A food log helps establish a baseline for eating habits, allowing individuals to reflect on their behaviors and make informed decisions. But tracking them can be time consuming and challenging.
A new feature within Fitbit’s food tracker to simplify the process of logging meals, enabling users to stay motivated and aware throughout their fitness journey. This enhancement encourages users to reflect on their eating habits and make informed decisions, promoting healthier, more sustainable changes in their lifestyle.
This was a four weeks long project executed as part of the HCI method elective.
4 UX designers
Literature review
interview
Crowdsourcing
Affinity mapping
Persona
brainstorming
qualitative analysis
Usability testing
Kano Analysis
Researcher
UI designer
4 weeks
View prototype
Notifications and widgets offer direct access to the meal logging flow, reducing the number of clicks and lowering the barrier to entry, making it easier to start tracking meals.
Three easy methods are provided for logging meals — AI-powered image recognition via camera, voice-annotated logging, and text search.
Nutritional and health-related information is introduced through ‘Smart Cookies,’ adding interest and entertainment. Users engage by learning new facts about nutritional value or health benefits, enhancing their experience while gaining tips to improve their habits.
Encouraging users to share their stories fosters a supportive community, motivating them to stay engaged and maintain healthy behaviors.
The diet tracking provides a visual record of meals and offers daily, weekly, and monthly statistics on macronutrient consumption. This enables users to reflect on their habits, identify dietary patterns, and make informed decisions.
We started this project by conducting semistructured interviews with participants (n=5; ages 22-35) about their fitness goals and how they integrate dietary targets into their daily lives.
Users believe that a good diet is essential for all aspects of life and consider it more important than physical activity.
While dietary goals varied among users, a common challenge was not knowing what changes to make to achieve those goals, along with a lack of motivation to take action.
It appeared paradoxical that interviewees acknowledged the importance of food tracking for weight management and the role of a healthy diet in supporting physical activity, yet they rarely used technology for these purposes.
Moreover, this insight came from only 5 user interviews. To validate the findings, we decide to use crowdsourcing method and asked a larger group to fill in the blanks for the statement below.
This revealed that users had diverse goals, ranging from wanting to change their weight to focusing on specific macronutrients. While some users were highly knowledgeable about nutrition and dietary management, others had less expertise.
We reviewed several academic papers that surveyed user perceptions of food logging apps and found that they do not simplify the meal logging process. The amount of information required upfront often deters users from starting, while the numerous steps needed for accurate logging reduce users’ confidence in completing the task effectively.
Said it takes too long to enter the data
Felt apps did not hold their interest long enough
Found existing diet apps confusing to use
Wanted control over data sharing
Left app because of hidden costs like features hidden behind freemium models
Users who have tried tracking and found it cumbersome, but still wish to understand their food consumption to make necessary improvements to their diets.
To encourage existing users to consistently log their meals, we need to design for behavior change. Our research identified a key challenge: users often abandon tracking due to the high cost of involvement, which must be addressed through our intervention.
The theory suggests that if the capability to change one's behavior exists, enabling self-efficacy, making the recipient aware of the potential health outcomes, and reinforcing these two aspects will help achieve one's behavior goal.
Our objective was to facilitate behavior change in the user by increasing their self-efficacy, providing positive external reinforcement, and managing outcome expectations through improved knowledge.
Reduce the barrier of entry to tracking the foods eaten in the course of a day.
Reward users with informational tidbits about the food they logged to reinforce the outcome expectations.
Providing social proof, and using engagement to drive the motivation to log.
Allow for a reflective analysis of foods consumed, giving the user agency in determining how they wish to modify their diet to meet their health needs.
Using the think-aloud protocol, five users were asked to perform four assigned tasks while their actions were observed. We then posed several questions to gain insight into their reasoning behind their approaches and evaluating the prototype. We also conducted a Kano Analysis to understand which features were the most desirable.
Central to the app, and most important feature for all user, most users found it clear, concise and useful
All users chose to log in using the camera function and thought it was a straightforward way of doing things.
Users like that it is informational, useful, and fun, but they have trouble recognizing and associating it with nutritional knowledge.
Users may not use the feature and share stories themselves, but like to look at others’ stories.
We identified five most essential features 1)Diet Tracking, 2) AI Image Recognition, 3) Smart Cookies, 4) Nutrition Information, 5) Social Engagement and posed two questions about each of these features to the same participants:
Based on user responses, we labeled 3 features:
Customers expect to be present in a product. If these features are not present, it will lead to dissatisfaction.
Customers are satisfied with when present and dissatisfied when not present. The level of satisfaction increases as the level of the feature increases.
Customers do not expect to be present in a product, but when present, they generate a high level of satisfaction and delight.
Must-have feature
Performance feature
Delightful feature
Must-have feature
Delightful feature
Accuracy of AI image recognition could be potential pain point.
We also need to find a way to enhance the amount of information that users can input while still benefiting from the ease of use provided by the lower barrier to entry.
Cookies feature is useful but needs improvements.
While Smart Cookies feature played a vital role in reinforcing expected outcomes, it did not meet user expectations during usability testing. We have yet to decide whether to improve or abandon it, and this decision will require further research.
Even if users do not directly engage with others through stories they find it useful to stay motivated.
Social Stories feature was well-received as a motivational tool, but it did not meet certain user needs, such as accessing shared food recipes. We need to explore ways to incorporate these missing features.