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The development of mobile applications ("mobile apps") is steadily increasing and appears to be a promising treatment method to help people change unwanted behaviors or maintain a regular relationship with the medical system. Mobile apps aimed at smoking cessation have been shown to be effective. However, if a treatment is not used regularly, it will not have the desired effect. The main objective of this study is to identify what makes a person decide to use a smoking cessation app and to do so regularly. The second objective is to determine what is necessary to achieve long-term change with a mobile app.
Despite the significant decrease in tobacco consumption in France (30% in 2000 vs. 25.4% in 2018), the prevalence of smokers aged 18 to 75 years is still a public issue. Among the new solutions proposed, mobile applications ("mobile apps") seem to be a promising treatment modality. Several advantages to their use are recognized for patients, health professionals and the health system itself. Mobile applications allow accessibility to care and information, the possibility of transposing several proven effective therapeutic principles, the possibility of integrating certain forms of information transmission such as messaging, behavioural feedback and audiovisual media.
Although mobile app development is a growing market, knowledge about the determinants of intention to use this type of technology is very limited, especially for smoking cessation apps. The investigators propose a theoretical model to examine what determines the regular use of mobile apps for smoking cessation among those who want to quit. The investigators use the TAMII model and the operational variables used in a more general study on e-health applications. A chronological organisation based on a three-part behavioural model (antecedent, target behaviour and outcome) is added to the TAMII model. The main objective is to identify the factors of Mobila App Sustain Use (MASU). All definitions of TAM-II will be used : perceived usefulness (PU), perceived ease of use (PEOU) and social norm (SN), as well as the definitions proposed by Choi et al (2014) on the predictors of PU, PEOU and SN.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| mobile app users | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Kwit SAS - smoking cessation app | Device | Kwit is a mobile app for smoking cessation. Different CBT techniques are used by the app already been proved as effective : Case analysis craving tool, Achievements badges,Diary, Goal (outcome) setting, A 9-steps preparation program, psychological education, Emotional monitoring, Access to groups on social networks, different strategies ( NRT/water/meditation), Motivational cards. |
| Measure | Description | Time Frame |
|---|---|---|
| First Use | The ratio of people accessing the app after giving them access to it. | Day 1 - First use |
| Mobile App Sustain Use (MASU) | The ratio of times the application is accessed per week.. | 90 days post firs use of the mobile apps |
| Mobile App Intention Use (MAIU): | Questionaire : please specify by selecting a number from 1 to 3, with 1 being "Just once", 2 being "Daily" and 3 being "Several times a day", how often you expect to use this application in the course of :
| Day 15 |
| Mobile App Intention Use (MAIU): | Questionaire : please specify by selecting a number from 1 to 3, with 1 being "Just once", 2 being "Daily" and 3 being "Several times a day", how often you expect to use this application in the course of :
| Day 30 |
| Mobile App Intention Use (MAIU): | Questionaire : please specify by selecting a number from 1 to 3, with 1 being "Just once", 2 being "Daily" and 3 being "Several times a day", how often you expect to use this application in the course of :
| Day 60 |
| Mobile App Intention Use (MAIU): | Questionaire : Please specify by selecting a number from 1 to 3, with 1 being "Just once", 2 being "Daily" and 3 being "Several times a day", how often you expect to use this application in the course of :
| Day 90 |
| Measure | Description | Time Frame |
|---|---|---|
| Smoking profile (SP) | The degree of dependence is assessed by the Fagerström Test, which is widely used. | 1 day before the first use of the mobile app |
| Craving intensity (CI) | The visual analog scale or VAS was used to measure the average craving intensity . |
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Participants must meet four inclusion criteria for the study:
Inclusion criteria
Exclusion criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Lucia ROMO | Pr. de psychologie clinique UNIVERSITE PARIS NANTERRE | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Universite Paris Nanterre, Epscp | La Defense | Nanterre | 92001 | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28869775 | Background | Taylor GMJ, Dalili MN, Semwal M, Civljak M, Sheikh A, Car J. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev. 2017 Sep 4;9(9):CD007078. doi: 10.1002/14651858.CD007078.pub5. | |
| 27060875 | Background | Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev. 2016 Apr 10;4(4):CD006611. doi: 10.1002/14651858.CD006611.pub4. |
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| ID | Term |
|---|---|
| D016540 | Smoking Cessation |
| ID | Term |
|---|---|
| D015438 | Health Behavior |
| D001519 | Behavior |
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| Mobile App Satisfaction assessment (MAS): | The Mobile App Ratting Scale (MARS) scale is a multidimensional metric that ranks and assesses the quality of mobile apps. The MARS total score can be used to evaluate and compare the quality of an application with others. The total score is calculated as the average of its five categories: user engagement, functionality, aesthetics, information and subjective quality. Each category is rated on a five-point scale ranging from inadequate-1 to excellent-5 (25).This scale has been used previously to assess the quality of smoking cessation apps in the Australian market with high inter-rater reliability (ICC =0.807) | Day 15 |
