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BackgroundThe prevalence of smoking is declining; however, it continues to be a major public health burden. In England, primary care is the health setting that provides smoking cessation support to most smokers. However, this setting has one of the lowest success rates. The iQuit in practice intervention (iQuit) is a tailored web-based and text message intervention developed for use in primary care consultations as an adjunct to routine smoking cessation support with the aim of increasing success rates. iQuit has demonstrated feasibility, acceptability, and potential effectiveness. ObjectiveThis definitive trial aims to determine the effectiveness and cost-effectiveness of iQuit when used as an adjunct to the usual support provided to patients who wish to quit smoking, compared with usual care alone. MethodsThe iQuit in Practice II trial is a two-arm, parallel-group, randomized controlled trial (RCT) with a 1:1 individual allocation comparing usual care (ie, pharmacotherapy combined with multisession behavioral support)—the control—with usual care plus iQuit—the intervention. Participants were recruited through primary care clinics and talked to a smoking cessation advisor. Participants were randomized during the initial consultation, and those allocated to the intervention group received a tailored advice report and 90 days of text messaging in addition to the standard support provided to all patients. ResultsThe primary outcome is self-reported prolonged abstinence biochemically verified using saliva cotinine at 6 months after the quit date. A sample size of 1700 participants, with 850 per arm, would yield 90% power to detect a 4.3% difference in validated quit rates between the groups at the two-sided 5% level of significance. The Cambridge East Research Ethics Committee approved the study in February 2016, and funding for the study was granted from May 2016. In total, 1671 participants were recruited between August 2016 and July 2019. Follow-up for all participants was completed in January 2020. Data analysis will begin in the summer of 2020. ConclusionsiQuit in Practice II is a definitive, pragmatic RCT assessing whether a digital intervention can augment the impact of routine smoking cessation support in primary care. Previous research has found good acceptability and feasibility for delivering iQuit among smoking cessation advisors working in primary care. If demonstrated to be cost-effective, iQuit could be delivered across primary care and other settings, such as community pharmacies. The potential benefit would likely be highest where less behavioral support is delivered. Trial RegistrationInternational Standard Randomized Controlled Trial Number (ISRCTN): 44559004; http://www.isrctn.com /ISRCTN44559004. International Registered Report Identifier (IRRID)DERR1-10.2196/17160

BackgroundThe efficacy of a highly tailored digital intervention to support medication adherence and feasibility to support clinical effectiveness as an adjunct to the primary care setting has not been evaluated. ObjectiveThis trial aimed to assess the behavioral efficacy of a highly tailored digital intervention to support medication adherence and to evaluate the feasibility of its clinical effectiveness, in patients with either or both hypertension and type 2 diabetes. We also examined quality of life and mechanisms of behavior change. Intervention fidelity, engagement, and satisfaction were also explored. MethodsThis was a multicenter, individually randomized controlled trial of 2 parallel groups: an intervention group that received a highly tailored text message and interactive voice response intervention for 12 weeks, and a control group that received usual care. Medication adherence was measured using self-reports and assessor-blinded practice records of a repeat prescription. Systolic blood pressure and glucose levels were assessed by nurses blinded to group allocation during practice visits at 3 months follow-up. Questionnaires obtained data to assess intervention mechanisms of action and satisfaction and digital log files captured data to evaluate fidelity and engagement. ResultsA total of 135 nonadherent patients (62/135, 46% female; 122/135, 90.3%; aged above 50 years) were randomly allocated in the intervention (n=79) or in the control group (n=56); of whom 13% (18/135) were lost at follow-up. Medication adherence was significantly improved in the intervention group compared with the control group (t116=2.27; P=.02, 2-tailed). Systolic blood pressure was 0.6 mmHg (95% CI −7.423 to 6.301), and hemoglobin A1c was 4.5 mmol/mol (95% CI −13.099 to 4.710) lower in the intervention group compared with the control group. Changes in intentional nonadherence and nonintentional nonadherence explained the improvements in medication adherence in the intervention group (beta=.074, SE=0.464; P=.04), but not in the control group (beta=.00, SE 1.35; P=.37). The intervention had 100% fidelity, a median of 12 days of engagement, and 76% overall satisfaction. ConclusionsOur trial is the first that has been conducted in the United Kingdom and showed that among nonadherent patients with either or both hypertension and type 2 diabetes, a highly tailored digital intervention was effective at improving treatment adherence and feasible to obtain clinically meaningful outcomes. Changes in intentional and nonintentional nonadherence predicted the improvements in medication adherence. The intervention had high fidelity, engagement, and satisfaction. Future research using a rigorous design is needed to evaluate the clinical effectiveness and cost-effectiveness of the intervention in primary care. Trial RegistrationInternational Standard Randomized Controlled Trial Number (ISRCTN) 10668149; http://www.controlled-trials.com/ISRCTN10668149.

