ArticleJMIR mHealth and uHealth2017
Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study.
Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07067151 (Use of Noninvasive Wearables Biomonitoring to Detect Pre-Smoking, Smoking, And Post-Smoking Stages), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Use of Noninvasive Wearables Biomonitoring to Detect Pre-Smoking, Smoking, And Post-Smoking Stages: An Observational Laboratory Study
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review.Sensors (Basel, Switzerland) · 2019Pooled it
- Smartband-based smoking detection and real-time brief mindfulness intervention: findings from a feasibility clinical trial.Annals of medicine · 2024Trial
- Feasibility and efficacy of a real-time smoking intervention using wearable technology.PLOS digital health · 2025Article
- Validating the information technology (IT) implementation framework to Implement mHealth technology for consumers: A case study of the Sense2Quit app for smoking cessation.International journal of medical informatics · 2025Article
- Article
- Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step.Sensors (Basel, Switzerland) · 2024Article
- Pilot Testing of an mHealth App for Tobacco Cessation in People Living With HIV: Protocol for a Pilot Randomized Controlled Trial.JMIR research protocols · 2023Article
- Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study.JMIR human factors · 2023Article
- The mutagenic effect of tobacco smoke on male fertility.Environmental science and pollution research international · 2022Review
- Application of Machine Learning to Sleep Stage Classification.Proceedings. International Conference on Computational Science and Computational Intelligence · 2021Article
- Are Machine Learning Methods the Future for Smoking Cessation Apps?Sensors (Basel, Switzerland) · 2021Article
- Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology.JMIR formative research · 2021Article
- A Report on Smoking Detection and Quitting Technologies.International journal of environmental research and public health · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundSmoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models.
objectiveThis study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting.
methodsA convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session.
resultsIn total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate.
conclusionsSmartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events.
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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.