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Background: 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: To undertake a feasibility randomised controlled trial to estimate key parameters to inform a definitive randomised controlled trial of Quit Sense. Design: 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: Online with recruitment, enrolment, randomisation and data collection (excluding manual telephone follow-up) automated through the study website. Participants: Smokers (323 screened, 297 eligible, 209 enrolled) recruited via online adverts on Google search, Facebook and Instagram. Interventions: Participants were allocated to 'usual care' arm (n = 105; text message referral to the National Health Service SmokeFree website) or 'usual care' plus Quit Sense (n = 104), via a text message invitation to install the Quit Sense app. Main outcome measures: 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: 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: 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: The trial design and procedures demonstrated feasibility and evidence was generated supporting the efficacy potential of Quit Sense. Future work: Progression to a definitive trial is warranted providing improved biochemical validation rates. Trial registration: This trial is registered as ISRCTN12326962. Funding: 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 Public Health Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information. Plain language summary: 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. Scientific summary: Background: Three million UK smokers attempt to stop smoking each year, but over 80% relapse. A lapse (any smoking) early on in a quit attempt is strongly associated with subsequent relapse back to smoking. Approximately half of all lapses are due to environmental smoking cues which can elicit cravings to smoke, such as the presence of cigarettes or being in a location which a smoker associates with smoking. Few interventions are effective at targeting cue-induced cravings. Smokers are more likely to prevent lapses if they better understand their smoking cues and use lapse prevention strategies, such as controlled deep breathing or avoiding other smokers, to combat these. This would likely help them avoid or cope with cue-induced cravings, but smokers typically lack skills in applying these effectively. If smokers trying to quit used effective lapse prevention strategies, this would very likely increase their chances of success. With patient and public involvement, we developed, refined and piloted a theory-guided Just-In-Time Adaptive Intervention smartphone app called Quit Sense that provides support to help smokers manage environmental cues to smoke as they arise. Pilot studies have shown that Quit Sense can provide 'in the moment' support to smokers, including lapse prevention strategies, and that users will engage with the app and find it acceptable. As Quit Sense is such a novel intervention, before a 'definitive' randomised controlled trial (RCT) can be conducted to test effectiveness, it is important to first undertake a RCT to establish whether such a study might be feasible. Objectives: The main objective was to conduct a feasibility RCT of Quit Sense to inform a definitive effectiveness trial, by estimating: completion rates for the anticipated primary outcome for a full trial (6-month self-reported abstinence with biochemical validation, based on the Russell standard); usual care arm cessation rate; cost of recruitment using online advertising; rates of app installation, use and acceptability; completion of smoking cessation-related resource use and quality-of-life data; intervention effect on anticipated primary outcome; intervention effect on hypothesised mechanisms of action of app at 6 weeks post enrolment; participant views of the app, as part of a qualitative process evaluation. Design: A parallel, two-arm RCT with an embedded qualitative interview process evaluation was undertaken. The evaluation included a preliminary cost-effectiveness analysis and a nested randomised study [Study Within A Trial (SWAT)] assessing the effects of different financial incentives on 6-month follow-up rates. Setting: The study setting was online and trial procedures were primarily automated. Participants: Participants were recruited via online adverts on Google search, Facebook and Instagram, screened for eligibility on the study website and enrolled if they were regular smokers, aged 16 years and above, were willing to make a quit attempt and owned an Android smartphone. Interventions: After completing the baseline questionnaire participants were then randomly allocated to a 'usual care' arm (text message referral to NHS SmokeFree website) or a 'usual care' plus Quit Sense arm, via a text message invitation to install the Quit Sense app. The Quit Sense app required users to set a quit date and then, leading up to their quit date, alongside other support features, invited them to report their smoking in real time and indicate the presence of key environmental smoking cues using a smoking behaviour logging tool. Once their quit date arrived, the app used location sensing and what it had learnt about the individual's smoking behaviour to tailor the timing and content of support messages when they spent time in self-identified high-risk locations. The support and advice aimed to help smokers learn about their smoking behaviour, prepare for their quit attempt and promoted the use of effective lapse prevention strategies during their quit attempt. Main outcomes