Abstract:Background: Understanding the characteristics of high and low utilizers of smartphone applications (apps) for smoking cessation would inform development of more engaging and effective apps, yet no studies to date have addressed this critical question. Informed by prior research on predictors of cessation Web site utilization, this study examines the degree to which baseline demographic factors (gender, age, and education), smoking-related factors (smoking level and friends' smoking), and psychological factors … Show more
“…Other randomised controlled trials comparing two mobile applications using action and commitment theory reported 13% (95% CI,6-22%) quit rate in the intervention (ATC based smart application) versus 8% (95% CI, 3-16%) in control groups (Quit Guide app) 36 . The odds of quitting were 2.9%, (95% CI 0.8-10.3).Two studies measuring 8-weeks cessation rate compared to conventional treatment only and full app adherence post-intervention were two times and four times more likely to achieve cessation, respectively 30,32 . When comparing the smartphone mobile applications with text message smoking cessation support, significantly higher number of quitters were found among the text message support group.…”
Section: Effect Of Intervention On the Outcomementioning
confidence: 97%
“…However, in interventional studies, the average number of the application openings was only 8.5 (SD = 9). Young age, knowledge level, heavier smoking, depression were predictive factors for app utilization studied in three studies 29,32,34 . Audio-visual features were most used aspects of the application followed by quit plan, tracking process and sharing features.…”
Section: App Utilization and Adherencementioning
confidence: 98%
“…The majority of the studies were conducted in the United States [29][30][31][32][33] . One unique intervention of mobile apps on the tablet was conducted among randomly selected hospital patients.…”
Section: Study Characteristics Participantsmentioning
confidence: 99%
“…Two studies included were a post-hoc analysis of the single arm pilot randomised control trials 31,32 . Three studies did not have a control group 29,33,34 .…”
Section: Design Features Of the Studiesmentioning
confidence: 99%
“…Two studies included hospital patient and smoking cessation clinic clients 29,35 . One study used an online screening survey for recruitment 32 . The format of delivery of intervention varied with each study.…”
INTRODUCTION Smartphone-based smoking cessation interventions are increasingly used around the world. However, the effects of smartphone applications on applicability and efficacy on cessation rate and prevention of relapses are not often evaluated. Therefore, this review aims to assess the evidence on effectiveness of smartphone applications as an intervention tool for smoking cessation support. METHODS We conducted the search using Ovid Medline/PubMed, CENTRAL and Scopus databases dated (January 2007-June 2016. Inclusion criteria include randomized control trials or intervention studies with mobile applications that offer smoking cessation support. Two assessors independently extracted and evaluated the data from each included study. RESULTS The review of eight selected studies illustrate the use of smartphone applications in increasing quit rates among smokers, however adherence to app features influences quit rates. Audiovisual features followed by a quit plan, tracking progress and sharing features are most accepted and utilised app features. However, inconsistency was observed in their association with abstinence or quit rate. App engagement features increase the statistical significance in the quit rate. Development of smartphone applications was supported by behavior change theories in all studies nevertheless; heterogeneous forms of intervention were adopted within studies. Similarly, reduction in relapse attributed to enhanced discussion among quitters using social media applications was observed. CONCLUSIONS Quality evidence is warranted with large sample size to measure effect size of the intervention. Future research on effectiveness and efficacy of smartphone alone and comparisons with other mHealth interventions, such as text messaging would be useful.
“…Other randomised controlled trials comparing two mobile applications using action and commitment theory reported 13% (95% CI,6-22%) quit rate in the intervention (ATC based smart application) versus 8% (95% CI, 3-16%) in control groups (Quit Guide app) 36 . The odds of quitting were 2.9%, (95% CI 0.8-10.3).Two studies measuring 8-weeks cessation rate compared to conventional treatment only and full app adherence post-intervention were two times and four times more likely to achieve cessation, respectively 30,32 . When comparing the smartphone mobile applications with text message smoking cessation support, significantly higher number of quitters were found among the text message support group.…”
Section: Effect Of Intervention On the Outcomementioning
confidence: 97%
“…However, in interventional studies, the average number of the application openings was only 8.5 (SD = 9). Young age, knowledge level, heavier smoking, depression were predictive factors for app utilization studied in three studies 29,32,34 . Audio-visual features were most used aspects of the application followed by quit plan, tracking process and sharing features.…”
Section: App Utilization and Adherencementioning
confidence: 98%
“…The majority of the studies were conducted in the United States [29][30][31][32][33] . One unique intervention of mobile apps on the tablet was conducted among randomly selected hospital patients.…”
Section: Study Characteristics Participantsmentioning
confidence: 99%
“…Two studies included were a post-hoc analysis of the single arm pilot randomised control trials 31,32 . Three studies did not have a control group 29,33,34 .…”
Section: Design Features Of the Studiesmentioning
confidence: 99%
“…Two studies included hospital patient and smoking cessation clinic clients 29,35 . One study used an online screening survey for recruitment 32 . The format of delivery of intervention varied with each study.…”
INTRODUCTION Smartphone-based smoking cessation interventions are increasingly used around the world. However, the effects of smartphone applications on applicability and efficacy on cessation rate and prevention of relapses are not often evaluated. Therefore, this review aims to assess the evidence on effectiveness of smartphone applications as an intervention tool for smoking cessation support. METHODS We conducted the search using Ovid Medline/PubMed, CENTRAL and Scopus databases dated (January 2007-June 2016. Inclusion criteria include randomized control trials or intervention studies with mobile applications that offer smoking cessation support. Two assessors independently extracted and evaluated the data from each included study. RESULTS The review of eight selected studies illustrate the use of smartphone applications in increasing quit rates among smokers, however adherence to app features influences quit rates. Audiovisual features followed by a quit plan, tracking progress and sharing features are most accepted and utilised app features. However, inconsistency was observed in their association with abstinence or quit rate. App engagement features increase the statistical significance in the quit rate. Development of smartphone applications was supported by behavior change theories in all studies nevertheless; heterogeneous forms of intervention were adopted within studies. Similarly, reduction in relapse attributed to enhanced discussion among quitters using social media applications was observed. CONCLUSIONS Quality evidence is warranted with large sample size to measure effect size of the intervention. Future research on effectiveness and efficacy of smartphone alone and comparisons with other mHealth interventions, such as text messaging would be useful.
ObjectiveSmartphone applications (i.e., apps) designed to target mental health symptoms have received increasing public and empirical attention, including in eating disorder|eating disorders (EDs) treatment. While some data have begun to characterise app users in non‐controlled settings, there is limited information on use of apps in higher levels of care (e.g., partial hospitalisation or residential treatment programs) for EDs.MethodThis study aimed to explore metrics of use while in treatment for a commonly used ED‐focused mobile app (Recovery Record) among individuals enroled in intensive outpatient, partial hospitalisation, residential, or inpatient treatments (N = 2042).ResultsResults indicated that older individuals and participants with binge eating disorder demonstrated more frequent app engagement compared to younger participants and other ED diagnoses, respectively. Individuals entering at intensive outpatient and partial hospitalisation levels of care, as well as those with routine discharges engaged more frequently with RR compared to individuals entering in inpatient or residential treatment, and those with non‐routine discharges.ConclusionsOur data provide initial descriptions of how RR may be used within higher levels of care for adults with EDs. Further work is needed to establish the benefit of these apps in clinical settings for EDs over and above standard treatment, better characterise for whom these apps provide benefit, and identify how best to tailor the experience to promote engagement across the full spectrum of ED patients.
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