Generalised partial linear single-index mixed models for repeated measures data

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

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Clinical characterization of dysautonomia in long COVID-19 patients

Scientific Reports ◽

10.1038/s41598-021-93546-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nicolas Barizien ◽

Morgan Le Guen ◽

Stéphanie Russel ◽

Pauline Touche ◽

Florent Huang ◽

...

Keyword(s):

Mixed Models ◽

Repeated Measures ◽

Physical Medicine ◽

Persistent Symptoms ◽

Index Changes ◽

Changes Over Time ◽

And Control ◽

Testing Group ◽

Over Time

AbstractIncreasing numbers of COVID-19 patients, continue to experience symptoms months after recovering from mild cases of COVID-19. Amongst these symptoms, several are related to neurological manifestations, including fatigue, anosmia, hypogeusia, headaches and hypoxia. However, the involvement of the autonomic nervous system, expressed by a dysautonomia, which can aggregate all these neurological symptoms has not been prominently reported. Here, we hypothesize that dysautonomia, could occur in secondary COVID-19 infection, also referred to as “long COVID” infection. 39 participants were included from December 2020 to January 2021 for assessment by the Department of physical medicine to enhance their physical capabilities: 12 participants with COVID-19 diagnosis and fatigue, 15 participants with COVID-19 diagnosis without fatigue and 12 control participants without COVID-19 diagnosis and without fatigue. Heart rate variability (HRV) during a change in position is commonly measured to diagnose autonomic dysregulation. In this cohort, to reflect HRV, parasympathetic/sympathetic balance was estimated using the NOL index, a multiparameter artificial intelligence-driven index calculated from extracted physiological signals by the PMD-200 pain monitoring system. Repeated-measures mixed-models testing group effect were performed to analyze NOL index changes over time between groups. A significant NOL index dissociation over time between long COVID-19 participants with fatigue and control participants was observed (p = 0.046). A trend towards significant NOL index dissociation over time was observed between long COVID-19 participants without fatigue and control participants (p = 0.109). No difference over time was observed between the two groups of long COVID-19 participants (p = 0.904). Long COVID-19 participants with fatigue may exhibit a dysautonomia characterized by dysregulation of the HRV, that is reflected by the NOL index measurements, compared to control participants. Dysautonomia may explain the persistent symptoms observed in long COVID-19 patients, such as fatigue and hypoxia. Trial registration: The study was approved by the Foch IRB: IRB00012437 (Approval Number: 20-12-02) on December 16, 2020.

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Changes in multiple sclerosis symptoms are associated with changes in work productivity of people living with multiple sclerosis

Multiple Sclerosis Journal ◽

10.1177/1352458521994557 ◽

2021 ◽

pp. 135245852199455

Author(s):

Barnabas Bessing ◽

Mohammad A Hussain ◽

Suzi B Claflin ◽

Jing Chen ◽

Leigh Blizzard ◽

...

Keyword(s):

Multiple Sclerosis ◽

Mixed Models ◽

Repeated Measures ◽

Work Productivity ◽

Annual Change ◽

Individual Variations ◽

Unit Increase ◽

The Mean ◽

Employment Rates ◽

Study Participants

Background: While employment rates have increased in people with multiple sclerosis (PwMS), little is known about the longitudinal trends of work productivity. Objective: To describe the longitudinal patterns of work productivity and examine the factors associated with annual change of work productivity of PwMS. Methods: Study participants were employed participants of the Australian MS Longitudinal Study (AMSLS) followed from 2015 to 2019 with at least two repeated measures ( n = 2121). We used linear mixed models to examine if the within-individual variations in MS symptoms are associated with changes in work productivity. Results: The mean annual change in work productivity between 2015 and 2019 was −0.23% ( SD = 18.68%). Not the actual severity of symptoms but rather the changes in severity of symptoms that are associated with change in work productivity in the same year. In a multivariable model, every unit increase in mean annual change in ‘pain and sensory symptoms’, ‘feelings of anxiety and depression’, and ‘fatigue and cognitive symptoms’ were independently associated with 2.43%, 1.55% and 1.01% annual reductions in work productivity, respectively. Conclusion: Individual changes in work productivity are largely driven by the changes in symptom severity rather than the absolute severity. Stabilising/improving MS symptoms might improve work productivity.

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