Orientation: Rising social media use has intensified concerns about its psychological effects, especially among teenagers and young adults, with prior research linking engagement to anxiety, depression, and reduced emotional well-being. Research purpose: This study examined how digital mental well-being interventions can mitigate the negative psychological effects of social media use. Motivation for the study: While extensive literature documents the risks of social media, few studies synthesise solution-oriented digital interventions or examine their ethical implications. This study addressed that gap by integrating psychological theory with emerging digital practices. Research approach/design and method: A qualitative systematic literature review of secondary data was adopted. Twenty-eight peer-reviewed sources were systematically selected and thematically analysed, guided by social comparison theory, cognitive behavioural therapy (CBT), and self-determination theory. Main findings: Social media distress was driven by social comparison, fear of missing out, and cyberpsychological pressures. CBT-based applications, mindfulness tools, and online support platforms showed potential to reduce anxiety and depressive symptoms when appropriately designed. Practical/managerial implications: Ethically designed interventions that prioritise user autonomy, data privacy, and inclusivity are essential. Practitioners, designers, and policymakers should adopt psychologically informed approaches to support sustainable well-being. Contribution/value-add: This study contributed to cyberpsychology literature by synthesising evidence on the effectiveness and ethical challenges of digital mental well-being interventions, offering a theoretical foundation for future research and responsible innovation.

Smart agriculture Internet of Things (IoT) systems are being deployed across South Africa and Sub-Saharan Africa faster than they are being secured, yet available security methods separate threat identification from risk quantification and assume the reliable power, connectivity and expertise of high-income settings. Following the Design Science Research Methodology, this study derived eight design requirements and constructed ISACRATF, an integrated six-phase framework that couples STRIDE-LM threat modelling to a Context-Adjusted Risk Engine (CARE). CARE amplifies a conventional likelihood–impact score by a bounded, transparently weighted factor built from five regional conditions: connectivity fragility, power instability, skills scarcity, supply-chain and device heterogeneity, and socio-economic criticality. Prioritised threats are then mapped to controls tiered by resource intensity. Demonstration on an assumed South African mixed-farm case showed that context re-orders risk priorities rather than inflating them uniformly: a denial-of-service threat moved from a three-way tie into the Critical band, and a spoofing threat rose from Medium to High. Because CARE reports base and adjusted scores side by side, the effect of context is explicit and auditable. Expert and field evaluation of the framework is reserved for future research.