase in popularity, there is a dearth of empirical studies that examine the factors influencing the continued usage intention of these applications (hereafter, apps). Drawing on stimulus-organism-response (S-O-R) theory, this paper investigates the features of stock trading apps that generate consumer engagement and consequently, continued app usage intention. In study 1, through semi-structured interviews, we establish four key drivers of customer engagement with stock trading apps, and two possible moderators influencing the relationship between customer app engagement and continued usage intention. In study 2, we examine these key drivers by surveying stock trading app users from three different Facebook stock trading communities. The results confirm that the social presence and security features of these apps are significantly associated with consumer stock trading app engagement. We also find that fear of uncertainty and perceived corporate hypocrisy weaken the effect of customer app engagement on continued app usage intention. The study findings add to the literature on app usage and customer engagement and provide insights for fintech service companies to help them understand the factors that enhance consumer engagement with these apps.

Households in energy transition: Promoting household energy-sufficient routines via app-based peer-to-peer interaction

We engage a community of ca. 200 voluntary Swiss households in using a smartphone app that provides energy consumption feedback and offers peer-to-peer interaction possibilities to share experiences on household routine change. Surveys prior to and three months after app use, in-app usage analytics, and analysis of in-app posts indicate that most households preferred individual-level consumption feedback: app-mediated peer interaction was only performed by a small household subsample, precluding community-level social learning. Most self-reported daily energy routines changed after app use, though effect size was generally small, apart from thermostat settings. Also, we found most app users were already well-informed on energy topics and engaged in energy savings at home. Future research could explore how to better reach alternative audiences for app-based interventions, and improve the effectiveness of social interactions to collectively experiment with new sustainable practices, therefore giving less prominence to individual-level app features.

Smartphone App Designed to Collect Health Information in Older Adults: Usability Study.

Studies evaluating the usability of mobile-phone assessments in older adults are limited. This study aims to identify design-based barriers and facilitators to mobile app survey completion among 2 samples of older adults; those in the Framingham Heart Study and a more diverse sample from a hospital-based setting. We used mixed methods to identify challenging and beneficial features of the mobile app in participants from the electronic Framingham Heart Study (n=15; mean age of 72 years; 6/15, 40% women; 15/15, 100% non-Hispanic and White) and among participants recruited from a hospital-based setting (n=15; mean age of 71 years; 7/15, 47% women; 3/15, 20% Hispanic; and 8/15, 53% non-White). A variety of app-based measures with different response formats were tested, including self-reported surveys, pictorial assessments (to indicate body pain sites), and cognitive testing tasks (eg, Trail Making Test and Stroop). Participants completed each measure using a think-aloud protocol, while being audio- and video-recorded with a qualitative interview conducted at the end of the session. Recordings were coded for participant usability errors by 2 pairs of coders. Participants completed the Mobile App Rating Scale to assess the app (response range 1=inadequate to 5=excellent). In electronic Framingham Heart Study participants, the average total Mobile App Rating Scale score was 7.6 (SD 1.1), with no significant differences in the hospital-based sample. In general, participants were pleased with the app and found it easy to use. A large minority had at least 1 navigational issue, most committed only once. Most older adults did not have difficulty completing the self-reported multiple-choice measures unless it included lengthy instructions but participants had usability issues with the Stroop and Trail Making Test. Our methods and results help guide app development and app-based survey construction for older adults, while also giving consideration to sociodemographic differences.

Exploring the Features of Mobile Language Learning Apps (MELLAs) for Improving English Language Skills in College Level Students

This study investigated the features of mobile language learning apps (MELLAs) that college students prefer for improving their English skills. The research surveyed 40 students from two government colleges in Khairpur. The researcher employed a mixed-methods approach, analyzing quantitative data in MS-Excel (likely survey responses agree, strongly agree, neutral, disagree, strongly disagree) in tables first, followed by qualitative data (potentially open-ended questions through face-to-face interviews) presented in graphs based on the quantitative findings. The study suggests that using MELLAs for self-directed learning is an effective way for college students to improve their English, favoring this approach over traditional methods in which students use themselves. It is said to be self-directed language learning that saves time. Further, this study recommends improving language learning apps and learning strategies. It aims to provide a foundation for future studies in this area, potentially benefiting learners, teachers, parents, and others interested in mobile language learning.

