Nuša Farič

BACKGROUND There is debate about the impact of video games on mental health. Understanding the effects of video gaming within families remains limited. OBJECTIVE To understand how video games affect people's mental health and their families' mental health based on the frequency and type of games played METHODS This was a secondary quantitative analysis of an international cross-sectional online survey. The survey data were collected in September 2025 from participants in India, the United Kingdom (UK), and the United States (US). The survey included 32 questions on demographics, video gaming habits, family relationships, mental health, social effects, loneliness, and family connections related to video gaming. RESULTS Of N=2336 people from India, the UK, and the US who completed the survey, n=1558 were eligible for analysis. People who gamed occasionally or regularly reported significantly higher family closeness scores (P<.001). Family gaming frequency (gaming together as a family) was significantly associated with stronger familial bonds (P<.001), while moderate gaming correlated with fewer conflicts, and frequent gaming produced mixed results. Shared family gaming frequency acted as a key predictor of both emotional closeness and communication comfort within households. Educational background did not significantly influence game-type preference (P=.78), with ‘Action and Adventure’ games dominating across all education levels. Participants with a diagnosed mental health condition(s) (n=269; 17.3%) were significantly more likely to report improvement in mood after video gaming (P<.001). The respondents with higher communication openness reported substantially lower loneliness (ρ=−0.68, P<.001). A large proportion, n=1186 (76.1%), would recommend family gaming to other families seeking to improve their relationships, especially when playing ‘Family and Party’ games, which appeared very effective at strengthening relationships with both parents and grandparents. CONCLUSIONS To our knowledge, this is the first international, cross-cultural survey analysis examining the relationship between family video gaming and mental health outcomes. Video gaming, practised in moderation, was associated with significant social and emotional outcomes for mental health across diverse populations. Because the data were cross-sectional and self-reported, interpret the findings as associations rather than causal effects. CLINICALTRIAL N/A

There is an urgent need to provide quality-of-life to a growing population of older adults living independently. Solutions that focus on the person and take into account their preferences and context are recognised as key. We introduce a framework for representing and reasoning about the Activities of Daily Living of older adults living independently at home. The framework integrates data from sensors and data from participants derived from semi-structured interviews, home layouts and additional contextual information, such as the researchers’ observations. These data are used to create formal models, personalised for each participant according to their preferences and context. Requirements specific to each individual are formulated and encoded in Linear Temporal Logic, and a model checker is used to verify whether each is satisfied by the model of the participant’s behaviour. We demonstrate the framework’s generalisability by applying it to two different participants, highlighting its potential to enhance the safety and well-being of older adults ageing in place.

BACKGROUND The global population is ageing at a rapid pace, raising new challenges for maintaining independence and quality of life in later years. Technology has the potential to act as a buffer, supporting successful ageing in place. Among these, unobtrusive sensing technologies are emerging as transformative tools that can monitor and detect deviations in Activities of Daily Living (ADLs) in a non-invasive manner OBJECTIVE This study explored older adults’ perceptions and lived experiences of sensing technologies integrated into their home environments. Using semi-structured interviews and in-home observations, we examined how older individuals interacted with motion, magnetic, and physiological sensors embedded in their everyday routines. METHODS Fourteen adults aged 60+ across 12 diverse socioeconomic households in Edinburgh and surrounding areas participated, contributing 27 interviews conducted both before and after installation. Participants logged their ADLs using either a digital app or a paper diary. Sensors were generally accepted and integrated seamlessly into daily life RESULTS Initial apprehensions about complexity diminished once participants experienced the discreet and passive design, highlighting the importance of unobtrusiveness for older populations. Personalisation of sensor placement and feedback emerged as important, emphasising the need to engage older adults in co-design. While paper-based diaries proved burdensome for some, the introduction of a digital version, shaped by participant feedback, streamlined the process and fostered greater awareness of daily routines, occasionally leading to small behavioural adjustments. Participants valued the potential of sensors for enhancing safety (e.g., fall detection) and supporting independence, and expressed comfort with data collection for research when it was clearly explained and purposeful. CONCLUSIONS This study contributes new insights and to the new research methodologies into the lived experience of older adults with sensing technologies installed directly in their own homes. By addressing a gap in the literature, it provides evidence to inform future research, design, and policy for supporting ageing in place. CLINICALTRIAL N/A

Digital support is a preferred source of information for pregnant women with personalisation being a key selling benefit for pregnancy apps. Pregnancy apps are the most popular type of health app (n= 2,900). To describe the variety and use of personalisation in pregnancy apps. A scoping review was performed using OvidSP databases using MEDLINE, EMBASE, PsychINFO to identify relevant articles up until August 2017. 1,121 articles were identified after excluding duplicates. Six studies met inclusion criteria (one interview study, one online survey, two app reviews; two studies using critical discourse analysis). Different dimensions of personalisation were described based on Blom’s (2000) definition of personalisation. Personalisation in pregnancy apps is largely determined by the functionality of the app (type of pregnancy app). There are no pregnancy apps that use all of the dimensions of personalisation described in this review. Future research should focus on personalising apps to reach underrepresented populations.

[This corrects the article DOI: 10.2196/47562.].

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).

