A smoking cessation smartphone app that delivers real-time 'context aware' behavioural support: the Quit Sense feasibility RCT.
BEHAVIOUR CHANGE
JUST-IN-TIME ADAPTIVE INTERVENTION (JITAI)
MHEALTH
SMARTPHONE APP
SMOKING CESSATION
Journal
Public health research (Southampton, England)
ISSN: 2050-439X
Titre abrégé: Public Health Res (Southampt)
Pays: England
ID NLM: 101653231
Informations de publication
Date de publication:
Apr 2024
Apr 2024
Historique:
medline:
27
4
2024
pubmed:
27
4
2024
entrez:
27
4
2024
Statut:
ppublish
Résumé
During a quit attempt, cues from a smoker's environment are a major cause of brief smoking lapses, which increase the risk of relapse. Quit Sense is a theory-guided Just-In-Time Adaptive Intervention smartphone app, providing smokers with the means to learn about their environmental smoking cues and provides 'in the moment' support to help them manage these during a quit attempt. To undertake a feasibility randomised controlled trial to estimate key parameters to inform a definitive randomised controlled trial of Quit Sense. A parallel, two-arm randomised controlled trial with a qualitative process evaluation and a 'Study Within A Trial' evaluating incentives on attrition. The research team were blind to allocation except for the study statistician, database developers and lead researcher. Participants were not blind to allocation. Online with recruitment, enrolment, randomisation and data collection (excluding manual telephone follow-up) automated through the study website. Smokers (323 screened, 297 eligible, 209 enrolled) recruited via online adverts on Google search, Facebook and Instagram. Participants were allocated to 'usual care' arm ( Follow-up at 6 weeks and 6 months post enrolment was undertaken by automated text messages with an online questionnaire link and, for non-responders, by telephone. Definitive trial progression criteria were met if a priori thresholds were included in or lower than the 95% confidence interval of the estimate. Measures included health economic and outcome data completion rates (progression criterion #1 threshold: ≥ 70%), including biochemical validation rates (progression criterion #2 threshold: ≥ 70%), recruitment costs, app installation (progression criterion #3 threshold: ≥ 70%) and engagement rates (progression criterion #4 threshold: ≥ 60%), biochemically verified 6-month abstinence and hypothesised mechanisms of action and participant views of the app (qualitative). Self-reported smoking outcome completion rates were 77% (95% confidence interval 71% to 82%) and health economic data (resource use and quality of life) 70% (95% CI 64% to 77%) at 6 months. Return rate of viable saliva samples for abstinence verification was 39% (95% CI 24% to 54%). The per-participant recruitment cost was £19.20, which included advert (£5.82) and running costs (£13.38). In the Quit Sense arm, 75% (95% CI 67% to 83%; 78/104) installed the app and, of these, 100% set a quit date within the app and 51% engaged with it for more than 1 week. The rate of 6-month biochemically verified sustained abstinence, which we anticipated would be used as a primary outcome in a future study, was 11.5% (12/104) in the Quit Sense arm and 2.9% (3/105) in the usual care arm (estimated effect size: adjusted odds ratio = 4.57, 95% CIs 1.23 to 16.94). There was no evidence of between-arm differences in hypothesised mechanisms of action. Three out of four progression criteria were met. The Study Within A Trial analysis found a £20 versus £10 incentive did not significantly increase follow-up rates though reduced the need for manual follow-up and increased response speed. The process evaluation identified several potential pathways to abstinence for Quit Sense, factors which led to disengagement with the app, and app improvement suggestions. Biochemical validation rates were lower than anticipated and imbalanced between arms. COVID-19-related restrictions likely limited opportunities for Quit Sense to provide location tailored support. The trial design and procedures demonstrated feasibility and evidence was generated supporting the efficacy potential of Quit Sense. Progression to a definitive trial is warranted providing improved biochemical validation rates. This trial is registered as ISRCTN12326962. This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 17/92/31) and is published in full in Smokers often fail to quit because of urges to smoke triggered by their surroundings (e.g. being around smokers). We developed a smartphone app (‘Quit Sense’) which learns about an individual’s surroundings and locations where they smoke. During a quit attempt, Quit Sense uses in-built sensors to identify when smokers are in those locations and sends ‘in the moment’ advice to help prevent them from smoking. We ran a feasibility study to help plan for a