Internet-Based Photoaging Within Australian Pharmacies to Promote Smoking Cessation: Randomized Controlled Trial
Curtin Health Innovation Research Institute, School of Pharmacy, Curtin University, Perth, Australia. . Journal of Medical Internet Research
(Impact Factor: 3.43).
03/2013; 15(3):e64. DOI: 10.2196/jmir.2337
Tobacco smoking leads to death or disability and a drain on national resources. The literature suggests that cigarette smoking continues to be a major modifiable risk factor for a variety of diseases and that smokers aged 18-30 years are relatively resistant to antismoking messages due to their widely held belief that they will not be lifelong smokers.
To conduct a randomized controlled trial (RCT) of a computer-generated photoaging intervention to promote smoking cessation among young adult smokers within a community pharmacy setting.
A trial was designed with 80% power based on the effect size observed in a published pilot study; 160 subjects were recruited (80 allocated to the control group and 80 to the intervention group) from 8 metropolitan community pharmacies located around Perth city center in Western Australia. All participants received standardized smoking cessation advice. The intervention group participants were also digitally photoaged by using the Internet-based APRIL Face Aging software so they could preview images of themselves as a lifelong smoker and as a nonsmoker. Due to the nature of the intervention, the participants and researcher could not be blinded to the study. The main outcome measure was quit attempts at 6-month follow-up, both self-reported and biochemically validated through testing for carbon monoxide (CO), and nicotine dependence assessed via the Fagerström scale.
At 6-month follow-up, 5 of 80 control group participants (6.3%) suggested they had quit smoking, but only 1 of 80 control group participants (1.3%) consented to, and was confirmed by, CO validation. In the intervention group, 22 of 80 participants (27.5%) reported quitting, with 11 of 80 participants (13.8%) confirmed by CO testing. This difference in biochemically confirmed quit attempts was statistically significant (χ(2) 1=9.0, P=.003). A repeated measures analysis suggested the average intervention group smoking dependence score had also significantly dropped compared to control participants (P<.001). These differences remained statistically significant after adjustment for small differences in gender distribution and nicotine dependence between the groups. The mean cost of implementing the intervention was estimated at AU $5.79 per participant. The incremental cost-effectiveness ratio was AU $46 per additional quitter. The mean cost that participants indicated they were willing to pay for the digital aging service was AU $20.25 (SD 15.32).
Demonstrating the detrimental effects on facial physical appearance by using a computer-generated simulation may be both effective and cost-effective at persuading young adult smokers to quit.
Australian New Zealand Clinical Trials Registry: ACTRN12609000885291; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12609000885291 (Archived by WebCite at http://www.webcitation.org/6F2kMt3kC).
Available from: Titus J. Brinker
Journal of Medical Internet Research 07/2015; 17(7):e186. DOI:10.2196/jmir.4792 · 3.43 Impact Factor
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ABSTRACT: This study reports how showing a person an illustration of themselves following future weight-loss might impact on their actual future weight-loss. Weight was recorded weekly, 8 weeks before and 8 weeks after the intervention. A significant proportion of the 44 participants lost weight following the intervention: 17 vs. 29 (P = 0.01, chi-squared = 6.559). After the first 8 weeks, the mean change in weight was -0.32 kg [standard deviation (SD): 2.2, P = 0.37]. The weight change after the second 8 weeks was -0.94 kg (SD: 1.7, P = 0.001). The mean difference in weight losses between the two periods was -0.62 kg (SD: 2.1, P = 0.08).
© The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
The European Journal of Public Health 02/2015; 25(2). DOI:10.1093/eurpub/cku249 · 2.59 Impact Factor
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