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Background: Alcohol dependence is common, yet highly undertreated. Smartphone applications (apps) have potential to enhance treatment accessibility and effectiveness, however evidence is limited, especially studies focussing on user experiences. The aim was to describe patient perceptions on the usability and acceptability of self-monitoring apps provided as treatment complement for alcohol dependence. Methods: Individual semi-structured interviews were conducted through video or phone calls with 21 participants, recruited from a randomized controlled trial at a dependency clinic in Stockholm. The participants had used two specific apps for self-monitoring consumption ("Glasklart" and "iBAC") during 12 wk prior to the interviews. Data was analyzed using Qualitative Content Analysis. Results: Two domains were identified: 1) Smartphone applications as facilitators to treatment, and 2) Barriers to smartphone application use. Using apps within the treatment context was believed to increase the accuracy of the reported consumption. Participants became more aware of their alcohol problem and described the apps as reinforcers that could increase both the motivation to change and the focus on the problem and commitment to treatment. The apps were further described as helpful to control alcohol consumption. However, app usage was constrained by technical problems, unfit app-specific features and procedures, and alcohol-related shame and stigma. Discussion and Conclusions: Self-monitoring alcohol apps have several beneficial features that can help assess, track, and control alcohol consumption, and improve communication with clinicians. The results indicate they can be useful complements to treatment for patients with alcohol dependence, but their use can be limited by different, foremost technical, issues. Smartphone applications for self-monitoring of alcohol consumption may help provide accurate data, increase consumption awareness, focus, motivation, and perceived control, Forskningsfinansiär: Systembolagets Alkoholforskningsråd

The incorporation of technologies in the field of education continues to grow, and contributes to the simplification of certain activities. A challenge for institutions is to have a support system for tutorial administrative management. The methodology is descriptive; the Scrum method was adapted for the management of the ICT project, the D3A method, and accessibility assessment were introduced according to the standard defined by the W3C. The results of the mobile application were present and the compliance with accessibility criteria was show. The process could be replicate in the design and implementation of other IT projects, considering the appropriation of the solution among its recipients as a challenge., La incorporación de las tecnologías en el ámbito educativo continúa creciendo, y contribuye en la simplificación de determinadas actividades. Un desafío en las instituciones es disponer de un sistema de apoyo a la gestión administrativa tutorial. La metodología es descriptiva, se adaptó el método Scrum para la gestión del proyecto TIC, el método D3A, y se introdujeron evaluación de la accesibilidad según estándar definido por la W3C. Se presentan los resultados de la aplicación móvil y se muestra el cumplimiento de criterios de accesibilidad. El proceso es replicable en el diseño y la realización de otros proyectos TI, considerando como reto la apropiación de la solución entre sus destinatarios

Introduction. The use of digital tools for health is a widespread practice. Objective. Determine the content validity and reliability of an instrument for measuring knowledge, appreciation, and usability of a mobile health and nutrition application with a database of native foods from Peru. Methods. A questionnaire of 8 questions was designed and applied to 32 participants randomly selected from adult patients at the Amistad Peru Korea Bellavista Health Center, Callao, who usually use cell phones with internet access and who use mobile applications. Results. The dichotomous items of the knowledge dimension had a KR20 of 0.72, in the appraisal dimension a Cronbach’s alpha coefficient of 0.74 was obtained and the usability dimension obtained a Cronbach’s alpha coefficient of 0.75. Conclusions. The instrument demonstrated high reliability in the three dimensions and has internal validity and adequate reliability to be applied., Introducción. El uso de herramientas digitales para la salud es una práctica extendida. Objetivo. Determinar la validez de contenido y confiabilidad de un instrumento de medición del conocimiento, valoración y usabilidad de un aplicativo móvil de salud y nutrición con base de datos de alimentos nativos del Perú. Métodos. Se diseñó un cuestionario de 8 preguntas que se aplicó en 32 participantes seleccionados aleatoriamente entre los pacientes adultos del Centro de Salud Amistad Perú Corea Bellavista, Callao, que habitualmente utilizan celular con acceso a internet y que usan aplicativos móviles. Resultados: Los ítems dicotómicos de la dimensión conocimiento tuvieron un KR20 de 0,72, en la dimensión valoración se obtuvo un coeficiente alfa de Cronbach de 0,74 y la dimensión usabilidad obtuvo un coeficiente alfa de Cronbach de 0,75. Conclusión. El instrumento demostró una alta confiabilidad en las tres dimensiones, con validez interna y confiabilidad adecuada para su aplicación.

Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individuals loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). Participants reported their loneliness feeling multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications.

BackgroundPeople with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.MethodsThe application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.ResultsThe mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.ConclusionThere is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

SignificanceA majority of visually impaired older adults were able to learn to proficiently use visual-assistive iPhone applications (apps) following a median 1 hour and/or multiple training sessions, which should be considered when planning vision rehabilitation service delivery, including the option for remote telerehabilitation for those who prefer that modality.PurposeOlder adults with low vision are increasingly using technology to improve their visual functioning. We examined whether age-related comorbidities were potential barriers to success in learning to use visual-assistive apps on a smartphone.MethodsA clinical trial assessed visual-assistive apps in 116 older adults aged 55+ years (mean [standard deviation], 72 [10] years). Subjects were randomized to use an app (SuperVision+, Seeing AI, or Aira) preloaded to a loaner iPhone and completed one-on-one training. App proficiency was measured by the participant's ability to use the iPhone/app without cueing at the end of training sessions. Training time was recorded for the initial session and totaled after subsequent sessions. Multiple regression models explored significant factors associated with training time and proficiency.ResultsMedian initial and total training times were 45 and 60 minutes, respectively. Increased initial and total training times were both significantly related to increased age (p<0.001), legal blindness (p<0.007), Seeing AI versus SuperVision+ app (p<0.03), and participants from New England versus California (p<0.001). Most (71%) achieved proficiency after the initial training session; those odds were significantly greater among younger participants (p=0.04), those who opted for telerehabilitation (p=0.03), those who had higher cognitive scores (p=0.04), or those who were from New England (p=0.04). The majority (90%) was ultimately proficient with the app; those odds were significantly greater among participants who already had an optical magnifier (p=0.008), but were unr

BackgroundFrontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged <65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown.ObjectiveThis study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD.MethodsParticipants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage v

The purpose of this project was to conduct a gap analysis that examined the needs of LIMMS users from system design and functionality to security. The analysis took into consideration the multiple platforms used by MassDOT currently for material and contract management. During the project, feedback was obtained from MassDOT users via private meetings and workshops to understand the scope of LIMMS usage. Given the increasing threat of software attacks, security was a particular focus of information gathering. A gap analysis report was created based on feedback from MassDOT users, a literature review, information gathered from other DOTs, and an evaluation of alternative material management products. Interviews with employees at other DOTs examined common use cases for alternative products. These products were evaluated via vendor interviews and software demonstrations. The gap analysis report findings resulted in a detailed and accurate scope of work for a sustainable and efficient LIMMS system.

This report presents the Mobile Unit for Sensing Traffic (MUST) project, which addresses the critical need for comprehensive traffic data in tribal and rural areas that often suffer from disproportionately high rates of traffic incidents due to limited infrastructure and resources. By implementing a low-cost, user-friendly data collection system, the MUST project aimed to provide real-time traffic information to significantly enhance traffic safety. The pilot installation in Yakama Nation, Washington, at a high-traffic intersection known for frequent accidents, marks a crucial step in this initiative. Collaboration with the Yakama Nation's Tribal Traffic Safety Coordinator and Yakama Power ensured the successful installation and operation of the sensor on a strategically selected telephone pole. Equipped with advanced machine learning technology, the MUST system collects detailed data on traffic flow, road surface conditions, and environmental factors such as temperature and humidity, visualized through a sophisticated dashboard for real-time monitoring and data-driven decision-making. This system allows for the identification of high-risk areas and the implementation of targeted safety measures, such as improved signage and road maintenance, while addressing specific concerns like pedestrian safety, visibility issues due to heavy fog, and speeding. By providing a robust dataset previously unavailable, the MUST project supports the Yakama Nation’s efforts to understand and mitigate traffic safety issues, ultimately enhancing the overall safety and quality of life for the community. This pilot project serves as a model for other tribal and rural areas looking to leverage advanced technology to improve their transportation safety infrastructure.

The extraction of traffic signs from Mobile Light Detection and Ranging (LiDAR) point cloud data has become a focal point in transportation research due to the increasing integration of LiDAR technologies. LiDAR, a remote sensing technology, captures detailed three-dimensional point cloud data, offering a comprehensive view of the surrounding environment. Mobile LiDAR systems mounted on vehicles enable efficient data collection, particularly for large-scale road networks. This study aims to develop and refine techniques for extracting traffic signs from Mobile LiDAR point cloud data, essential for enhancing road safety, navigation systems, and intelligent transportation solutions. By leveraging LiDAR technology, new possibilities for automating traffic sign recognition and mapping emerge. The research focuses on detecting traffic signs using Mobile LiDAR point cloud data, employing an intensity-based sign extraction method to identify traffic signs, traffic signals, and other retro-reflective objects. The workflow involves managing LiDAR Aerial Survey (LAS) datasets, including tasks such as merging/splitting, gridding, and detecting high-intensity features. Identified signs are visualized in Google Earth Pro, facilitating their display in Geographic Information Systems (GIS). Furthermore, the study explores point density analysis, establishing connections with potential grid resolutions for additional extraction or analysis, such as road condition assessments or crack detection.

Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21-0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators ( https://nda.nih.gov/abcd/ ). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.

To achieve the research objectives of this project, four specific tasks are involved: Task 1: A comprehensive literature review is conducted to explore and examine existing technologies and practices related to transportation asset collection and inspection, including emerging AI technologies. Task 2: A mobile phone mounted on a vehicle is used to collect data by recording videos while driving on Utah highways and streets. The capability of AI in transportation asset identification and condition assessment is pre-evaluated. Task 3: Based on self-collected images and utilizing AI algorithms, multiple AI models are developed to inspect and identify transportation assets, including assessing pavement marking conditions, identifying the various traffic signs, and detecting common litter on the roads. The performance of each model is evaluated. Task 4: An AI prototype model is also developed to identify concrete barriers and steel guardrails.

Transportation asset management requires timely information collection to inform relevant maintenance practices. Traditional data collection methods often necessitate manual operation or the use of specialized equipment, e.g., light detection and ranging (LiDAR), which can be labor-intensive and costly to implement. With advancements in computing techniques, artificial intelligence (AI) has emerged as a powerful tool for automatically detecting objects in images and videos. Therefore, this project aims to develop an implementable AI package that streamlines the inspection of transportation assets through automated processes. Specifically, a smartphone was mounted on the vehicle’s front windshield to record videos of transportation assets on both highways and local roads in Utah. These videos were then converted and processed into labeled images, which served as training and test datasets for the AI algorithms. Based on a deep learning framework, i.e., You Only Look Once (YOLO), we developed accurate and efficient AI algorithms to automatically detect and identify transportation assets, which include pavement marking issues, traffic signs, litter and trash, and steel guardrails and concrete barriers. The developed AI models demonstrate good performance in identifying targeted objects, achieving over 85% accuracy. The AI package developed in this project is expected to enable timely and efficient information collection for transportation assets, thereby improving road safety.