| Mobile App Satisfaction assessment (MAS): | The Mobile App Ratting Scale (MARS) scale is a multidimensional metric that ranks and assesses the quality of mobile apps. The MARS total score can be used to evaluate and compare the quality of an application with others. The total score is calculated as the average of its five categories: user engagement, functionality, aesthetics, information and subjective quality. Each category is rated on a five-point scale ranging from inadequate-1 to excellent-5 (25).This scale has been used previously to assess the quality of smoking cessation apps in the Australian market with high inter-rater reliability (ICC =0.807) | Day 30 |
| Mobile App Satisfaction assessment (MAS): | The Mobile App Ratting Scale (MARS) scale is a multidimensional metric that ranks and assesses the quality of mobile apps. The MARS total score can be used to evaluate and compare the quality of an application with others. The total score is calculated as the average of its five categories: user engagement, functionality, aesthetics, information and subjective quality. Each category is rated on a five-point scale ranging from inadequate-1 to excellent-5 (25).This scale has been used previously to assess the quality of smoking cessation apps in the Australian market with high inter-rater reliability (ICC =0.807) | Day 60 |
| Mobile App Satisfaction assessment (MAS): | The Mobile App Ratting Scale (MARS) scale is a multidimensional metric that ranks and assesses the quality of mobile apps. The MARS total score can be used to evaluate and compare the quality of an application with others. The total score is calculated as the average of its five categories: user engagement, functionality, aesthetics, information and subjective quality. Each category is rated on a five-point scale ranging from inadequate-1 to excellent-5.This scale has been used previously to assess the quality of smoking cessation apps in the Australian market with high inter-rater reliability (ICC =0.807). | Day 90 |
| 1 day before before the first use of the mobile app |
| Craving intensity (CI) | The visual analog scale or VAS was used to measure the average craving intensity . | Day 15 |
| Craving intensity (CI) | The visual analog scale or VAS was used to measure the average craving intensity . | Day 30 |
| Craving intensity (CI) | The visual analog scale or VAS was used to measure the average craving intensity . | Day 60 |
| Craving intensity (CI) | The visual analog scale or VAS was used to measure the average craving intensity . | Day 90 |
| Behavior change : Smoking cessation | Self-reported 15-day point prevalence smoking status. Since your first use of the app have you smoked a cigarette (even a puff)? | Day 15 |
| Behavior change : Smoking cessation | Self-reported 30-day point prevalence smoking status. Since your first use of the app have you smoked a cigarette (even a puff)? | Day 30 |
| Behavior change : Smoking cessation | Self-reported 60-day point prevalence smoking status. Since your first use of the app have you smoked a cigarette (even a puff)? | Day 60 |
| Behavior change : Smoking cessation | Self-reported 90-day point prevalence smoking status. Since your first use of the app have you smoked a cigarette (even a puff)? | Day 90 |
| 31638271 | Background | Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y, Dobson R. Mobile phone text messaging and app-based interventions for smoking cessation. Cochrane Database Syst Rev. 2019 Oct 22;10(10):CD006611. doi: 10.1002/14651858.CD006611.pub5. |
| 32432186 | Background | Regmi K, Kassim N, Ahmad N, Tuah NA. Effectiveness of Mobile Apps for Smoking Cessation: A Review. Tob Prev Cessat. 2017 Apr 12;3:12. doi: 10.18332/tpc/70088. eCollection 2017. |
| 26045249 | Background | Hoeppner BB, Hoeppner SS, Seaboyer L, Schick MR, Wu GW, Bergman BG, Kelly JF. How Smart are Smartphone Apps for Smoking Cessation? A Content Analysis. Nicotine Tob Res. 2016 May;18(5):1025-31. doi: 10.1093/ntr/ntv117. Epub 2015 Jun 4. |
| 31196062 | Background | Rajani NB, Weth D, Mastellos N, Filippidis FT. Adherence of popular smoking cessation mobile applications to evidence-based guidelines. BMC Public Health. 2019 Jun 13;19(1):743. doi: 10.1186/s12889-019-7084-7. |
| 25207512 | Background | Cho J, Quinlan MM, Park D, Noh GY. Determinants of adoption of smartphone health apps among college students. Am J Health Behav. 2014 Nov;38(6):860-70. doi: 10.5993/AJHB.38.6.8. |
| 15312915 | Background | Cotten SR, Gupta SS. Characteristics of online and offline health information seekers and factors that discriminate between them. Soc Sci Med. 2004 Nov;59(9):1795-806. doi: 10.1016/j.socscimed.2004.02.020. |
| 25760773 | Background | Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015 Mar 11;3(1):e27. doi: 10.2196/mhealth.3422. |
| 33929336 | Background | Rajani NB, Mastellos N, Filippidis FT. Self-Efficacy and Motivation to Quit of Smokers Seeking to Quit: Quantitative Assessment of Smoking Cessation Mobile Apps. JMIR Mhealth Uhealth. 2021 Apr 30;9(4):e25030. doi: 10.2196/25030. |
| 30112741 | Background | Rahimi B, Nadri H, Lotfnezhad Afshar H, Timpka T. A Systematic Review of the Technology Acceptance Model in Health Informatics. Appl Clin Inform. 2018 Jul;9(3):604-634. doi: 10.1055/s-0038-1668091. Epub 2018 Aug 15. |
| 39357053 | Derived | Bustamante Perez LA, Romo L, Zerhouni O. Feasibility and Engagement of a Mobile App Preparation Program (Kwit) for Smoking Cessation in an Ecological Context: Quantitative Study. JMIR Mhealth Uhealth. 2024 Oct 2;12:e51025. doi: 10.2196/51025. |