Smoking-cessation acceptance via mobile health

2019 ◽  
Vol 38 (3) ◽  
pp. 313-327 ◽  
Author(s):  
Rahib Ali ◽  
Ziqiong Zhang ◽  
Muhammad Bux Soomro
Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Shimeng Liu ◽  
Wuwei Feng ◽  
Pratik Y Chhatbar ◽  
Bruce I Ovbiagele

Background: The overwhelming majority of strokes can be prevented via optimal vascular risk factor control. However, there remains an evidence practice gap with regard to treatment of vascular risk factors. With the rapid growth worldwide in cell-phone use, Internet connectivity, and digital health technology, mobile health (mHealth) technology may offer a promising approach to bridge these treatment gaps and reduce the global burden of stroke. Objective: To evaluate the effectiveness of mHealth in vascular risk factor control through a systemic review and meta-analysis. Methods: We searched PubMed from January 1, 2000 to May 17, 2016 using keywords: mobile health, mhealth, short message, cellular phone, mobile phone, stroke prevention and control, diabetes mellitus, hypertension, hyperlipidemia and smoking cessation. We performed a meta-analysis of all eligible randomized control clinical trials that assessed the long-term (at 6 months) effect of mHealth. Results: Of 79 articles identified, 13 of them met eligibility criteria (6 for glycemic control and 7 for smoking cessation) and were included for the final meta-analysis. There were no eligible studies for dyslipidemia or hypertension. mHealth resulted in greater HbA1c reduction at 6 months (6 studies; 663 subjects; SMD: -0.44; 95% CI: [-0.82, -0.06], P =0.02; Mean difference of decrease in HbA1c: -0.39%; 95% CI: [-0.74,-0.04], P =0.03). mHealth also led to relatively higher smoking abstinence rates at 6 months (7 studies; 9,514 subjects; OR: 1.54; 95% CI: [1.24, 1.90], P <0.0001). Conclusion: Use of mHealth improves glycemic control and smoking abstinence rates, two factors that may lead to better stroke outcomes. Future mHealth studies should focus on modifying premier vascular risk factors like hypertension, specifically in people with or at risk of stroke.


2019 ◽  
Vol 4 ◽  
Author(s):  
Nick Noguez And Michael Gonzalez

  Despite the ubiquity of smartphone ownership and the increasing integration of social engagement features in smoking cessation apps to engage users, thesocial engagement features that exist in current smoking cessation apps and how effective these social features are in engaging users remain unclear. To fill the gap in the literature, a content analysis of free and paid smoking cessation mobile apps isconducted to examine a) the presence of socialengagement features(e.g., social support, social announcement, social referencing) and non-social engagement features (e.g., personal environmental changes, goal setting), and b) their relationship with user ratingsand engagement scores (e.g., Mobile App rating scale [MARS]). The findings will not only extend the mobile health apps engagement typology,but also inform smoking cessation mobile apps design.


2017 ◽  
Author(s):  
Kristopher Abbate ◽  
Melanie Hingle ◽  
Julie Armin ◽  
Peter Giacobbi Jr ◽  
Judith S. Gordon

2020 ◽  
Vol 16 (4) ◽  
pp. 420-428
Author(s):  
Alyssa M. Medenblik ◽  
Adam M. Mann ◽  
Tiffany A. Beaver ◽  
Eric A. Dedert ◽  
Sarah M. Wilson ◽  
...  

2018 ◽  
Author(s):  
Ulrika Müssener ◽  
Catharina Linderoth ◽  
Marcus Bendtsen