Bakgrund Diabetes mellitus är ett samlingsnamn för olika diabetessjukdomar, däribland typ 1 diabetes och typ 2 diabetes. Typ 1 diabetes och typ 2 diabetes är kroniska sjukdomar där insulinproduktionen helt har upphört eller inte är tillräckligt för att täcka kroppens behov. Den tekniska utvecklingen inom hälso-och sjukvård har blivit alltmer digitaliserad och skapat förutsättningar för en ny typ av stöd med mobila applikationer. Egenvård är en viktig del av diabetesbehandlingen och mobila applikationer kan vara ett hjälpmedel för personer med diabetes. Sjuksköterskans roll behöver anpassas för att möta kraven inom en mer digitaliserat hälso-och sjukvårds miljö. Avsikten var att ge patienten adekvat information och utbildning av mobila applikationer för att stärka patientens egenvårdsförmåga. Syfte Syftet är att belysa patienters upplevelser och erfarenheter av mobila applikationer som stöd för egenvård med diabetes typ 1 och typ 2. Metod Studien genomfördes som en icke-systematisk litteraturöversikt som inkluderade 14 vetenskapliga artiklar från databaserna PubMed och CINAHL. De vetenskapliga artiklarna var publicerade från de fem senaste åren och var av både kvalitativ och kvantitativ ansats. Vidare hade studien en systematisk struktur där samtliga artiklarna hade kvalitetsgranskat i enlighet med Sophiahemmets bedömningsunderlag. Resultatet erhölls genom en integrerad dataanalys. Resultat Personernas upplevelser och erfarenheter sammanställdes i tre huvudkategorier och flera underkategorier. Av de 14 utvalda artiklarna var sex kvalitativa, fem kvantitativa och tre utav en mixad metod. Resultaten visar på en övervägande positiv respons mot mobila applikationer, där de bidrar till förbättrad egenvård och engagemang. Trots detta kvarstår frågor om användarvänlighet och tekniska begränsningar. Slutsats Mobila applikationer har stor potential att förbättra diabetesvården genom att stärka patienters egenvård och erhålla viktig information om sjukdomen till vårdgivaren. F, Background Diabetes mellitus is an umbrella term for various diabetes diseases, including type 1 diabetes and type 2 diabetes. Type 1 and type 2 diabetes are chronic conditions where insulin production has completely ceased or is insufficient to meet the body's requirements. The technological advancements in healthcare have gradually become more digitalized and created opportunities for a new type of support through mobile applications. Self-care is a crucial component of diabetes treatment, and mobile applications can serve as a tool for patients with diabetes. The nurse’s role needs to be adapted to meet the demands of a more digitized healthcare environment. This is to provide patients with adequate information and education on mobile applications to strengthen the patient's self-care ability. Aim The purpose is to shed light on patients' experiences and perceptions of mobile applications as support for self-care in type 1 and type 2 diabetes. Method The study was conducted as a non-systematic literature review, which included 14 scientific articles retrieved from the databases PubMed and CINAHL. These articles were published within the last five years and encompassed both qualitative and quantitative approaches. Furthermore, the study followed a systematic structure where all articles were quality-assessed according to Sophiahemmet's evaluation criteria. The results were obtained through an integrated data analysis. Results The individuals' experiences and perceptions were compiled into three main categories and several subcategories. Out of the 14 selected articles, six were qualitative, five were quantitative, and three utilized a mixed methods approach. The results indicate a predominantly positive response towards mobile applications, as they contribute to improved self-care and engagement. However, questions regarding usability and technical limitations remain. Conclusions Mobile applications have significant potential to enhance diabetes care by empowering

Introduktion: Fysisk aktivitet är viktigt för både psykiskt och fysiskt välbefinnande. Idag är vi allt mer fysisk inaktiva och stillasittande ökar. Fysisk aktivitet minskar risken för bland annat hjärt- och kärlsjukdomar och typ 2-diabetes. För de som har typ 2- diabetes eller graviditetsdiabetes funkar fysisk aktivitet även behandlande. I takt med digitaliseringens framfart ökar hälsoappar. Dessa har potential att främja hälsosamma beteendeförändringar. Syfte: Syftet med studien var att utvärdera vad för faktorer i hälsoappar som gör att vissa hälsoappar är effektiva för att öka fysisk aktivitet hos vuxna individer med risk för typ 2-diabetes, diagnostiserad typ 2- diabetes eller graviditetsdiabetes. Metod: Studien genomfördes som en systematisk litteraturstudie med 12 vetenskapliga artiklar. Studien analyserades genom en tematisk analys. Resultat: Effektiva appar är multifaktoriella, fler faktorer påverkar. Analysen genererades i tre teman; användarupplevelse och engagemang, beteendeteorier och funktioner i hälsoappar. Dessa tre teman är viktiga för att lyckas med beteendeförändring i en hälsoapp. Användarupplevelse och engagemang kan främjas genom regelbunden feedback. Även individanpassade mål kan ge bra effekt på engagemanget för hälsobeteenden. Appdesign som integrerar beteende teorier kan stödja motivation och hjälpa användare att övervinna hinder för fysisk aktivitet. Slutsats: Effektiva hälsoappar är multifaktoriella och kräver integration av funktioner som förbättrar användarupplevelsen, inkorporerar beteendeteorier och anpassas efter målgruppen för att stödja beteendeförändring. Framtida forskning bör fokusera på att utvärdera del ångsiktiga effekterna av digitala hälsointerventioner för att främja hållbara beteendeförändringar över tid., Introduction: Physical activity is crucial for both physical and mental well-being. Unfortunately, society is becoming increasingly physically inactive, leading to more sedentary lifestyles. Engaging in physical activity reduces the risk of diseases such as cardiovascular conditions and type 2 diabetes. For individuals with type 2 diabetes or gestational diabetes, physical activity can be an important part of treatment. The rise of digitalization has brought about a proliferation of health apps, which have the potential to promote healthy behavior changes. Purpose: The purpose of the study was to evaluate the factors in health apps that make some of them effective in increasing physical activity among adults at risk for type 2 diabetes, diagnosed with type 2 diabetes, or gestational diabetes. Method: The study was conducted as a systematic literature review with 12 scientific articles. The analysis was performed using thematic analysis. Results: Effective health apps are multifactorial, influenced by multiple factors. The analysis generated three themes: user experience and engagement, behavioral theories, and app functionalities. These three themes are crucial for achieving behavior change in a health app. User experience and engagement can be enhanced through regular feedback. Additionally, personalized goals can positively impact engagement in health behaviors. App design that integrates behavioral theories can support motivation and help users overcome barriers to physical activity. Conclusion: Effective health apps are multifactorial and require the integration of features that enhance user experience, incorporate behavior theories, and are tailored to the target audience to support behavior change. Future research should focus on evaluating the long-term effects of digital health interventions to promote sustainable behavior changes over time.

