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Spousal cancer caregivers' emotional and relational health can become compromised over time due to ongoing challenges related to the cancer experience. This warrants a call for the assessment of interventions aimed at improving aspects of caregiver well-being. The current study employed a randomized controlled trial to determine whether emotional disclosure via the use of expressive writing improved spousal cancer caregivers' perceived caregiver burden, stress, and depression. Participants (N = 64) were assigned to one of the two disclosure conditions: expressive disclosure or benefit finding-or to a time-management control condition. Participants completed three at-home writing sessions at one-week intervals. Results indicated that written forms of emotional disclosure might improve burden, stress, and depression contingent on writing condition. Specifically, both forms of emotional disclosure outperformed the control condition in reducing caregivers' depression. The control condition outperformed treatments in reducing caregiver stress. Finally, posttest caregiver burden was significantly lower than pretest burden across all writing conditions. This trial was registered with clinicaltrials.gov, ID: NCT02339870.

Background Screening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus. MethodsIn this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlationbased feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals.Findings The BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlationbased feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84-0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83-0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74-0·84; sensitivity set at 90%; specificity of 58%). Interpretation Our diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients withsymptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care.

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