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Large-scale analysis of test-retest reliabilities of self-regulation measures.

ABSTRACT: The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (i) providing a comprehensive literature review on an extensive set of self-regulation measures and (ii) empirically evaluating test-retest reliability of this battery in a new sample. We find that dependent variables (DVs) from self-report surveys of self-regulation have high test-retest reliability, while DVs derived from behavioral tasks do not. This holds both in the literature and in our sample, although the test-retest reliability estimates in the literature are highly variable. We confirm that this is due to differences in between-subject variability. We also compare different types of task DVs (e.g., model parameters vs. raw response times) in their suitability as individual difference DVs, finding that certain model parameters are as stable as raw DVs. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.

SUBMITTER: Enkavi AZ

PROVIDER: S-EPMC6431228 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Large-scale analysis of test-retest reliabilities of self-regulation measures.

Enkavi A Zeynep AZ Eisenberg Ian W IW Bissett Patrick G PG Mazza Gina L GL MacKinnon David P DP Marsch Lisa A LA Poldrack Russell A RA

Proceedings of the National Academy of Sciences of the United States of America 20190306 12

The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measuremen ...[more]

PMID: 30842284

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Project description:BackgroundBack pain negatively impacts a person's quality of life and can cause major disability or even death. The measurement of spinal stiffness can be utilized as a promising tool to guide therapeutic decisions regarding physical therapy that result in effective back pain management. This study aimed to determine the reliability of instrumented postero-anterior (PA) stiffness assessment of the lumbar spine in asymptomatic participants by novice assessors using a portable algometer and the Kinovea program.MethodsThirty asymptomatic participants aged 18-25 years were enrolled in this study. Two novice assessors examined the participants for lumbar spinal stiffness at L1-L5 for two consecutive days. The algometer was applied to measure the PA force that applied to each lumbar. The stiffness assessment of each lumbar region was recorded as a video. The 600 data sets of assessment videos were imported into the Kinovea program to perform displacement measurements of each lumbar level. Spinal displacement values at 15 N were defined by graph plotting between force and displacement. The spinal stiffness values were defined by slope calculation. Both variables were analyzed for inter-rater and test-retest reliabilities using intra-class correlation coefficients (ICCs) and standard error of measurement (SEM). Bland-Altman analysis was applied to assess the inter-rater and test-retest systematical bias and limits of agreement of measuring displacement and stiffness.ResultsThe inter-rater reliability of measuring the displacement and the stiffness of L1-L5 was moderate to good (displacement ICCs: 0.67-0.83, stiffness ICCs: 0.60-0.83). The test-retest reliability of measuring the displacement and stiffness of L1-L5 were moderate to good, ICCs: 0.57-0.86 and ICCs: 0.51-0.88, respectively. The inter-rater analysis's Bland-Altman plot showed that the systematic bias was 0.83 when measuring displacement and 0.20 when measuring stiffness and the bias of both parameters were in both directions. While the test-retest systematically biased measurements of displacement and stiffness were -0.26 mm and 0.22 N/mm, respectively, and the bias of both parameters were in both directions.ConclusionsThe moderate-to-good inter-rater and test-retest reliabilities of the portable instrumented spinal stiffness assessment using a digital algometer and the Kinovea program by novice assessors were demonstrated in this study. Bland-Altman analysis showed that measuring stiffness was more stable and had less systematic bias than measuring displacement. To figure out how reliable the device is in general, more comprehensive studies should be comparatively conducted in the future on subgroups of patients with normal vertebra, hypomobile or hypermobile conditions.

Project description:Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient???0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet???0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.

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Large-scale analysis of test-retest reliabilities of self-regulation measures.

Publications

Large-scale analysis of test-retest reliabilities of self-regulation measures.

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