AI-Enhanced Accessibility Solutions using Chatbot

The "AI-Enhanced Accessibility Solutions" project addresses the real-world challenge of enhancing accessibility for individuals through advanced technologies. Utilizing React.js, Next.js, Tailwind, and AI APIs, this study focuses on generating inclusive content, including text, images, videos, code, and chatbot interactions. The primary objectives are to improve user interfaces, employ AI-driven content generation techniques, and ensure compliance with accessibility standards. The platform allows users to input self-generated prompts to produce customized data, such as text, images, videos, and code, tailored to their needs. An interactive AI-powered chatbot assists users in daily activities, adapting responses based on the quality of prompts provided, ensuring personalized and effective results. Key features include AI-generated content designed for various abilities. This includes alternative text for images, simplified text for cognitive accessibility, and adaptive chatbot responses. The project also focuses on generating accessible code, adhering to best web development practices. Tailwind CSS is used to optimize content presentation, addressing factors like color contrast and readability. The study includes a comparative analysis of accessibility features in popular apps versus those generated by our system. User testing and feedback mechanisms are employed to validate the project's effectiveness in meeting diverse user needs. This project not only advances AI-driven content creation but also represents a significant step towards creating inclusive digital environments. It emphasizes the importance of accessibility in technology, ensuring that digital content is accessible to all individuals, regardless of their abilities. Keywords: AI-Enhanced Accessibility, Inclusive Content, React.js, Next.js, AI APIs, User Interfaces, Accessibility Standards, AI-Powered Chatbot, Cognitive Accessibility

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.

Preferences for and intention to use an app for premenstrual mental health symptoms using the Health Behaviour Model (HBM)

Premenstrual symptoms are common, with premenstrual syndrome and premenstrual dysphoric disorder associated with decreased wellbeing and increased suicidality. Apps can offer convenient support for premenstrual mental health symptoms. We aimed to understand app preferences and Health Belief Model (HBM) constructs driving app use intention. An online survey was delivered. Structural equation modelling (SEM) explored HBM constructs. Data from 530 United Kingdom based participants who reported their mental health was impacted by their menstrual cycle (mean age = 35.85, SD = 7.28) were analysed. In terms of preferred app features, results indicated that symptom monitoring (74.72%, n = 396) and psychoeducation (57.92%, n = 307) were sought after, with 52.64% (n = 279) indicating unwillingness to pay for an app for mental health symptoms related to the menstrual cycle. Regarding HBM results, Satorra–Bentler-scaled fit statistics indicated a good model fit (χ2(254) = 565.91, p < 0.001; CFI = 0.939, RMSEA = 0.048, SRMR = 0.058). HBM constructs explained 58.22% of intention to use, driven by cues to action (β = 0.49, p < 0.001), perceived barriers (β = −0.22, p < 0.001), perceived severity (β = 0.16, P = 0.012), and perceived benefits (β = 0.10, p = 0.035). Results indicate that app developers should undertake co-design, secure healthcare professional endorsement, highlight therapeutic benefits, and address barriers like digital discomfort, privacy concerns, and quality.

Evaluating MediBetter: A Mobile Application for Health Monitoring and Medication Management.

This study introduces MediBetter, a mobile application designed to empower patients undergoing routine medication in health monitoring and medication adherence. It is a mobile application designed to serve as a supportive health technology for patients to monitor their health status and manage their routine medication. It offers three main features: text-based daily self health report, AI-based summarization of the health report, and medication taking reminder. To evaluate the quality of generated summaries generated by both the user and AI (ChatGPT), we conducted human expert evaluation process. Furthermore, we also evaluated the usefulness of existing features in the app. The experiment results show that ChatGPT-generated summaries outperformed user-generated ones, demonstrating superior informativeness, coherence, fluency, consistency, and contradiction handling. Participants found the app's features highly useful for health monitoring and medication adherence, with strong agreement on their utility.

Identifying Patterns of Smoking Cessation App Feature Use That Predict Successful Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning.

Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness. We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors). Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the National Cancer Institute's quitSTART app. Participants' (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants' probability of cessation from 28 variables reflecting participants' use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals' SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation. The SML model's sensitivity (0.67) and specificity (0.67) in the held-aside test set indicated that individuals' patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model-predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16). Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps. ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736.

“Run Forrest Run!”: Measuring the Impact of App-Enabled Performance and Social Feedback on Athletic and Usage Outcomes

