Team Rakali: Intervention Study Synthesis

by Greg Kalman, Austin Konig, Ananya Navale, Shuman Wang, Jasmine Xu

Summary

Study goal:

Our intervention aimed to help participants make healthier late-night eating decisions using pre-commitment and pre-planning. The core hypothesis was that decisions made in the morning, before fatigue, stress, or convenience pressures accumulate, would be more intentional and health-aligned.

The key question that we aimed to answer was: “Can pre-commitment earlier in the day meaningfully shift late-night food choices?”

Participants:

A total of 7 participants took part in the study, including 2 who had completed the baseline phase and 5 who were newly recruited. 

Procedure:

Each morning for five consecutive days, participants received a Google Form guiding them through a brief pre-commitment exercise. The Google Form was divided into two parts:

Part 1: Pre-commitment for that night

The first section of the form focused on pre-commitment for that evening. Participants were asked whether they anticipated staying up late and, if so, were guided through selecting a late-night snack or meal in advance. They chose from a curated healthy menu, featuring options such as nut bars, heart-healthy oatmeal, and other balanced snacks, provided through our partnership with One Step Foods for the study. Participants were then prompted to indicate when and where they planned to eat the snack and optionally confirm their intention with a simple commitment statement (e.g., “I plan to follow through with this plan tonight”).

Part 2: Accountability

Participants reflected on whether they followed through with their previous night’s commitment.

The full process took ~1–2 minutes per day.

Key insights and implications for design

Insight 1: The core mechanism of pre-commitment worked- but through salience, not control.

What we learnt:

Participants consistently reported that the biggest impact of the intervention was that the form made healthy late-night eating top-of-mind. Moreover, the optional commitment checkbox was particularly well received. Participants indicated that because it was optional, it felt like an active, autonomous choice rather than a coercive element.

Overall, it seemed that the intervention’s power came less from restriction and more from increased salience and intentionality.

Design Implications: 

The core structure of the intervention should be preserved in the final app, particularly its emphasis on autonomy and light-touch commitment. Because the impact came from increased salience rather than restriction, the solution should continue to frame pre-commitment as a supportive planning tool rather than a rule to follow. Retaining the optional “I plan to follow through” prompt is important, as it reinforces intentional choice without feeling coercive. 

Insight 2: Pre-Commitment Requires External Cues

What we learnt: 

Participants shared that although they were motivated to complete the form, they would likely forget without the team’s daily reminders. This suggests that pre-commitment is not yet an automatic habit and depends on external cues to prompt action. One participant noted that on particularly overwhelming days, when her workload was heavy, she might not have the mental bandwidth to complete it or might simply forget. However, participants also noted that the brevity of the form, taking only one to two minutes, significantly increased compliance, as its low time cost made it feel manageable even on busy days.

Therefore, this signifies that pre-commitment is not self-sustaining. At least in the earlier stages before it becomes a habit, it requires environmental scaffolding.

Design implications: 

This suggests that the final app must integrate built-in notification and reminder features, while keeping the pre-commitment process as quick and frictionless as possible. Reminders can explicitly emphasize the low time investment, such as “Take 1–2 minutes to plan your snack”, to reduce perceived effort and increase follow-through.

Insight 3: Cravings Are Unpredictable

What we learnt:

Several participants noted that their nighttime cravings were hard to predict in the morning. They might plan one thing but want something completely different later. This can become a barrier for using the pre-commitment mechanism. 

Design implications:

The final solution should build flexibility directly into the pre-commitment process. Because cravings and internal states are dynamic and often unpredictable, the app should allow users to edit or adjust their planned choice without framing changes as failure. Morning selections can be positioned as a “best guess” rather than a binding decision, reinforcing that plans can evolve. 

Insight 4: The Menu can be a Bottleneck

What we learnt:

Many participants did not choose from the provided delivery menu because the options did not align with their cravings. Several expressed interest in fresh fruit, which was unavailable. Furthermore, one participant shared that she would prefer to pay out-of-pocket for something that truly matched her preferences rather than receive a free option that didn’t fully align with what she was craving.

This indicated that cost was not the primary constraint, but food appeal and fit were. We initially assumed free food would increase adherence. Instead, food desirability was the limiting factor. It is therefore crucial to design a menu that actually caters to people’s tastes. 

Design Implications:

Rather than offering a fixed, prescriptive menu, the final solution should prioritize personalization and flexibility, allowing users to select foods that genuinely appeal to them while subtly guiding choices toward healthier variations. Alternatively, we can also conduct deeper research into what people actually gravitate toward at night and design a menu that meaningfully reflects their real cravings and taste preferences, rather than relying on assumptions about what they might want.

System Paths & Story Maps

We decided to refine our initial system path based on three personas by starting with the morning push notification and then mapping the full decision tree that follows. From there, we layered in a calendar scan and past consumption history so the system could distinguish between real need and habitual ordering. That shift helped us move from a simple pre-order flow to a conditional system that adapts to workload, free dinner windows, and prior waste. The mindfulness prompt became a key intervention point, slowing down automatic behavior while still allowing students to proceed if they truly need fuel for a heavy study night.

