Process and Information Needs When Searching for and Selecting Apps for Smoking Cessation: Qualitative Study Using Contextual Inquiry
Bibliographic record
Abstract
BACKGROUND: Hundreds of apps are available to support people in their quest to quit smoking. It has been hypothesized that selecting an app from a sizable volume without any aid can be overwhelming and difficult. However, little is known about how people choose apps for smoking cessation and what exactly people want to know about an app before choosing to install it. Understanding the decision-making process may ultimately be helpful in creating tools to help people meaningfully select apps. OBJECTIVE: The aim of this study is to obtain insights into the process of searching and selecting mobile apps for smoking cessation and map the range of actions and the accompanying reasons during the search, focusing on the information needs and experiences of those who aim to find an app. METHODS: Contextual inquiries were conducted with 10 Dutch adults wanting to quit smoking by using an app. During the inquiries, we observed people as they chose an app. In addition, 2 weeks later, there was a short semistructured follow-up interview over the phone. Through convenience and purposive sampling, we included participants differing in gender, age, and educational level. We used thematic analysis to analyze the transcribed interviews and leveraged a combination of video and audio recordings to understand what is involved in searching and selecting apps for smoking cessation. RESULTS: The process of finding smoking cessation apps is comprehensive: participants explored, evaluated, and searched for information; imagined using functions; compared apps; assessed the trustworthiness of apps and information; and made several decisions while navigating the internet and app stores. During the search, the participants gained knowledge of apps and developed clearer ideas about their wishes and requirements. Confidence and trust in these apps to help quitting remained quite low or even decreased. Although the process was predominantly a positive experience, the whole process took time and energy and caused negative emotions such as frustration and disappointment for some participants. In addition, without the participants realizing it, errors in information processing occurred, which affected the choices they made. All participants chose an app with the explicit intention of using it. After 2 weeks, of the 10 participants, 6 had used the app, of whom only 1 extensively. CONCLUSIONS: Finding an app in the current app stores that contains functions and features expected to help in quitting smoking takes considerable time and energy, can be a negative experience, and is prone to errors in information processing that affect decision-making. Therefore, we advise the further development of decision aids, such as advanced filters, recommender systems and curated health app portals, and make a number of concrete recommendations for the design of such systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".