This study analyzes the robustness of the linear mixed model (LMM) with the Kenward–Roger (KR) procedure to violations of normality and sphericity when used in split-plot designs with small sample ...sizes. Specifically, it explores the independent effect of skewness and kurtosis on KR robustness for the values of skewness and kurtosis coefficients that are most frequently found in psychological and educational research data. To this end, a Monte Carlo simulation study was designed, considering a split-plot design with three levels of the between-subjects grouping factor and four levels of the within-subjects factor. Robustness is assessed in terms of the probability of type I error. The results showed that (1) the robustness of the KR procedure does not differ as a function of the violation or satisfaction of the sphericity assumption when small samples are used; (2) the LMM with KR can be a good option for analyzing total sample sizes of 45 or larger when their distributions are normal, slightly or moderately skewed, and with different degrees of kurtosis violation; (3) the effect of skewness on the robustness of the LMM with KR is greater than the corresponding effect of kurtosis for common values; and (4) when data are not normal and the total sample size is 30, the procedure is not robust. Alternative analyses should be performed when the total sample size is 30.
The aim of this study was to examine changes in sexist attitudes and beliefs in a group of Spanish adolescents over a period of three consecutive years, with specific attention being paid to gender ...differences. Participants were 279 students (mean age at first assessment of 12.10 years) who, in each of the three years, completed the Ambivalent Sexism Inventory and the Questionnaire on Attitudes towards Diversity and Violence. Longitudinal analysis showed that hostile sexism did not vary over time, whereas scores on benevolent sexism and on sexist beliefs and justification of violence all fell between the ages of 12 and 14, there being an equivalent decrease in boys and girls. Boys scored significantly higher than girls on hostile sexism, as well as on sexist beliefs. These results illustrate how sexist attitudes and beliefs change during adolescence and provide further confirmation that these variables show gender differences from an early age.
Using a Monte Carlo simulation and the Kenward–Roger (KR) correction for degrees of freedom, in this article we analyzed the application of the linear mixed model (LMM) to a mixed repeated measures ...design. The LMM was first used to select the covariance structure with three types of data distribution: normal, exponential, and log-normal. This showed that, with homogeneous between-groups covariance and when the distribution was normal, the covariance structure with the best fit was the unstructured population matrix. However, with heterogeneous between-groups covariance and when the pairing between covariance matrices and group sizes was null, the best fit was shown by the between-subjects heterogeneous unstructured population matrix, which was the case for all of the distributions analyzed. By contrast, with positive or negative pairings, the within-subjects and between-subjects heterogeneous first-order autoregressive structure produced the best fit. In the second stage of the study, the robustness of the LMM was tested. This showed that the KR method provided adequate control of Type I error rates for the time effect with normally distributed data. However, as skewness increased—as occurs, for example, in the log-normal distribution—the robustness of KR was null, especially when the assumption of sphericity was violated. As regards the influence of kurtosis, the analysis showed that the degree of robustness increased in line with the amount of kurtosis.
Objectives: We explore the association between different patterns of change in depressive symptoms and memory trajectories in US and European Mediterranean (Spain, France, Italy, and Israel) and ...non-Mediterranean (Sweden, Denmark, Netherlands, Germany, Belgium, Switzerland, and Austria) older adults. Methods: Samples consisted of 3,466 participants from the Health Retirement Study (HRS) and 3,940 participants from the Survey of Health, Aging and Retirement (SHARE). Individuals were grouped as follows: non-case depression (NO DEP), persistent depression (DEP), depression onset (ONSET), depression recovery (RECOV), and fluctuating (FLUCT). Memory was measured using immediate and delayed recall tests. Linear mixed models were used. Results: DEP and RECOV had significantly lower baseline memory scores compared to NO DEP, at intercept level. At slope level, ONSET had a significantly faster decline in both tasks compared to NO DEP. Discussion: Cross-cohort robust and consistent new empirical evidence on the association between depression onset and faster decline in memory scores is provided.
Objective:
We assessed the feasibility and validity of remote researcher-led administration and self-administration of modified versions of two cognitive tasks sensitive to ADHD, a four-choice ...reaction time task (Fast task) and a combined Continuous Performance Test/Go No-Go task (CPT/GNG), through a new remote measurement technology system.
Method:
We compared the cognitive performance measures (mean and variability of reaction times (MRT, RTV), omission errors (OE) and commission errors (CE)) at a remote baseline researcher-led administration and three remote self-administration sessions between participants with and without ADHD (n = 40).
Results:
The most consistent group differences were found for RTV, MRT and CE at the baseline researcher-led administration and the first self-administration, with 8 of the 10 comparisons statistically significant and all comparisons indicating medium to large effect sizes.
Conclusion:
Remote administration of cognitive tasks successfully captured the difficulties with response inhibition and regulation of attention, supporting the feasibility and validity of remote assessments.