measures: All participants were contacted at 6-week and 6-month follow-up. This was done initially by text message with an embedded link to the follow-up questionnaires or, if no response, manually by telephone. As part of a SWAT , participants underwent secondary randomisation to receive either a £10 or £20 incentive for completion of the 6-month questionnaire. Four progression criteria and corresponding thresholds for seeking to undertake a definitive trial were set a priori. Criteria were met if each threshold was included in or lower than the 95% confidence interval (CI) of the estimate. Trial 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%), the app's effect on biochemically verified abstinence at 6 months (anticipated primary outcome of definitive trial) and hypothesised mechanisms of action. The qualitative process evaluation sought to identify participants' views of the trial and the Quit Sense app. The protocol and statistical and health economics analysis plan were pre-specified and published (open access). Results: Of the people screened (N = 323), 299 (93%) were eligible and 209 (65% of screened) were consented and randomised either to the usual care arm (n = 105) or to the Quit Sense arm (n = 104). Completion of self-reported smoking questions was 71% (149/209; 95% CI 65% to 77%) and 77% (160/209; 95% CI 71% to 82%) at 6 weeks and 6 months, respectively, and at 6 months 70% provided both cessation support resource use data and EuroQol-5 Dimensions five level (EQ-5D-5L) data. Among those self-reporting abstinence at 6 months follow-up, 39% (95% CI 24% to 54%) returned a viable sample for biochemical verification, although return rates were higher in the Quit Sense than the usual care arm (52% vs. 19%). Online recruitment was completed successfully within the planned 6-week period, split into two campaigns, with a per-participant cost of £19.20, split into advert (£5.82) and running costs (£13.38). In the Quit Sense arm, 75% (95% CI 67% to 83%) of participants installed the app and, of these, 100% set a quit date within the app and 51% (95% CI 39% to 63%) engaged with it for more than 1 week. At final follow-up, 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% CI 1.23 to 16.94). Similar effects were observed for biochemically verified secondary abstinence outcomes at 6-month follow-up and sensitivity analyses. Effect sizes were smaller and non-significant for self-report only abstinence and there was no evidence of a between-arm difference in abstinence at 6-week follow-up. Participants in the Quit Sense arm had higher rates of lapse avoidance in the first 2 weeks of their quit attempt or post enrolment compared to usual care though this was not statistically significant (29.6% vs. 19.2%; p = 0.14). There was no evidence of between-arm differences in hypothesised mechanisms of action of the app, including the mean frequency of lapse prevention strategy use [mean difference (MD) −0.07; p = 0.46], smoking cessation self-efficacy (MD 0.18; p = 0.39), strength (p = 0.23) and frequency (p = 0.83) of urges to smoke or the Wisconsin Inventory of Smoking Dependence Motives subscales of automaticity (p = 0.51) and associative processes (p = 0.58). The qualitative process evaluation identified several potential pathways to abstinence among Quit Sense arm participants. Interviewed participants reported finding the insights gained from engaging with the smoking behaviour learning tool before their quit attempt started particularly valuable. They reported that this reinforced their commitment to quit, helped them better understand the drivers of their smoking behaviour and challenged the need for them to smoke. Some also reported finding regular and location-specific support messages encouraging and motivational and that this reinforced the goal of quitting and made them feel equipped when they spent time in locations that they used to smoke in. Participants highlighted several factors which led to disengagement with the app, including relapse, no longer feeling they needed support, not finding the app met their needs and technical issues. Participants also provided suggestions for app improvement relating to logging smoking, adding gamification elements and improved support triggering. Interviewed participants from both arms highlighted how COVID-19 measures had affected their smoking behaviour and restricted the time they spent outside of their home, which would have had implications for location-based cessation support. In terms of the main cost-drivers, e-cigarettes/vaporisers and nicotine replacement therapy accounted for more than 70% of the total non-intervention costs. The total intervention cost (recruitment advertising and maintaining the Quit Sense app) was estimated to be £28.51 per participant. In both groups, the mean EQ-5D-5L score at both 6-week and 6-month follow-up was lower than that at baseline. A preliminary cost-effectiveness analysis using EQ-5D-5L score as the outcome estimated that the intervention was both more costly and less effective, compared to standard care, though there was no significant difference in cost or effect between the two groups. The SWAT analysis indicated that increasing the incentive for completing follow-up from £10 to £20 did not increase response rates at 6 months (74% vs. 79%; p = 0.36) but did reduce the proportion of participants requiring manual follow-up (62% vs. 46%; p = 0.018) and the median response time to responding (15 days vs. 7 days; p = 0.016). Conclusions: The Quit Sense RCT design and procedures demonstrated feasibility and generated preliminary efficacy evidence of the app on abstinence at 6-month follow-up, although how the app may achieve this is not clear. Three out of the four pre-specified feasibility progression criteria for moving to a definitive trial – completion rate of self-reported abstinence at final follow-up and the rate of Quit Sense installation and engagement – were met. The return of saliva samples among quitters was lower than anticipated, though this can likely be increased in a future trial through increased incentivisation and enhanced procedures. Progression to a definitive trial is warranted. Trial registration: This trial is registered as ISRCTN12326962. Funding: 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 Public Health Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information. [ABSTRACT FROM AUTHOR]