Background: Wikipedia is the largest free online encyclopedia and the seventh most visited website worldwide, containing >45,000 freely accessible English-language medical articles accessed nearly 1.6 billion times annually. Concerns have been expressed about the balance of content related to biological sex on Wikipedia. Objective: This study aims to categorize the top 1000 most-read (most popular) English-language Wikipedia health articles for June 2019 according to the relevance of the article topic to each sex and quality. Methods: In the first step, Wikipedia articles were identified using WikiProject Medicine Popular Pages. These were analyzed on 13 factors, including total views, article quality, and total number of references. In the second step, 2 general medical textbooks were used as comparators to assess whether Wikipedia’s spread of articles was typical compared to the general medical coverage. According to the article’s content, we proposed criteria with 5 categories: 1=“exclusively female,” 2=“predominantly female but can also affect male individuals,” 3=“not sex specific or neutral,” 4=predominantly male but can affect female individuals,” and 5=“exclusively male.” Results: Of the 1000 Wikipedia health articles, 933 (93.3%) were not sex specific and 67 (6.7%) were sex specific. There was no statistically significant difference in the number of reads per month between the sex-specific and non–sex-specific articles (P=.29). Coverage of female topics was higher (50/1000, 5%) than male topics (17/1000, 1.7%; this difference was also observed for the 2 medical textbooks, in which 90.2% (2330/2584) of content was not sex specific, female topics accounted for 8.1% (209/2584), and male topics for accounted for 1.7% (45/2584; statistically significant difference; Fisher exact test P=.03). Female-category articles were ranked higher on the Wikipedia medical topic importance list (top, high, or mid importance) than male-category articles (borderline statistical significance; Fisher exact test P=.05). Female articles had a higher number of total and unique references; a slightly higher number of page watchers, pictures, and available languages; and lower number of edits than male articles (all were statistically nonsignificant). Conclusions: Across several metrics, a sample of popular Wikipedia health-related articles for both sexes had comparable quality. Wikipedia had a lower number of female articles and a higher number of neutral articles relative to the 2 medical textbooks. These differences were small, but statistically significant. Higher exclusively female coverage, compared to exclusively male coverage, in Wikipedia articles was similar to the 2 medical textbooks and can be explained by inclusion of sections on obstetrics and gynecology. This is unlike the imbalance seen among biographies of living people, in which approximately 77.6% pertain to male individuals. Although this study included a small sample of articles, the spread of Wikipedia articles may reflect the readership and the population’s content consumption at a given time. Further study of a larger sample of Wikipedia articles would be valuable.

Artificial Intelligence (AI) based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based e-learning imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. We performed semi-structured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations and Macro-environmental factors framework (TPOM). We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure. These under-researched factors require attention and further research.

Objectives: Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. Materials and methods: We performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework. Results: We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. Discussion: Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure. Conclusion: The socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research.

BACKGROUND Wikipedia is the largest free online encyclopedia and the seventh most visited website worldwide, containing >45,000 freely accessible English-language medical articles accessed nearly 1.6 billion times annually. Concerns have been expressed about the balance of content related to biological sex on Wikipedia. OBJECTIVE This study aims to categorize the top 1000 most-read (most popular) English-language Wikipedia health articles for June 2019 according to the relevance of the article topic to each sex and quality. METHODS In the first step, Wikipedia articles were identified using WikiProject Medicine Popular Pages. These were analyzed on 13 factors, including total views, article quality, and total number of references. In the second step, 2 general medical textbooks were used as comparators to assess whether Wikipedia’s spread of articles was typical compared to the general medical coverage. According to the article’s content, we proposed criteria with 5 categories: 1=“exclusively female,” 2=“predominantly female but can also affect male individuals,” 3=“not sex specific or neutral,” 4=predominantly male but can affect female individuals,” and 5=“exclusively male.” RESULTS Of the 1000 Wikipedia health articles, 993 (93.3%) were not sex specific and 67 (6.7%) were sex specific. There was no statistically significant difference in the number of reads per month between the sex-specific and non–sex-specific articles (P=.29). Coverage of female topics was higher (50/1000, 5%) than male topics (17/1000, 1.7%; this difference was also observed for the 2 medical textbooks, in which 90.2% (2330/2584) of content was not sex specific, female topics accounted for 8.1% (209/2584), and male topics for accounted for 1.7% (45/2584; statistically significant difference; Fisher exact test P=.03). Female-category articles were ranked higher on the Wikipedia medical topic importance list (top, high, or mid importance) than male-category articles (borderline statistical significance; Fisher exact test P=.05). Female articles had a higher number of total and unique references; a slightly higher number of page watchers, pictures, and available languages; and lower number of edits than male articles (all were statistically nonsignificant). CONCLUSIONS Across several metrics, a sample of popular Wikipedia health-related articles for both sexes had comparable quality. Wikipedia had a lower number of female articles and a higher number of neutral articles relative to the 2 medical textbooks. These differences were small, but statistically significant. Higher exclusively female coverage, compared to exclusively male coverage, in Wikipedia articles was similar to the 2 medical textbooks and can be explained by inclusion of sections on obstetrics and gynecology. This is unlike the imbalance seen among biographies of living people, in which approximately 77.6% pertain to male individuals. Although this study included a small sample of articles, the spread of Wikipedia articles may reflect the readership and the population’s content consumption at a given time. Further study of a larger sample of Wikipedia articles would be valuable.