future large study to see if Quit Sense helps smokers to quit. This feasibility study was designed to tell us how many participants complete study measures; recruitment costs; how many participants install and use Quit Sense; and estimate whether Quit Sense may help smokers to stop and how it might do this. We recruited 209 smokers using online adverts on Google search, Facebook and Instagram, costing £19 per participant. Participants then had an equal chance of receiving a web link to the National Health Service SmokeFree website (‘usual care group’) or receive that same web link plus a link to the Quit Sense app (‘Quit Sense group’). Three-quarters of the Quit Sense group installed the app on their phone and half of these used the app for more than 1 week. We followed up 77% of participants at 6 months to collect study data, though only 39% of quitters returned a saliva sample for abstinence verification. At 6 months, more people in the Quit Sense group had stopped smoking (12%) than the usual care group (3%). It was not clear how the app helped smokers to quit based on study measures, though interviews found that the process of training the app helped people quit through learning about what triggered their smoking behaviour. The findings support undertaking a large study to tell us whether Quit Sense really does help smokers to quit.
Sections du résumé
Background
UNASSIGNED
During a quit attempt, cues from a smoker's environment are a major cause of brief smoking lapses, which increase the risk of relapse. Quit Sense is a theory-guided Just-In-Time Adaptive Intervention smartphone app, providing smokers with the means to learn about their environmental smoking cues and provides 'in the moment' support to help them manage these during a quit attempt.
Objective
UNASSIGNED
To undertake a feasibility randomised controlled trial to estimate key parameters to inform a definitive randomised controlled trial of Quit Sense.
Design
UNASSIGNED
A parallel, two-arm randomised controlled trial with a qualitative process evaluation and a 'Study Within A Trial' evaluating incentives on attrition. The research team were blind to allocation except for the study statistician, database developers and lead researcher. Participants were not blind to allocation.
Setting
UNASSIGNED
Online with recruitment, enrolment, randomisation and data collection (excluding manual telephone follow-up) automated through the study website.
Participants
UNASSIGNED
Smokers (323 screened, 297 eligible, 209 enrolled) recruited via online adverts on Google search, Facebook and Instagram.
Interventions
UNASSIGNED
Participants were allocated to 'usual care' arm (
Main outcome measures
UNASSIGNED
Follow-up at 6 weeks and 6 months post enrolment was undertaken by automated text messages with an online questionnaire link and, for non-responders, by telephone. Definitive trial progression criteria were met if a priori thresholds were included in or lower than the 95% confidence interval of the estimate. Measures included health economic and outcome data completion rates (progression criterion #1 threshold: ≥ 70%), including biochemical validation rates (progression criterion #2 threshold: ≥ 70%), recruitment costs, app installation (progression criterion #3 threshold: ≥ 70%) and engagement rates (progression criterion #4 threshold: ≥ 60%), biochemically verified 6-month abstinence and hypothesised mechanisms of action and participant views of the app (qualitative).
Results
UNASSIGNED
Self-reported smoking outcome completion rates were 77% (95% confidence interval 71% to 82%) and health economic data (resource use and quality of life) 70% (95% CI 64% to 77%) at 6 months. Return rate of viable saliva samples for abstinence verification was 39% (95% CI 24% to 54%). The per-participant recruitment cost was £19.20, which included advert (£5.82) and running costs (£13.38). In the Quit Sense arm, 75% (95% CI 67% to 83%; 78/104) installed the app and, of these, 100% set a quit date within the app and 51% engaged with it for more than 1 week. The rate of 6-month biochemically verified sustained abstinence, which we anticipated would be used as a primary outcome in a future study, was 11.5% (12/104) in the Quit Sense arm and 2.9% (3/105) in the usual care arm (estimated effect size: adjusted odds ratio = 4.57, 95% CIs 1.23 to 16.94). There was no evidence of between-arm differences in hypothesised mechanisms of action. Three out of four progression criteria were met. The Study Within A Trial analysis found a £20 versus £10 incentive did not significantly increase follow-up rates though reduced the need for manual follow-up and increased response speed. The process evaluation identified several potential pathways to abstinence for Quit Sense, factors which led to disengagement with the app, and app improvement suggestions.
