Engagement with Digital Behaviour Change Interventions: Conceptualisation, Measurement and Promotion

Digital behaviour change interventions (DBCIs) can help people change various health behaviours; however, engagement is low on average and there is a positive association of engagement with intervention effectiveness. The extent to which this relationship is confounded or subject to reverse causality is unclear, and evidence-based models of how to promote engagement are lacking. Progress is hindered by the existence of multiple definitions and measures of engagement; this hampers attempts to aggregate data in meta-analyses. Using smartphone applications (apps) for smoking cessation and alcohol reduction as case studies, this thesis investigated how to conceptualise and measure engagement and identified factors that influence engagement with DBCIs in general, and with apps for smoking cessation and alcohol reduction in particular. Six studies using qualitative and quantitative methods were conducted. Study 1 was a systematic, interdisciplinary literature review, which synthesised existing conceptualisations and generated an integrative definition of engagement with behavioural and experiential dimensions, and a conceptual framework of factors that influence engagement with DBCIs. Studies 3 and 4 involved the development and evaluation of a self-report measure of the behavioural and experiential dimensions of engagement. Studies 2, 5 and 6 used mixed-methods to identify factors that influence engagement with apps for smoking cessation and alcohol reduction. Engagement with DBCIs can usefully be defined in both behavioural and experiential terms: the self-report measure demonstrated promising psychometric properties and was underpinned by two distinct factors, labelled ‘Experiential Engagement’ and ‘Behavioural Engagement’. Design features that support users’ motivation to change, foster their beliefs about the perceived usefulness and relevance of the technology, and spark their interest were found to be most important in the promotion of engagement with apps for smoking cessation and alcohol reduction. These findings can be used to inform the design of new, or modification of existing, apps for these behaviours

Estimated failure to report unsuccessful quit attempts by type of cessation aid: A population survey of smokers in England

Introduction. It has been estimated that smokers tend to fail to report unsuccessful quit attempts that lasted a short time and occurred a longer time ago. However, it is unclear whether the failure to report unsuccessful quit attempts varies by the type of cessation aid used. Methods. A total of 5,892 smokers aged 16+ years who had made 1+ quit attempts in the past year were surveyed between January 2014 and December 2020 as part of the Smoking Toolkit Study. Respondents indicated when their most recent quit attempt started, how long it lasted, and which cessation aid(s) were used (e.g., unaided, varenicline, and behavioural support). The percentage failure to report for each cessation aid and 95% bootstrap confidence intervals (CIs) were estimated with an established method. Test for equality of proportions was performed to examine whether quit attempts lasting between one day and one week and that started >6 months ago failed to be reported at a different rate depending on the cessation aid used. Results. We estimated that after three months, 97% (95% -98%) of unaided quit attempts lasting less than one day, 80% (95% -81%) of those lasting between one day and one week, and 60% (95% -61%) of those lasting between one week and one month fail to be reported. Compared with unaided attempts, the estimated percentage failure to report quit attempts that lasted between one day and one week and that started >6 months ago was significantly lower for attempts involving behavioural support (92% of unaided attempts vs. 75% of attempts involving behavioural support, , ). No other significant differences were detected. Conclusions. Smokers in England appear to fail to report a substantial proportion of unsuccessful quit attempts. This failure appears particularly prominent for attempts that last a short time or occurred longer ago and appears lower for attempts involving behavioural support compared with unaided attempts

A pilot randomised trial of a brief virtual reality scenario in smokers unmotivated to quit: Assessing the feasibility of recruitment

Individual-level interventions for smokers unmotivated to quit remain scarce and have had limited success. Little is known about the potential of virtual reality (VR) for delivering messaging to smokers unmotivated to quit. This pilot trial aimed to assess the feasibility of recruitment and acceptability of a brief, theory-informed VR scenario and estimate proximal quitting outcomes. Unmotivated smokers (recruited between February-August 2021) aged 18+ years who had access to, or were willing to receive via post, a VR headset were randomly assigned (1:1) using block randomisation to view the intervention (i.e., a hospital-based scenario with motivational stop smoking messaging) or a ‘sham’ VR scenario (i.e., a scenario about the human body without any smoking-specific messaging) with a researcher present via teleconferencing software. The primary outcome was feasibility of recruitment (i.e., achieving the target sample size of 60 participants within 3 months of recruitment). Secondary outcomes included acceptability (i.e., positive affective and cognitive attitudes), quitting self-efficacy and intention to stop smoking (i.e., clicking on a weblink with additional stop smoking information). We report point estimates and 95% confidence intervals (CIs). The study protocol was pre-registered (osf.io/95tus). A total of 60 participants were randomised within 6 months (intervention: n = 30; control: n = 30), 37 of whom were recruited within a 2-month period of active recruitment following an amendment to gift inexpensive (£7) cardboard VR headsets via post. The mean (SD) age of participants was 34.4 (12.1) years, with 46.7% identifying as female. The mean (SD) cigarettes smoked per day was 9.8 (7.2). The intervention (86.7%, 95% CI = 69.3%-96.2%) and control (93.3%, 95% CI = 77.9%-99.2%) scenarios were rated as acceptable. Quitting self-efficacy and intention to stop smoking in the intervention (13.3%, 95% CI = 3.7%-30.7%; 3.3%, 95% CI = 0.1%-17.2%) and control (26.7%, 95% CI = 12.3%-45.9%; 0%, 95% CI = 0%-11.6%) arm were comparable. The target sample size was not achieved within the feasibility window; however, an amendment to gift inexpensive headsets via post appeared feasible. The brief VR scenario appeared acceptable to smokers unmotivated to quit