In today's digital age, the concept of combining finance and technology called FinTech, has revolutionized financial services, making them more accessible through mobile applications. However, a significant knowledge gap exists among older adults (aged 70-85), who face usability challenges with these banking apps (Wahab et al.,2021). Despite an increase in Internet use among older age groups in recent years (SCB, 2022), many still struggle with FinTech applications due to physical, cognitive, and motivational factors (Gomez-Hernandez et al., 2023). The shift towards digital services and lack of familiarity is something that leads to older adults feeling excluded (Clarke, 2022). This study aims to gather and evaluate previous design guidelines regarding mobile UI and UX for older adults, with the goal of finding ways to improve the general user interaction, inclusivity, and independence for this demographic in Sweden. Previous guidelines and recommendations were gathered and existing Swedish banking apps were looked into. These guidelines and findings were then used to create a prototype of a fictitious banking app that was tested by usability testing. Five participants aged 71-82 were recruited through purposive sampling. During the data collection, qualitative methods such as semi-structured interviews and usability tests were used. Participants shared their experiences with mobile apps, completed tasks in the prototype while thinking aloud, and provided feedback. This helped to understand what is lacking in FinTech apps and how previous guidelines work for older adults. Thematic analysis revealed that FinTech mobile applications often struggle with small text size, information overload, and navigation difficulties, leading to exclusion in digital society. While some older participants found banking apps easy to use, others faced significant challenges, highlighting the need for improved user interaction. Established design guidelines and recommendations provide a

OBJECTIVE: Mobile health (mHealth) services suffer from high attrition rates yet represent a viable strategy for adults to improve their health. There is a need to develop evidence-based mHealth services and to constantly evaluate their feasibility. This study explored the acceptability, usability, engagement and optimisation of a co-developed mHealth service, aiming to promote healthy lifestyle behaviours. METHODS: The service LongLife Active® (LLA) is a mobile app with coaching. Adults were recruited from the general population. Quantitative results and qualitative findings guided the reasoning for the acceptability, usability, engagement and optimisation of LLA. Data from: questionnaires, log data, eight semi-structured interviews with users, feedback comments from users and two focus groups with product developers and coaches were collected. Inductive content analysis was used to analyse the qualitative data. A mixed method approach was used to interpret the findings. RESULTS: The final sample was 55 users (82% female), who signed up to use the service for 12 weeks. Engagement data was available for 43 (78%). The action plan was the most popular function engaged with by users. The mean scores for acceptability and usability were 3.3/5.0 and 50/100, respectively, rated by 15 users. Users expressed that the service's health focus was unique, and the service gave them a 'kickstart' in their behaviour change. Many ways to optimise the service were identified, including to increase personalisation, promote motivation and improve usability. CONCLUSION: By incorporating suggestions for optimisation, this service has the potential to support peoples' healthy lifestyle behaviours.

Objective: Fibromyalgia (FM) is a prevalent pain syndrome with significant healthcare and societal costs. The aim of the SMART-FM-SP study is to determine the effectiveness, cost-utility, and physiological effects in patients with FM of a digital intervention (STANZA (R)) currently marketed in the United States, which delivers smartphone-based, fully self-guided Acceptance and Commitment Therapy (Digital ACT) for treating FM-related symptoms. Methods: A single-site, parallel-group, superiority, randomized controlled trial (RCT) will be conducted, including a total of 360 adults diagnosed with FM. Individuals will be randomly allocated (1:1:1) to treatment as usual (TAU), to TAU plus 12 weeks of treatment with Digital ACT, or to TAU plus 12 weeks of treatment with digital symptom tracking (i.e. FibroST). Participants will be assessed at baseline, post-treatment, and 6-month follow-up. An intention-to-treat analysis using linear mixed models will be computed to analyze the effects of Digital ACT on functional impairment (primary outcome), as measured by the Fibromyalgia Impact Questionnaire Revised at 6 months from the inception of the treatment. Secondary outcomes include impression of change, symptoms of distress, pain catastrophising, quality of life, cost-utility, and selected biomarkers (cortisol and cortisone, immune-inflammatory markers, and FKBP5 gene polymorphisms). The role of ACT-related processes of change will be tested with path analyses. Conclusions: This study is the first RCT that tests Digital ACT for Spanish patients with FM. Results will be important not only for patients and clinicians, but also for policy makers by examining the cost-utility of the app in a public healthcare context.