Our audience includes students who visit late night dining spots like TAP occasionally as well as those who rely on it during intense academic periods. For heavy courseload students, scheduled pre-orders aligned with long study blocks provide structured, balanced fuel instead of last minute impulse decisions. For students who tend to spiral, reminders and pattern insights make the link between deadlines, social nights, and eating more visible. The system supports planning ahead when fuel is necessary and introduces friction when ordering is driven by habit rather than need, creating a balance between autonomy and guidance.

We created the story map by organizing the experience around two primary activities: ordering food and tracking late night eating habits. We then broke each into clear steps and granular actions, mapping every interaction from login to checkout and from opening the tracker to submitting progress. 

Revisiting our raw intervention data showed that reducing friction would be vital to break the cycle for the most extreme users, particularly those with heavy late night eating habits who only partially completed surveys. This led directly to our MVP features, which focus on the most valuable and non-negotiable components: streamlined ordering, pre scheduled delivery aligned with workload, simple habit tracking, and timely notifications. Prioritizing these elements ensures the system supports behavior change without adding complexity that could discourage consistent use.

MVP Features

Core Functionality

1. Menu browser with curated healthy late-night snack options sourced from partner restaurants and cloud kitchens
2. Cart and checkout system with individual payment processing
3. Real-time delivery tracking with ETA and order status updates
4. User profile with authentication, dietary preferences, and order history

UI/UX Specifications

1. Home feed serving as a centralized hub for navigation and quick reordering
2. Push notifications for delivery updates and essential alerts
3. Eat-rate confirmation prompt post-delivery to track consumption percentage
4. Streak tracking system visible on user profile
5. Notification triggers for group invites, streak-at-risk warnings, and peer activity

Accountability and Social Aspect Specifications

1. Friends system with the ability to view peer streaks and eat-rate statistics
2. Group ordering with shared cart and split payment functionality
3. Accountability nudges triggered by streak breaks or declining eat-rate
4. Structured challenges tied to streak and eat-rate goals
5. Health tips and contextual nutritional guidance surfaced on the home feed

Enterprise/Revenue Specifications

1. Partner restaurant onboarding portal and vendor management system
2. Group discounts and incentive structures for consistent ordering behavior

Bubble Maps

We are developing a centralized home feed as the main hub connecting users to key features: browsing curated healthy late-night menus, initiating group orders, tracking streaks, and accessing daily health guidance. The ordering flow guides users from menu browsing through cart and checkout, supporting individual and group payments, followed by real-time delivery tracking. All orders come from a curated network of partner restaurants and cloud kitchens. Social features integrate the friends system into group ordering and accountability, allowing users to invite peers, compare streaks, and encourage members when their healthy eating consistency declines.

Our tracking and engagement layer forms the core of the accountability framework. After delivery, users confirm if they consumed their order, updating their eat-rate percentage and streak in real time. These metrics are visible to peers and reflected in profile statistics and order history. We add gamification through structured challenges aligned with streak and eat-rate goals, enabling friend groups to pursue shared targets like sustained healthy snacking. Our notification system supports this by delivering group invites, delivery updates, streak-at-risk alerts, and accountability prompts to maintain engagement and adherence.

Assumption Map

Our Assumptions

Do they want this? (Desirability)

• Who are the target customers for our solution?
  • Busy, health-conscious college students who frequently study late into the night and struggle with unhealthy snacking.
• What problem do our customers want to solve?
  • They want to avoid making poor food choices when they are tired and stressed, and they need a convenient way to access healthy options without disrupting their workflow.
• How do our customers solve this problem today?
  • They resort to vending machines, food-delivery apps (like DoorDash), or convenience-store snacks. Some may keep a stash of snacks in their dorm, but some of these snacks or foods are unhealthy.
• Why can’t our customers solve this problem today?
  • Decision fatigue is high late at night, making it difficult to resist convenient but unhealthy options. Healthy alternatives are often less accessible or require more effort than students are willing to expend in the moment.
• What is the outcome our customers want to achieve?
  • To feel energized, productive, and good about their food choices, even during intense study sessions, without wasting time or energy.
• Why will our customers stop using their current solution?
  • Our solution removes the in-the-moment decision, making the healthy choice the easiest choice. It offers a curated, convenient, and pre-committed alternative that aligns with their long-term health goals.

Can we do this? (Feasibility)

• What are our biggest technical or engineering challenges?
  • The most critical technical challenge is building a seamless and engaging app that integrates pre-ordering, payment, notifications, and gamification features reliably.
• What are our biggest legal or regulatory risks?
  • Ensuring food safety and proper handling in partnership with the food provider (Step One Foods) and complying with data privacy regulations for user data.
• What are our internal governance or policy hurdles?
  • Establishing a clear partnership agreement with the food supplier that defines responsibilities, quality control, and revenue sharing.
• Why does our leadership team support this solution?
  • We don’t have leadership.
• Where does our funding for this solution come from?
  • Initial funding (i.e., food) is provided by the sponsoring partner, Step One Foods, as part of a study to promote healthier eating habits.
• Why is our team uniquely positioned to win?
  • Our team has a unique combination of behavioral science insights (leveraging pre-commitment) and a direct partnership with a health food company, allowing us to create a targeted, effective intervention.

