Overview
Fun Kit成功为慢性病患者的“日常规律服药的难题”提供了一个优秀的解决方式。通过将功能性与人性化的有趣设计相结合，它将一项枯燥且平凡的任务转变为愉快的体验。
SPONSORED BY
My contribution
Design
Modeling
The team
Raj V Shirolkar | PM & Engineer
Yuqi Huang | PM & Designer
Year
Oct. 2022
problem
我们发现用户在服药过程中面临3个主要问题：用户容易忘记是否已经服药，手动装药耗时且令人沮丧，以及长期服药导致情绪低落。这些问题让用户的服药体验变得复杂且不愉快，还影响了药物依从性，降低了他们的生活质量。
Strategy
为了解决这些问题，我们进行了用户调查和半结构化访谈，并使用Thematic Analysis进行数据编码和分析，明确用户的主要痛点。通过共创研讨会与利益相关者头脑风暴潜在解决方案，并优先考虑用户反馈的可行性和影响力。然后快速的迭代，开发和优化原型，并通过用户测试收集反馈，不断优化设计。
Solution
我们设计了一个通过扭转机制分配药丸并结合时间提醒和LED反馈的智能药盒。用户只需简单地扭转按钮即可获取药丸，减少了单次取药的操作负担。同时通过齿轮带动实现物理的早、中、晚的可视化，使用户记住服药时间，每次取药时LED灯自动亮起，给予用户正面反馈，提升服药体验。
同时有多个工具包可以自由拼接，方便用户存储和管理多种药物。减少了手动操作的繁琐，使整个服药过程更加高效和愉快。
Where it all begain?
During the COVID-19 pandemic, the demand for food delivery surged, and reports about the survival struggles of delivery workers increased accordingly. As delivery workers lack a voice in society, we wanted to try to do something to help.
Initial design question
What should we create?
To address the issues or challenges that delivery workers "care about most," thereby improving their job satisfaction?
Desk Research
What we found?
| To gain a comprehensive and rapid understanding of the problem, we first conducted desk/secondary research.
Online Questionnaire Survey
Based on the three findings from the "desk research" above, we created a "questionnaire survey" to further understand the situation of delivery workers.
For this, we collected 64 valid questionnaire responses and used the Affinity mapping method for qualitative analysis to categorize the data results, with the aim of further narrowing the research scope.
Conclusion:
Delivery workers are generally dissatisfied with workplace safety levels, working hours, and the rating system.
Who Should We Interview?
Through these recruitment criteria, we aim to gain deep insights into the actual working and living conditions of delivery workers.
Our goal is to collect high-quality, representative data for subsequent analysis and strategy development, thereby helping the delivery worker community improve their career and quality of life.
🔵 "The ideal interviewee is a mainland Chinese delivery worker who is willing to maintain high work intensity and is interested in improving work and living conditions."
We established the following criteria to identify suitable participants.
Recruitment Strategy
• Conduct preliminary online surveys through delivery workers' WeChat groups to identify interested participants.
• Conduct in-depth interviews with selected candidates, either virtually or in-person.
• Ensure diversity in participant selection to cover a range of different experiences and perspectives.
Inclusion Criteria
• Active delivery workers on major food delivery service platforms in first- and second-tier cities in mainland China.。
• Open to using technology (such as apps, wearable devices, etc.) to optimize work processes.
• Willing to share experiences regarding job satisfaction and health issues, and willing to participate in discussions and evaluations of technologies or strategies that "may improve their working conditions and quality of life."
Exclusion Criteria
• People who are uncomfortable discussing their work experiences in depth or using technology.
• Delivery workers who cannot work at high intensity due to health issues or other personal reasons.
User Interviews
| 基本上一轮的调查问卷中总结出的10个发现，我们设计了一份访谈问卷（30-40分钟/人），并按照招募规则筛选出8名合格的参与者开展访谈。最终确定了4个关键发现。
本研究旨在深入理解外卖员面临的挑战，这有助于创建更契合的支持系统来改善其工作生活条件。
Persona
|
Deeper Exploration
Experiential Research
Why did I conduct "Experiential Research"?
To get closer to the essence of the problem.
To compensate for the limitation of personas in determining user need priorities.
To participate in the delivery workers' community and daily life, rather than just being an observer.
This gave me an immersive, insider perspective to understand and empathize with the real lives of delivery workers.
How did I conduct "Experiential Research"?
I registered for Meituan Crowdsourcing (part-time) and became a delivery worker.
I collected data through observation, documentation, and inquiry, and used this data to guide ideation and design.
What did I gain from "Experiential Research"?
It revealed problems that were not fully discussed in previous surveys and interviews. I distilled 3 new findings:
High-intensity work triggers health crises.
Long-term, high-intensity outdoor work exposes delivery workers to various health risks, such as fatigue, joint pain, and respiratory problems.
Platform algorithms & extreme weather jointly lead to high accident rates.
There is a high positive correlation between food delivery platform algorithms, extreme weather, and accident rates.
High-intensity work leads to cognitive decline.
After working continuously for several hours, delivery workers experience drowsiness, reduced attention, and cognitive decline.
However, under the dual constraints of economic pressure and platform rules, they have almost no choice but to continue working.
After integrating these 3 hidden issues into the previous insights, we identified the core pain points of delivery workers 👇
Problem Identification
Solution Generation
技术原理
的可视化
Visualization of
technical principles
功能图示
技术逻辑原理
我们的收获
What We Learned ？
Thanks for Watching