With three UX researchers, I conducted a diary study to understand how people use wearable devices and related mobile apps for weight management “in-the-wild.” This project was sponsored by Samsung Research America, Mobile Innovation Lab.
We investigated how experienced and novice users incorporated Fitbit into their lives over one week, and analyzed how their behaviors differed. Our primary goal was to understand why users continue to use Fitbit and why they stop.
The objective was to inform Samsung on their development of new tools and services for managing weight and preventing chronic diseases. The diary study contributed to their foundational knowledge about users’ environments that might influence their behaviors.
- Key findings from the current evaluation of tools and services
- Recommendations for future research
The R&D team at Mobile Innovation Lab used our study results to inform their future exploration and product strategy. Due to the NDA (non-disclosure agreement), I will only include our method and approach. Specific findings and recommendations are excluded.
Project Team & Links
- Kim Lambert
- Meena Sujanani
- Jessica Bao
My contributions: Literature review, competitive analysis, screener design, and data synthesis. Acted as the research operations lead and point of contact.
- User Profile for Recruitment (PDF)
- Project Personas (PDF)
- Project Study Plan (PDF)
- Final Report (PDF)
In the U.S., over 29.1 million people had diabetes in 2014, and about 600,000 Americans die from heart disease each year. (Source: National Diabetes Statistics Report, 2014) Obesity is a major contributor to both diabetes and heart disease, which means effectively managing one’s weight is critical to improving health and wellness.
Our stakeholders at Samsung Research America were interested in studying how people use emerging technologies for weight management, which ones resonate with them, and why. In particular, they were interested in comparing different approaches to weight management and what factors influence long-term user retention.
Our goal was to understand:
- Why people start using tools to track their health?
- How they use tools to track their health?
- What motivates them to continue tracking their health?
- What obstacles they encounter in track their health?
We determined that a diary study on wearable device usage would help us understand the emerging behaviors within the burgeoning tracking device market. The method was well-suited for gathering behavioral data in context (i.e. “in the wild”) compared to in-lab interviews.
1) Secondary Research
We surveyed 11 academic papers and conducted online competitive analysis of 7 wearable devices on the market to inform our study plan. I lead the literature review and synthesis of industry reports. As a group, we selected Fitbit Flex to use in our study based on its sales, marketshare, and feature set.
2) Usability Study
We segmented our audience based on their motivations for managing weight, and developed user profiles in order to identify our target population.
Our segmented user groups:
- Novice users – hadn’t used a Fitbit nor any other smart wearable devices
- Experienced users – used their Fitbit device for at least 3 months
We defined each user group with a persona:
We created an online screener, and relied on convenience sampling for recruitment. We selected a total of 10 participants: 5 experienced users and 5 novice users. I developed the online screener while my team focused on recruiting participants.
We constructed our 7-day diary study in which participants received daily prompts through email. The prompts contained a set of same core questions and a set of variable questions specific to each participant group. I co-facilitated writing sessions for our daily prompts by helping our team prioritize what we want to learn.
Each day of the study covered a different theme regarding device use. We avoided event- and signal-contingent protocol to allow more flexibility for participants to record their responses. I was responsible for sending out daily prompts and reminders for the study while my team collected responses and performed personal follow-ups.
3) Data Analysis
Pre-analysis, we decided to bucket responses in terms of:
- User behaviors
- Design opportunities
- Personal preferences
All members collaborated on coding each participant response. Collectively, we grouped these codes based on relationships to each other to let natural categories emerge. Our analysis produced 4 main themes out of 490 codes.
4) Post-study Interviews
We conducted 4 post-study interviews with participants whose responses were particularly interesting. The purpose of these interviews was to explore outliers whose reasoning could challenge our current understanding of diary study results. Our team conducted these interviews while I continued to analyze our findings.
We produced 11 findings, each with an incidence rate and 1-2 representative quotes from novice and experienced participants. From those, we came up with 9 high-level recommendations that operationalized our findings. Our team delivered a final presentation to the user research and design team in the Samsung Mobile Innovation Lab.
The biggest lesson I learned was to get buy-in for the research design earlier to allow for stakeholder feedback and avoid delays. Since this was a generative research study, the challenge was learning how to properly scope a study so we could execute it within our constraints.
Another lesson is to communicate research findings at an appropriate level of detail that address stakeholder concerns. This helps to ensure that our recommendations align with the decisions that stakeholders have to make. The value of research comes from helping them consider the trade-offs and payoffs of critical decisions.