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In light of the substantial global production of biomass waste, effective waste management and energy recovery solutions are of paramount importance. Hydrothermal liquefaction (HTL) and anaerobic digestion (AD) have emerged as innovative techniques for converting biomass waste into valuable resources. Their integration creates a synergistic framework that mitigates inherent limitations, leading to improved efficiency, enhanced product quality, and the comprehensive utilization of biomass. This review paper investigates the integration of HTL and AD, highlighting its significance and potential benefits as well as the optimal sequencing (HTL followed by AD and AD followed by HTL). The review encompasses experimental procedures, factors influencing both sequencing options, energy recovery characterizations, final product outcomes, as well as toxicological assessments and discussions on reduction. Additionally, it delves into the transition towards a circular bioeconomy and discusses the challenges and opportunities intrinsic to these processes. The findings presented in this review offer valuable insights to shape future research in this evolving field., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Delivery of craving management tools via smartphone applications (apps) may improve smoking cessation rates, but research on such programmes remains limited, especially in real-world settings. This study evaluated the effectiveness of adding craving management tools in a cessation app (BupaQuit)., Methods: The study was a two-arm pragmatic pilot parallel randomised controlled trial, comparing a fully-automated BupaQuit app with craving management tool with a control app version without craving management tool. A total of 425 adult UK-based daily smokers were enrolled through open online recruitment (February 2015-March 2016), with no researcher involvement, and individually randomised within the app to the intervention ( n  = 208) or control ( n  = 217). The primary outcome was self-reported 14-day continuous abstinence assessed at 4-week follow-up. Secondary outcomes included 6-month point-prevalence and sustained abstinence, and app usage. The primary outcome was assessed with Fisher's exact test using intent to treat with those lost to follow-up counted as smoking. Participants were not reimbursed., Results: Re-contact rates were 50.4% at 4 weeks and 40.2% at 6 months. There was no significant difference between intervention and control arms on the primary outcome (13.5% vs 15.7%; p  = 0.58; relative risk = 0.86, 95% confidence interval = 0.54-1.36) or secondary cessation outcomes (6-month point prevalence: 14.4% vs 17.1%, p  = 0.51; relative risk = 0.85, 95% confidence interval = 0.54-1.32; 6-month sustained: 11.1% vs 13.4%, p  = 0.55; relative risk = 0.83, 95% confidence interval = 0.50-1.38). Bayes factors supported the null hypothesis ( B [0, 0, 1.0986] = 0.20). Usage was similar across the conditions (mean/median logins: 9.6/4 vs 10.5/5; time spent: 401.8/202 s vs 325.8/209 s)., Conclusions: The addition of craving management tools did not affect cessation, and the limited engagement with the app may have contributed to this., (© The Author(s) 2021.)

Introduction: Verifying self-reports of smoking abstinence is challenging in studies that involve remote data collection. Resting heart rate (HR) decreases during smoking abstinence. This study assessed whether a decrease in resting HR measured using freely available smartphone apps could potentially be used to verify smoking abstinence., Methods: This study involved a repeated measures experimental design, with data collection in natural setting. Participants were 18 adult, daily smokers. They recorded resting HR in beats per minute (bpm) using freely available smartphone apps during five timepoints (two in the morning and three postnoon) on each of 3 days. The outcome measure was the mean of the postnoon HR recordings. The experimental condition for each of the 3 days (counterbalanced order) was as follows: (1) smoking as usual, (2) not smoking without nicotine replacement therapy (NRT), or (3) not smoking but using NRT. Abstinence was verified using expired-air carbon monoxide (CO) concentration., Results: Compared with the smoking as usual condition, mean HR was 13.4 bpm lower (95% confidence interval [CI] = 5.4 to 21.4, p = .001) in the not smoking without NRT condition and 10.4 bpm lower (95% CI = 3.1 to 17.8, p = 0.004) in the not smoking with NRT condition. There was no statistically significant difference in HR between the two not smoking conditions (p = .39). Abstinence during not smoking days without and with NRT was CO-verified in 18/18 and in 16/18 cases, respectively., Conclusions: Self-recording of resting HR in natural setting using smartphone apps shows a reliable decrease in response to smoking abstinence and may provide a basis for remote verification in smoking cessation studies., Implications: Remote verification of self-reported abstinence in smoking cessation studies remains challenging. Smoking abstinence has been shown to decrease resting HR under laboratory conditions. This study demonstrated that self-recording using freely available smartphone apps shows reliable decreases in resting HR during smoking abstinence and may provide a basis for inexpensive remote verification of smoking abstinence., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco.)

