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Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate "what if" scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems. Systems thinking is essential to study complex problems that arise from many interacting system parts at different levels. An example of a complex problem is depression, related to individual biological and psychological characteristics, but also our society and environment. To schematically describe which system parts are important in explaining a complex problem, it is common to draw a visual representation of the system that produces the problem, i.e., a causal loop diagram (CLD). Even though a CLD can tell us a lot about the origins of a complex problem, it cannot show what the effect would be if a system part were changed. For example, even if the CLD indicates that income inequality is an important system part in explaining depression, we still cannot tell from just this visual representation whether depression rates would change if income inequality were reduced. To answer such questions, we need to develop a computational model reflecting the CLD: a computer program that can calculate what would happen under different conditions. This article describes how to create a CLD that can be converted into a computational system dynamics model. We propose to achieve this conversion by asking experts to communicate their knowledge about individual system parts and storing this information within the CLD. This expert knowledge then helps us calculate what happens when all system parts interact simultaneously. This method can aid in answering questions about the effects of changes in system parts for a broader range of complex problems. [ABSTRACT FROM AUTHOR]

Age-related difficulties and quarantine restrictions impede the possibilities to maintain contact with one's social network. Maintaining these contacts may be supported by digital games. To develop effective and feasible digital tools to foster social interaction, we aimed to explore what older adults find important in social contact and what barriers and enablers they foresee in digital gaming interventions as network support aids. Two focus groups and 20 semi-structured interviews (N = 29) with older adults (aged 55–87) were held to explore the research questions. Furthermore, a questionnaire was administered (N = 29) containing measures of loneliness, frailty, and social network size. Participants found 'reciprocity', 'in-person contact', and 'personal connection' important in contact with strong ties. Online games were not used much for socializing but may be used in the future, particularly by less mobile older adults. Future social gaming interventions should be challenging, user-friendly, and offer the possibility to communicate. Digital co-designed interventions that are feasible, challenging, intuitive, and trigger meaningful communication may strengthen social interactions in older adults. They may be a relevant social support tool in periods of interaction limitations due to functional impairment or social isolation. [ABSTRACT FROM AUTHOR]

Background: Digital mental health interventions could sustainably and scalably prevent and reduce loneliness in older adults. We designed an app containing 29 text-based games and a questionnaire-administering chatbot to stimulate intergenerational contact., Objective: This study aims to evaluate the feasibility of a social gaming app in reducing loneliness among older adults by evaluating recruitment strategies, data collection procedures, and gameplay activity., Methods: This mixed methods study recruited participants via newsletters, articles, and a social media campaign. We used semistructured interviews and descriptive analysis of questionnaire answers and game data to assess feasibility. Key measures included recruitment reach and efficiency, participant demographics, in-app activity, and app usability and engagement feedback., Results: The social media campaign reached 192,641 potential participants, resulting in 1363 game downloads. A total of 155 participants (aged 65 years and older: n=34, 21.9% and aged less than 65 years: n=121, 78.1%) provided informed consent, yielding a conversion rate of 0.08%. The recruitment campaign focusing on distanced playful interaction had a significantly (P<.001) higher click-through rate (1.98%) than a campaign focusing on research participation (click-through rate=0.51%; P<.001). The overall conversion rate from advertisement exposure to research participation was 0.08%. Participants had a mean age of 48 (SD 16) years. The 65 years and older group averaged 70 (SD 5) years, while the less 65 years group averaged 42 (SD 12) years. Additionally, 45.2% (57/126) reported at least moderate levels of loneliness at baseline. Of the initial 554 players, 91 (16.4%) remained active after the first week, and 32 (5.8%) remained active for more than 90 days. Active participants tended to interact with those within their own age group, as indicated by a Pearson correlation of r=0.31 between the ages of the message sender and receiver. Interviews with 12 (48%) participants (aged 65-79 years; female: n=12, 83%) revealed barriers such as excessive chatbot questions and a mismatch between the target group and app design focus. Questionnaire completion rates dropped from 46% at baseline to 10% at follow-up., Conclusions: These findings underscore the challenges of recruitment and retention for older adults in a fully digital social gaming intervention. Effective recruitment strategies and targeted app design are crucial for engagement. Based on these insights, future interventions should focus on simplified interfaces, clear guidance for gameplay, and addressing the specific needs and preferences of older adults, thereby enhancing the effectiveness of digital mental health interventions., (©Bas D L Châtel, Jeroen H M Janssen, Geeske M E E Peeters, Rense Corten, Rob Tieben, Menno Deen, Elmy J M Hendriks, Marcel G M Olde Rikkert. Originally published in JMIR Serious Games (https://games.jmir.org), 16.10.2024.)