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Objective: The aim of this study was to test the effectiveness of a tailored quitline tobacco treatment ('Quitlink') among people receiving support for mental health conditions. Methods: We employed a prospective, cluster-randomised, open, blinded endpoint design to compare a control condition to our 'Quitlink' intervention. Both conditions received a brief intervention delivered by a peer researcher. Control participants received no further intervention. Quitlink participants were referred to a tailored 8-week quitline intervention delivered by dedicated Quitline counsellors plus combination nicotine replacement therapy. The primary outcome was self-reported 6 months continuous abstinence from end of treatment (8 months from baseline). Secondary outcomes included additional smoking outcomes, mental health symptoms, substance use and quality of life. A within-trial economic evaluation was conducted. Results: In total, 110 participants were recruited over 26 months and 91 had confirmed outcomes at 8 months post baseline. There was a difference in self-reported prolonged abstinence at 8-month follow-up between Quitlink (16%, n = 6) and control (2%, n = 1) conditions, which was not statistically significant (OR = 8.33 [0.52, 132.09] p = 0.131 available case). There was a significant difference in favour of the Quitlink condition on 7-day point prevalence at 2 months (OR = 8.06 [1.27, 51.00] p = 0.027 available case). Quitlink costs AU$9231 per additional quit achieved. Conclusion: The Quitlink intervention did not result in significantly higher rates of prolonged abstinence at 8 months post baseline. However, engagement rates and satisfaction with the 'Quitlink' intervention were high. While underpowered, the Quitlink intervention shows promise. A powered trial to determine its effectiveness for improving long-term cessation is warranted. [ABSTRACT FROM AUTHOR]

Purpose: Calibration of a radiotherapy electronic portal imaging device (EPID) using the pixel‐sensitivity‐map (PSM) in place of the flood field correction improves the utility of the EPID for quality assurance applications. Multiple methods are available for determining the PSM and this study provides an evaluation to inform on which is superior. Methods: Three different empirical methods ("Calvary Mater Newcastle" [CMN], "Varian," and "WashU") and a Monte Carlo‐based method of PSM determination were investigated on a single Varian TrueBeam STx linear accelerator (linac) with an aS1200 EPID panel. PSM measurements were performed for each empirical method three successive times using the 6 MV beam. The resulting PSM from each method was compared to the Monte Carlo method as a reference using 2D percentage deviation maps and histograms plus crossplane profiles. The repeatability of generated PSMs was also assessed via 2D standard deviation (SD) maps and histograms. Additionally, the Beam‐Response generated by removal of the PSM from a raw EPID image for each method was visually contrasted. Finally, the practicality of each method was assessed qualitatively and via the measured time required to acquire and export the required images. Results: The median pixel‐by‐pixel percentage deviation between each of the empirical PSM methods and the Monte Carlo PSM was ‐0.36%, 0.24%, and 0.74% for the CMN, Varian, and WashU methods, respectively. Ninety‐five percent of pixels were found to be repeatable to within ‐0.21%, 0.08%, 0.19%, and 0.35% (1 SD) for the CMN, Monte Carlo, Varian, and WashU methods, respectively. The WashU method was found to be quickest for data acquisition and export and the CMN the slowest. Conclusion: For the first time four methods of generating the EPID PSM have been compared in detail and strengths and weaknesses of each method have been identified. All methods are considered likely to be clinically acceptable and with similar practical requirements. [ABSTRACT FROM AUTHOR]

Purpose: The EPID PSM is a useful EPID calibration method for QA applications. The dependence of the EPID PSM on the photon beam used to acquire it has been investigated in this study for the four available PSM methods. The aim is to inform upon the viability of applying a single PSM for all available photon beams to simplify PSM implementation and maintenance. Methods: Four methods of PSM determination were each measured once in a single session on a single TrueBeam ® STx linac using 6 MV, 10 MV, 6 MV Flattening‐Filter‐Free (FFF), and 10 MV FFF photon beams. The resultant PSM was assessed for both intra‐ and inter‐method beam dependence via comparison between PSM of the same method compared to the 6 MV PSM and via comparison between PSM of the same beam with the corresponding Monte Carlo PSM. Comparisons were performed via 2D percentage deviation plots with associated histograms, 1D crossplane profiles, and via mean, median, and standard deviation percentage deviation statistics. Generated beam‐response was compared qualitatively via 1D crossplane profile comparison and quantitatively via symmetry assessment with comparison to the IC profiler device. Results: The Varian method provided the most consistent PSM with varying photon beam, with median percent deviation from the 6 MV PSM within 0.14% for all other beams. Qualitatively, each method provided similar beam‐response profiles. The measured beam‐response symmetry agreed to within 0.2% between the Calvary Mater Newcastle (CMN) method and IC profiler, but agreement reduced to within 0.9% and 2.2% for the Varian and WashU methods. PSM percent deviation with Monte Carlo PSM was within 0.75% for all methods and beams. Conclusion: Results suggest that the PSM may be independent of photon beam to clinically relevant levels. The Varian method of PSM determination introduces the least beam dependence into the measured PSM. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions To (1) evaluate the efficacy of OptimAAPP, a smartphone insulin dose calculator for carbohydrate, fat, and protein in managing glycaemia compared with carbohydrate counting in adolescents and adults with type 1 diabetes using flexible multiple daily injection therapy (MDI, ≥4 injections/day) and (2) assess user acceptability of OptimAAPP.In this free‐living trial, participants aged 12–50 years were randomised to use carbohydrate counting or OptimAAPP for meal insulin dose calculation for 3 months, then use the alternate method for 3 months. The primary outcome, time‐in‐range (3.9–10.0 mmol/L) was measured in weeks 3–4 of each arm using continuous glucose monitoring. The acceptability of OptimAAPP was assessed at end intervention using a purpose‐designed questionnaire.An intention‐to‐treat analysis of 41 participants, mean age 28 ± 12 years and HbA1c 56 ± 10 mmol/mol (7.3 ± 0.9%) found no significant difference in glycaemic outcomes when using OptimAAPP compared with carbohydrate counting including time‐in‐range (70.5 vs. 67.6%, p = 0.102), above range (24.5% vs. 28.0%, p = 0.068), below range (4.9% vs. 4.4%, p = 0.318), and coefficient of variation (32.2% vs. 33.3%, p = 0.136). There was no severe hypoglycaemia. Participants reported that OptimAAPP was easy to use (79%), and they were confident in giving the recommended doses (82%). Barriers to use were the small food database and the time associated with food entry.In adolescents and adults using flexible MDI therapy, OptimAAPP use did not produce glycaemic outcomes that were significantly different from carbohydrate counting. Participant views of OptimAAPP indicate a high level of acceptability. Increasing the size of the food database will likely enhance the user experience. [ABSTRACT FROM AUTHOR]