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Smoking cessation | health behavior change | social support | self-reflection




Class: INST838 Health Informatics

Professor: Eun Kyoung Choe

Timeline: 2018.9 - 2018.12


Design background: Peiyi Liu

Design background: Jessica Yuan

Engineer background: Xiaoyu Sun

Business background: James Chi



Literature review:

User research:

Paper prototype:

User testing:

UI design:


Many studies and sources have shown that the success rate for smokers who are in the action stage and attempt to quit smoking alone is lower than 10%. Many people fail to quit smoking because it usually takes about 30 attempts and is a challenge for them to fight their habit.

How can we help action stage smokers reduce failure in smoking cessation?


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Who are users?

What are existed solutions?

None of our teammate has experience in smoking. To understand the smoking behaviors and existed solutions of smoking cessation, we went through papers, articles, and social platforms to dive deeper into the subject matter. 

1. Improve mindfulness to achieve behavior change

Tracking and reflecting on smoking behavior can promote the mindfulness and effectively help smokers quit. Previous research enriches our understanding of addictive behaviors and the nature of behavior change [6, 10]. When the urge of smoking appears, smokers unconsciously ignore all the negative aspects of smoking and repeat the habit of smoking [3].

Digital Mind

Currently, people use social network websites such as Facebook and Twitter [8, 11], and other online communities[4] to look for information, seek help, post achievement, and share experience [9]. Furthermore, studies show that people are more comfortable to share experience related to health and behavior change, and respond to other people in a supportive manner when they are on anonymous online communities[7].

2. People need social support to stay motivated

Holding Hands

3. Targeting smokers who are already in action


Previous research identified five stages of change of quitting smoking, which are: precontemplation, contemplation, preparation, action, and maintenance [5]. Research shows that people at different stages have different preferences for information exchange[9]. People at the action stage are the most active—they seek information and plans, share achievement and stories, and ask and answer questions.

4. Lack of social interaction in smoking cessation apps

Common features that existing smoking cessation apps employ are tracking benefits (e.g., money saved and health benefits accrued), tracking the number of quitting days, hypnosis techniques, goal setting, education, measuring lung health, and games for quitting[1, 2].

Man Trying App
literature review


Does tracking urges improve mindfulness?

Our team designed a diary study research to learn more about the context of their smoking behavior. This study served the two purposes.

1. Explore the context of each urge.

2. Validate the effectiveness of self-tracking and mindfulness on the participant’s perception towards their own behavior.


Six participants who are active smoker, between 18 to 35 years old, and have the experience of smoking for more than six months are recruited.


Diary Study

Record daily smoking behaviors


Answer follow-up questions

Qualitative Coding

Assesses the user data

Research record and analysis examples

smoking behavior records

We asked participant to record data of when they smoked, where they smoked, whether they smoked alone or with others, and why they smoked.

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Smoking Time and qualitative coding

The team assessed the user data and found there were typically six reasons for the smoking behaviors, habits, positive reinforcements, negative reinforcements, relaxations, social activities and the combination of frustrations and social activities. They were the qualitative codings for the research analysis.


The three peak values are 2am, 3pm and 11pm. The number of cigarettes under the period from 9pm to 11pm increases tremendously. The reason for smoking behavior on 2am is P1’s habit before sleeping. The reason
for smoking behavior on 3pm is either relaxation or social activity. The increasing amount of cigarettes between 9pm to 11pm are due to social activities and frustrations.

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1. Most of the smoking urges are unconsciously done, and moods did not contribute much.


Based on the diary entries collected, most of the smoking are being done because of habits or when people try to fill in spare times. Those occurrence did not involved with any negative or positive emotions and participants could not think of particular reasons why they choose to smoke at those moments. This implied the lack of mindfulness among smokers.

2. Self-tracking and reflection improve mindfulness.

The seven-day data tracking process boosted people’s consciousness of their smoking behavior. For example, P1 said he would stop smoking the cigarette before sleeping because he realized that he coughed while sleeping, and smoking might be the reason.

3. Smokers prefer to get support from other people if they decide to quit smoking.


Five out of six participants prefer to form a group because other smokers understand their situation, provide suggestions and celebrate efforts they put into overcoming urges. Those potentially motivate smokers a lot rather than quit smoking by themselves.



