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Malnutrition affects up to 80% of patients with head and neck cancer (HNC) and is associated with higher burden of disease, poorer treatment outcomes, and greater mortality. The Eating As Treatment (EAT) intervention is a behavioral intervention previously demonstrated to be effective in improving nutritional status, depression, and quality of life in patients with HNC. This article examines the effects of the EAT intervention on 5-year mortality among participants. A multicenter, stepped-wedge, randomized controlled trial was conducted in 5 Australian hospitals. Dietitians were trained to deliver EAT, a combination of motivational interviewing and cognitive behavior therapy strategies, to patients with HNC receiving radiation therapy. Secondary analyses of survival benefit on an intention-to-treat basis were performed. Differences in proportions of 5-year all-cause mortality between the control and EAT intervention arms were analyzed using multivariable logistic regression, and 5-year survival rates were analyzed using Cox proportional hazards regression. Analyses controlled for temporal effects (study duration), hospital site (clustering), and baseline nutritional status differences. Overall, there were 64 deaths in the 5 years after enrollment, 36 (24%) among those assigned to the control condition and 28 (18%) among those assigned to EAT. Logistic regression showed statistically significant reduced odds in favor of EAT (odds ratio, 0.33; 95% CI, 0.11-0.96), with an absolute risk reduction of 17% (95% CI, 0.01-0.33) and a relative risk reduction of 55% (95% CI, 0.22-0.92), resulting in a number needed to treat of 6 (95% CI, 4-13). Survival analysis revealed that risk of death was significantly reduced by the EAT intervention (hazard ratio, 0.39; 0.16-0.96). Participation in EAT provided a statistically and clinically meaningful survival benefit, likely via improved nutrition during radiation therapy. This survival benefit strengthens the finding of the main trial, showing that a behavioral intervention focused on nutrition could improve HNC outcomes. Replication studies using stepped-wedge designs for implementation into clinical practice may be warranted. [ABSTRACT FROM AUTHOR]

ObjectiveSmoke-free policies have been introduced in inpatient psychiatric facilities in most developed nations. Such a period of supported abstinence during hospitalization may impact smoking behaviours post discharge, yet little quantitative evidence exists. The aim of this review was to provide the first synthesis of the research evidence examining the impact of a smoke-free psychiatric hospitalization on patients' smoking-related behaviours, motivation, and beliefs.MethodsWe conducted a systematic review of electronic databases PubMed, MEDLINE, PsycINFO, and EMBASE from inception to June 2013. Studies were included if they were conducted in an inpatient psychiatric facility with a smoke-free policy and if they examined any change in patients' smoking-related behaviours, motivation, or beliefs either during admission, post discharge, or both. Risk of bias was assessed using the Cochrane Collaboration Risk of Bias Tool.ResultsFourteen studies were included in the review. Of the four studies that assessed change in smoking from admission to post discharge, two indicated a significant decline in cigarette consumption up to 3 months post discharge. Positive changes in motivation to quit and beliefs about quitting ability were identified in two studies. One study reported an increase in the rate of quit attempts and one reported a decline in nicotine dependence levels.ConclusionsA smoke-free psychiatric hospitalization may have a positive impact on patients' smoking-related behaviours, motivation, and beliefs, both during admission and up to 3 months post discharge. Further controlled studies with more rigorous designs are required to confirm this potential.