Limitations
UNASSIGNED
Biochemical validation rates were lower than anticipated and imbalanced between arms. COVID-19-related restrictions likely limited opportunities for Quit Sense to provide location tailored support.
Conclusions
UNASSIGNED
The trial design and procedures demonstrated feasibility and evidence was generated supporting the efficacy potential of Quit Sense.
Future work
UNASSIGNED
Progression to a definitive trial is warranted providing improved biochemical validation rates.
Trial registration
UNASSIGNED
This trial is registered as ISRCTN12326962.
Funding
UNASSIGNED
This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 17/92/31) and is published in full in
Smokers often fail to quit because of urges to smoke triggered by their surroundings (e.g. being around smokers). We developed a smartphone app (‘Quit Sense’) which learns about an individual’s surroundings and locations where they smoke. During a quit attempt, Quit Sense uses in-built sensors to identify when smokers are in those locations and sends ‘in the moment’ advice to help prevent them from smoking. We ran a feasibility study to help plan for a future large study to see if Quit Sense helps smokers to quit. This feasibility study was designed to tell us how many participants complete study measures; recruitment costs; how many participants install and use Quit Sense; and estimate whether Quit Sense may help smokers to stop and how it might do this. We recruited 209 smokers using online adverts on Google search, Facebook and Instagram, costing £19 per participant. Participants then had an equal chance of receiving a web link to the National Health Service SmokeFree website (‘usual care group’) or receive that same web link plus a link to the Quit Sense app (‘Quit Sense group’). Three-quarters of the Quit Sense group installed the app on their phone and half of these used the app for more than 1 week. We followed up 77% of participants at 6 months to collect study data, though only 39% of quitters returned a saliva sample for abstinence verification. At 6 months, more people in the Quit Sense group had stopped smoking (12%) than the usual care group (3%). It was not clear how the app helped smokers to quit based on study measures, though interviews found that the process of training the app helped people quit through learning about what triggered their smoking behaviour. The findings support undertaking a large study to tell us whether Quit Sense really does help smokers to quit.
Autres résumés
Type: plain-language-summary
(eng)
Smokers often fail to quit because of urges to smoke triggered by their surroundings (e.g. being around smokers). We developed a smartphone app (‘Quit Sense’) which learns about an individual’s surroundings and locations where they smoke. During a quit attempt, Quit Sense uses in-built sensors to identify when smokers are in those locations and sends ‘in the moment’ advice to help prevent them from smoking. We ran a feasibility study to help plan for a future large study to see if Quit Sense helps smokers to quit. This feasibility study was designed to tell us how many participants complete study measures; recruitment costs; how many participants install and use Quit Sense; and estimate whether Quit Sense may help smokers to stop and how it might do this. We recruited 209 smokers using online adverts on Google search, Facebook and Instagram, costing £19 per participant. Participants then had an equal chance of receiving a web link to the National Health Service SmokeFree website (‘usual care group’) or receive that same web link plus a link to the Quit Sense app (‘Quit Sense group’). Three-quarters of the Quit Sense group installed the app on their phone and half of these used the app for more than 1 week. We followed up 77% of participants at 6 months to collect study data, though only 39% of quitters returned a saliva sample for abstinence verification. At 6 months, more people in the Quit Sense group had stopped smoking (12%) than the usual care group (3%). It was not clear how the app helped smokers to quit based on study measures, though interviews found that the process of training the app helped people quit through learning about what triggered their smoking behaviour. The findings support undertaking a large study to tell us whether Quit Sense really does help smokers to quit.
Types de publication
Journal Article
Randomized Controlled Trial
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-99Références
World Health Organization. Report on Global Tobacco Epidemic. Geneva: WHO; 2011.