BackgroundA major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. ObjectiveWe sought to (1) assess smokers’ compliance with reporting their smoking in real time and identify reasons for noncompliance, (2) assess the app's accuracy in identifying user-specific high-risk locations for smoking, (3) explore the feasibility and user perspective of geofence-triggered support, and (4) identify any technological issues or privacy concerns. MethodsAn explanatory sequential mixed-methods design was used, where data collected by the app informed semistructured interviews. Participants were smokers who owned an Android mobile phone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, end-of-day (EoD) surveys of smoking beliefs and behavior, support message ratings, and app interaction data. Interviews were undertaken and analyzed thematically (N=13). Quantitative and qualitative data were analyzed separately and findings presented sequentially. ResultsOut of 15 participants, 3 (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 37.8 (SD 21.2) per participant, or 2.0 (SD 2.2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56.2% of days. Geolocation was collected in 97.0% of smoking reports with a mean accuracy of 31.6 (SD 16.8) meters. A total of 5 out of 9 (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50.0% (137/274) of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 78.2% (158/202) of delivered messages were rated by participants. Qualitative findings identified multiple reasons for noncompliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app’s identification of their smoking locations, were largely positive about the value of geofence-triggered support, and had no privacy concerns about the data collected by the app. ConclusionsUser-initiated self-report is feasible for training a cessation app about an individual’s smoking behavior, although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants.

BackgroundMobile apps present a potentially cost-effective tool for delivering behavior change interventions at scale, but no known studies have tested the efficacy of apps as a tool to specifically increase vegetable consumption among overweight adults. ObjectiveThe purpose of this pilot study was to assess the initial efficacy and user acceptability of a theory-driven mobile app to increase vegetable consumption. MethodsA total of 17 overweight adults aged 42.0 (SD 7.3) years with a body mass index (BMI) of 32.0 (SD 3.5) kg/m2 were randomized to the use of Vegethon (a fully automated theory-driven mobile app enabling self-monitoring of vegetable consumption, goal setting, feedback, and social comparison) or a wait-listed control condition. All participants were recruited from an ongoing 12-month weight loss trial (parent trial). Researchers who performed data analysis were blinded to condition assignment. The primary outcome measure was daily vegetable consumption, assessed using an adapted version of the validated Harvard Food Frequency Questionnaire administered at baseline and 12 weeks after randomization. An analysis of covariance was used to assess differences in 12-week vegetable consumption between intervention and control conditions, controlling for baseline. App usability and satisfaction were measured via a 21-item post-intervention questionnaire. ResultsUsing intention-to-treat analyses, all enrolled participants (intervention: 8; control: 9) were analyzed. Of the 8 participants randomized to the intervention, 5 downloaded the app and logged their vegetable consumption a mean of 0.7 (SD 0.9) times per day, 2 downloaded the app but did not use it, and 1 never downloaded it. Consumption of vegetables was significantly greater among the intervention versus control condition at the end of the 12-week pilot study (adjusted mean difference: 7.4 servings; 95% CI 1.4-13.5; P=.02). Among secondary outcomes defined a priori, there was significantly greater consumption of green leafy vegetables, cruciferous vegetables, and dark yellow vegetables (adjusted mean difference: 2.6, 1.6, and 0.8 servings; 95% CI 0.1-5.0, 0.1-3.2, and 0.3-1.4; P=.04, P=.04, and P=.004, respectively). Participants reported positive experiences with the app, including strong agreement with the statements “I have found Vegethon easy to use” and “I would recommend Vegethon to a friend” (mean 4.6 (SD 0.6) and 4.2 (SD 0.8), respectively, (on a 5-point scale). ConclusionsVegethon demonstrated initial efficacy and user acceptability. A mobile app intervention may be useful for increasing vegetable consumption among overweight adults. The small sample size prevented precise estimates of effect sizes. Given the improved health outcomes associated with increases in vegetable consumption, these findings indicate the need for larger, longer-term evaluations of Vegethon and similar technologies among overweight adults and other suitable target groups. Trial RegistrationClinicalTrials.gov NCT01826591; https://clinicaltrials.gov/ct2/show/NCT01826591 (Archived by WebCite at http://www.webcitation.org/6hYDw2AOB)