This thesis addresses the design of mobile applications for elderly users, focusing on cognitive and visual challenges. Using a user-centered approach, the study combines literature review and expert interviews in Europe to identify effective design practices. Key findings highlight that elderly users struggle with small text, low-contrast visuals, and complex navigation, worsened by cognitive declines. Recommended design strategies include larger text and buttons, high-contrast color schemes, simplified navigation, and voice navigation features. These insights are compiled into guidelines for developers, stressing the importance of user research, iterative design, and adaptive features. Inclusive design can improve the digital experience for the elderly, enhancing their engagement and accessibility.

With the emergence of video-streaming applications extending beyond the entertainment section and becoming more accessible in everyday interactions. However, developing a video live-streaming application and choosing which services to use can be a hard task whether to go for a pre-built solution or build your own foundation. When deciding which services to include in your product a lot of aspects have to be taken into consideration, regarding performance and so on. The aim of this thesis is to emphasize the importance and relevancy of the development efficiency metric, including soft metrics such as maintainability and understandability. During this thesis three different video live-streaming services were chosen to be compared against each other regarding development efficiency: WebRTC, Agora and GetStream. Baseline applications were developed where each respective chosen service was implemented and further evaluated based on development efficiency. According to development efficiency, GetStream had the highest score compared to the other services and therefore should be the easiest to maintain and understand. On the other hand, WebRTC had the lowest development efficiency score and therefore should be the hardest to maintain and understand, however by choosing WebRTC you are not opting into a third-party service and therefore have more control over the implementation. Overall, it can be concluded that deciding which services to include a multitude of aspects has to be considered.

Background: The usage of complex artificial intelligence (AI) models demands expensive computational resources. While currently, available high-performance computing environments can support such complexity, the deployment of AI models in mobile devices, which is an increasing trend, is challenging. Environments with low computational resources imply limitations in the design decisions during the AI-enabled software engineering lifecycle that balance the trade-off between the accuracy and the complexity of the mobile applications. Objective: Our objective is to systematically assess the trade-off between accuracy and complexity when deploying complex AI models (e.g. neural networks) to mobile devices in pursuit of greener AI solutions. We aim to cover (i) the impact of the design decisions on the achievement of high-accuracy and low resource-consumption implementations; and (ii) the validation of profiling tools for systematically promoting greener AI. Method: We implement neural networks in mobile applications to solve multiple image and text classification problems on a variety of benchmark datasets. We then profile and model the accuracy, storage weight, and time of CPU usage of the AI-enabled applications in operation with respect to their design decisions. Finally, we provide an open-source data repository following the EMSE open science practices and containing all the experimentation, analysis, and reports in our study. Results: We find that the number of parameters in the AI models makes the time of CPU usage scale exponentially in convolutional neural networks and logarithmically in fully-connected layers. We also see the storage weight scales linearly with the number of parameters, while the accuracy does not. For this reason, we argue that a good practice for practitioners is to start small and only increase the size of the AI models when their accuracy is low. We also find that Residual Networks (ResNets) and Transformers have a higher baseline cost in t, This work has been partially supported by the GAISSA project (TED2021-130923B-I00, which is funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR); and, by the “Beatriz Galindo” Spanish Program (BEAGAL18/00064)., Peer Reviewed, Postprint (author's final draft)

This article explores the utilization of interactive tools in the creation of interactive exercises for German language lessons and the assessment of learners' performance. The concept of didacticization, which involves adapting educational materials and authentic texts, is examined in detail. The article outlines the essential steps for didacticizing authentic texts and provides examples of how digital tools can be employed to accomplish this task, thereby facilitating their integration into the educational process. The primary objectives of employing these tools are to foster language competence development and evaluate the attainment of learning outcomes. The study establishes that the utilization of mobile applications and Web 2.0 interactive tools effectively organizes students' work both within and outside the classroom, leading to the enhancement and refinement of their language competence.

Background: Home blood pressure monitoring has many benefits, even more so, in populations prone to high blood pressure, such as persons with diabetes. Objective: The purpose of this research was to validate the QardioArm mobile device in a sample of individuals with noninsulin-dependent type 2 diabetes in accordance with the guidelines of the second International Protocol of the European Society of Hypertension. Methods: The sample consisted of 33 patients with type 2 diabetes. To evaluate the validity of QardioArm by comparing its data with that obtained with a digital sphygmomanometer (Omron M3 Intellisense), two nurses collected diastolic blood pressure, systolic blood pressure, and heart rate with both devices. Results: The analysis indicated that the test device QardioArm met all the validation requirements using a sample population with type 2 diabetes. Conclusions: This paper reports the first validation of QardioArm in a population of individuals with noninsulin-dependent type 2 diabetes. QardioArm for home monitoring of blood pressure and heart rate met the requirements of the second International Protocol of the European Society of Hypertension., Depto. de Enfermería, Fac. de Enfermería, Fisioterapia y Podología, TRUE, pub