GBD 2019. Tobacco collaborators. spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet 2021;397:2337–60. https://doi.org/10.1016/S0140-6736(21)01169-7
Murray CJ, Richards MA, Newton JN, Fenton KA, Anderson HR, Atkinson C, et al. UK health performance: findings of the global burden of disease study 2010. Lancet 2013;381:997–1020. https://doi.org/10.1016/S0140-6736(13)60355-4
NHS Digital. Statistics on Smoking – England 2018: Statistics on Smoking. Part 1: Smoking-Related Ill Health and Mortality. 2018. URL: https://digital.nhs.uk/data-and-information/publications/statistical/statistics-on-smoking/statistics-on-smoking-england-2018/part-1-smoking-related-ill-health-and-mortality (accessed 27 April 2020).
West R, Brown J. Smoking Toolkit Study (STS). 2017. URL: www.smokinginengland.info/ (accessed 1 July 2017).
Kenford SL, Fiore MC, Jorenby DE, Smith SS, Wetter D, Baker TB. Predicting smoking cessation: who will quit with and without the nicotine patch. JAMA 1994;271:589–94. https://doi.org/10.1001/jama.1994.03510320029025
Deiches JF, Baker TB, Lanza S, Piper ME. Early lapses in a cessation attempt: lapse contexts, cessation success, and predictors of early lapse. Nicotine Tob Res 2013;15:1883–91. https://doi.org/10.1093/ntr/ntt074
Shadel WG, Martino SC, Setodji C, Cervone D, Witkiewitz K, Beckjord EB, et al. Lapse-induced surges in craving influence relapse in adult smokers: an experimental investigation. Health Psychol 2011;30:588–96. https://doi.org/10.1037/a0023445
Shiffman S, Paty JA, Gnys M, Kassel JA, Hickcox M. First lapses to smoking: within-subjects analysis of real-time reports. J Consult Clin Psychol 1996;64:366–79. https://doi.org/10.1037/0022-006X.64.2.366
Naughton F, Hopewell S, Lathia N, Schalbroeck R, Brown C, Mascolo C, et al. A context-sensing mobile phone app (Q Sense) for smoking cessation: a mixed-methods study. JMIR Mhealth Uhealth 2016;4:e106. https://doi.org/10.2196/mhealth.5787
Conklin CA, McClernon FJ, Vella EJ, Joyce CJ, Salkeld RP, Parzynski CS, Bennett L. Combined smoking cues enhance reactivity and predict immediate subsequent smoking. Nicotine Tob Res 2019;21:241–8. https://doi.org/doi:10.1093/ntr/nty009
Conklin CA, Perkins KA, Robin N, McClernon FJ, Salkeld RP. Bringing the real world into the laboratory: personal smoking and nonsmoking environments. Drug Alcohol Depend 2010;111:58–63. https://doi.org/doi:10.1016/j.drugalcdep.2010.03.017
Ahnallen CG, Tidey JW. Personalized smoking environment cue reactivity in smokers with schizophrenia and controls: a pilot study. Psychiatry Res 2011;188:286–8. https://doi.org/10.1016/j.psychres.2011.04.005
Ferguson SG, Shiffman S. Effect of high-dose nicotine patch on craving and negative affect leading up to lapse episodes. Psychopharmacology (Berl) 2014;231:2595–602. https://doi.org/10.1007/s00213-013-3429-6
Ferguson SG, Shiffman S. Effect of high-dose nicotine patch on the characteristics of lapse episodes. Health Psychol 2010;29:358–66. https://doi.org/10.1037/a0019367
Ferguson SG, Shiffman S. The relevance and treatment of cue-induced cravings in tobacco dependence. J Subst Abuse Treat 2009;36:235–43. https://doi.org/10.1016/j.jsat.2008.06.005
Versace F, Stevens EM, Robinson JD, Cui Y, Deweese MM, Engelmann JM, et al. Brain responses to cigarette-related and emotional images in smokers during smoking cessation: no effect of varenicline or bupropion on the late positive potential. Nicotine Tob Res 2019;21:234–40. https://doi.org/10.1093/ntr/ntx264
Balmford J, Borland R, Hammond D, Cummings KM. Adherence to and reasons for premature discontinuation from stop-smoking medications: data from the ITC Four-Country Survey. Nicotine Tob Res 2011;13:94–102. https://doi.org/10.1093/ntr/ntq215
Buss V, West R, Kock L, Kale D, Brown J. Smoking in England. 2022. URL: https://smokinginengland.info/ (accessed 23 November 2023).