Background and Aim: It is useful, for theoretical and practical reasons, to be able to specify functions for continuous abstinence over time in smoking cessation attempts. This study aimed to find the best-fitting models of mean proportion abstinent with different smoking cessation pharmacotherapies up to 52 weeks from the quit date., Methods: We searched the Cochrane Database of Systematic Reviews to identify randomized controlled trials (RCTs) of pharmacological treatments to aid smoking cessation. For comparability, we selected trials that provided 12 weeks of treatment. Continuous abstinence rates for each treatment at each follow-up point in trials were extracted along with methodological details of the trial. Data points for each pharmacotherapy at each follow-up point were aggregated where the total across contributing studies included at least 1000 participants per data point. Continuous abstinence curves were modelled using a range of different functions from the quit date to 52-week follow-up. Models were compared for fit using R 2 and Bayesian information criterion (BIC)., Results: Studies meeting our selection criteria covered three pharmacotherapies [varenicline, nicotine replacement therapy (NRT) and bupropion] and placebo. Power functions provided the best fit (R 2  > 0.99, BIC < 17.0) to continuous abstinence curves from the target quit date in all cases except for varenicline, where a logarithmic function described the curve best (R 2  = 0.99, BIC = 21.2). At 52 weeks, abstinence rates were 22.5% (23.0% modelled) for varenicline, 16.7% (16.0% modelled) for bupropion, 13.0% (12.4% modelled) for NRT and 8.3% (8.9% modelled) for placebo. For varenicline, bupropion, NRT and placebo, respectively, 55.9, 65.0, 62.3 and 56.5% of participants who were abstinent at the end of treatment were still abstinent at 52 weeks., Conclusions: Mean continuous abstinence rates up to 52 weeks from initiation of smoking cessation attempts in clinical trials can be modelled using simple power functions for placebo, nicotine replacement therapy and bupropion and a logarithmic function for varenicline. This allows accurate prediction of abstinence rates from any time point to any other time point up to 52 weeks., (© 2019 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Background and Aims: Tobacco smoking is prevalent among Arab smokers. Interventions to support smoking cessation may differ in effectiveness in this population from Western populations usually studied. This review assessed evidence of effectiveness of clinical smoking interventions in Arab smokers., Methods: A systematic search for comparative trials evaluating tobacco cessation interventions in Arab populations was conducted in the MEDLINE, EMBASE, PyschINFO, CINHAL and Web of Science databases. Behavioural, pharmacological and combined interventions were included. Reference lists of included studies were hand searched. The outcome measure was self- reported tobacco abstinence at the final follow-up, with biochemical verification where available. Assessment of evidence for effectiveness was undertaken using Bayes Factors., Results: A total of 659 titles and abstracts were identified. Five studies met the inclusion criteria. Four of these were randomized controlled trials and one was a non-randomized comparative trial. Differences between study features precluded meaningful aggregation for a meta-analysis. The four randomized trials all yielded Bayes Factors <1, suggesting no effect of the intervention compared with the control condition. The non-randomized trial, conducted in tuberculosis clinics in Sudan, yielded an extremely high Bayes Factor (>1000), supporting the hypothesis of effectiveness; however, the study was judged to have a high risk of bias., Conclusions: As yet, there is no convincing direct evidence that clinical smoking cessation interventions, which are found to be effective in Western populations, are also effective for Arab smokers. There is an urgent need for high quality randomized trials evaluating interventions in this population., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Smartphone applications (apps) are popular aids for smoking cessation. Smoke Free is an app that delivers behaviour change techniques used in effective face-to-face behavioural support programmes. The aim of this study was to assess whether the full version of Smoke Free is more effective than the reduced version. Methods:  This was a two-arm randomised controlled trial. Smokers who downloaded Smoke Free were randomly offered the full or reduced version; 28,112 smokers aged 18+ years who set a quit date were included. The full version provided updates on benefits of abstinence, progress (days smoke free), virtual 'badges' and daily 'missions' with push notifications aimed at preventing and managing cravings. The reduced version did not include the missions. At baseline the app recorded users': device type (iPhone or Android), age, sex, daily cigarette consumption, time to first cigarette of the day, and educational level. The primary outcome was self-reported complete abstinence from the quit date in a 3-month follow-up questionnaire delivered via the app. Analyses conducted included logistic regressions of outcome on to app version (full versus reduced) with adjustment for baseline variables using both intention-to-treat/missing-equals smoking (MES) and follow-up-only (FUO) analyses. Results: The 3-month follow-up rate was 8.5% (n=1,213) for the intervention and 6.5% (n=901) for the control. A total of 234 participants reported not smoking in the intervention versus 124 in the control, representing 1.6% versus 0.9% in the MES analysis and 19.3% versus 13.8% in the FUO analysis. Adjusted odds ratios were 1.90, 95%CI=1.53-2.37 (p<0.001) and 1.50, 95%CI=1.18-1.91 (p<0.001) in the MES and FUO analyses respectively. Conclusions: Despite very low follow-up rates using in-app follow up, both intention-to-treat/missing equals smoking and follow-up only analyses showed the full version of the Smoke Free app to result in higher self-reported 3-month continuous smoking abstinence rates than the reduced version., Competing Interests: Competing interests: David Crane is originator of the Smoke Free app and derives income from it. Harveen Kaur Ubhi has no conflict of interest. Jamie Brown has received unrestricted smoking cessation research funding from Pfizer. Robert West has undertaken research and consultancy for companies that develop and manufacture smoking cessation medications. JB & RW are both unpaid members of the scientific steering group of the Smoke Free mobile application.

