Mentastic
Supporting mental well-being through personalised insights and behaviour-aware support
The challenge
Mentastic is a digital mental wellness platform that combines data from wearable devices, mood and behaviour tracking, sleep, digital habits and information provided by users. The platform uses AI and psychological guidance to turn this data into clear insights, personalised routines and support. The idea is to go beyond simple tracking and general recommendations, which do not always reflect users’ needs, everyday experiences or readiness to change.
The challenge was to understand what Mentastic could offer beyond existing apps and what kind of digital support users would find useful, practical 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 used a human-centred research approach to understand how people manage their mental well-being, what makes healthy habits difficult to maintain and what they expect from a digital mental wellness app. The findings were used to create personas, behaviour-change scenarios, user journey maps and the Mentastic design concept.
→ Interview guide development
A semi-structured interview guide was created to understand how mental well-being fits into participants’ daily lives and how technology could support them. The questions covered daily habits, mental health, technology use, well-being practices, health tracking and previous experiences with mental health apps. The interviews also explored what users expected from personalised support. This structure made it possible to compare participants’ experiences while also exploring their individual needs and concerns.
→ User interviews
Twenty-one semi-structured interviews were conducted with potential Mentastic users. Participants included people from Mentastic’s target groups, such as high-achieving professionals and frontline workers, as well as people with different backgrounds. The interviews explored their daily routines, well-being challenges and current coping strategies. The goal was to understand why people may stop using existing mental health tools and what kind of personalised support they would find useful.
→ Affinity diagramming
Interview findings were organised and analysed using affinity diagramming in FigJam. This helped identify common patterns and key themes that guided the design.
→ Persona development
The interviews identified two user groups with different approaches to managing their mental well-being. Based on these findings, two personas were created: Analytical Alex and Mindful Max. Analytical Alex values data, structure and measurable progress, while Mindful Max prefers a simpler experience with empathetic guidance and emotional support. The personas helped guide the design and identify features that support the needs of both user groups.
→ User scenarios & journey maps
Detailed scenarios were created to show how each persona could move through different stages of behaviour change, from life before Mentastic and first use to long-term use and possible relapse. The scenarios were tested in interviews with users who matched the persona profiles. Their feedback helped check whether the scenarios and proposed solutions 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 it difficult to keep tracking their well-being manually over time. To make this easier, the design focused on simple interactions and automatic data collection, allowing users to focus on understanding their well-being instead of 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 wanted the AI companion to communicate clearly, be honest about its limitations and provide empathetic support without sounding too clinical or overly positive.
Design Solutions
The research showed that Mentastic should be more than just another tracking app. The concept focused on making self-reflection easier, turning personal data into useful guidance and adapting support to different user needs and stages of behaviour change.
→ Trust-building onboarding
Problem – Users may hesitate to share personal information if they do not understand how the platform works, how their data is used or what the system can and cannot do.
Solution – The onboarding clearly explains what Mentastic does, what it cannot do and why certain information or permissions are needed. Connecting devices is optional, and users are given clear explanations before doing so.
→ Meaningful insights dashboard
Problem – Participants want more than raw data or general well-being advice. They need help understanding what their personal information actually means.
Solution – The dashboard supports both quick check-ins and deeper reflection through summaries and weekly trends. Clear explanations help users understand their data. Each insight also includes one clear next step, helping users turn information into action.
→ 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 that provides personal and empathetic support. Conversations are short and focused, with open questions and gentle follow-ups. Patrick connects conversations with users’ data and provides clear insights or practical recommendations.
Usability Testing
The concept was tested with participants who matched the two personas before moving to feature development. The sessions explored whether the scenarios felt realistic, met participants’ needs and provided value in everyday life. Participants found the scenarios realistic and relatable, especially those focused on improving mental well-being through small everyday actions. The feedback confirmed that the concept supported the needs of both user groups and helped guide the final design recommendations for Mentastic.
Outcome
The project turned findings from 21 interviews into a clear product concept for Mentastic. The research was used to create two personas, user scenarios, journey maps and a high-fidelity prototype based on different approaches to mental well-being. The concept showed how personalised insights, simple interactions and empathetic guidance could support users in their daily lives. The final prototype and design recommendations provide a foundation for the future development of Mentastic.