BACKGROUND Tobacco smoking is the primary cause of preventable premature disease and death worldwide. Evidence of the efficacy of text messaging interventions to reduce smoking behavior is well established, but there is still a need for studies targeting young people, especially because young adult smokers are less likely to seek treatment than older adults. A mobile health intervention, Nicotine Exit (NEXit), targeting smoking among university students was developed to support university students to quit smoking. Short-term effectiveness was measured through a randomized controlled trial, which found that immediately after the 12-week intervention, 26% of smokers in the intervention group had prolonged abstinence compared with 15% in the control group. OBJECTIVE The objective of this study was to explore the experience of being allocated to the control group in the NEXit smoking cessation intervention. METHODS We asked students who were allocated to the control group in the main NEXit randomized controlled trial to report their experiences. An email was sent to the participants with an electronic link to a short questionnaire. We assessed the distribution of the responses to the questionnaire by descriptive analysis. We analyzed free-text comments to 4 questions. RESULTS The response rate for the questionnaire was 33.8% (258/763), and we collected 143 free-text comments. Of the responders, 60.9% (157/258) experienced frustration, disappointment, and irritation about being allocated to the control group; they felt they were being denied support by having to wait for the intervention. Monthly text messages during the waiting period thanking them for taking part in the trial were perceived as negative by 72.3% (189/258), but for some the messages served as a reminder about the decision to quit smoking. Of the responders, 61.2% (158/258) chose to wait to quit smoking until they had access to the intervention, and 29.8% (77/258) decided to try to quit smoking without support. Of the respondents, 77.5% (200/258) claimed they were still smoking and had signed up or were thinking about signing up for the smoking cessation program at the time of the questionnaire. CONCLUSIONS Most of the respondents reported negative feelings about having to wait for the support of the intervention and that they had decided to continue smoking. A similar number decided to wait to quit smoking until they had access to the intervention, and these respondents reported a high interest in the intervention. Free-text comments indicated that some control group participants believed that they had been excluded from the trial, while others were confused when asked to sign up for the intervention again. CLINICALTRIAL ISRCTN Registry ISRCTN75766527; http://www.isrctn.com/ISRCTN75766527 (Archived by WebCite at http://www.webcitation.org/7678sUKbR)


2017 ◽  
Author(s):  
Carina Tudor-Sfetea ◽  
Riham Rabee ◽  
Muhammad Najim ◽  
Nima Amin ◽  
Mehak Chadha ◽  
...  

BACKGROUND Mobile health (mHealth) apps can offer users numerous benefits, representing a feasible and acceptable means of administering health interventions such as cognitive behavioral therapy (CBT). CBT is commonly used in the treatment of mental health conditions, where it has a strong evidence base, suggesting that it represents an effective method to elicit health behavior change. More importantly, CBT has proved to be effective in smoking cessation, in the context of smoking-related costs to the National Health Service (NHS) having been estimated to be as high as £2.6bn in 2015. Although the evidence base for computerized CBT in mental health is strong, there is limited literature on its use in smoking cessation. This, combined with the cost-effectiveness of mHealth interventions, advocates a need for research into the effectiveness of CBT-based smoking cessation apps. OBJECTIVE The objective of this study was, first, to explore participants’ perceptions of 2 mHealth apps, a CBT-based app, Quit Genius, and a non-CBT-based app, NHS Smokefree, over a variety of themes. Second, the study aimed to investigate the perceptions and health behavior of users of each app with respect to smoking cessation. METHODS A qualitative short-term longitudinal study was conducted, using a sample of 29 smokers allocated to one of the 2 apps, Quit Genius or Smokefree. Each user underwent 2 one-to-one semistructured interviews, 1 week apart. Thematic analysis was carried out, and important themes were identified. Descriptive statistics regarding participants’ perceptions and health behavior in relation to smoking cessation are also provided. RESULTS The thematic analysis resulted in five higher themes and several subthemes. Participants were generally more positive about Quit Genius’s features, as well as about its design and information engagement and quality. Quit Genius users reported increased motivation to quit smoking, as well as greater willingness to continue using their allocated app after 1 week. Moreover, these participants demonstrated preliminary changes in their smoking behavior, although this was in the context of our limited sample, not yet allowing for the finding to be generalizable. CONCLUSIONS Our findings underscore the use of CBT in the context of mHealth apps as a feasible and potentially effective smoking cessation tool. mHealth apps must be well developed, preferably with an underlying behavioral change mechanism, to promote positive health behavior change. Digital CBT has the potential to become a powerful tool in overcoming current health care challenges. The present results should be replicated in a wider sample using the apps for a longer period so as to allow for generalizability. Further research is also needed to focus on the effect of greater personalization on behavioral change and on understanding the psychological barriers to the adoption of new mHealth solutions.


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