After the research, sketches were created for the key wireframes to help the target audience become more active. Various heuristics for user interface design were applied during this process.
StressLoos
A good & an evil app
In this project, I collaborated with a partner to create two interactive applications using Figma. These applications were based on data experiments that we executed, and analyzed.
Data research
For this project, we conducted three data studies related to the mental health of young people to generate predictions using Machine Learning.
More data conclusions based on questionnaire research
Questions were asked about physical activity, social interactions, stress levels, favorite activities, and stress triggers.
Physical activity and social interaction are crucial factors that contribute to a positive mood and energy level.
Self-care and relaxation through favorite activities like cooking, exercising, and listening to music enhance this effect.
Reducing stress seems possible through a combination of physical and social activities.
Participants often experience stress daily, with work, study, and health being the main causes.
Overall, social interactions are generally perceived as positive.
Participants report managing stress through exercise, social support, or taking time for themselves.
Persona
Based on the research data, one persona was developed. Outlined below in the persona hypothesis.
About StressLoos
The StressLoos app helps users understand their stress patterns and create a balanced lifestyle. It focuses on providing personalized support to reduce stress and promote a healthy balance between physical, social, and relaxing activities.
The good app
The app ensures privacy by giving users control over their data, with consent options that can be updated later, and maintaining transparency. Recommendations are clear, explaining the data and algorithms used. Advice is fair and unbiased, with no preference for specific groups or discrimination based on age, gender, status, or limitations.
The evil app
The malicious app poses as a mental health tool but is designed to collect data for commercial purposes. It lacks transparency, giving users no control over their data and privacy. Activities are also offered based on gender, age, and ethnicity, categorizing users into specific groups.
Explainability
Good app
Transparent communication
We communicate transparently by explaining how activities are determined. The stress level is clearly stated as a prediction based on permitted data usage, and the ‘i’ provides details on how the data is calculated.
Evil app
Unclear predictions
Using unclear language and lacking transparency about predictions ultimately confuses the user and provides no useful explanation.
Bias
Good app
Adaptability
Recommended activities usually match user needs but can be adjusted in intensity or duration to accommodate individual preferences and limitations.
Evil app
Distinction
Activities are offered based on sensitive demographics like gender, age, and ethnicity, while they should instead be based on preferred activities, time, and activity level.
Privacy
Good app
Data collection
The user can specify which data we may use at the start and later through settings. Separate consent is requested for each data collection.
Evil app
Data exploitation
Sensitive personal data, such as stress levels and behavior, are used for advertisements. The focus should instead be on relevant activities for the user, without commercial interests.
Figma design
Below you can see the figma design and the user flow of the evil app I designed