In recent years, there has been an increased interest in the environment, and various efforts has been made in order to reduce human impact upon the planet. One such effort is transitioning the transport industry from using fossil-fuels to more sustainable fuels, such as electricity. This has led to a heightened interest in electric vehicles. Although electric vehicles have risen in popularity, they still face some challenges. One of these challenges is the lack of charging stations, and the hassle of charging the car. The complicated charging experience has even been expressed as a barrier, making some people hesitant to buy an electric vehicle. This calls for making the charging experience more intuitive and easy. One possible way to achieve this is by providing the drivers of electric vehicles with complementary tools, such as mobile applications, to aid them with charging. However, for these tools to be effective, it is important that they are designed with usability in mind. The focus of this thesis is enhancing usability in mobile applications for charging stations. In order to achieve this, a mobile application called Greencharge Infra is used as a case. The current design of this application is evaluated using Nielsen’s 10 heuristics of usability. Additionally, a user survey is sent out to representative users in order to better understand the user needs in these kinds of applications. Based on the findings from the evaluation and the user survey, and knowledge from established usability principles, a redesigned application is proposed. This proposal is later tested on representative users to ensure that the new design is living up to usability expectations. During the test, five criteria of usability are evaluated, these are efficiency, errors, satisfaction, memorability and learnability. The usability test result show that the new design of the application was efficient, induced few errors, was pleasant to use, and easy to learn. Therefore, the overall res

Introduction: mobile applications (Apps) are transforming learning perspectives in healthcare settings. Objective: to evaluate the effectiveness and usability of EpiApp as a support tool in the consultation and resolution of epidemiology problems, compared to traditional methods (TM). Methodology: EpiApp was designed using a methodological structure. A randomized controlled experimental study was conducted with two groups: EpiApp and TM. A standardized questionnaire and the MARS scale were used to evaluate effectiveness and usability, respectively. Results: a total of 176 students were randomized: EpiApp (n=88) and TM (n=88). Response time was shorter for EpiApp users (23.99 min [±SD 8.38] vs. 29.16 min [±SD 11.61], p=0.005). There were no differences in the number of correct responses (p=0.635) or the opportunity to pass the questionnaire (p=0.054). Usability was good (MARS>4). Discussion: mobile Apps have great potential to be useful in healthcare education and decision-making in epidemiology, as well as in general medical care. However, it is essential to conduct rigorous studies to ensure that these applications are effective, useful, and easy to use and understand. Conclusions: EpiApp reduced response time in solving epidemiology problems without affecting the number of correct answers. The usability was good, although there is room for improvement in terms of enjoyment, interest, individual adaptability, and interactivity. The effectiveness and usability of EpiApp need to be validated through more rigorous studies in different contexts., Introducción: las aplicaciones móviles (Apps) están transformando las perspectivas de aprendizaje en entornos de salud. Objetivo: evaluar la efectividad y usabilidad de EpiApp como herramienta de apoyo en la consulta y resolución de problemas de epidemiología comparada con métodos tradicionales (MT). Materiales y métodos: se diseñó EpiApp usando una estructura metodológica. Se realizó un estudio experimental controlado y aleatorizado con dos grupos: EpiApp y MT. La fectividad se evaluó con un cuestionario estandarizado y la usabilidad con la escala MARS. Resultados: se aleatorizaron 176 estudiantes: EpiApp (n=88) y MT (n=88). El tiempo de respuesta fue menor para los usuarios de EpiApp (23,99 min [± DE 8,38] vs. 29,16 min [± DE 11,61], p = 0,005). No hubo diferencias en el número de respuestas correctas (p = 0,635), ni en la oportunidad de aprobar el cuestionario (p = 0,054). La usabilidad fue buena (MARS > 4). Discusión: las Apps móviles tienen un gran potencial para ser útiles en la educación en salud y en la toma de decisiones en epidemiología, así como en la atención médica en general. No obstante, es esencial realizar estudios rigurosos para garantizar que estas aplicaciones sean efectivas, útiles, fáciles de usar y comprender. Conclusiones: EpiApp redujo el tiempo de respuesta en la resolución de problemas de epidemiología sin afectar el número de respuestas correctas. La usabilidad fue buena, aunque hay oportunidad de mejora en diversión, interés, adaptabilidad individual e interactividad. La efectividad y usabilidad de EpiApp requiere ser validada con estudios más rigurosos y en diferentes contextos.