Background: Smartphone applications (apps) are popular aids for smoking cessation. Smoke Free is an app that delivers behaviour change techniques used in effective face-to-face behavioural support programmes. The aim of this study was to assess whether the full version of Smoke Free is more effective than the reduced version. Methods:  This was a two-arm exploratory randomised controlled trial. Smokers who downloaded Smoke Free were randomly offered the full or reduced version; 28,112 smokers aged 18+ years who set a quit date were included. The full version provided updates on benefits of abstinence, progress (days smoke free), virtual 'badges' and daily 'missions' with push notifications aimed at preventing and managing cravings. The reduced version did not include the missions. At baseline the app recorded users': device type (iPhone or Android), age, sex, daily cigarette consumption, time to first cigarette of the day, and educational level. The primary outcome was self-reported complete abstinence from the quit date in a 3-month follow-up questionnaire delivered via the app. Analyses conducted included logistic regressions of outcome on to app version (full versus reduced) with adjustment for baseline variables using both intention-to-treat/missing-equals smoking (MES) and follow-up-only (FUO) analyses. Results: The 3-month follow-up rate was 8.5% (n=1,213) for the intervention and 6.5% (n=901) for the control. A total of 234 participants reported not smoking in the intervention versus 124 in the control, representing 1.6% versus 0.9% in the MES analysis and 19.3% versus 13.8% in the FUO analysis. Adjusted odds ratios were 1.90, 95%CI=1.53-2.37 (p<0.001) and 1.50, 95%CI=1.18-1.91 (p<0.001) in the MES and FUO analyses respectively. Conclusions: Despite very low follow-up rates using in-app follow up, both intention-to-treat/missing equals smoking and follow-up only analyses showed the full version of the Smoke Free app to result in higher self-reported 3-month continuous smoking abstinence rates than the reduced version., Competing Interests: Competing interests: David Crane is originator of the Smoke Free app and derives income from it. Harveen Kaur Ubhi has no conflict of interest. Jamie Brown has received unrestricted smoking cessation research funding from Pfizer. Robert West has undertaken research and consultancy for companies that develop and manufacture smoking cessation medications. JB & RW are both unpaid members of the scientific steering group of the Smoke Free mobile application.