Background and Aims Haemodynamic instability is associated with peri-operative myocardial injury, particularly in patients receiving renin–angiotensin system (RAS) inhibitors (angiotensin-converting-enzyme inhibitors/angiotensin II receptor blockers). Whether stopping RAS inhibitors to minimise hypotension, or continuing RAS inhibitors to avoid hypertension, reduces peri-operative myocardial injury remains unclear. Methods From 31 July 2017 to 1 October 2021, patients aged ≥60 years undergoing elective non-cardiac surgery were randomly assigned to either discontinue or continue RAS inhibitors prescribed for existing medical conditions in six UK centres. Renin–angiotensin system inhibitors were withheld for different durations (2–3 days) before surgery, according to their pharmacokinetic profile. The primary outcome, masked to investigators, clinicians, and patients, was myocardial injury [plasma high-sensitivity troponin-T (hs-TnT) ≥ 15 ng/L within 48 h after surgery, or ≥5 ng/L increase when pre-operative hs-TnT ≥15 ng/L]. Pre-specified adverse haemodynamic events occurring within 48 h of surgery included acute hypertension (>180 mmHg) and hypotension requiring vasoactive therapy. Results Two hundred and sixty-two participants were randomized to continue (n = 132) or stop (n = 130) RAS inhibitors. Myocardial injury occurred in 58 (48.3%) patients randomized to discontinue, compared with 50 (41.3%) patients who continued, RAS inhibitors [odds ratio (for continuing): 0.77; 95% confidence interval (CI) 0.45–1.31]. Hypertensive adverse events were more frequent when RAS inhibitors were stopped [16 (12.4%)], compared with 7 (5.3%) who continued RAS inhibitors [odds ratio (for continuing): 0.4; 95% CI 0.16–1.00]. Hypotension rates were similar when RAS inhibitors were stopped [12 (9.3%)] or continued [11 (8.4%)]. Conclusions Discontinuing RAS inhibitors before non-cardiac surgery did not reduce myocardial injury, and could increase the risk of clinically significant acute hypertension. These findings require confirmation in future studies. [ABSTRACT FROM AUTHOR]

BackgroundMost smokers attempt to stop using cigarettes numerous times before successfully quitting. Cigarette cravings may undermine perceived competence to quit and thus constitute psychological threats to the individual’s self-concept. Self-affirmation may promote smoking cessation by offsetting these threats. ObjectiveThis study examines whether self-affirmation is associated with smoking cessation in the context of a cessation app. Two types of self-affirmation are examined: tendency to spontaneously self-affirm, and self-affirmation inductions added to a publicly available smoking cessation app (Smoke-Free Quit Smoking Now). In addition, this study explores whether optimism and emotional states (happiness, anger, anxiousness, hopefulness, sadness) predict smoking cessation. MethodsAll users who met the inclusion criteria, provided consent to participate, and completed a baseline assessment, including all individual difference measures, were randomized to 1 of 4 conditions. Half of the participants were randomly assigned to complete a self-affirmation induction upon study entry. Orthogonally, half of the participants were randomly assigned to receive self-affirming text notifications during their quit attempt or to receive conventional notifications. The induction and the text notifications were fully automated, and all data were collected through self-assessments in the app. Self-reported smoking cessation was assessed 1 month and 3 months following study entry. ResultsThe study enrolled 7899 participants; 647 completed the 1-month follow-up. Using an intent-to-treat analysis at the 1-month follow-up, 7.2% (569/7899) of participants self-reported not smoking in the previous week and 6.4% (503/7899) self-reported not smoking in the previous month. Greater tendency to spontaneously self-affirm predicted a greater likelihood of cessation (P

In November-December 2007 a widespread seabird mortality event occurred in Monterey Bay, California, USA, coincident with a massive red tide caused by the dinoflagellate Akashiwo sanguinea. Affected birds had a slimy yellow-green material on their feathers, which were saturated with water, and they were severely hypothermic. We determined that foam containing surfactant-like proteins, derived from organic matter of the red tide, coated their feathers and neutralized natural water repellency and insulation. No evidence of exposure to petroleum or other oils or biotoxins were found. This is the first documented case of its kind, but previous similar events may have gone undetected. The frequency and amplitude of red tides have increased in Monterey Bay since 2004, suggesting that impacts on wintering marine birds may continue or increase.