Should we do this? (Viability)

• What are our main acquisition channels for obtaining customers?
  • Channels could include on-campus promotions, social media marketing, and partnerships with university wellness programs.
• How will our customers repeatedly use our solution?
  • Habit formation will be encouraged through gamification (e.g., keeping a virtual pet alive), reflection prompts, and social accountability features that reward consistent use.
• Why will our customers refer us to new customers?
  • If the app successfully helps them feel healthier and more productive, they will naturally share it with friends who face the same late-night study struggles. Social features that encourage group participation will also drive referrals.
• How does this solution support our company vision?
  • It directly supports the vision of making healthy choices easier and more accessible, using behavioral science to build sustainable, positive habits.
• Who are our primary competitors to our solution?
  • Primary competitors are established food delivery giants like UberEats and DoorDash, as well as on-campus vending machines and convenience stores that offer immediate gratification.
• How will our solution generate revenue?
  • Through the sale of snacks. A margin will be earned on each product sold through the app, based on the partnership agreement with some food companies like One Step Foods.

The most important assumptions here are the three that we chose for our assumption tests below:

1. Can social features & peer accountability drive habit adoption? This assumption tests the durability of the habit, to see if communal behavior could impact an individual’s desire to eat healthier at night.
2. Will students follow through on orders placed in the morning? This assumption tests the mindset of the students, to see if they can stick to a plan they formulated for themselves at a different time and potentially in a different headspace.
3. Could students find some healthy menu appealing enough? This assumption tests the larger question of whether students inherently desire only unhealthy food to sustain and entertain them late at night or if there are possibilities of healthier options (that aren’t currently available and weren’t during the intervention study) that could entice students enough to subconsciously choose healthier?

Key Insights from the Map

1. The Core Risk is Behavioral Follow-Through: The single most important and unknown assumption is: Will students actually follow through on their morning pre-orders? The entire concept hinges on the idea that a pre-commitment made in the morning will hold up against the spontaneous realities of a student’s evening—changing social plans, fluctuating appetite, or simply not feeling like the snack they ordered hours ago. This is a massive leap of faith in the power of pre-commitment and must be the top priority for testing.
2. Engagement and Motivation are Critical Unknowns: We are assuming that social accountability features will be compelling enough to drive repeat usage and sustain engagement. However, it is unknown if these mechanics will be perceived as genuinely motivating or as trivial gimmicks. The success of turning this intervention into a long-term habit, rather than a one-time novelty, depends on getting this right. Therefore, testing the appeal and effectiveness of these motivational loops is crucial.
3. Desirability of the “Healthy” Constraint: We assume students want to be healthier, but will they find a curated menu of only healthy snacks appealing enough to choose our app over competitors that offer a variety of indulgent options? This assumption in the Evaluate quadrant highlights a potential conflict between the students’ stated goals (to be healthy) and their in-the-moment desires. We must validate whether the convenience and pre-commitment are strong enough to overcome the allure of a late-night burger or ice cream-covered cookie from another service.

Assumption Tests

Main Assumption 1: Can social features & peer accountability drive habit adoption?

We believe that: Social features and peer accountability can help individuals to maintain their habits.

To verify that, we will: Create a simple sample task and a fictional leaderboard to show students their progress compared to their fictionalized peers.

And measure: Whether or not they complete the task subsequently after checking their relative scores on the leaderboard.

We are right if: All students complete the given task in a timely manner based on the percentage of “peers” who have completed the same task before them during the same day- this is similar to Duolingo’s encouragement notifications (e.g. “Only 30% of learners maintained their streak earlier than you did today!”).

Main Assumption 2: Will students follow through on orders placed in the morning?

We believe that: Students will stay committed later at night to orders they placed in the morning.

To verify that, we will: Have students commit to completing a simple task in the morning and give themselves a time to actually complete the task later in the day at the time they specified in the pre-planning phase.

And measure: How many students completed the task by the time they noted and/or how long students took to complete the task after the time they specified.

We are right if: Students complete the task they committed to within 1 hour of the time they specified in their pre-commitment statements.

Main Assumption 3: Could students find a healthy menu appealing enough?

We believe that: Although our menu during the intervention study may not have offered the ideal food choices that a student would gravitate towards naturally, students could potentially take interest in other healthier menu options that are equally as enticing as the current offerings.

To verify that, we will: Create a fake menu that combines current menu items and fictional healthier options to share with participants to complete when they are hungry at night, and have them submit their “orders” to us.

And measure: How many times the fake healthier foods are selected over the foods that are known to be already available.

We are right if: The students still find themselves craving the newer fake options we place on the menu, despite their knowing that these foods don’t exist. We hope that their hunger and intuitive desire are strong enough to point them towards the healthier options regardless of the facts.