Exercise-tracking apps are digital tools for delivering personalized behavioral interventions. Despite the growing usage of exercise applications, the efficacy of in-exercise app features in driving usage and athletic outcomes remains poorly understood. To remain competitive, sports organizations now need to leverage tracking tools to efficiently allocate resources and streamline training regimens and interventions for their core assets (i.e., athletes). In response to these operational needs, we examine two specific forms of such in-exercise interventions, namely performance feedback and social feedback. We conducted an 18-month-long field study with 1,037 uniformed group servicemen to assess the effect of these feedback types on running and usage outcomes. Results from the field study provided evidence that these two app features improved the servicemen’s running times and frequency of application usage, on average. Contrary to the common belief that more features are better, the joint usage of two feedback features does not produce additive effects. Tests at more granular levels suggest that users who received both feedback types in exercise episodes exhibit overconfidence behavior by participating in fewer subsequent exercises. The receipt of both feedback may be redundant and can cause user annoyance. Heterogeneity tests revealed that while performance feedback benefited most runners, social features were effective only for already stronger runners. Also, only positive social feedback had a significant impact on running performance. The results further indicate that performance feedback generated a slow but sustained increase in usage frequency, while social feedback spurred quick initial growth in usage but dwindled in effectiveness over time. Implications for theory and practice, as well as directions for further research, are discussed.

The dark triad, dating app use and online disinhibition positively predict technology-facilitated sexual violence perpetration

Technology-facilitated sexual violence (TFSV) describes the perpetration of a variety of sexual offences committed through digital communication technologies. The current study aims to investigate the similarity of risk factors between TFSV and in-person sexual offending. The sample consisted of 105 young adult, Australian males who responded to measures of sexual violence acceptance, dating app usage, online disinhibition, and the dark triad. Overall, 45.3% of the sample reported at least one instance of TFSV perpetration in the past 5 years. Standard and hierarchical linear regressions revealed dating app usage, the dark triad, and online disinhibition positively predicted TFSV perpetration. Neither measure of attitudes accepting of sexual violence predicted TFSV perpetration, but their positive correlation is indicative of cultural norms enabling sexual violence against women. As illuminated here, the congruence between TFSV and in-person violence extends beyond conceptual and prevalence trends to predictive risk factors. Recommendations for interventions and prevention include raising awareness of risk-generating app features that create vulnerability to sexual harm and identifying heterogenous groups of TFSV offenders based on key characteristics to develop optimal intervention strategies.

“I think it was helpful but not as helpful as it could have been” - a qualitative study of the experiences and perspectives of using fitness apps among manual wheelchair users with spinal cord injury

Purpose To gain knowledge of the experiences and perceptions of using fitness apps by full-time wheelchair users with spinal cord injury (SCI) and to explore experiences and perceptions of using these apps in relation to barriers to and facilitators of physical activity (PA). Materials and Methods A qualitative design was employed. Semi-structured interviews were conducted with ten manual wheelchair users with SCI. Data were analysed using reflexive thematic analysis. Results Four themes were developed: 1) App functionality and experience, 2) Enhancing motivation, 3) Precursors to engagement and 4) Knowledge gaps. Experience and perception were strongly influenced by app design, particularly the presence or absence of features tailored for wheelchair users. Many apps incorporated the use wearable or mountable sensors. Fitness apps were perceived to increase motivation, although, this had its limitations. Conversely, apps were reported to have limited influence on commonly reported barriers to PA. Furthermore, the data highlighted gaps in knowledge which created barriers to effective app use. Conclusions Fitness apps were perceived to increase motivation to engage in PA in manual wheelchair users with SCI. However, more needs to be done to further develop app features for wheelchair users with SCI and to address remaining barriers to engagement.

Understanding Patient Perspectives on the Use of Gamification and Incentives in mHealth Apps to Improve Medication Adherence: Qualitative Study.

Medication nonadherence remains a significant health and economic burden in many high-income countries. Emerging smartphone interventions have started to use features such as gamification and financial incentives with varying degrees of effectiveness on medication adherence and health outcomes. A more consistent approach to applying these features, informed by patient perspectives, may result in more predictable and beneficial results from this type of intervention. This qualitative study aims to identify patient perspectives on the use of gamification and financial incentives in mobile health (mHealth) apps for medication adherence in Australian patients taking medication for chronic conditions. A total of 19 participants were included in iterative semistructured web-based focus groups conducted between May and December 2022. The facilitator used exploratory prompts relating to mHealth apps, gamification, and financial incentives, along with concepts raised from previous focus groups. Transcriptions were independently coded to develop a set of themes. Three themes were identified: purpose-driven design, trust-based standards, and personal choice. All participants acknowledged gamification and financial incentives as potentially effective features in mHealth apps for medication adherence. However, they also indicated that the effectiveness heavily depended on implementation and execution. Major concerns relating to gamification and financial incentives were perceived trivialization and potential for medication abuse, respectively. The study's findings provide a foundation for developers seeking to apply these novel features in an app intervention for a general cohort of patients. However, the study highlights the need for standards for mHealth apps for medication adherence, with particular attention to the use of gamification and financial incentives. Future research with patients and stakeholders across the mHealth app ecosystem should be explored to formalize and validate a set of standards or framework.