Brodbeck J, Bachmann MS, Znoj H. Distinct coping strategies differentially predict urge levels and lapses in a smoking cessation attempt. Addict Behav 2013;38:2224–9. https://doi.org/10.1016/j.addbeh.2013.02.001
O’Connell KA, Hosein VL, Schwartz JE. Thinking and/or doing as strategies for resisting smoking. Res Nurs Health 2006;29:533–42. https://doi.org/10.1002/nur.20151
Naughton F, McEwen A, Sutton S. Use and effectiveness of lapse prevention strategies among pregnant smokers. J Health Psychol 2015;20:1427–33. https://doi.org/10.1177/1359105313512878
Abroms LC, Lee WJ, Bontemps-Jones J, Ramani R, Mellerson J. A content analysis of popular smartphone apps for smoking cessation. Am J Prev Med 2013;45:732–6. https://doi.org/10.1016/j.amepre.2013.07.008
Thornton L, Quinn C, Birrell L, Guillaumier A, Shaw B, Forbes E, et al. Free smoking cessation mobile apps available in Australia: a quality review and content analysis. Aust N Z J Public Health 2017;41:625–30. https://doi.org/10.1111/1753-6405.12688
Vilardaga R, Casellas-Pujol E, McClernon JF, Garrison KA. Mobile applications for the treatment of tobacco use and dependence. Curr Addict Rep 2019;6:86–97. https://doi.org/10.1007/s40429-019-00248-0
Chu KH, Matheny SJ, Escobar-Viera CG, Wessel C, Notier AE, Davis EM. Smartphone health apps for tobacco cessation: a systematic review. Addict Behav 2021;112:106616. https://doi.org/10.1016/j.addbeh.2020.106616
Bricker JB, Watson NL, Mull KE, Sullivan BM, Heffner Jaimee L. Efficacy of smartphone applications for smoking cessation: a randomized clinical trial. JAMA Intern Med 2020;180:1472. https://doi.org/10.1001/jamainternmed.2020.4055
Garrison KA, Pal P, O’Malley SS, Pittman BP, Gueorguieva R, Rojiani R, et al. Craving to quit: a randomized controlled trial of smartphone app-based mindfulness training for smoking cessation. Nicotine Tob Res 2020;22:324–31. https://doi.org/10.1093/ntr/nty126
Perski O, Crane D, Beard E, Brown J. Does the addition of a supportive chatbot promote user engagement with a smoking cessation app? An experimental study. Digit Health 2019;5:80676. https://doi.org/10.1177/2055207619880676
Etter JF, Khazaal Y. The Stop-tabac smartphone application for smoking cessation: a randomized controlled trial. Addiction 2021;117:1406–15. https://doi.org/10.1111/add.15738
Perski O, Hebert ET, Naughton F, Hekler EB, Brown J, Businelle MS. Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review. Addiction 2021;117:1220–41. https://doi.org/10.1111/add.15687
Free C, Knight R, Robertson S, Whittaker R, Edwards P, Zhou W, et al. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. Lancet 2011;378:49–55. https://doi.org/10.1016/S0140-6736(11)60701-0
Devries KM, Kenward MG, Free CJ. Preventing smoking relapse using text messages: analysis of data from the txt2stop trial. Nicotine Tob Res 2012;15:77–82. https://doi.org/10.1093/ntr/nts086
Naughton F, Cooper S, Foster K, Emery J, Leonardi-Bee J, Sutton S, et al. Large multi-centre pilot randomized controlled trial testing a low-cost, tailored, self-help smoking cessation text message intervention for pregnant smokers (MiQuit). Addiction 2017;112:1238–49. https://doi.org/10.1111/add.13802
Naughton F, Jamison J, Boase S, Sloan M, Gilbert H, Prevost AT, et al. Randomized controlled trial to assess the short-term effectiveness of tailored web- and text-based facilitation of smoking cessation in primary care (iQuit in Practice). Addiction 2014;109:1184–93. https://doi.org/10.1111/add.12556
