Mentastic
Supporting mental well-being through personalised insights and behaviour-aware support
The challenge
Mentastic is a preventive digital mental wellness platform that combines information from wearable devices, self-reported inputs, mood and behaviour tracking, sleep data, and digital habits. Using AI-supported analysis and evidence-based psychological guidance, the platform aims to translate personal health information into understandable insights, adaptive routines, and contextual support. However, many existing mental health and well-being applications focus primarily on tracking or provide generic recommendations that do not always reflect users’ emotional needs, everyday experiences, or readiness to change.
The challenge was to understand what Mentastic could offer beyond existing applications and identify what types of digital support users would consider meaningful, actionable, and trustworthy.
Methods
Desk Research, Semi-Structured Interviews, Affinity Diagramming, Persona Development, Behaviour Change Mapping, User Scenario Development, User Journey Mapping, Prototyping
Team
Barsha K C, Maret Luud, Alp Kadir Türedi, Emrah G. Candan
Duration
3 Months
Research
The project followed a human-centred research approach to understand how people perceive and manage their mental well-being, what prevents them from maintaining healthy behaviours, and what they expect from a digital mental wellness application. The findings were used to develop research-based personas, behaviour-change scenarios, user journey maps, and a design concept for Mentastic.
→ Interview guide development
A semi-structured interview guide was developed to explore how mental well-being fits into participants’ everyday lives and what role technology could realistically play in supporting it. The guide covered five areas: participant background, daily habits, mental health, technology use, and final reflections.
The questions examined daily routines, emotionally demanding situations, existing well-being practices, health-tracking behaviour, previous experiences with mental health applications, and expectations of personalised support. This structure made it possible to compare participants’ experiences while leaving enough flexibility to explore individual needs, behaviours, and concerns in greater depth.
→ User interviews
Twenty-one semi-structured interviews were conducted with potential Mentastic users recruited through purposive sampling. Participants included people from Mentastic’s target groups, such as high-achieving professionals and frontline workers, alongside exploratory participants with different backgrounds.
The interviews explored participants’ daily routines, well-being challenges, and current coping strategies to better understand what prevents long-term engagement with existing mental health tools and what kind of personalised support they would find valuable.
→ Affinity diagramming
Interview findings were transcribed into a standardised analysis template and analysed collaboratively using affinity diagramming in FigJam. This process revealed recurring patterns and key themes that informed the design direction.
→ Persona development
The interview analysis revealed two distinct user groups with different approaches to managing their mental well-being. These findings were translated into two research-driven personas—Analytical Alex and Mindful Max—to represent the contrasting needs, motivations, and behaviours identified during the study.
While Analytical Alex values data, structure, and measurable progress, Mindful Max prefers a simpler, low-effort experience with empathetic guidance and emotional support. The personas became a foundation for designing features that address the needs of both user groups.
→ User scenarios & journey maps
Detailed scenarios were created to show how each persona might move through the different stages of behaviour change. The scenarios illustrated life before using Mentastic, the first interaction with the platform, active use, long-term maintenance, and possible relapse. The scenarios were evaluated through validation interviews with users who matched the persona profiles. Their feedback helped confirm whether the situations and proposed interventions felt realistic.
Key Insights
→ Trust comes first
If insights feel unclear or unreliable, users quickly lose confidence in the system. The research showed that transparency, consistency, and clear communication are fundamental design requirements for building long-term trust.
→ Lower effort, higher value
Participants found manual tracking difficult to maintain over time. To reduce effort, the design prioritised simple interactions and passive data collection, allowing users to focus on understanding their well-being rather than entering data.
→ Insights should be meaningful and actionable
Users did not want raw charts or generic wellbeing advice. Instead, they looked for clear and understandable patterns, such as relationships between sleep and mood.
→ Transparent and supportive conversations
Participants expected the AI companion to communicate clearly, acknowledge its limitations, and provide empathetic support without sounding overly clinical or artificially positive.
Design Solutions
The research showed that Mentastic should not operate as another passive tracking application. The concept focused on reducing the effort required to reflect, transforming personal data into actionable guidance, and adapting support to different user needs and stages of behaviour change.
→ Trust-building onboarding
Problem – Users may hesitate to share sensitive personal information when they do not clearly understand what the platform does, how their data will be used, or where the system’s responsibilities end.
Solution – The onboarding experience clearly explains what Mentastic does, what it does not do, and why specific information or permissions are requested. Device integrations remain optional and are introduced with clear explanations to reduce early uncertainty.
→ Meaningful insights dashboard
Problem – Participants wanted more than raw data or generic wellbeing advice. They needed help understanding what their personal information actually meant.
Solution – The dashboard supports both quick check-ins and deeper reflection through summaries and weekly trends. Visualisations are always accompanied by plain-language explanations, making personal data easier to understand. Every insight is paired with one clear next step, helping users move from reflection to action instead of simply viewing information.
→ Conversational AI companion
Problem – Traditional wellness applications often require repetitive input or provide generic interactions that feel impersonal.
Solution – Mentastic introduces Patrick, an AI companion designed as a reflective support tool rather than a therapist or crisis service. Conversations remain short and focused, using open-ended questions and gentle follow-ups. Patrick connects conversations with tracked data and guides users toward visual evidence or one actionable recommendation, making the interaction both supportive and explainable.
Usability Testing
The proposed concept was validated with participants representing the two research-based personas before moving into feature development. The sessions focused on whether the scenarios reflected realistic experiences, addressed participants’ needs, and provided meaningful value in everyday life.
Participants found the scenarios realistic and relatable, particularly those centred on improving mental well-being through small, everyday actions. Participants representing Analytical Alex appreciated the personalised, data-driven guidance, while those matching Mindful Max responded positively to the conversational interaction and voice-first journaling experience.
The feedback confirmed that the concept addressed the needs of both user groups and informed the final design recommendations for Mentastic.
Outcome
The project translated qualitative research into a practical product vision for Mentastic. Insights from 21 interviews were synthesised into two research-based personas, user scenarios, journey maps, and a high-fidelity prototype designed around the different ways people approach mental well-being.
The concept demonstrated how personalised insights, low-effort interactions, and empathetic guidance could support everyday mental well-being while accommodating different user needs and preferences. Instead of introducing a one-size-fits-all solution, the design explored how preventive mental health support could be tailored to different behavioural patterns and levels of engagement.
The resulting prototype and design recommendations provide a research-based foundation for the future development of the Mentastic platform.