Are you smoking? Automatic alert system helping people keep away from cigarettes

Chen, Taiyu; Zhang, Xiaoliang; Jiang, Haotian; Asaeikheybari, Golnoush; Goel, Nikhil; Hooper, Monica Webb; Huang, Ming-Chun

doi:10.1016/j.smhl.2018.07.008

Cited by 19 publications

(15 citation statements)

References 21 publications

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“…The work presented by Chen et al [8] also provides support for quitting smoking. In particular, a personalised quitting plan is created based on the answers provided by the user to several questions related to demographics and smoking habits (e.g., sex, age, smoking history, daily smoking quantity, etc.).…”

Section: Smoking Quitting Applicationsmentioning

confidence: 89%

“…The Case Western Reserve University (CWRU) developed a smoking cessation system able to perform the detection of smoking activities in real-time, based on the analysis of the movements registered by two armbands [8]. The system has three main components: the software application component, which consists of the quitting plan and mindfulness training, the activity recognition component, composed by the armbands' raw signal data collection and the classification algorithm, and the Internet service component, that includes the message service and cloud sharing.…”

Section: Smoking Detection Technologiesmentioning

confidence: 99%

“…Such a structured and constrained way to record the signals associated to activities affects the interpretation of the very high achieved performances. Hand-to-mouth and chest sensors Research Prototype 61% Lopez-Meyer et al [3] Hand-to-mouth sensor Research Prototype 90% Cole et al [4] App + Smartwatch Research Prototype 89% Smokesense [7] Smartwatch Research Prototype 89% Research by CWRU [8] App + 2 Armbands Not on the market 98% CigFree [9] App + Property Smartband Starting from $69 100 N/A SmokeBeat [10] App + Smartwatch Free 80% StopWatch [11] Smartwatch Research Prototype 92% Lu et al [12] Prototypal wrist sensors Research Prototype 5 88% Senyurek et al [13] Prototypal wrist sensors Research Prototype 93% Senyurek et al [14] Prototypal wrist sensors Research Prototype 97% RisQ [15] App + wrist sensors Research Prototype 81%…”

Section: Smoking Detection Technologiesmentioning

confidence: 99%

“…Several recent scientific works show very promising results but, at the same time, present obstacles for the application in real-life daily scenarios. As an example, the work presented in Reference [8] shows impressive results. However, the evaluation protocol takes into account only six movements performed by only right-hand smokers, and the actions have been performed in a very constrained way, as previously detailed.…”

Section: Final Remarksmentioning

confidence: 99%

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A Report on Smoking Detection and Quitting Technologies

Ortis

Caponnetto

Polosa

et al. 2020

IJERPH

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Mobile health technologies are being developed for personal lifestyle and medical healthcare support, of which a growing number are designed to assist smokers to quit. The potential impact of these technologies in the fight against smoking addiction and on improving quitting rates must be systematically evaluated. The aim of this report is to identify and appraise the most promising smoking detection and quitting technologies (e.g., smartphone apps, wearable devices) supporting smoking reduction or quitting programs. We searched PubMed and Scopus databases (2008-2019) for studies on mobile health technologies developed to assist smokers to quit using a combination of Medical Subject Headings topics and free text terms. A Google search was also performed to retrieve the most relevant smartphone apps for quitting smoking, considering the average user’s rating and the ranking computed by the search engine algorithms. All included studies were evaluated using consolidated criteria for reporting qualitative research, such as applied methodologies and the performed evaluation protocol. Main outcome measures were usability and effectiveness of smoking detection and quitting technologies supporting smoking reduction or quitting programs. Our search identified 32 smoking detection and quitting technologies (12 smoking detection systems and 20 smoking quitting smartphone apps). Most of the existing apps for quitting smoking require the users to register every smoking event. Moreover, only a restricted group of them have been scientifically evaluated. The works supported by documented experimental evaluation show very high detection scores, however the experimental protocols usually lack in variability (e.g., only right-hand patients, not natural sequence of gestures) and have been conducted with limited numbers of patients as well as under constrained settings quite far from real-life use scenarios. Several recent scientific works show very promising results but, at the same time, present obstacles for the application on real-life daily scenarios.

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Section: Smoking Quitting Applicationsmentioning

confidence: 89%

Section: Smoking Detection Technologiesmentioning

confidence: 99%

Section: Smoking Detection Technologiesmentioning

confidence: 99%

Section: Final Remarksmentioning

confidence: 99%

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A Report on Smoking Detection and Quitting Technologies

Ortis

Caponnetto

Polosa

et al. 2020

IJERPH

View full text Add to dashboard Cite

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“…Pragmatic trials are a next step common to both traditional and digital interventions; they seek to understand effectiveness in a real-world setting and with diverse populations [22, 66]. Pragmatic studies of digital technology could be undertaken completely outside of the context of healthcare settings such as with studies that recruit participants and deliver interventions through social media [67], that deliver interventions and collect data from biometric sensors and smartphone apps [68], or that market health-related services such as genetic testing directly to consumers [69]. The FDA’s move toward the use of real-world evidence and real-world data suggests that digital health tools used by consumers may assume greater importance for testing drugs and devices [65] as well as nonregulated products.…”

Section: Testing Drugs and Devices Versus Digital Solutionsmentioning

confidence: 99%

An Algorithm for Digital Medicine Testing: A NODE.Health Perspective Intended to Help Emerging Technology Companies and Healthcare Systems Navigate the Trial and Testing Period prior to Full-Scale Adoption

et al. 2018

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Over the last several years, there has been rapid growth of digital technologies attempting to transform healthcare. Unique features of digital medicine technology lead to both challenges and opportunities for testing and validation. Yet little guidance exists to help a health system decide whether to undertake a pilot test of new technology, move right to full-scale adoption or start somewhere in between. To navigate this complexity, this paper proposes an algorithm to help choose the best path toward validation and adoption. Special attention is paid to considering whether the needs of patients with limited digital skills, equipment (e.g., smartphones) and connectivity (e.g., data plans) have been considered in technology development and deployment. The algorithm reflects the collective experience of 20+ health systems and academic institutions that have established the Network of Digital Evidence for Health, NODE.Health, plus insights from existing clinical research taxonomies, syntheses or frameworks for assessing technology or for reporting clinical trials.

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