RESUMEN
BACKGROUND: Although many smoking cessation smartphone apps exist, few have been independently evaluated, particularly in older populations. In 2017, of the 112 commercially available smoking cessation apps in Australia, only 6 were deemed to be of high quality, in that they partially adhered to Australian guidelines. Mobile health (mHealth) apps have the potential to modify smoking behavior at a relatively low cost; however, their acceptability in older smokers remains unknown. Rigorous scientific evaluation of apps is thus urgently needed to assist smokers and clinicians alike. OBJECTIVE: We conducted a pilot randomized controlled trial to evaluate the feasibility of a large-scale trial to assess the use and acceptability of a high-quality smoking cessation app in older smokers. METHODS: Adult inpatient and outpatient smokers with computer and smartphone access were recruited face to face and via telephone interviews from Metropolitan Hospitals in Brisbane, Australia. Participants were randomized 1:1 to the intervention (requested to download the "My QuitBuddy" smoking cessation app on their smartphone) or the control group (provided access to a tailored smoking cessation support webpage [Quit HQ]). The My QuitBuddy app is freely available from app stores and provides personalized evidenced-based smoking cessation support. Quit HQ offers regular email support over 12 weeks. No training or instructions on the use of these e-resources were given to participants. Outcomes at 3 months included recruitment and retention rates, use and acceptability of e-resource (User Version of the Mobile App Rating Scale [uMARS]), changes in quitting motivation (10-point scale), and self-reported smoking abstinence. RESULTS: We randomized 64 of 231 potentially eligible individuals (27.7%). The mean age of participants was 62 (SD 8). Nicotine dependence was moderate (mean Heaviness of Smoking Index [HSI] 2.8 [SD 1.2]). At 3 months the retention rate was (58/64, 91%). A total of 15 of 31 participants in the intervention arm (48%) used the app at least once, compared with 10 of 33 (30%) in the control arm. uMARS scores for e-resource use and acceptability were statistically similar (P=.29). Motivation to quit was significantly higher in the intervention arm compared with the control arm (median 6 [IQR 4-8] versus 4 [IQR 4-5], respectively, P=.02). According to the intention-to-treat analysis, smoking abstinence was nonsignificantly higher in the intervention group (4/31 [13%], 95% CI 4%-30%, versus 2/33 [6%], 95% CI 1%-20%; P=.42). The estimated number needed to treat was 14. CONCLUSIONS: Internet and mHealth smoking cessation resources appear acceptable to a minority of older smokers. Smokers who engaged with the allocated e-resources rated them equally, and there were trends toward greater uptake, increased motivation, and higher abstinence rates in the app group; however, only the change in motivation reached statistical significance (median score 6 versus 4, respectively, P=.02). This results of this pilot study suggest that apps may improve quit outcomes in older adults who are willing to use them. Further research into user-app interactions should be undertaken to facilitate improvements in app design and consumer engagement. These favorable trends should be explored in larger trials with sufficient statistical power. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12619000159156; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=376849&isReview=true.
RESUMEN
BACKGROUND AND OBJECTIVE: A new taxonomic and management approach, termed treatable traits, has been proposed for airway diseases including severe asthma. This study examined whether treatable traits could be identified using registry data and whether particular treatable traits were associated with future exacerbation risk. METHODS: The Australasian Severe Asthma Web-Based Database (SAWD) enrolled 434 participants with severe asthma and a comparison group of 102 participants with non-severe asthma. Published treatable traits were mapped to registry data fields and their prevalence was described. Participants were characterized at baseline and every 6 months for 24 months. RESULTS: In SAWD, 24 treatable traits were identified in three domains: pulmonary, extrapulmonary and behavioural/risk factors. Patients with severe asthma expressed more pulmonary and extrapulmonary treatable traits than non-severe asthma. Allergic sensitization, upper-airway disease, airflow limitation, eosinophilic inflammation and frequent exacerbations were common in severe asthma. Ten traits predicted exacerbation risk; among the strongest were being prone to exacerbations, depression, inhaler device polypharmacy, vocal cord dysfunction and obstructive sleep apnoea. CONCLUSION: Treatable traits can be assessed using a severe asthma registry. In severe asthma, patients express more treatable traits than non-severe asthma. Traits may be associated with future asthma exacerbation risk demonstrating the clinical utility of assessing treatable traits.
