E-cigarette support for smoking cessation: Identifying the effectiveness of intervention components in an online randomised optimisation trial
Dataset
Dawkins, L. and Kimber, C. (2023). E-cigarette support for smoking cessation: Identifying the effectiveness of intervention components in an online randomised optimisation trial. London South Bank University. https://doi.org/10.18744/lsbu.92xq8
Authors | Dawkins, L. and Kimber, C. |
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Abstract | This is an online study of 1214 UK smokers which aimed to determine which combination(s) of five intervention components can help smokers to stop smoking by using an e-cigarette. A balanced five-factor randomised factorial design was used, guided by the Multiphase Optimisation Strategy (MOST). The five intervention components were: i) tailored advice on which e-cigarette device to purchase; ii) tailored advice on which e-liquid nicotine strength to purchase; iii) tailored advice on which flavour to purchase; iv) brief information on relative harms of e-cigarettes vs smoking; v) text message support. Participants completed a baseline survey online and were then randomised to either receive or not receive each intervention component (resulting in 32 possible combinations). Tailored advice on device, nicotine strength and flavour were based on responses to baseline questions and were displayed at the end of the survey (for participants randomised to receive that condition). Follow up data was collected via a second online survey after 12 weeks to collect information on abstinence rates and adherence to intervention components. Logistic regressions were used to model the main effects and two-way interactions on the primary outcome (4-weeks abstinence) and secondary outcomes (7-day point prevalence and ≥50% smoking reduction). The full dataset and syntax (as SPSS files) are available. Link to protocol: https://www.qeios.com/read/9RDLJA.3 Link to trial registry: https://www.isrctn.com/ISRCTN54776958 Link to published paper: To follow (currently under review with Addiction) |
Keywords | Digital interventions; Tailored advice; Smoking cessation; Smoking reduction; E-cigarettes; Multi-phase Optimisation Strategy (MOST); Tobacco; Nicotine; Vaping |
Year | 2023 |
Publisher | London South Bank University |
Digital Object Identifier (DOI) | https://doi.org/10.18744/lsbu.92xq8 |
Funder/Client | Medical Research Council |
Data files | License Data type Spreadsheet Contents Data File Access Level Open |
Data collection period | 01 Apr 2020 to end of 31 Oct 2020 |
Data collection method | Baseline data was collected from 1455 smokers (N=1214 after removing 241 duplicates and bots – see data processing below) between April and July 2020. Between July and October 2020, 529 of the participants completed the 12-week follow up questionnaire in full and a further 107 provided information on the primary outcome variable only via text/email. Five intervention components were tested with each participant randomised to receive each component (ON) or not (OFF) resulting in 32 experimental conditions. Participants were eligible for inclusion if they were aged 18 or over, a daily smoker, resident in the UK, fluent in English, interested in quitting and using an e-cigarette, had access to a mobile phone and able to make an online purchase. Participants were provided with a voucher (to the value of £50) for making a purchase at the online store. Information collected at baseline included: demographic, smoking and vaping-related information; questions around preferences and nicotine dependence to inform tailoring of advice around device, flavour and nicotine strength; motivation and confidence in quitting; identity questions; and e-cigarette harm perceptions. At the 12-week follow up, data was collected on: smoking cessation and reduction outcome variables (continuous 4-weeks abstinence, 7-day point prevalence abstinence, smoking reduction from baseline); use of the product; adherence to recommendations and suitability of the advice, text messages and written information; identity questions and covid questions (given that the study commenced at the beginning of the first covid-19 lockdown). All variables are clearly labelled in SPSS and a description of the variables is provided below. |
Data preparation and processing activities | Our inclusion criteria specified that an individual could only take part once. However, despite adding reCAPTCHA and blocking repeat entries from the same IP address, e-mail and phone number, our automated randomisation in Qualtrics failed to detect all duplicates and bots so some individuals were erroneously randomised. Duplicates and bots are protocol violations and were defined as multiple completions from the same individual. These include participants who completed the survey in its entirety multiple times (often in quick succession) thus were randomised to more than one condition. Offending participants managed to circumvent our systems by supplying different email addresses or providing fake telephone numbers. Polite emails were also sent out to repeat offenders to ask them to stop taking the survey and the survey was halted (for a few days) on two occasions to allow us to implement further measures (e.g. blocking repeat post-codes and updating inclusion criteria to only one entry per household). On every instance, only the first entry from that individual was included in the intention-to-treat analysis. All subsequent entries from that same individual were removed. Violations were systematically identified as follows: Data cleaning Variable List (Data dictionary) |
Publication process dates | |
Deposited | 19 Jan 2023 |
https://openresearch.lsbu.ac.uk/item/92xq8
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