Meal Planning for Food Security
Background
For my User Research Methods course at DePaul, I worked in a group with 3 teammates that focused on answering the question: How can a technology-based solution assist in organizational meal planning for young adults to increase a user's level of food security? We conducted observations and interviews to better understand user needs for meal planning technologies, and developed design implications for a technology-based meal planning solution.
Food Insecurity and Barriers
Food insecurity is a growing issue in the United States. Along with rising food prices, young adults often don't have the time necessary to prepare affordable, home cooked meals. (Hover for more)
The research shows…
Research Question
How can a technology-based solution assist in organizational meal planning for young adults, to increase a user's level of food security?
Methodology and Process
To understand these user needs and propose a solution, my team and I conducted the following over 10 weeks:
Competitive Analysis
Analyze the strengths and weaknesses of existing meal planning solutions to discover potential design implications for a solution.
Observations
"Imagine you are having a conversation with the other people in your household, and the discussion turns to planning your shared meals for the week ahead. You want to help each other figure out what to cook for the week and when based on the food you already have available at home. Please walk us through how you would meal plan for the next seven days. Feel free to make use of any tools you normally would when accomplishing this (e.g. pen and paper, whiteboard, any mobile apps, etc.), and make any comments explaining your choices."
Data Analysis
We used FigJam to group together our notes from our observations, and created an affinity diagram to help identify any themes or patterns. After analyzing the results of our observations, we created a sequence diagram to visualize the general process our participants take when meal planning. This helped us synthesize design implications for a meal planning solution and informed the writing of our interview questions.
Interviews
We conducted interviews to gather in-depth, qualitative data about the struggles, experiences, and goals of those who engage in structured meal planning. Similar to our observation protocol, we recruited 8 participants through word of mouth referrals or the DePaul CDM participant pool (See Appendix for participant data). Interviews took place over Zoom, and the audio was recorded for later reviewing and analysis. These are some examples of the questions asked:
What resources or tools do you use for planning what you will cook?
How does your schedule affect how you meal plan?
What has your experience been with meal planning tools?
Data Analysis (again)
We consolidated our interview findings, then performed inductive coding using atlas.ti to extract major themes and responses to interview questions. We then used Figjam to create another affinity diagram based on these responses. These findings informed the creation of our user persona spectrums, that group participants based on certain meal planning factors. After using these spectrums to create user personas and scenarios, we made user journey maps and a priority matrix to better understand user needs for a technology-based meal planning solution.
Findings
Observations and Interviews
Our main findings from our observations and interviews are organized into three main themes (Hover to discover more details):
Competitive Analysis
HelloFresh
HelloFresh helps users overcome meal planning barriers by providing the exact amount of ingredients needed for a given recipe. It eliminates the need for grocery shopping, as they ship the kits with everything needed to make a nutritious meal at home. However, there is limited customization in that it doesn't allow users to incorporate pre-owned ingredients, and may not be affordable for food insecure populations.
MealPrepPro
MealPrepPro is a meal planning app that provides users with recipes and meal plans based on health goals, food preferences, and cooking schedules. It consolidates critical information such as grocery lists, recipes, and meal plans in one locations, which is something participants struggled with using other tools. Similar to HelloFresh, there isn't a direct way for users to find recipes including pre-owned ingredients with MealPrepPro and doesn't allow users to add ingredients to shopping lists that aren't included in a provided recipe.
Implications of Competitive Analysis
While these services address some of the barriers we discovered through our research — like the limitations of current tools to centralize meal planning information and accessibility of grocery stores or healthy foods — there is still room for a solution to address food waste through use of pre-owned ingredients, create personalized grocery lists, and optimize these processes to improve time scarcity.
Persona Spectrums
Based on our interview responses, we grouped participants based on their responses to certain questions. We then looked at these groups to determine qualities of our personas.
From our spectrums, the identified groups were consolidated into two distinct personas with different meal planning preferences. These allowed us to better visualize the needs of different user types.
User Journey Maps
The user journey maps show a scenario in which our personas experience common frustrations expressed by our participants. These maps assist us in contextualizing the barriers involved in meal planning, so we can brainstorm features that accurately address user needs.
