Study Results
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Basic Information
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COMPLETED
NA
255 participants
INTERVENTIONAL
2022-03-21
2022-08-08
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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NA
SINGLE_GROUP
BASIC_SCIENCE
NONE
Study Groups
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mobile app users
Kwit SAS - smoking cessation app
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.
Interventions
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Kwit SAS - smoking cessation app
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.
Eligibility Criteria
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Inclusion Criteria
* Smoking Status: consider themselves an active smoker
* Motivation to quit: be willing to quit smoking, in the short and medium term.
* Agreement to participate: They must also agree to participate in the study. They will have read the information note where the procedure is described; the researchers presented and their rights to withdraw from the study are recalled.
Exclusion Criteria
* Access to the internet to complete the questionnaires
* Download the application and receive the updates it offers.
18 Years
ALL
No
Sponsors
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Paris Nanterre University
OTHER
Responsible Party
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Luz BUSTAMANTE
Principal Investigator
Principal Investigators
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Lucia ROMO
Role: PRINCIPAL_INVESTIGATOR
Pr. de psychologie clinique UNIVERSITE PARIS NANTERRE
Locations
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Universite Paris Nanterre, Epscp
La Defense, Nanterre, France
Countries
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References
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Other Identifiers
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A20273336
Identifier Type: -
Identifier Source: org_study_id
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