Predictors of physical activity and public safety perception regarding technology adoption for promoting physical activity in Jeddah, Saudi Arabia

BackgroundThis study aims to identify the predictors of levels of physical activity among the residents of Jeddah, Saudi Arabia. Additionally, it seeks to understand the public's safety perception regarding the adoption of technology for promoting physical activity. MethodThis is an analytical cross-sectional study using self-administered web-based survey. A stratified sampling technique was used to select participants from adult population in Jeddah and data collection took place from May 2023 to December 2023. Multivariable logistic regression models were applied to identify predictors of low-intensity (walking steps) and high-intensity physical activities. ResultA total of 462 participated in the study. Gender significantly influenced walking habits, with males more likely to walk than females (AOR = 2.37; 95 % CI: 1.55–3.62; P < 0.001). Smoking status was also a predictor, with smokers more inclined to walk compared to non-smokers (AOR = 2.15; 95 % CI: 1.20–3.82; P = 0.010). High-intensity physical activity strongly correlated with increased walking, especially among those active 3–7 days per week (AOR = 3.03; 95 % CI: 1.91–4.78; P < 0.001). Chronic diseases negatively impacted walking frequency (AOR = 0.57; 95 % CI: 0.34–0.95; P = 0.031). Furthermore, males were likelier to engage in high-intensity activities compared to females (AOR = 1.61; 95 % CI: 1.04–2.50; P = 0.033). Those walking ≥ 10,000 steps weekly were more likely to participate in high-intensity activities (AOR = 3.07; 95 % CI: 1.95–4.82; P < 0.001). Excellent self-rated health was associated with higher participation in such activities (AOR = 2.00; 95 % CI: 1.30–3.06; P = 0.002). Most respondents (84.9 %) felt safe on public walkways, and 78.1 % indicated safety perceptions influenced their walkway use. Technology use was divided, with about half (49.1 %) using apps for tracking physical activity. The majority (90.9 %) supported adding motivational features to the 'Sehhaty' app, and 73.6 % favored financial rewards for walking. However, only 45.5 % believed that public walkways are designed to encourage walking. ConclusionThe study highlights significant predictors of physical activity among general population in Jeddah. The study also revealed the impact of safety perceptions and technology use on physical activity, with strong support for motivational features in health apps. However, there is a need for improved design of public walkways to encourage walking.

UX Design for an E-commerce Application: A Design Thinking Perspective

The development of e-commerce applications has revolutionized the way people conduct business, but challenges still exist in designing applications that align with user needs and expectations. This study aims to document the design-thinking (DT) approach used in the development of a customized e-commerce application based on the user's perspective, with the goal of extracting the user experience of traditional commerce processes to identify its features. In this study, we focused on the first four phases of DT, which include empathize, define, ideate, and prototype, to gather insights and understand the experiences, needs, pain points, and solutions of the local producers. The data gathered from interviews and observations was subjected to content analysis to identify patterns and themes related to desired features. After analysis, the researchers identified key app features, including integrating data analytics for product performance and demand forecasting, introducing a peer-vendor marketplace, a vendor-managed inventory management system, and a product verification system for authenticity. A mockup prototype design was created and presented to users for feedback. The study highlights the importance of user perspectives in e-commerce app design and the value of design thinking in creating a customized user experience that aligns with user needs.

Human-Centered Design and Development of a Fall Prevention Exercise App for Older Adults in Primary Care Settings.

Falls in older adults are a serious public health problem that can lead to reduced quality of life or death. Patients often do not receive fall prevention guidance from primary care providers (PCPs), despite evidence that falls can be prevented. Mobile health technologies may help to address this disparity and promote evidence-based fall prevention. Our main objective was to use human-centered design to develop a user-friendly, fall prevention exercise app using validated user requirements. The app features evidence-based behavior change strategies and exercise content to support older people initiating and adhering to a progressive fall prevention exercise program. We organized our multistage, iterative design process into three phases: gathering user requirements, usability evaluation, and refining app features. Our methods include focus groups, usability testing, and subject-matter expert meetings. Focus groups (total n = 6), usability testing (n = 30) including a posttest questionnaire [Health-ITUES score: mean (standard deviation [SD]) = 4.2 (0.9)], and subject-matter expert meetings demonstrate participant satisfaction with the app concept and design. Overall, participants saw value in receiving exercise prescriptions from the app that would be recommended by their PCP and reported satisfaction with the content of the app. This study demonstrates the development, refinement, and usability testing of a fall prevention exercise app and corresponding tools that PCPs may use to prescribe tailored exercise recommendations to their older patients as an evidence-based fall prevention strategy accessible in the context of busy clinical workflows.