02 Opportunity Discovery.
1 Research Methods.
Approach.
In order to comply with social distancing rule, all researches have been conducted remotely. We primarily looked at literature related to eating rate detection and behavior intervention method. User research participants were recruited through Slack and Reddit in the Georgia Tech community.
Key Findings.
Current studies focus more on eating detection technologies rather than intervention methods, and the few existing intervention tools have poor user experience and lack supporting evidence of effectiveness from long-term studies. We found that users are interested in this idea, but are looking for a more effortless and enjoyable solution when using a product as such.
8
Semi-structured User Interviews
20 Questions, 60 min.
Recruited on Reddit.
12
Existing Systems Evaluated
Including commercial and academic projects.
14
ER Detection Methods Examined
Based on 27 research papers.
From ACM Digital Library.
2 Journey Map
Approach.
We created a journey map to better understand the painpoints along the process of eating rate tracking. Because the lack of commercial products in this category, we identified necessary stages of establishing proper eating rate based on literature review and user interviews.
Debrief.
We noticed that users are having a difficult time properly evaluating their eating speed and derive actionable insights. User's self evaluation on eating speed is highly depending on their family members and friends, and lack an objective metrics to reach more reliable conclusion. Besides, users do not know how to adjust their eating speed on a daily basis and develop a habit.
Detailed Insights.
Preparation stage:
Users want to understand how it can help/harm their health goal.
Users do not have sufficient understanding on the importance of eating rate. Even for users who have a vague idea on the importance, they are not associating their health goal with their eating rates.
Collection & Integration stage:
Users want a reliable method to evaluate their eating rate.
The unreliability of subjective evaluation has shown both in literature review and our user research. In order to get un-biased result and suggestions, users need an objective monitor method.
Reflection stage:
Users want to analyze their eating rate and to know how they can improve.
In addition to having an objective tracking method, users need to be able to understand the data and get insights on how to improve their habits based on their goal.
Action stage:
Users want to make real-time/long-term adjustment on eating rate.
Most importantly, users need a solution that can motivate and remind behavior change, both on a daily basis and on the long run.
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Our Design Should...
Catered to individual's heath goal, and provide personalized suggestions.
#strategy
#added value
Require minimal effort after initial setup to encourage retenion.
#learning curve
#retention
Provide actionable insights to help build a healthier habit.
#strategy
#PHI
Provide more personalized options for notifications based on preference and progress.
#mindless computing
#personalization