Background and Aims: Models of tobacco smoking behaviour propose that anticipated pleasure or satisfaction, the need to alleviate a nicotine-induced drive state and a stimulus-driven impulse potentially play an important role. This study aimed to provide a preliminary assessment of how far urges to smoke are reported by smokers and whether the strength of such urges prior to a quit attempt predicts short-term success at quitting., Methods: In a prospective study, 566 smokers attending a treatment programme to help smokers quit completed a written questionnaire covering frequency of different types of urge to smoke (automatic impulse - 'automatic urges', anticipated pleasure - 'pleasure urges', and fulfilling a need - 'need urges'). They were asked to rate this for whichever of these urges was dominant for them. The questionnaire also assessed daily cigarette consumption, time to first cigarette of the day, age and gender. Carbon monoxide verified smoking status was recorded at 1 and 4weeks after the target quit date., Findings: A total of 47.9% (271) of smokers reported that automatic urges were dominant, 21.7% (123) reported pleasure urges to be dominant, and 30.4% (172) reported need urges to be dominant. The strength of automatic urges predicted abstinence at both 1week (OR=0.74, p=0.005, 95%CI=0.60-0.92) and 4weeks (OR=0.73, p=0.008, 95%CI=0.59-0.92). Associations between other urge types and abstinence were not statistically significant., Conclusions: A substantial proportion of smokers attending a clinic for help with quitting report that their dominant urges to smoke occur without any anticipation of pleasure or relief and the strength of the automatic urges predicts failure to sustain abstinence following a quit attempt., (Copyright © 2017. Published by Elsevier Ltd.)

iOS and Android smartphone users may differ in ways that affect their use and likelihood of success when using a smoking cessation application (app). If so, it may be necessary to take the device type (iOS and Android) into account when designing smoking cessation apps and in studies evaluating app effectiveness. How do socio-demographic and smoking characteristics, potentially relevant to engagement and cessation outcomes, of the SF28 app users differ between those using the iOS version and those using the Android version? Data were collected between October 2013 and April 2015. The variables measured were age, gender, social grade, time since the most recent quit attempt, choice of medication use (nicotine replacement therapy or varenicline), weekly expenditure on cigarettes, cigarettes smoked per day, reason for using the app and quit date set. The alpha was set to p < 0.006 to adjust for multiple comparisons. A total of 1368 users were included in the analysis. iOS and Android device users were similar in terms of age, social grade, weekly expenditure on cigarettes and cigarettes smoked per day. Compared with Android users, iOS users were more likely to have downloaded the app for a serious quit attempt (74.3 versus 69.6%, p = 0.001), made a quit attempt within the last 12 months (59.6 versus 45.9%, p < 0.001) and set their quit date on the day of registration (61 versus 46.2%, p < 0.001). They were less likely to have used stop-smoking medication to support their quit attempt (31.5 versus 48.6%, p < 0.001). Differences between smokers using the iOS version of smoking cessation apps and those using the Android version may influence quit success.

Background: Public health organisations such as the National Health Service in the United Kingdom and the National Institutes of Health in the United States provide access to online libraries of publicly endorsed smartphone applications (apps); however, there is little evidence that users rely on this guidance. Rather, one of the most common methods of finding new apps is to search an online store. As hundreds of smoking cessation and alcohol-related apps are currently available on the market, smokers and drinkers must actively choose which app to download prior to engaging with it. The influences on this choice are yet to be identified. This study aimed to investigate 1) design features that shape users' choice of smoking cessation or alcohol reduction apps, and 2) design features judged to be important for engagement., Methods: Adult smokers (n = 10) and drinkers (n = 10) interested in using an app to quit/cut down were asked to search an online store to identify and explore a smoking cessation or alcohol reduction app of their choice whilst thinking aloud. Semi-structured interview techniques were used to allow participants to elaborate on their statements. An interpretivist theoretical framework informed the analysis. Verbal reports were audio recorded, transcribed verbatim and analysed using inductive thematic analysis., Results: Participants chose apps based on their immediate look and feel, quality as judged by others' ratings and brand recognition ('social proof'), and titles judged to be realistic and relevant. Monitoring and feedback, goal setting, rewards and prompts were identified as important for engagement, fostering motivation and autonomy. Tailoring of content, a non-judgmental communication style, privacy and accuracy were viewed as important for engagement, fostering a sense of personal relevance and trust. Sharing progress on social media and the use of craving management techniques in social settings were judged not to be engaging because of concerns about others' negative reactions., Conclusions: Choice of a smoking cessation or alcohol reduction app may be influenced by its immediate look and feel, 'social proof' and titles that appear realistic. Design features that enhance motivation, autonomy, personal relevance and credibility may be important for engagement.