BackgroundDigital interventions to reduce excessive alcohol consumption have the potential to have a broader reach and be more cost-effective than traditional brief interventions. However, there is not yet strong evidence for their ability to engage users or their effectiveness. ObjectiveThis study aimed to identify the behavior change techniques (BCTs) and engagement strategies most worthy of further study by inclusion in a smartphone app to reduce alcohol consumption, using formal expert consensus methods. MethodsThe first phase of the study consisted of a Delphi exercise with three rounds. It was conducted with 7 international experts in the field of alcohol and/or behavior change. In the first round, experts identified BCTs most likely to be effective at reducing alcohol consumption and strategies most likely to engage users with an app; these were rated in the second round; and those rated as effective by at least four out of seven participants were ranked in the third round. The rankings were analyzed using Kendall’s W coefficient of concordance, which indicates consensus between participants. The second phase consisted of a new, independent group of experts (n=43) ranking the BCTs that were identified in the first phase. The correlation between the rankings of the two groups was assessed using Spearman’s rank correlation coefficient. ResultsTwelve BCTs were identified as likely to be effective. There was moderate agreement among the experts over their ranking (W=.465, χ211=35.8, P

BackgroundMobile phone apps have the potential to reduce excessive alcohol consumption cost-effectively. Although hundreds of alcohol-related apps are available, there is little information about the behavior change techniques (BCTs) they contain, or the extent to which they are based on evidence or theory and how this relates to their popularity and user ratings. ObjectiveOur aim was to assess the proportion of popular alcohol-related apps available in the United Kingdom that focus on alcohol reduction, identify the BCTs they contain, and explore whether BCTs or the mention of theory or evidence is associated with app popularity and user ratings. MethodsWe searched the iTunes and Google Play stores with the terms “alcohol” and “drink”, and the first 800 results were classified into alcohol reduction, entertainment, or blood alcohol content measurement. Of those classified as alcohol reduction, all free apps and the top 10 paid apps were coded for BCTs and for reference to evidence or theory. Measures of popularity and user ratings were extracted. ResultsOf the 800 apps identified, 662 were unique. Of these, 13.7% (91/662) were classified as alcohol reduction (95% CI 11.3-16.6), 53.9% (357/662) entertainment (95% CI 50.1-57.7), 18.9% (125/662) blood alcohol content measurement (95% CI 16.1-22.0) and 13.4% (89/662) other (95% CI 11.1-16.3). The 51 free alcohol reduction apps and the top 10 paid apps contained a mean of 3.6 BCTs (SD 3.4), with approximately 12% (7/61) not including any BCTs. The BCTs used most often were “facilitate self-recording” (54%, 33/61), “provide information on consequences of excessive alcohol use and drinking cessation” (43%, 26/61), “provide feedback on performance” (41%, 25/61), “give options for additional and later support” (25%, 15/61) and “offer/direct towards appropriate written materials” (23%, 14/61). These apps also rarely included any of the 22 BCTs frequently used in other health behavior change interventions (mean 2.46, SD 2.06). Evidence was mentioned by 16.4% of apps, and theory was not mentioned by any app. Multivariable regression showed that apps including advice on environmental restructuring were associated with lower user ratings (Β=-46.61, P=.04, 95% CI -91.77 to -1.45) and that both the techniques of “advise on/facilitate the use of social support” (Β=2549.21, P=.04, 95% CI 96.75-5001.67) and the mention of evidence (Β=1376.74, P=.02, 95%, CI 208.62-2544.86) were associated with the popularity of the app. ConclusionsOnly a minority of alcohol-related apps promoted health while the majority implicitly or explicitly promoted the use of alcohol. Alcohol-related apps that promoted health contained few BCTs and none referred to theory. The mention of evidence was associated with more popular apps, but popularity and user ratings were only weakly associated with the BCT content.