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;10:CD006611. https://doi.org/10.1002/14651858.CD006611.pub5
Hebert ET, Ra CK, Alexander AC, Helt A, Moisiuc R, Kendzor DE, et al. A mobile just-in-time adaptive intervention for smoking cessation: pilot randomized controlled trial. J Med Internet Res 2020;22:e16907. https://doi.org/10.2196/16907
McClernon FJ, Roy CR. I am your smartphone and I know you are about to smoke: the application of mobile sensing and computing approaches to smoking research and treatment. Nicotine Tob Res 2013;15:1651–4. https://doi.org/10.1093/ntr/ntt054
Naughton F. Delivering ‘Just-In-Time’ smoking cessation support via mobile phones: current knowledge and future directions. Nicotine Tob Res 2016;19(3):379–83. https://doi.org/10.1093/ntr/ntw143
Naughton F, Brown C, High J, Notley C, Mascolo C, Coleman T, et al. Randomised controlled trial of a just-in-time adaptive intervention (JITAI) smoking cessation smartphone app: the Quit Sense feasibility trial protocol. BMJ Open 2021;11:e048204. https://doi.org/10.1136/bmjopen-2020-048204
Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al.; REDCap Consortium. The REDCap consortium: building an international community of software platform partners. J Biomed Inform 2019;95:103208. https://doi.org/10.1016/j.jbi.2019.103208
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap) – a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42:377–81. https://doi.org/10.1016/j.jbi.2008.08.010
Office for National Statistics. The National Statistics Socio-Economic Classification User Manual. Basingstoke: ONS; 2005.
Naughton F, Prevost AT, Gilbert H, Sutton S. Randomised controlled trial evaluation of a tailored leaflet and SMS text message self-help intervention for pregnant smokers (MiQuit). Nicotine Tob Res 2012;14:569–77. https://doi.org/10.1093/ntr/ntr254
Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall; 1986.
Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013;46:81–95. https://doi.org/10.1007/s12160-013-9486-6
Michie S, Hyder N, Walia A, West R. Development of a taxonomy of behaviour change techniques used in individual behavioural support for smoking cessation. Addict Behav 2011;36:315–9. https://doi.org/10.1016/j.addbeh.2010.11.016
West R, Hajek P. Evaluation of the mood and physical symptoms scale (MPSS) to assess cigarette withdrawal. Psychopharmacology (Berl) 2004;177:195–9. https://doi.org/10.1007/s00213-004-1923-6
Fidler JA, Shahab L, West R. Strength of urges to smoke as a measure of severity of cigarette dependence: comparison with the Fagerstrom Test for Nicotine Dependence and its components. Addiction 2011;106:631–8. https://doi.org/10.1111/j.1360-0443.2010.03226.x
Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ 2008;337:a1655. https://doi.org/10.1136/bmj.a1655
Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res 2011;20:1727–36. https://doi.org/10.1007/s11136-011-9903-x
West R, Hajek P, Stead L, Stapleton J. Outcome criteria in smoking cessation trials: proposal for a common standard. Addiction 2005;100:299–303. https://doi.org/10.1111/j.1360-0443.2004.00995.x
Benowitz NL, Bernert JT, Foulds J, Hecht SS, Jacob P, Jarvis MJ, et al. Biochemical verification of tobacco use and abstinence: 2019 update. Nicotine Tob Res 2019;22:1086–97. https://doi.org/10.1093/ntr/ntz132
Jacob P, 3rd, Hatsukami D, Severson H, Hall S, Yu L, Benowitz Neal L. Anabasine and anatabine as biomarkers for tobacco use during nicotine replacement therapy. Cancer Epidemiol Biomarkers Prev 2002;11:1668–73.