Research Based Design Implications
Based on the findings from our observations, interviews, and subsequent analysis, our team developed features for a proposed technology-based solution to assist users in organizational meal-planning. We used our personas, scenarios, and user journey maps aside our qualitative data to inform our process. Additionally, we rated the priority, impact, feasibility of each feature, and determined the user type that would benefit most from the feature (See Appendix for full priority matrix). These were the features we deemed high priority (hover to read more details):
User Scenarios
Limitations and Next Directions
One of the main limitations of this study was the demographic of participants we recruited. Our focus on meal planning was based on prior research that implicated meal planning as a means of improving food security. Due to logistical and ethical reasons, we were unable to recruit participants in food-insecure populations to fully understand the struggles they face in cooking nutritious, affordable meals. While we discovered user needs for meal planning tools among young adults, the full scope is not yet realized from this project without insights from the most impacted population.
Further, we mainly interviewed and observed DePaul students in the College of Digital Media because the participant pool was easily accessible and included primarily young adults. In future work, we should expand our samples to include non CDM students and include more members of shared households to understand how meal planning works in a communal setting to assist food insecure families.
If given more time and resources, next steps would include:
Ideate and prototype a technology-based solution based on our findings.
Design and test our solution among users to gather feedback and iterate.
Expand recruiting to include members of food-insecure populations to better understanding their distinct experiences, struggles, and backgrounds.
Reflection
Throughout this project and course, I focused on applying qualitative methods in user research to discover issues people face in their meal planning process. These qualitative research methods allowed us to dig deeper into the underlying processes, and expand upon observed user behaviors and thoughts.
One of the main challenges I faced was not jumping to a flashy solution. Instead, I had to focus on what was learned from our participants. While we could have taken the idea of meal planning and jumped straight to designing a mobile app, that — for good reason — wasn't part of this course. This served as blinders of a sort, that forced my focus onto the users and their experiences, struggles, and goals. Several times we found that an idea we had before conducting research turned out to be totally uninformed.
For example, we had ideas of a messaging system within the platform for shared households and image-sharing to incorporate social aspects. Our prospective users claimed that these weren't appealing, as they didn't solve any problem they faced in their previous experiences.
Overall, this project reinforced that in order to develop a solution that appropriately addresses the issues a user may face, you have to first conduct thorough user research to paint the bigger picture of a given problem. This project changed the way I approach design and research, and made me understand more deeply why empathy is an important quality for designers and researchers alike to have.
References
Aurélie, B., Hamada, M., and Tanguay, Doucet I.( 2024). COOKNOOK: Intelligent Meal Planning Application. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 619, 1–6. https://doi.org/10.1145/3613905.3647979
Baker, J. (2024, September 4). USDA Food Security Report Highlights Startling Hunger Crisis in America. FRAC. https://frac.org/news/usdafoodsecurityreportsept2024.
Aberg, J. (2009). An evaluation of a meal planning system: ease of use and perceived usefulness. In Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology (BCS-HCI '09). BCS Learning & Development Ltd., Swindon, GBR, 278–287. https://dl.acm.org/doi/10.5555/1671011.1671045
Appendix
Observation Participants
Participant
Age
Gender Identity
Occupation Industry
Household Size
Cooking Comfort Level (1-5)
1
29
Female
Biotech
2
4
2
37
Female
Higher Education
3
5
3
26
Male
Research
2
3
4
26
Female
Finance
2
4
5
24
Male
Student
5
4
6
30
Female
Student
2
5
7
29
Female
UX
3
5
8
23
Male
Consultant
2
4
Interview Participants
Participant
Age
Gender Identity
Occupation Industry
Household Size
Cooking Comfort Level (1-5)
1
29
Female
Biotech
2
4
2
37
Female
Higher Education
3
5
3
26
Male
Research
2
3
4
26
Female
Finance
2
4
5
24
Male
Student
5
4
6
30
Female
Student
2
5
7
29
Female
UX
3
5
8
23
Male
Consultant
2
4
Priority Matrix
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