BackgroundMental health and alcohol use problems are among the most common causes of disease burden in young Australians, frequently co-occur (comorbidity), and lead to significant lifetime burden. However, comorbidities remain significantly underdetected and undertreated in health settings. Digital mental health tools designed to identify at-risk individuals, encourage help-seeking, or deliver treatment for comorbidity have the potential to address this service gap. However, despite a strong body of evidence that digital mental health programs provide an effective treatment option for a range of mental health and alcohol use problems in young adults, research shows that uptake rates can be low. Thus, it is important to understand the factors that influence treatment satisfaction and quality-of-life outcomes for young adults who access e–mental health interventions for comorbidity. ObjectiveIn this study, we seek to understand the factors that influence treatment satisfaction and quality-of-life outcomes for young adults who access e–mental health interventions for comorbid alcohol and mood disorders. The aim is to determine the importance of personality (ie, Big Five personality traits and intervention attitudes), affective factors (ie, depression, anxiety, and stress levels), and baseline alcohol consumption in predicting intervention trial engagement at sign-up, satisfaction with the online tool, and quality of life at the end of the iTreAD (Internet Treatment for Alcohol and Depression) trial. MethodsAustralian adults (N=411) aged between 18 and 30 years who screened positive for depression and alcohol use problems signed up for the iTreAD project between August 2014 and October 2015. During registration, participants provided information about their personality, current affective state, alcohol use, treatment expectations, and basic demographic information. Subsequent follow-up surveys were used to gauge the ongoing trial engagement. The last follow-up questionnaire, completed at 64 weeks, assessed participants’ satisfaction with web-based treatment and quality-of-life outcomes. ResultsMultiple linear regression analyses were used to assess the relative influence of predictor variables on trial engagement, treatment satisfaction, and quality-of-life outcomes. The analyses revealed that the overall predictive effects of personality and affective factors were 20% or lower. Neuroticism constituted a unique predictor of engagement with the iTreAD study in that neuroticism facilitated the return of web-based self-assessments during the study. The return of incentivized follow-up assessments predicted treatment satisfaction, and state-based depression predicted variance in quality-of-life reports at study completion. ConclusionsOur findings suggest that traditional predictors of engagement observed in face-to-face research may not be easily transferable to digital health interventions, particularly those aimed at comorbid mental health concerns and alcohol misuse among young adults. More research is needed to identify what determines engagement in this population to optimally design and execute digital intervention studies with multiple treatment aims. Trial RegistrationAustralian New Zealand Clinical Trials Registry (ACTRN): 12614000310662; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=365137&isReview=true. International Registered Report Identifier (IRRID)RR2-10.1186/s12889-015-2365-2

BackgroundDespite the importance and popularity of mutual support groups, there have been no systematic attempts to implement and evaluate routine outcome monitoring (ROM) in these settings. Unlike other mutual support groups for addiction, trained facilitators lead all Self-Management and Recovery Training (SMART Recovery) groups, thereby providing an opportunity to implement ROM as a routine component of SMART Recovery groups. ObjectiveThis study protocol aims to describe a stage 1 pilot study designed to explore the feasibility and acceptability of a novel, purpose-built mobile health (mHealth) ROM and feedback app (Smart Track) in SMART Recovery groups coordinated by SMART Recovery Australia (SRAU) The secondary objectives are to describe Smart Track usage patterns, explore psychometric properties of the ROM items (ie, internal reliability and convergent and divergent validity), and provide preliminary evidence for participant reported outcomes (such as alcohol and other drug use, self-reported recovery, and mental health). MethodsParticipants (n=100) from the SMART Recovery groups across New South Wales, Australia, will be recruited to a nonrandomized, prospective, single-arm trial of the Smart Track app. There are 4 modes of data collection: (1) ROM data collected from group participants via the Smart Track app, (2) data analytics summarizing user interactions with Smart Track, (3) quantitative interview and survey data of group participants (baseline, 2-week follow-up, and 2-month follow-up), and (4) qualitative interviews with group participants (n=20) and facilitators (n=10). Feasibility and acceptability (primary objectives) will be analyzed using descriptive statistics, a cost analysis, and a qualitative evaluation. ResultsAt the time of submission, 13 sites (25 groups per week) had agreed to be involved. Funding was awarded on August 14, 2017, and ethics approval was granted on April 26, 2018 (HREC/18/WGONG/34; 2018/099). Enrollment is due to commence in July 2019. Data collection is due to be finalized in October 2019. ConclusionsTo the best of our knowledge, this study is the first to use ROM and tailored feedback within a mutual support group setting for addictive behaviors. Our study design will provide an opportunity to identify the acceptability of a novel mHealth ROM and feedback app within this setting and provide detailed information on what factors promote or hinder ROM usage within this context. This project aims to offer a new tool, should Smart Track prove feasible and acceptable, that service providers, policy makers, and researchers could use in the future to understand the impact of SMART Recovery groups. Trial RegistrationAustralian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12619000686101; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377336. International Registered Report Identifier (IRRID)PRR1-10.2196/15113