Cheung KL, de Ruijter D, Hiligsmann M, Elfeddali I, Hoving C, Evers SMAA, de Vries H. Exploring consensus on how to measure smoking cessation. A Delphi study. BMC Public Health 2017;17:890. https://doi.org/10.1186/s12889-017-4902-7
Gwaltney CJ, Shiffman S, Balabanis MH, Paty JA. Dynamic self-efficacy and outcome expectancies: prediction of smoking lapse and relapse. J Abnorm Psychol 2005;114:661–75. https://doi.org/10.1037/0021-843X.114.4.661
Smith SS, Piper ME, Bolt DM, Fiore MC, Wetter DW, Cinciripini PM, Baker TB. Development of the brief Wisconsin Inventory of Smoking Dependence Motives. Nicotine Tob Res 2010;12:489–99. https://doi.org/10.1093/ntr/ntq032
Teare MD, Dimairo M, Shephard N, Hayman A, Whitehead A, Walters SJ. Sample size requirements to estimate key design parameters from external pilot randomised controlled trials: a simulation study. Trials 2014;15:264. https://doi.org/10.1186/1745-6215-15-264
Brown J, Michie S, Geraghty AW, Yardley L, Gardner B, Shahab L, et al. Internet-based intervention for smoking cessation (StopAdvisor) in people with low and high socioeconomic status: a randomised controlled trial. Lancet Respir Med 2014;2:997–1006. https://doi.org/10.1016/S2213-2600(14)70195-X
Bricker JB, Mull KE, Kientz JA, Vilardaga R, Mercer LD, Akioka KJ, Heffner JL. Randomized, controlled pilot trial of a smartphone app for smoking cessation using acceptance and commitment therapy. Drug Alcohol Depend 2014;143:87–94. https://doi.org/10.1016/j.drugalcdep.2014.07.006
Taylor GMJ, Dalili MN, Semwal M, Civljak M, Sheikh A, Car J. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev 2017;9:CD007078. https://doi.org/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;4:CD006611. https://doi.org/10.1002/14651858.CD006611.pub4
Iacoviello BM, Steinerman JR, Klein DB, Silver Theodore L, Berger AG, Luo SX, Schork NJ. Clickotine, a personalized smartphone app for smoking cessation: initial evaluation. JMIR Mhealth Uhealth 2017;5:e56. https://doi.org/10.2196/mhealth.7226
Mason D, Gilbert H, Sutton S. Effectiveness of web-based tailored smoking cessation advice reports (iQuit): a randomized trial. Addiction 2012;107:2183–90. https://doi.org/10.1111/j.1360-0443.2012.03972.x
Burton PR, Gurrin LC, Campbell MJ. Clinical significance not statistical significance: a simple Bayesian alternative to p values. J Epidemiol Community Health 1998;52:318–23. https://doi.org/10.1136/jech.52.5.318
Fleiss JL. Statistical Methods for Rates and Proportions. 2nd edn. New York: John Wiley; 1981.
Jones KC, Burns A. Unit Costs of Health and Social Care 2021. Kent, UK: Personal Social Services Research Unit; 2021. https://doi.org/10.22024/UniKent/01.02.92342
National Institute for Health and Clinical Excellence (NICE). Position Statement on Use of the EQ-5D-5L Valuation Set (updated October 2019). 2019. URL: https://www.nice.org.uk/about/what-we-do/our-programmes/nice-guidance/technology-appraisal-guidance/eq-5d-5l (accessed 1 March 2024).
van Hout B, Janssen MF, Feng YS, Kohlmann T, Busschbach J, Golicki D, et al. Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets. Value Health 2012;15:708–15. https://doi.org/10.1016/j.jval.2012.02.008
Willan AR, Briggs AH, Hoch JS. Regression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Econ 2004;13:461–75. https://doi.org/10.1002/hec.843
National Institute for Health and Clinical Excellence (NICE). Guide to the Methods of Technology Appraisal 2013. 2013. URL: https://www.nice.org.uk/process/pmg9/chapter/foreword (accessed 1 March 2024).
Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ 2015;350:h1258. https://doi.org/10.1136/bmj.h1258
Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006;3:77–101. https://doi.org/10.1191/1478088706qp063oa
Stinnett AA, Mullahy J. Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making 1998;18:S68–80. https://doi.org/10.1177/0272989X98018002S09
Masaki K, Tateno H, Nomura A, Muto T, Suzuki S, Satake K, et al. A randomized controlled trial of a smoking cessation smartphone application with a carbon monoxide checker. NPJ Digit Med 2020;3:35. https://doi.org/10.1038/s41746-020-0243-5
McCarthy DE, Minami H, Yeh VM, Bold KW. An experimental investigation of reactivity to ecological momentary assessment frequency among adults trying to quit smoking. Addiction 2015;110:1549–60. https://doi.org/10.1111/add.12996
Berg ML, Cheung KL, Hiligsmann M, Evers S, de Kinderen RJA, Kulchaitanaroaj P, Pokhrel S. Model-based economic evaluations in smoking cessation and their transferability to new contexts: a systematic review. Addiction 2017;112:946–67. https://doi.org/10.1111/add.13748
Coleman T, Clark M, Welch C, Whitemore R, Leonardi-Bee J, Cooper S, et al. Effectiveness of offering tailored text message, self-help smoking cessation support to pregnant women who want information on stopping smoking: MiQuit3 randomised controlled trial and meta-analysis. Addiction 2022;117:1079–94. https://doi.org/10.1111/add.15715
Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, et al. Understanding and promoting effective engagement with digital behavior change interventions. Am J Prev Med 2016;51:833–42. https://doi.org/10.1016/j.amepre.2016.06.015
Munafo M. Open science and research reproducibility. ecancermedicalscience 2016;10:ed56. https://doi.org/10.3332/ecancer.2016.ed56
Watson NL, Mull KE, Heffner JL, McClure JB, Bricker JB. Participant recruitment and retention in remote eHealth intervention trials: methods and lessons learned from a large randomized controlled trial of two web-based smoking interventions. J Med Internet Res 2018;20:e10351. https://doi.org/10.2196/10351
BinDhim NF, McGeechan K, Trevena L. Who uses smoking cessation apps? A feasibility study across three countries via smartphones. JMIR Mhealth Uhealth 2014;2:e4. https://doi.org/10.2196/mhealth.2841
BinDhim NF, McGeechan K, Trevena L. Smartphone Smoking Cessation Application (SSC App) trial: a multicountry double-blind automated randomised controlled trial of a smoking cessation decision-aid ‘app’. BMJ Open 2018;8:e017105. https://doi.org/10.1136/bmjopen-2017-017105
Jackson SE, Beard E, Angus C, Field M, Brown J. Moderators of changes in smoking, drinking and quitting behaviour associated with the first COVID-19 lockdown in England. Addiction 2022;117:772–83. https://doi.org/10.1111/add.15656
Ubhi HK, Kotz D, Michie S, van Schayck OCP, West R. A comparison of the characteristics of iOS and Android users of a smoking cessation app. Transl Behav Med 2017;7:166–71. https://doi.org/10.1007/s13142-016-0455-z
Szinay D, Jones A, Chadborn T, Brown J, Naughton F. Influences on the uptake of and engagement with health and well-being smartphone apps: systematic review. J Med Internet Res 2020;22:e17572. https://doi.org/10.2196/17572
Szinay D, Perski O, Jones A, Chadborn T, Brown J, Naughton F. Perceptions of factors influencing engagement with health and well-being apps in the United Kingdom: qualitative interview study. JMIR Mhealth Uhealth 2021;9:e29098. https://doi.org/10.2196/29098
Office for National Statistics. Population Estimates by Ethnic Group and Religion, England and Wales: 2019. 2019. URL: www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/articles/populationestimatesbyethnicgroupandreligionenglandandwales/2019 (accessed 20 March 2022).