Objective: To describe the characteristics of the use of a preventive app by the Peruvian population during its first year of operations between 2019 and 2020. Materials and methods: A descriptive, cross-sectional and retrospective study, in which the database of the Salud Total preventive app, concerning the demographic characteristics of and usage by the Peruvian population, was reviewed during its first year of operations. Sociodemographic variables (sex and age) and usage (health data, pathological findings, frequency of use, and type and number of services requested) were analyzed. The analysis and processing of descriptive data were performed using Microsoft Excel Results: The number of users who downloaded and registered in the app was 9,737 people. A similar sex ratio was found, and the 21- to 50-year-old group prevailed. A total of 2,254 health data voluntarily entered by the users, out of which the most reported were weight and height (45.3 %), blood pressure (20.4 %), hemoglobin (17.1 %) and waist circumference, were collected. Pathological findings such as high blood pressure (25.7 %) and overweight (58.5 %), among others, were found in the recorded data. Likewise, 1,072 different health services were requested through the app, among which medical care (58 %) and laboratory tests (32 %) stood out. Conclusions: Users were able to register in and interact with a preventive app in order to monitor their well-being. Moreover, they used it both to add their health data and to request remote medical care. This could be a new intervention tool for taking preventive actions, managing timely care of diseases and offering a new data source for health research., Objetivo: Describir las características del uso de una app preventiva en la población peruana durante su primer año de operaciones, en el periodo comprendido entre el 2019 y 2020. Materiales y métodos: Estudio de tipo descriptivo, transversal y retrospectivo, donde se revisó la base de datos de la app preventiva Salud Total respecto a las características demográficas y forma de uso en la población peruana durante su primer año de operaciones. Se analizaron las variables sociodemográficas (sexo y edad) y las formas de uso (los datos de salud ingresados, los hallazgos patológicos encontrados, la recurrencia de uso y el tipo y la cantidad de servicios solicitados). El análisis y procesamiento de los datos descriptivos se realizaron con el programa Microsoft Excel. Resultados: El número de usuarios que descargaron y se registraron en la app fue 9737 personas. Con respecto al sexo, la proporción fue similar, y el grupo etario predominante fue entre 21 y 50 años. Se obtuvieron 2304 datos de salud que los usuarios registraron voluntariamente, de los cuales los más reportados fueron el peso y la talla (45,3 %), seguidos de la presión arterial (20,4 %), la hemoglobina (17,1 %) y la circunferencia abdominal. Se encontraron hallazgos patológicos en los datos registrados tales como la presión arterial elevada (25,7 %) y el peso elevado (58,5 %), entre otros. Asimismo, los usuarios solicitaron por medio de la app un total de 1072 servicios de salud de diversos tipos, entre los cuales destacan la atención médica (58 %) y el laboratorio clínico (32 %). Conclusiones: Los usuarios pudieron registrarse e interactuar con una app preventiva con el fin de controlar su bienestar, y la usaron tanto para añadir sus datos de salud como para solicitar servicios médicos de forma remota, lo que puede significar una nueva herramienta de intervención para direccionar acciones preventivas, la atención oportuna de enfermedades y una nueva fuente de datos para la investigación en salud.

Background: Resting tremor is a motor manifestation present in most Parkinson’s disease (PD) patients. For its assessment, several scales have been created, but mobile applications could help in objectively assessing resting tremor in PD patients in person and/or remotely in a more ecological scenario. (2) Methods: a systematic review following the PRISMA recommendations was conducted in scientific databases (PubMed, Medline, Science Direct, Academic Search Premier, and Web of Science) and in the main mobile application markets (Google Play, iOS App Store, and Windows Store) to determine the applications available for the assessment of resting tremor in patients with PD using only the measurement components of the phone itself (accelerometers and gyroscopes). (3) Results: 14 articles that used mobile apps to assess resting tremor in PD were included, and 13 apps were identified in the mobile application markets for the same purpose. The risk of bias and of applicability concerns of the articles analyzed was low. Mobile applications found in the app markets met an average of 85.09% of the recommendations for the development of medical mobile applications. (4) Conclusions: the use of mobile applications for the evaluation of resting tremor in PD patients has great potential, but validation studies for this purpose are scarce., Sección Deptal. de Radiología, Rehabilitación y Fisioterapia (Enfermería), Fac. de Enfermería, Fisioterapia y Podología, TRUE, pub

BACKGROUND // Since 2003, the National Cancer Institute (NCI) of the United States of America has been one of the world leaders in classifying adverse effects (AEs). Currently, smartphones allow, among many other things, the monitoring of these AEs of chemotherapy from home to improve the safety and quality of life of patients. The aim was to perform a descriptive comparative analysis of the AEs content of the Abeona Health® app and the latest version of the CTCAE (Common Terminology Criteria for Adverse Events). METHODS // The Abeona Health® app and the CTCAE v5 guide were used. Subsequently, the most recurrent AEs in the existing chemotherapy treatment were analysed according to the NCI and the Spanish Society of Medical Oncology (SEOM) and finally, whether patients could identify them. RESULTS // The CTCAE v5 (collects 837 AEs), where two hundred and twenty-five are signs and symptoms. The NCI classifies fifty- five signs and symptoms as the most recurrent, and the SEOM sixteen, of which fifteen coincide with the NCI. The Abeona Health® application has seven AEs, all included in the CTCAE v5. Of these seven, six appear in the NCI lists of most recurrent AEs and four in the SEOM list, all identifiable by the patient. CONCLUSIONS // The Abeona Health® app is considered adequate for the patient participation in their self-care, although some fields could be expanded., FUNDAMENTOS // Desde 2003, el Instituto Nacional del Cáncer (NCI) de los Estados Unidos de América ha sido uno de los líderes mundiales en la clasificación de los Efectos Adversos (EA). Actualmente, los teléfonos inteligentes permiten, entre otras muchas cosas, la monitorización de estos EA de la quimioterapia desde el domicilio para mejorar la seguridad y la calidad de vida de los pacientes. El objetivo de este estudio fue realizar un análisis comparativo descriptivo del contenido de los EA de la aplicación Abeona Health® y la última versión de los CTCAE (Common Terminology Criteria for Adverse Events). MÉTODOS // Se utilizó la app Abeona Health® y la guía CTCAE v5. Posteriormente, se analizaron los EA más recurrentes en el tratamiento quimioterápico, según la NCI y la Sociedad Española de Oncología Médica (SEOM) y, finalmente, si los pacientes podían identificarlos. RESULTADOS // El CTCAE v5 recoge 837 EA, donde 225 son signos y síntomas. El NCI clasifica cincuenta y cinco signos y síntomas como los más recurrentes, y la SEOM dieciséis, de los cuales quince coinciden con el NCI. La aplicación Abeona Health® dispone de siete EA, y todos se incluyen en el CTCAE v5. De estos siete, seis aparecen en las listas de EA más recurrentes del NCI y cuatro en la de la SEOM, todos ellos identificables por el paciente. CONCLUSIONES // La app de Abeona Health® se considera adecuada para la participación del paciente en su autocuidado, si bien se podrían ampliar algunos campos.

