ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2020
SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Data-driven design guide for vibrotactile display layouts by continuous mapping.Scientific reports · 2025Article
- Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.JAMIA open · 2025Review
- Relative importance of temporal and location features in predicting smoking events.NPJ digital medicine · 2025Article
- Additive Manufacturing of Organic Electrochemical Transistors: Methods, Device Architectures, and Emerging Applications.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
- Sensors for Smoking Detection in Epidemiological Research: Scoping Review.JMIR mHealth and uHealth · 2024Article
- Towards Predicting Smoking Events for Just-in-time Interventions.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023Article
- Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder.Cogent mental health · 2023Article
- mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022Article
- The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol.Contemporary clinical trials · 2021Article
- OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2021Article
- A Robust Functional EM Algorithm for Incomplete Panel Count Data.Advances in neural information processing systems · 2020Article
- Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot: Smoking cessation use case.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Context plays a key role in impulsive adverse behaviors such as fights, suicide attempts, binge-drinking, and smoking lapse. Several contexts dissuade such behaviors, but some may trigger adverse impulsive behaviors. We define these latter contexts as
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.