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Proposed Design

Based on the findings from the user research and secondary research, we came up with and evaluated design ideas. Our final design, Quitogether, will help people reflect on their smoking behavior by tracking and visualizing data regarding smoking and smoking urges. People will give and receive social support from group members who work closely to quit smoking and from other people who are using Quitogether. We created a low fidelity paper prototype of our initial product concepts.

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Test and improve the product

We conducted user testing with three participants  The three participants were current smokers with more than 12-month smoking experience. During each session, the participant was asked to complete a few tasks of three scenarios, which were about exploring smoking data, reporting smoking urges and getting help from group members, and browsing smoking plans and stories. After each scenario, participants talked about their overall experience and whether it will effectively help them to quit smoking. We have the following feedback that later has been applied to our final design.




Make message more customized for the interaction between group mates
Group chat and messages from group members instead of from the system would better guide users’ behavior. Participants mentioned the content of the intervention message from group members should be personalized based on the user’s preferences and the user context.

Track and show urges to promote mindfulness
Tracking smoking urges instead of tracking the times of smoking would help users effectively quit smoking, as it can help them overcome some of the urges. While tracking the reasons of smoking can help users reflect on their smoking data, few participants said they would manually track it because of the high tracking burden.

Show overcome urges to promote positive reinforcement and mindfulness
Highlighting how many times users overcame the urge of smoking can also offer them a sense of
success and help them stick to their quitting plan. The visualization of daily goal has a great impact on how users perceive the process toward their goals.




[1] Lorien C Abroms, Nalini Padmanabhan, Lalida Thaweethai, and Todd Phillips. 2011. iPhone apps for smoking cessation: a content analysis. American journal of preventive medicine 40, 3 (2011), 279–285.

[2] Lorien C Abroms, J Lee Westmaas, Jeuneviette Bontemps-Jones, Rathna Ramani, and Jenelle Mellerson. 2013. A content analysis of popular smartphone apps for smoking cessation. American journal of preventive medicine 45, 6 (2013), 732–736.

[3] Judson A Brewer, Sarah Mallik, Theresa A Babuscio, Charla Nich, Hayley E Johnson, Cameron M Deleone, Candace A Minnix-Cotton, Shannon A Byrne, Hedy Kober, Andrea JWeinstein, et al. 2011. Mindfulness training for smoking cessation: results from a randomized controlled trial. Drug and alcohol dependence 119, 1-2 (2011), 72–80.

[4] Nathan K Cobb, Amanda L Graham, and David B Abrams. 2010. Social network structure of a large online community for smoking cessation. American journal of public health 100, 7 (2010), 1282–1289.

[5] Carlo C DiClemente, James O Prochaska, Scott K Fairhurst, Wayne F Velicer, Mary M Velasquez, and Joseph S Rossi. 1991. The process of smoking cessation: an analysis of precontemplation, contemplation, and preparation stages of change. Journal of consulting and clinical psychology 59, 2 (1991), 295.

[6] Susan Michie, Natasha Hyder, Asha Walia, and Robert West. 2011. Development of a taxonomy of behaviour change techniques used in individual behavioural support for smoking cessation. Addictive behaviors 36, 4 (2011), 315–319.

[7] Meredith Ringel Morris, Jaime Teevan, and Katrina Panovich. 2010. What do people ask their social networks, and why?: a survey study of status message q&a behavior. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 1739–1748.

[8] Elizabeth L Murnane and Scott Counts. 2014. Unraveling abstinence and relapse: smoking cessation reflected in social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1345–1354.

[9] Bernd Ploderer,Wally Smith, Steve Howard, Jon Pearce, and Ron Borland. 2013. Patterns of support in an online community for smoking cessation. In Proceedings of the 6th International Conference on Communities and Technologies. ACM, 26–35.

[10] Robert West and Jamie Brown. 2013. Theory of addiction. John Wiley & Sons.

[11] Mi Zhang and Christopher C Yang. 2013. The effectiveness of smoking cessation intervention on Facebook: a preliminary study of posts and users. In International Conference on Smart Health. Springer, 7–17.

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