Stay With Mirror.
Stay Beyond Self.
Mobile App 2020
Mirror Mirror is a versatile mobile system designed to help you track and reflect on your personal health. It encourages regular data recording by allowing you to log your mood, sleep patterns, exercise, and medical records through photos, voice memos, and text entries. Follow the intuitive flow and let it evolve with your health journey.
Connect Your Mood with Daily Sleep to Form A Seamless User Flow
Using emojis to record your mood makes your mood shown vividly. Sleeping time surrounded by the emoji makes you connect the data with your daily mood. Sleeping data includes quality of sleep, dreams, feelings after waking up, and audio or visual notes.
Text, microphone, and photos are the main qualitative recording methods in Mirror Mirror. Audio/Visual records after waking up allow you easily to record your feelings.
Mood, sleep, food & drink are circulated, and through time, AI can identify and show how they influence each other.
Focus on Your Physical Status And Reflect From Daily Diary
Recording exercise, symptoms, medication, and supplements, helps you to figure out the small or irregular body reaction in the long term. Also, the overall dashboard summarises your day and helps you review your daily self-status.
AI-Powered Personalization Brings an Interactive Dialog
Mirror Mirror will find out the most frequent recorded categories and analyze your needs. The system is presented in three scenarios. This one presents the basic analysis and interactive dialog.
One of the scenarios is as your assistant involves his/her emotion to comfort you when you need encouragement. The other is as a professional practitioner to give professional suggestions. Here it gives balanced nutrition as an example. The other is as a professional practitioner to give professional suggestions. Here it gives balanced nutrition as an example.
Defining the Problem
Before directly jumping to solutions, I wanted to focus on designing for non-chronic users. Therefore, I researched the literature and the current products to understand the different needs of people with and without chronic disease. We found that most of the health-tracking applications focused on one category, e.g., menstruation.
Also, people with and without chronic conditions were recorded differently. People with chronic use them for communicating with the medical staff. On the other hand, people without chronic aim to develop routines, satisfying their curiosity by using devices.
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‘ Systems are designed for doctors. Whenever there is no emergent health danger, it is very difficult to motivate the patients to track their data for prevention.' -- dr. Hareld, 2020
Hypothesize, Findings, and Concepts
According to the user research above, I make the hypothesis that the users want to feel more intimate about their health data; the system could motivate users to record health data; and the system would be adapted automatically according to the different needs of the users.
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The corresponding conceptual designs are to let the users reflect on themselves; able to take care of users' feelings through friendly system navigation; and finally provide an influential framework for the system to adapt with the users automatically. From here, we need to further investigate the elements within this framework.
User Research to Define the Context of User Interaction
To understand which data categories and how users usually collect data intuitively, I started the contextual step by asking 12 participants to record their 'health status' daily without restriction and not giving a guide.
We found that among the participants, 83% started to reflect and would self-diagnose speculatively; 92% recorded their feelings; and 67% identified their health patterns, e.g. headache, tearing eyes, etc. Lastly, 7 kinds of data: Food & Drink, Medication & Supplements, Mood, Sleep, Exercise, Activities, and Symptoms are identified to be recorded.
Meanwhile, we identified the interactive behaviors of the tools they used. Video, audio recording, and note-taking are common to be used. What could also become a good tracking method is the content in 'friend/family calling'. Also, having a guide to record for users is essential.
1st Research Artifact : High Personalization is Required
According to the pilot test, the prototype collected data from food, drink & mood. They can be adapted for 7 days according to the users' needs. We ran the research remotely with an online questionnaire and semi-structured interviews.
We discovered that customized prototypes reflect the needs of the users and the wish to have more different kinds of data for an enjoyable experience. This has reflected a high-personalization need for habit recording.
1st Research Prototypes
'I wanted to record by photos as well'.
2nd Research Artifact: Drive Dynamic Data Quality to AI Empowerment
More categories are added in the second iterations, which allow users to increase the flexibility of recording. It is designed with the flow of a day, from waking up to the end of a day. It is highly personalized that the research has mimicked an AI system in the background.
The result shows that the order of categories is important. Also, the dynamic framework could be a design tool for designers to design better. This structure with the 7 categories helps designers to design a better tool for collecting health data. It provides a dynamic for the prototypes to create an intelligent solution. Among these data, symptoms have been a high-sensitive data type for privacy.
2nd Research Prototypes
'It helps me reflect on my physical body and mental phases..'.
MVP of Mirror Mirror
From Food
To Overall Data
From Sleep
To Physical Data
Remote Validation
12 participants were conducted with 5 people with chronic diseases. They experienced different degrees of intelligent automation. Afterward, the qualitative data are analyzed by thematic analysis.
The interesting findings are: the designers could apply different degrees of automotive, ranging from closeness, and autonomy, to personal interests. The users want to (1 )receive the direct results, e.g. weather, and date. (2) flexibility of automation: categories influenced by a certain extent of routine, e.g. eating time, food portion, etc. (3) full flexibility: categories changed day by day, e.g. food, mood, condition of the exercise, etc.
We discovered that adjustable functionality, dynamic adoption, and creating adherence are related to the users' recording behaviors. For example, to create an adherence to recording could be related to their rhythm of life, such as the type of job, interests in qualitative recording, etc.
Learnings and Retrospect