The association of smoking status with SARS-CoV-2 infection, hospitalisation and mortality from COVID-19: A living rapid evidence review (version 6)

Aims: To estimate the association of smoking status with rates of i) infection, ii) hospitalisation, iii) disease severity, and iv) mortality from SARS-CoV-2/COVID-19 disease. Design: Living rapid review of observational and experimental studies with random-effects hierarchical Bayesian meta-analyses. Published articles and pre-prints were identified via Ovid MEDLINE and medRxiv. Setting: Community or hospital with no restrictions on location. Participants: Adults who had received a test for SARS-CoV-2 infection or a diagnosis of COVID-19. Measurements: Outcomes were SARS-CoV-2 infection, hospitalisation, disease severity and mortality stratified by smoking status. Study quality was assessed. Findings: Version 6 with searches up to 17 July 2020 included 174 studies with 26 included in meta-analyses. Thirty-nine studies reported current, former and never smoking status. Notwithstanding recording uncertainties, compared with adult national prevalence estimates, recorded current smoking rates were generally lower than expected. Current compared with never smokers were at reduced risk of SARS-CoV-2 infection (RR = 0.74, 95% Credible Interval (CrI) = 0.56-0.97, τ = 0.46). Former compared with never smokers were at somewhat increased risk of infection but data were inconclusive (RR = 1.06, 95% CrI = 0.94-1.20, τ = 0.19). Current (RR = 1.05, CrI = 0.82-1.34, τ = 0.29) and former (RR = 1.20, CrI = 1.03-1.44, τ = 0.19) compared with never smokers were both at somewhat increased risk of hospitalisation with COVID-19, but data for current smokers were inconclusive. Current (RR = 1.15, CrI = 0.80-1.66, τ = 0.29) and former (RR = 1.51, CrI = 1.06-2.15, τ = 0.36) compared with never smokers were at increased risk of greater disease severity, but data for current smokers were inconclusive. Current (RR = 1.89, 95% CrI = 0.77-3.41, τ = 0.51) and former (RR = 1.93, 95% CrI = 1.33-2.66, τ = 0.19) compared with never smokers had increased risk of in-hospital death, but data for current smokers were inconclusive. Conclusions: There is uncertainty about the associations of smoking with COVID-19 outcomes. Recorded smoking prevalence among people with COVID-19 was generally lower than national prevalence. Current smokers were at reduced risk of infection. Former smokers were at increased risk of hospitalisation, disease severity and mortality, while data for current smokers favoured no important associations but were inconclusive.</jats:p

Assessing the psychometric properties of the digital behavior change intervention engagement scale in users of an app for reducing alcohol consumption:Evaluation study

Background: The level and type of engagement with digital behavior change interventions (DBCIs) are likely to influence their effectiveness, but validated self-report measures of engagement are lacking. The DBCI Engagement Scale was designed to assess behavioral (ie, amount, depth of use) and experiential (ie, attention, interest, enjoyment) dimensions of engagement. Objective: We aimed to assess the psychometric properties of the DBCI Engagement Scale in users of a smartphone app for reducing alcohol consumption. Methods: Participants (N=147) were UK-based, adult, excessive drinkers recruited via an online research platform. Participants downloaded the Drink Less app and completed the scale immediately after their first login in exchange for a financial reward. Criterion variables included the objectively recorded amount of use, depth of use, and subsequent login. Five types of validity (ie, construct, criterion, predictive, incremental, divergent) were examined in exploratory factor, correlational, and regression analyses. The Cronbach alpha was calculated to assess the scale’s internal reliability. Covariates included motivation to reduce alcohol consumption. Results: Responses on the DBCI Engagement Scale could be characterized in terms of two largely independent subscales related to experience and behavior. The experiential and behavioral subscales showed high (α=.78) and moderate (α=.45) internal reliability, respectively. Total scale scores predicted future behavioral engagement (ie, subsequent login) with and without adjusting for users’ motivation to reduce alcohol consumption (adjusted odds ratio [ORadj]=1.14; 95% CI 1.03-1.27; P=.01), which was driven by the experiential (ORadj=1.19; 95% CI 1.05-1.34; P=.006) but not the behavioral subscale. Conclusions: The DBCI Engagement Scale assesses behavioral and experiential aspects of engagement. The behavioral subscale may not be a valid indicator of behavioral engagement. The experiential subscale can predict subsequent behavioral engagement with an app for reducing alcohol consumption. Further refinements and validation of the scale in larger samples and across different DBCIs are needed

Smoking and COVID-19: Rapid evidence review for the Royal College of Physicians, London (UK)