BACKGROUND: Post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly prevalent, and co-occurring among post-9/11 veterans. Mobile health (mHealth) applications, specifically those focused on mindfulness-based techniques, may be an effective avenue to intervene with veterans who cannot or will not seek care at traditional in-person settings. Thus, to address areas of improvement in mHealth for veterans, we developed Mind Guide and prepared it for testing in a pilot randomized controlled trial (RCT) with veterans. METHODS: We have completed phase 1 (treatment development) and Phase 2 (beta test) of our mobile mHealth app, Mind Guide. In this paper we describe the methods for Phase 1 as well as results for our beta test (n = 16; inclusion criteria included screen for PTSD, AUD, a post-9/11 veteran, and not currently receiving treatment) for Mind Guide as well as outline procedures for our pilot RCT of Mind Guide (Phase 3). The PTSD Checklist, self-reported alcohol use, the Perceived Stress Scale, Penn Alcohol Craving Scale, and the Emotion Regulation Questionnaire were used. RESULTS: Results of our beta test of Mind Guide show promising past 30 day effects on PTSD (d = -1.12), frequency of alcohol use (d = -0.54), and alcohol problems (d = -0.44), and related mechanisms of craving (d = -0.53), perceived stress (d = -0.88), and emotion regulation (d = -1.22). CONCLUSION: Our initial beta-test of Mind Guide shows promise for reducing PTSD and alcohol related problems among veterans. Recruitment is ongoing for our pilot RCT in which 200 veterans will be recruited and followed up for 3 months. CLINICALTRIALS: gov Identifier: NCT04769986.

INTRODUCTION: Deep brain stimulation (DBS) is an effective treatment for Parkinsons disease (PD), but its efficacy is tied to DBS programming, which is often time consuming and burdensome for patients, caregivers, and clinicians. Our aim is to test whether the Mobile Application for PD DBS (MAP DBS), a clinical decision support system, can improve programming. METHODS: We conducted an open-label, 1:1 randomized, controlled, multicenter clinical trial comparing six months of SOC standard of care (SOC) to six months of MAP DBS-aided programming. We enrolled patients between 30 and 80 years old who received DBS to treat idiopathic PD at six expert centers across the United States. The primary outcome was time spent DBS programming and secondary outcomes measured changes in motor symptoms, caregiver strain and medication requirements. RESULTS: We found a significant reduction in initial visit time (SOC: 43.8 ± 28.9 min n = 37, MAP DBS: 27.4 ± 13.0 min n = 35, p = 0.001). We did not find a significant difference in total programming time between the groups over the 6-month study duration. MAP DBS-aided patients experienced a significantly larger reduction in UPDRS III on-medication scores (-7.0 ± 7.9) compared to SOC (-2.7 ± 6.9, p = 0.01) at six months. CONCLUSION: MAP DBS was well tolerated and improves key aspects of DBS programming time and clinical efficacy.

Technology incorporation in speech therapy has been growing over the years. Mobile applications are among the adoptions that facilitate delivering speech therapy services. The situation in Malaysia is discouraging because there are not enough speech-language pathologists (SLPs) to serve the growing number of populations. Despite the abundance of available speech therapy mobile applications in the market, there is a lack of information focusing on the SLP’s knowledge and usage perspectives, especially in Malaysia. The objectives of this study are to describe the knowledge and usage perspectives of speech therapy mobile applications among SLPs in Malaysia and to analyze the instructional features and functional features relationships within the perspectives of SLPs. Surveys are established in three parts, with demographic questions in Part A, Likert scale responses for statements in Part B, and open-ended questions in Part C. This study is co-designed to relate to the results from an initial study that adopted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and features analysis. The data from the initial study includes a review of 161 apps out of 1797 that have been identified. Five instructional features and nine functional features are presented. There are 35 SLPs participating in the survey. Their responses demonstrate evidence of SLPs’ knowledge and usage of speech therapy mobile applications. We will propose a conceptual framework for the features of speech therapy mobile applications, using people with aphasia as a point of reference for users with speech and language disorders.

Objective: To evaluate whether, in patients with trauma and soft tissue injuries of the wrist, hand and/or fingers, an exercise program performed on a touchscreen tablet-based app reduces the consumption of face-to-face resources and improves clinical recovery, compared to a conventional home exercise program prescribed on paper. Design: Pragmatic, multicentre, parallel, two-group, controlled clinical trial with blinded assessor. Participants and setting: Eighty-one patients with traumatic bone and/or soft tissue injuries of the hand, wrist and/or fingers recruited in four hospitals of the Andalusian Public Health System. Interventions: The experimental group received a home exercise program using a touchscreen tablet application and the control group received a home exercise program on paper. Both groups received the same treatment of face-to-face physiotherapy. Primary outcome: Number of physiotherapy sessions. Secondary outcomes were the duration of physiotherapy and clinical variables such as functional ability, grip strength, pain and manual dexterity. Results: The experimental group required fewer physiotherapy sessions (MD -11,5 sessions; 95% CI -21.4 to -1.4), showed a shorter duration of physiotherapy (MD -3.8 weeks, 95% CI -7 to -1) and had better recovery of grip strength, pain and dexterity compared to the control group. Conclusions: In patients with trauma and soft tissue injuries of the wrist, hand and/or fingers, an exercise program performed on a touchscreen tablet-based app in combination with face-to-face physiotherapy reduces the consumption of face-to-face resources and improves clinical recovery, compared to conventional home exercise program prescribed on paper.