Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach’s feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.

Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults.

In recent years, research in tracking and assessing wound severity using computerized image processing has increased. With the emergence of mobile devices, powerful functionalities and processing capabilities have provided multiple non-invasive wound evaluation opportunities in both clinical and non-clinical settings. With current imaging technologies, objective and reliable techniques provide qualitative information that can be further processed to provide quantitative information on the size, structure, and color characteristics of wounds. These efficient image analysis algorithms help determine the injury features and the progress of healing in a short time. This paper presents a systematic investigation of articles that specifically address the measurement of wounds’ sizes with image processing techniques, promoting the connection between computer science and health. Of the 208 studies identified by searching electronic databases, 20 were included in the review. From the perspective of image processing color models, the most dominant model was the hue, saturation, and value (HSV) color space. We proposed that a method for measuring the wound area must implement different stages, including conversion to grayscale for further implementation of the threshold and a segmentation method to measure the wound area as the number of pixels for further conversion to metric units. Regarding devices, mobile technology is shown to have reached the level of reliable accuracy.

The digital divide remains a persistent challenge for older adults, who often face structural, cognitive, and motivational barriers to effective participation in digital environments. This study presents and empirically evaluates a blended game-based assessment (GBA) and game-based learning (GBL) approach designed to support digital competences development in later life. Two complementary analogue-hybrid games, INFINITY and DiGiUP, were developed by operationalising the European DigComp 2.2 framework within concrete game mechanics. Motivational design was grounded in self-determination theory (SDT). INFINITY focuses on baseline competence elicitation and confidence building through structured board–game interaction, while DiGiUP integrates scenario-based digital challenges to combine assessment and learning within gameplay. The study was conducted within the Erasmus+ DIGIBLEND project and involved 45 older adults participating in facilitated small-group sessions in Slovenia. Digital competence outcomes were analysed using DigComp proficiency levels, and motivational experiences were measured using an SDT-based Gameplay Questionnaire. Quantitative analyses revealed statistically significant increases in demonstrated proficiency levels under DiGiUP compared to INFINITY across Information and Data Literacy, Safety, and Problem Solving. Qualitative observations and questionnaire responses further indicated high perceived competence, relatedness, and autonomy, suggesting strong motivational engagement in the game-based environment. While the exploratory design and single-country convenience sample limit generalisability and causal inference, the study provides design-informed empirical evidence that a blended GBA–GBL pathway can be feasibly implemented in real-world adult education settings. By translating competence frameworks into playable mechanics and embedding motivational principles within accessible formats, this work offers a replicable model for inclusive digital competence development among older adults.

Background: Artificial intelligence is rapidly disrupting high-level cognitive and analytical labour. Yet, while emerging macroeconomic analyses increasingly map AI exposure across broad demographic boundaries, there is a scarcity of qualitative research exploring how these shifts are experienced by highly skilled older immigrant professionals navigating the workforce at the compounding intersection of age, migration status, and professional isolation. Methods: This qualitative study explored the lived experiences of 32 older immigrant professionals aged 50–55 across four AI-disrupted sectors: Healthcare, Finance, Marketing, and Information Technology. Semi-structured interviews averaging 55.4 min were analysed using reflexive thematic analysis. Participants also completed the General Attitudes Toward Artificial Intelligence Scale (GAAIS) and the Perceived Job Insecurity Scale, used descriptively to contextualise interview narratives. Results: Ten themes revealed a pattern termed Double Displacement: the compounding of chronic socio-cultural exclusion from domestic professional networks with the acute techno-cognitive demotion from autonomous expert to passive machine validator. A further finding, the foreignness premium, describes how AI systems expropriate the distinctive multicultural and regional professional assets that migration history conferred, turning these assets into drivers of late-career obsolescence. Additional themes document enforced professional silence near the retirement threshold, fragmented cross-border pension entitlements as a structural amplifier of displacement, and an identifiable set of buffer conditions under which Double Displacement does not fully materialise. Discussion: Contextualised within the WHO Active Ageing framework, cognitive automation simultaneously undermines income security, social participation, and psychosocial health among this population. The findings call for age-decoupled reskilling provision, anonymous organisational reporting channels for AI-related dissent, and cross-border pension coordination to address the specific vulnerabilities of internationally mobile professionals approaching retirement.