BackgroundMobile technologies for health (mHealth) represent a promising strategy for reducing type 2 diabetes (T2DM) risk. The PROPELS trial investigates whether structured group-based education alone or supplemented with a follow-on support program combining self-monitoring with pedometers and tailored text-messaging is effective in promoting and maintaining physical activity among people at high risk of T2DM. ObjectiveThis paper describes the iterative development of the PROPELS follow-on support program and presents evidence on its acceptability and feasibility. MethodsWe used a modified mHealth development framework with four phases: (1) conceptualization of the follow-on support program using theory and evidence, (2) formative research including focus groups (n=15, ages 39-79 years), (3) pre-testing focus groups using a think aloud protocol (n=20, ages 52-78 years), and (4) piloting (n=11). Analysis was informed by the constant comparative approach, with findings from each phase informing subsequent phases. ResultsThe first three phases informed the structure, nature, and content of the follow-on support program, including the frequency of text messages, the need for tailored content and two-way interaction, the importance of motivational messages based on encouragement and reinforcement of affective benefits (eg, enjoyment) with minimal messages about weight and T2DM risk, and the need for appropriate language. The refined program is personalized and tailored to the individual’s perceived confidence, previous activity levels, and physical activity goals. The pilot phase indicated that the program appeared to fit well with everyday routines and was easy to use by older adults. ConclusionsWe developed a feasible and innovative text messaging and pedometer program based on evidence and behavior change theory and grounded in the experiences, views, and needs of people at high diabetes risk. A large scale trial is testing the effectiveness of this 4-year program over and above structured group education alone. Trial RegistrationInternational Standard Randomized Controlled Trial Number (ISRCTN): 83465245; http://www.controlled-trials.com/ISRCTN83465245/83465245 (Archived by WebCite at http://www.webcitation.org/6dfSmrVAe)

The COVID-19 pandemic has presented unique challenges and stressors that are testing the resilience of residential life staff in significant ways. Like many colleges and universities, this campus of study provided almost exclusively remote instruction during the 2020-21 academic year while providing housing for approximately 30% of the undergraduate population. In 2021-22, this campus was near the residential capacity while providing mostly in-person instruction. Even with a 97% vaccination rate for students, monitoring the safety and wellness of residents created unique stressors for both professional and paraprofessional staff. The varied impacts of the pandemic brought constant change, and the results of such change were no more apparent than in the 24/7 environments of the residential community. This article explores the depth and breadth of issues related to the stress, coping, and resilience of live-in student staff and identifies variables that contributed to their well-being. Using a mixed methods approach, a comparison was made between student hall staff and the rest of the campus population on self-reported mental health concerns. These data were used to create changes in practice to improve the paraprofessional experience, such as professional staff assuming more in-person responsibilities and making mental health support more readily accessible to paraprofessional staff. [Discussion questions developed by Zakery Earp.]

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]

Smong is an indigenous knowledge related to the tsunami. The word of smong comes from the Devayan language of Simeulue and refers to the complex of earthquake/sea receding/giant wave that is typical of tsunami events in Indonesia. Now their pride in the word smong has spread to Aceh and is being adopted in their efforts to adapt to earthquake and tsunami disasters. Simeuluean people, grateful to their nation for the reconstruction after 2004 and want to grow their pride in smong to a national level. They wanted to make a gift of smong to be owned by the Indonesian people. To do this, they feel this study initiates that the word smong should be part of the narrative in Indonesian as the basis for a new understanding of the tsunami disaster by Indonesian people. Initiating the word smong in Indonesian vocabulary does not mean removing the word tsunami that now an international word. But the acceptance of smong should be a source of pride in the protection of our greatest wealth of people.