If we are asked to name that one evil of the society which is gradually hallowing it up from the core, it would inevitably have to be the habit of smoking. Even though smoking is a subjective choice and depends on the tendencies of the individual, the overall effects produced by it are excruciatingly objective and glaring. With the already surging strata of air pollution, smoking seems to simply accelerate the consequences that a man’s lungs are exposed to; furthermore, the aftermaths of smoking are not wholly restricted within the realms of the individual. If studies are to be believed, people around the smoker who inhabits in passive smoking have greater possibilities of developing physical vulnerabilities when compared to the former kind. To combat the increasing dangers of smoking, the market has introduced e-liquids that are used in the vaping equipment and is known to pose little or no dangers when placed alongside tobacco cigarettes. In a recent prospective observational study that was conducted to delineate the physical activity with smoking urges illustrated an evidence that indulging in regular exercise can indeed limit smoking urges; however, this trend is limited in scope to acute effects and invariably dependent on measures ensued by the retrospective self. Mobile health technologies work as a catalyst by offering effective techniques that can track real-time data of the human behaviors directing from the natural environment.
Apart from outlining the association of physical activity with smoking urges, the chief objective of the study was to browse through the real-world, one dimensional setting that is rendered by mobile health tools to analyze and trace the link formed between exercise and recurring wills to smoke in a 12-week observational study.
The study was essentially conducted on a small-scale platform and included about 60 smokers who resorted to smoking more than or equal to three cigarettes in a day. This also took into account their day-to-day activities such as physical exercise, demographics and usual smoking behaviors by capitalizing upon a web-based questionnaire. Here, with “smoking behavior” we mean the instincts that prompted the candidates to smoke during a day. This introduction was followed by step counts that were, in turn, measured continuously using the Fitbit charge HR. The intervention of technology in counting the number of steps taken in a day allowed the calculation to be based upon sheer accuracy and transparency thus, leaving no room for any mistake in the study. The participants were asked to report their instantaneous smoking urges through text messages using the Likert scale that ranged from 1 to 9; one being the lowest urge while 9 is the highest. After the study was completed, participants also registered their feedbacks in follow-up smoking behaviors in an online exit survey.
Out of all the 60 participants that were enrolled in the study, a total of 53 candidates whose age were more than 40 years, recorded consequent 6 weeks of data and were thus successfully laid down in the analysis. Let us also tell you that 57% of these participants were women and 49% of the population accounted to non-white people. The entire course of study recorded about 15,365 messages with a mean of 290, and a standard of 62 messages sent by each participant. Interestingly, the mean urge over this course was associated with a positive string amongst the daily cigarette consumption, their usual willingness to smoke and physical activities at the follow up (Pearson r=0.33, P=0.02). Nonetheless, the regressive models of acute effects had a distinct story to tell altogether; it proclaimed generous inverse associations between steps that fell within the scopes of 30, 60 and 120 minute time windows of a reported urge (beta=0.0191 per 100 steps where P is less than 0.001). Additionally, the remaining 6 people who formed around 10% of the study population displayed a more strapping and streamlined association between the steps taken in every single day and the urge to smoke at both the day level and a minimum of 30 minutes level. In the first case, the mean individualized beta equaled to 0.153 per 1000 steps, and on the other hand, the mean individualized beta conformed to 1.66 per 1000 steps.
Even though there was no concrete association between the number of daily physical activities measured objectively and concurrently reported smoking urges, there appeared to be a hint of proportionate inverse relationship between smoking urge and recent step counts that fell within the window of 30 and 120 minutes. Out of the entire lot, 10% of the participants seemed to have a more powerful and coherent relation between the physical activity and smoking urge. Also, let us not steer away from accepting the fact that this provocative finding will require greater probation and further study to look within the deep layers of this association.