The aim of this study was to assess whether or not behaviour change techniques (BCTs) as well as engagement and ease-of-use features used in smartphone applications (apps) to aid smoking cessation can be identified reliably. Apps were coded for presence of potentially effective BCTs, and engagement and ease-of-use features. Inter-rater reliability for this coding was assessed. Inter-rater agreement for identifying presence of potentially effective BCTs ranged from 66.8 to 95.1 % with 'prevalence and bias adjusted kappas' (PABAK) ranging from 0.35 to 0.90 (p < 0.001). The intra-class correlation coefficients between the two coders for scores denoting the proportions of (a) a set of engagement features and (b) a set of ease-of-use features, which were included, were 0.77 and 0.75, respectively (p < 0.001). Prevalence estimates for BCTs ranged from <10 % for medication advice to >50 % for rewarding abstinence. The average proportions of specified engagement and ease-of-use features included in the apps were 69 and 83 %, respectively. The study found that it is possible to identify potentially effective BCTs, and engagement and ease-of-use features in smoking cessation apps with fair to high inter-rater reliability.

Background and Aims: Smartphone applications (apps) offer a potentially cost-effective and a wide-reach aid to smoking cessation. In 2012, a content analysis of smoking cessation apps suggested that most apps did not adopt behaviour change techniques (BCTs), which according to previous research had suggested would promote higher success rates in quitting smoking. This study examined whether or not, this situation had changed by 2014 for free smoking cessation apps available in the Apple App Store. It also compared the use of engagement and ease-of-use features between the two time points., Methods: 137 free apps available in the Apple App Sore in 2014 were coded using an established framework for the presence or absence of evidence-based BCTs, and engagement and ease-of-use features. The results from the 2014 data were compared with a similar exercise conducted on 83 free apps available in 2012., Results: BCTs supporting identity change, rewarding abstinence and advising on changing routines were less prevalent in 2014 as compared with 2012 (14.6% vs. 42.2%, 18.2% vs. 48.2%, and 17.5% vs. 24.1%, respectively). Advice on coping with cravings and advice on the use of stop-smoking medication were more prevalent in 2014 as compared with 2012 (27.7% vs. 20.5% and 14.6% vs 3.6%, respectively). The use of recognised engagement features was less common in 2014 than in 2012 (45.3% vs. 69.6%) while ease-of-use features remained very high (94.5% vs. 82.6%)., Conclusion: There was little evidence of improvement in the use of evidence-based BCTs in free smoking cessation iPhone-based apps between 2012 and 2014., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Little is known about the effectiveness of mobile apps in aiding smoking cessation or their validity for automated collection of data on smoking cessation outcomes., Objective: We conducted a preliminary evaluation of SF28 (SF28 is the name of the app, short for SmokeFree28)-an app aimed at helping smokers to be smoke-free for 28 days., Methods: Data on sociodemographic characteristics, smoking history, number of logins, and abstinence at each login were uploaded to a server from SF28 between August 2012 and August 2013. Users were included if they were aged 16 years or over, smoked cigarettes at the time of registration, had set a quit date, and used the app at least once on or after their quit date. Their characteristics were compared with data from a representative sample of smokers trying to stop smoking in England. The percentage of users recording 28 days of abstinence was compared with a value of 15% estimated for unaided quitting. Correlations were assessed between recorded abstinence for 28 days and well-established abstinence predictors., Results: A total of 1170 users met the inclusion criteria. Compared with smokers trying to quit in England, they had higher consumption, and were younger, more likely to be female, and had a non-manual rather than manual occupation. In total, 18.9% (95% CI 16.7-21.1) were recorded as being abstinent from smoking for 28 days or longer. The mean number of logins was 8.5 (SD 9.0). The proportion recording abstinence for 28 days or longer was higher in users who were older, in a non-manual occupation, and in those using a smoking cessation medication., Conclusions: The recorded 28-day abstinence rates from the mobile app, SF28, suggest that it may help some smokers to stop smoking. Further evaluation by means of a randomized trial appears to be warranted.

Early orthodontic interventions are often initiated in the developing dentition to promote favourable developmental changes. Interceptive orthodontic can eliminate or reduce the severity of a developing malocclusion, the complexity of orthodontic treatment, overall treatment time and cost. The safest way to prevent future malocclusions from tooth loss is to place a space maintainer that is effective and durable. An appropriate use of space maintainer is advocated to hold the space until the eruption of permanent teeth. This case report describes the various changing trends in use of space maintainers: conventional band and loop, prefabricated band with custom made loop and glass fibre reinforced composite resins as space maintainers.