SARS-CoV-2 is the causative agent of COVID-19, an emergent zoonotic disease which has reached pandemic levels and is designated a public health emergency of international concern by the World Health Organisation. Early data suggest that older age, male sex and a diagnosis of hypertension or diabetes independently increase the risk of hospitalisation and death from COVID-19; however, the biological mechanisms underpinning these associations are currently unclear. The SARS-CoV-2 virus enters human cells through the ACE2 receptor. Current and past tobacco smoking is associated with changes in the ACE2 receptor expression, hypertension, diabetes and worse outcomes following other viral infections. It is hence plausible that smoking is an independent risk factor for hospitalisation and death from COVID-19. Here, we review available evidence for an association between smoking status and hospitalisation for COVID-19. We highlight concerns regarding the quality of available data and call for more systematic collection of smoking status in future studies

Smoking and COVID Living Review (v11): a bayesian analysis

Aims: To estimate the association of smoking status with rates of i) infection, ii) hospitalisation, iii) disease severity, andiv) mortality from SARS-CoV-2/COVID-19 disease.Design: Living rapid review of observational and experimental studies with random-effects hierarchical Bayesian meta-analyses. Published articles and pre-prints were identified via MEDLINE and medRxiv.Setting: Community or hospital. No restrictions on location.Participants: Adults who received a SARS-CoV-2 test or a COVID-19 diagnosis.Measurements: Outcomes were SARS-CoV-2 infection, hospitalisation, disease severity and mortality stratified bysmoking status. Study quality was assessed (i.e. ‘good’, ‘fair’ and ‘poor’).Findings: v11 (searches up to 2021-02-16) included 405 studies with 62 ‘good’ and ‘fair’ quality studies included inunadjusted meta-analyses. 121 studies (29.9%) reported current, former and never smoking status with the remainderusing broader categories. Recorded smoking prevalence among people with COVID-19 was generally lower thannational prevalence. Current compared with never smokers were at reduced risk of SARS-CoV-2 infection (RR = 0.71,95% Credible Interval (CrI) = 0.61-0.82, τ = 0.34). Data for former smokers were inconclusive (RR = 1.03, 95% CrI =0.95-1.11, τ = 0.17) but favoured there being no important association (4% probability of RR ≥1.1). Former comparedwith never smokers were at increased risk of hospitalisation (RR = 1.19, CrI = 1.1-1.29, τ = 0.13), greater diseaseseverity (RR = 1.8, CrI = 1.27-2.55, τ = 0.46) and mortality (RR = 1.56, CrI = 1.23-2, τ = 0.43). Data for current smokerson hospitalisation, disease severity and mortality were inconclusive (RR = 1.1, 95% CrI = 0.99-1.21, τ = 0.15; RR 1.26,95% CrI = 0.92-1.73, τ = 0.32; RR = 1.12, 95% CrI = 0.84-1.47, τ = 0.42, respectively) but favoured there being noimportant associations with hospitalisation and mortality (49% and 56% probability of RR ≥1.1, respectively) and a smallbut important association with disease severity (83% probability of RR ≥1.1

Do Daily Fluctuations in Psychological and App-Related Variables Predict Engagement With an Alcohol Reduction App? A Series of N-Of-1 Studies

Background: Previous studies have identified psychological and smartphone app–related predictors of engagement with alcohol reduction apps at a group level. However, strategies to promote engagement need to be effective at the individual level. Evidence as to whether group-level predictors of engagement are also predictive for individuals is lacking. Objective: The aim of this study was to examine whether daily fluctuations in (1) the receipt of a reminder, (2) motivation to reduce alcohol, (3) perceived usefulness of the app, (4) alcohol consumption, and (5) perceived lack of time predicted within-person variability in the frequency and amount of engagement with an alcohol reduction app. Methods: We conducted a series of observational N-of-1 studies. The predictor variables were measured twice daily for 28 days via ecological momentary assessments. The outcome variables were measured through automated recordings of the participants’ app screen views. A total of nine London-based adults who drank alcohol excessively and were willing to set a reduction goal took part. Each participant’s dataset was analyzed separately using generalized additive mixed models to derive incidence rate ratios (IRRs) for the within-person associations of the predictor and outcome variables. Debriefing interviews, analyzed using thematic analysis, were used to contextualize the findings. Results: Predictors of the frequency and amount of engagement differed between individuals, and for the variables 'perceived usefulness of the app' and 'perceived lack of time', the direction of associations also differed between individuals. The most consistent predictors of within-person variability in the frequency of engagement were the receipt of a daily reminder (IRR=1.80-3.88; P<.05) and perceived usefulness of the app (IRR=0.82-1.42; P<.05). The most consistent predictors of within-person variability in the amount of engagement were motivation to reduce alcohol (IRR=1.67-3.45; P<.05) and perceived usefulness of the app (IRR=0.52-137.32; P<.05). Conclusions: The utility of the selected psychological and app-related variables in predicting the frequency and amount of engagement with an alcohol reduction app differed at the individual level. This highlights that key within-person associations may be masked in group-level designs and suggests that different strategies to promote engagement may be required for different individuals