Abstract
Research to date on multimorbidity and cognitive impairment is mainly cross-sectional or with limited history information of the health conditions. The present study explores the association ...between cognitive performance and previous history of health conditions over 24 years in a sample of 4858 respondents of the Health Retirement Study. Data from health conditions between 1998 and 2014 included self-reports for hypertension, diabetes, arthritis, stroke, cancer, lung and heart diseases and psychiatric problems. Duration of the health condition was categorized as more than 10 years, between 4 and 10 years, less than 4 years and no condition. Cognition was assessed using a summary index of cognitive performance including measures of memory, working memory, speed processing, knowledge, and language. ANOVA and post hoc tests were performed to explore the association between cognition and the duration of each health condition independently. Multiple linear regression analyses were performed to explore the association between multiple health conditions and cognitive performance. Results showed significant independent associations between cognitive performance in 2014 and each health condition, except for cancer F(1,4)=2.60; p =.51. When all the health conditions were considered in the regression models, we found that cognitive performance is negatively associated with high blood pressure and stroke (independently of the duration of the condition), long-term diabetes and lung diseases (i.e., for more than 10 years) and recent cancer (i.e., in the last 4 years). Our findings highlight that considering duration of co-existent health conditions is key for identifying individuals at greater risk of cognitive impairment.
Abstract
Polypharmacy is associated with increased health care costs and adverse health outcomes. Traditional research on polypharmacy uses dichotomous measures which overlook its multidimensional ...nature. We propose a new approach to grouping older adults based on the number and type of medications taken as well as other indicators of polypharmacy. Data was extracted from 1328 respondents of the 2007 Prescription Drug Survey (a sub-study of the Health Retirement Study) who were between 50 and 70 years old and taking ≥1 medication each month. Latent class analysis was carried out with the optimal number of classes assessed based on relative model fit (AIC, adjusted BIC) and interpretability. Latent classes were formed based on the number of medications, drug types, duration of medication intake, side effects, and presence of chronic health conditions. A four-class model was selected based on model fit and interpretability of the solutions. Although there was some overlap when we compared our model with standard cut-offs for polypharmacy (i.e., ‘high polypharmacy’ classes were more likely to take 5+ and 9+ medications), chi-square tests showed significant differences between our latent classes and cut-offs based on 5+ X2 = 894; p<0.001 and 9+ medications X2 = 398; p<0.001. Among individuals taking <5 medications, our model differentiated two distinct types of ‘low polypharmacy’ based on the types of drugs reported. Our proposal to incorporate a multidimensional assessment of polypharmacy considers the wider context of medication use and chronic health in older age, moving beyond crude medication counts.
Student satisfaction with podcasts is frequently used as an indicator of the effectiveness of educational podcasting. This aspect has usually been assessed through surveys or interviews in ...descriptive studies, but no standard questionnaire exists that can be used to compare results. The main aim of this study was to present the Student Satisfaction with Educational Podcasts Questionnaire (SSEPQ). The SSEPQ consists of 10 items that are scored on a Likert-type scale. The items address the opinions of students on content adequacy, ease of use, usefulness, and the benefits of podcasts for learning. 3-5 minute podcasts were assessed as a supplementary tool within the research methods and statistics course of their psychology undergraduate degree. Confirmatory factor analysis with cross-validation showed a one-factor structure, supporting the use of the total score as a global index of student satisfaction with podcasts. The results suggest that there is a high level of satisfaction with podcasts as a tool to improve learning. The questionnaire is a brief and simple tool that can provide lecturers with direct feedback from their students, and may prove useful in improving the teaching-learning process.
Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, ...the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings.
The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8).
Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature.
We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires.
We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.
An electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. ...Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments).
Foresight is a novel transformer-based pipeline that uses named entity recognition and linking tools to convert EHR document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events, such as disorders, substances, procedures, and findings. The Foresight pipeline has four main components: (1) CogStack (data retrieval and preprocessing); (2) the Medical Concept Annotation Toolkit (structuring of the free-text information from EHRs); (3) Foresight Core (deep-learning model for biomedical concept modelling); and (4) the Foresight web application. We processed the entire free-text portion from three different hospital datasets (King's College Hospital KCH, South London and Maudsley SLaM, and the US Medical Information Mart for Intensive Care III MIMIC-III), resulting in information from 811 336 patients and covering both physical and mental health institutions. We measured the performance of models using custom metrics derived from precision and recall.
Foresight achieved a precision@10 (ie, of 10 forecasted candidates, at least one is correct) of 0·68 (SD 0·0027) for the KCH dataset, 0·76 (0·0032) for the SLaM dataset, and 0·88 (0·0018) for the MIMIC-III dataset, for forecasting the next new disorder in a patient timeline. Foresight also achieved a precision@10 value of 0·80 (0·0013) for the KCH dataset, 0·81 (0·0026) for the SLaM dataset, and 0·91 (0·0011) for the MIMIC-III dataset, for forecasting the next new biomedical concept. In addition, Foresight was validated on 34 synthetic patient timelines by five clinicians and achieved a relevancy of 33 (97% 95% CI 91–100) of 34 for the top forecasted candidate disorder. As a generative model, Foresight can forecast follow-on biomedical concepts for as many steps as required.
Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes.
National Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK.
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