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      Assessing an Internet-Delivered, Emotion-Focused Intervention Compared With a Healthy Lifestyle Active Control Intervention in Improving Mental Health in Cancer Survivors: Protocol for a Randomized Controlled Trial


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          Cancer survivors are vulnerable to experiencing symptoms of anxiety and depression and may benefit from accessible interventions focused on improving emotion regulation. CanCope Mind (CM) was developed as an internet-delivered intervention adapted from the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders to improve emotion regulation and support the mental health of cancer survivors.


          This protocol aims to provide an outline of the CanCope Study, a trial comparing the efficacy of a Unified Protocol–adapted internet-delivered intervention (CM) designed for cancer survivors compared with an active control condition—an internet-delivered healthy lifestyle intervention, CanCope Lifestyle (CL). The primary aim is to assess and compare the efficacy of both interventions in improving emotion regulation, anxiety and depressive symptoms, and quality of life. The secondary aims involve assessing the mechanisms of the CM intervention.


          This trial is a 2-arm randomized controlled trial that allocates cancer survivors to either CM or CL. Both interventions comprise 4 web-based modules and are expected to take participants at least 8 weeks to complete. Participants’ mental and physical health will be assessed via self-reported surveys at baseline (T 0), between each module (T 1, T 2, and T 3), immediately after the intervention (T 4), and at 3-month follow-up (T 5). The study aims to recruit 110 participants who have completed T 4.


          The CanCope study began recruitment in September 2020. A total of 224 participants have been randomized to the CM (n =110, 49.1%) and CL (n=114, 50.9%) groups.


          This is one of the first trials to develop and investigate the efficacy of a web-based intervention for cancer survivors that specifically targets emotion regulation.

          Trial Registration

          Australian Clinical Trials ACTRN12620000943943; https://tinyurl.com/b3z9cjsp

          International Registered Report Identifier (IRRID)


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          Most cited references74

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

            Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
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              Multiple imputation of discrete and continuous data by fully conditional specification.

              The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data. Two approaches for imputing multivariate data exist: joint modeling (JM) and fully conditional specification (FCS). JM is based on parametric statistical theory, and leads to imputation procedures whose statistical properties are known. JM is theoretically sound, but the joint model may lack flexibility needed to represent typical data features, potentially leading to bias. FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable. FCS provides tremendous flexibility and is easy to apply, but its statistical properties are difficult to establish. Simulation work shows that FCS behaves very well in the cases studied. The present paper reviews and compares the approaches. JM and FCS were applied to pubertal development data of 3801 Dutch girls that had missing data on menarche (two categories), breast development (five categories) and pubic hair development (six stages). Imputations for these data were created under two models: a multivariate normal model with rounding and a conditionally specified discrete model. The JM approach introduced biases in the reference curves, whereas FCS did not. The paper concludes that FCS is a useful and easily applied flexible alternative to JM when no convenient and realistic joint distribution can be specified.

                Author and article information

                JMIR Res Protoc
                JMIR Res Protoc
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                July 2022
                27 July 2022
                : 11
                : 7
                : e36658
                [1 ] School of Psychological Sciences and Turner Institute for Brain and Mental Health Monash University Melbourne Australia
                [2 ] Biostatistics Center Massachusetts General Hospital Boston, MA United States
                [3 ] Department of Psychiatry College of Medicine University of Arizona Tucson, AZ United States
                [4 ] Peter MacCallum Cancer Centre Melbourne Australia
                Author notes
                Corresponding Author: Joshua F Wiley joshua.wiley@ 123456monash.edu
                Author information
                ©Isabelle S Smith, Rebecca Wallace, Cornelia Wellecke, Marie-Abèle Bind, Karen L Weihs, Bei Bei, Joshua F Wiley. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 27.07.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                : 6 February 2022
                : 21 June 2022
                : 26 June 2022
                : 27 June 2022

                cancer survivor,depressive symptoms,anxiety symptoms,emotion regulation,unified protocol,transdiagnostic,internet-delivered intervention,quality of life,ehealth,randomized controlled trial,psycho-oncology,mobile phone


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