ArticleJMIR formative research2021
Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology.
Article in JMIR formative research, 2021. 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 9 papers.
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
9 citing papers in PubMed, 14 citations in OpenAlex.
- 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
- Article
- Monitoring Opioid-Use-Disorder Treatment Adherence Using Smartwatch Gesture Recognition.Sensors (Basel, Switzerland) · 2025Article
- Acceptability of heart rate-based remote monitoring of smoking status.Addictive behaviors reports · 2024Article
- Sensors for Smoking Detection in Epidemiological Research: Scoping Review.JMIR mHealth and uHealth · 2024Article
- 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
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 at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile there have been many technological advances in studying the neurobiological and clinical basis of tobacco use disorder and nicotine addiction, there have been relatively minor advances in technologies for monitoring, characterizing, and intervening to prevent smoking in real time. Better understanding of real-time smoking behavior can be helpful in numerous applications without the burden and recall bias associated with self-report.
objectiveThe goal of this study was to test the validity of using a smartwatch to advance the study of temporal patterns and characteristics of smoking in a controlled laboratory setting prior to its implementation in situ. Specifically, the aim was to compare smoking characteristics recorded by Automated Smoking PerceptIon and REcording (ASPIRE) on a smartwatch with the pocket Clinical Research Support System (CReSS) topography device, using video observation as the gold standard.
methodsAdult smokers (N=27) engaged in a video-recorded laboratory smoking task using the pocket CReSS while also wearing a Polar M600 smartwatch. In-house software, ASPIRE, was used to record accelerometer data to identify the duration of puffs and interpuff intervals (IPIs). The recorded sessions from CReSS and ASPIRE were manually annotated to assess smoking topography. Agreement between CReSS-recorded and ASPIRE-recorded smoking behavior was compared.
resultsASPIRE produced more consistent number of puffs and IPI durations relative to CReSS, when comparing both methods to visual puff count. In addition, CReSS recordings reported many implausible measurements in the order of milliseconds. After filtering implausible data recorded from CReSS, ASPIRE and CReSS produced consistent results for puff duration (R
conclusionsAgreement between ASPIRE and other indicators of smoking characteristics was high, suggesting that the use of ASPIRE is a viable method of passively characterizing smoking behavior. Moreover, ASPIRE was more accurate than CReSS for measuring puffs and IPIs. Results from this study provide the foundation for future utilization of ASPIRE to passively and accurately monitor and quantify smoking behavior in situ.
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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 Socratic graph.