Search Results (6)

Introduction: Few applications worldwide focus on psychosocial rehabilitation, and none specifically address psychosocial rehabilitation projects. This justifies the need for an application to assist mental health professionals in constructing and managing such projects in the Brazilian mental health scenario. Objective: This study aimed to present a web application, the “Psychosocial Rehabilitation Application” (Psychosocial Rehab App), and describe its development in detail through a technological survey conducted between May 2024 and February 2025. Method: The development process of the web app was carried out in the following four stages, adapted from the Novak method: theoretical basis, requirements survey, prototyping, and development with alpha testing. The active and collaborative participation of the main researcher (a psychiatric nurse) and two undergraduate software engineers, supervised by a software engineer and a professor of nursing and psychology, was essential for producing a suitable operational product available to mental health professionals. Interactions were conducted via video calls, WhatsApp, and email. These interactions were transcribed using the Transkriptor software and inserted into the ATLAS.ti software for thematic analysis. Results: The web app “Psychosocial Rehabilitation Application” displays a home screen for registration and other screens structured into the stages of the psychosocial rehabilitation project (assessment, diagnosis, goals, intervention, agreements, and re-assessment). It also has a home screen, a resource screen, and a function screen with options to add a new project, search for a project, or search for mental health support services. These features facilitate the operation and streamline psychosocial rehabilitation projects by mental health professionals. Thematic analysis revealed three themes and seven codes describing the entire development process and interactions among participants in collaborative, interrelational work. A collaborative approach between researchers and developers was essential for translating the complexity of the psychosocial rehabilitation project into practical and usable functionalities for future users, who will be mental health professionals. Discussion: The Psychosocial Rehab App was developed collaboratively by mental health professionals and developers. It supports the creation of structured rehabilitation projects, improving decision-making and documentation. Designed for clinical use, the app promotes autonomy and recovery by aligning technology with psychosocial rehabilitation theory and the actual needs of mental health services. Conclusions: The Psychosocial Rehab App was developed through collaborative work between mental health and technology professionals. The lead researcher mediated this process to ensure that the app’s functionalities reflected both technical feasibility and therapeutic goals. Empathy and dialog were key to translating complex clinical needs into usable and context-appropriate technological solutions. Full article

Evidence is increasing for digital health programs targeting the secondary prevention of stroke. We aimed to determine the feasibility of the novel Care Assistant and support Program for people after Stroke (CAPS) or transient ischaemic attack (TIA) by combining person-centred goal setting and risk-factor monitoring through a web-based clinician portal, SMS messages, a mobile application (app), and a wearable device. We conducted a 12-week mixed-methods, open-label feasibility study. Participants (6 months–3 years after stroke or TIA, access to the internet via a smartphone/tablet) were recruited via the Australian Stroke Clinical Registry. Participants set one or two secondary prevention goals with a researcher and provided access and training in technology use. Feasibility outcomes included recruitment, retention, usability, acceptability, and satisfaction. Secondary outcomes included goal attainment, health outcomes, and program costs. Following 600 invitations, 58 responded, 34/36 (94%) eligible participants commenced the program (one withdrawal; 97% retention), and 10 were interviewed. Participants (27% female, 33% TIA) generally rated the usability of the mobile application as ‘Good’ to ‘Excellent’ (System Usability Scale). Most (94%) agreed the program helped with engagement in health self-monitoring. Overall, 52 goals were set, predominantly regarding exercise (21/52), which were the most frequently achieved (9/21). At 12 weeks, participants reported significant improvements (p < 0.05) in self-efficacy (Cohen’s d = 0.40), cardiovascular health (d = 0.71), and the mental health domain of the PROMIS GH (d = 0.63). CAPS was acceptable, with good retention and engagement of participants. Evaluation of this program in a randomised controlled trial is warranted. Full article

Gender inequalities continue to pose a significant issue across various aspects of life, adversely impacting the well-being of both females and males. These disparities often stem from the ingrained gender stereotypes passed down to young individuals through parental guidance, educational systems, and media portrayal. For this reason, within the psycho-pedagogical field, various intervention models have been developed in recent years, leveraging digital tools to combat stereotypes and enhance well-being among adolescents. The aim of this systematic review is, therefore, to identify studies employing digital tools, particularly serious games, to promote well-being from a gender perspective. The review was conducted using the PRISMA guidelines and collected articles from four databases: Scopus, the Web of Science, PubMed, and PsycInfo. The screening process culminated in the selection of 15 articles. The findings reveal a proliferation of platforms, applications, and programs aimed at promoting well-being by addressing emotional, cognitive (or mental), physical, and sexual health dimensions. Some contributions emphasize nurturing positive attributes within individuals or fostering empowerment as a precursor to well-being. Additionally, certain articles delve into the effect of the COVID-19 pandemic on the well-being of young men and women; in particular, the authors investigated the effect of using an app to improve well-being before and after the pandemic. This systematic review aims to expand the knowledge base on technology-based interventions for social change. It endeavors to empower educators and advance the creation of innovative, evidence-based digital tools that can enhance positive mental health, promote gender equality education, and foster the well-being of young people. Full article

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance. Full article

Sports injuries have become a real health concern. Particularly noticeable is the increasing number of severe sports injuries among young people. Sports injury (SI) is a multifactorial event where many internal and external, proximal and remote factors play a role in a recursive way, including physical and psychological variables. Accordingly, many voices expressing the need of tackling that and several prevention programs have arisen. Nevertheless, different barriers and limitations prevent a wide extension of well-controlled programs, closely monitored by highly specialized professionals in ordinary sports grass-root organizations. These have helped flourishing different low intensity (LI)-interventions and e-Health apps focusing on both physical warmup, training and fitness, and mental skills aimed at reducing athlete’s vulnerability to SIs. This kind of intervention usually uses self-administered techniques and/or non-specialized staff that can effectively monitoring the program. In fact, LI-interventions have shown to be effective coping with different health and psychological issues. However, these interventions face an important challenge: the lack of engagement people usually show. The current paper proposes how gamification can contribute to the engagement to such interventions. Based on the mechanics–dynamics–aesthetics framework to analyze game design, the paper suggests a set of guidelines app- and web-LI interventions aimed at preventing SIs should include to foster motivation and reduce attrition. Full article

Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH₃, CO, NO₂, CH₄, CO₂, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth. Full article