scholarly journals Neuropathological Associates of Multiple Cognitive Functions in Two Community-Based Cohorts of Older Adults

2010 ◽  
Vol 17 (4) ◽  
pp. 602-614 ◽  
Author(s):  
N. Maritza Dowling ◽  
Sarah Tomaszewski Farias ◽  
Bruce R. Reed ◽  
Joshua A. Sonnen ◽  
Milton E. Strauss ◽  
...  
Keyword(s):  
Older Adults ◽  
Lewy Bodies ◽  
Lewy Body Disease ◽  
Small Sample ◽  
Cognitive Testing ◽  
Brain Weight ◽  
Cognitive Domains ◽  
Using Data ◽  
Small Sample Sizes ◽  
Independent Effects

AbstractStudies of neuropathology-cognition associations are not common and have been limited by small sample sizes, long intervals between autopsy and cognitive testing, and lack of breadth of neuropathology and cognition variables. This study examined domain-specific effects of common neuropathologies on cognition using data (N = 652) from two large cohort studies of older adults. We first identified dimensions of a battery of 17 neuropsychological tests, and regional measures of Alzheimer’s disease (AD) neuropathology. We then evaluated how cognitive factors were related to dimensions of AD and additional measures of cerebrovascular and Lewy Body disease, and also examined independent effects of brain weight. All cognitive domains had multiple neuropathology determinants that differed by domain. Neocortical neurofibrillary tangles were the strongest predictors of most domains, while medial temporal tangles showed a weaker relationship with episodic memory. Neuritic plaques had relatively strong effects on multiple domains. Lewy bodies and macroscopic infarcts were associated with all domains, while microscopic infarcts had more limited associations. Brain weight was related to all domains independent of specific neuropathologies. Results show that cognition is complexly determined by multiple disease substrates. Neuropathological variables and brain weight contributed approximately a third to half of the explained variance in different cognitive domains. (JINS, 2011, 17, 602–614).

scholarly journals BFF: Bayesian, Fiducial, and Frequentist Analysis of Cognitive Engagement among Cognitively Impaired Older Adults

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 428
Author(s):  
Shevaun D. Neupert ◽  
Claire M. Growney ◽  
Xianghe Zhu ◽  
Julia K. Sorensen ◽  
Emily L. Smith ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Engagement ◽  
Small Sample ◽  
Asymptotic Distributions ◽  
Cognitive Testing ◽  
Cognitively Impaired ◽  
Physical And Mental Health ◽  
Engagement Theory ◽  
Hair Samples ◽  
Small Sample Sizes

Engagement in cognitively demanding activities is beneficial to preserving cognitive health. Our goal was to demonstrate the utility of frequentist, Bayesian, and fiducial statistical methods for evaluating the robustness of effects in identifying factors that contribute to cognitive engagement for older adults experiencing cognitive decline. We collected a total of 504 observations across two longitudinal waves of data from 28 cognitively impaired older adults. Participants’ systolic blood pressure responsivity, an index of cognitive engagement, was continuously sampled during cognitive testing. Participants reported on physical and mental health challenges and provided hair samples to assess chronic stress at each wave. Using the three statistical paradigms, we compared results from six model testing levels and longitudinal changes in health and stress predicting changes in cognitive engagement. Findings were mostly consistent across the three paradigms, providing additional confidence in determining effects. We extend selective engagement theory to cognitive impairment, noting that health challenges and stress appear to be important moderators. Further, we emphasize the utility of the Bayesian and fiducial paradigms for use with relatively small sample sizes because they are not based on asymptotic distributions. In particular, the fiducial paradigm is a useful tool because it provides more information than p values without the need to specify prior distributions, which may unduly influence the results based on a small sample. We provide the R code used to develop and implement all models.

Confidence intervals for a difference between lognormal means in cluster randomization trials

2014 ◽  
Vol 26 (2) ◽  
pp. 598-614 ◽  
Author(s):  
Julia Poirier ◽  
GY Zou ◽  
John Koval
Keyword(s):  
Confidence Intervals ◽  
Community Acquired Pneumonia ◽  
Small Sample ◽  
Cluster Randomization ◽  
Critical Pathway ◽  
Arithmetic Means ◽  
Multiple Parameters ◽  
The Difference ◽  
Using Data ◽  
Small Sample Sizes

Cluster randomization trials, in which intact social units are randomized to different interventions, have become popular in the last 25 years. Outcomes from these trials in many cases are positively skewed, following approximately lognormal distributions. When inference is focused on the difference between treatment arm arithmetic means, existent confidence interval procedures either make restricting assumptions or are complex to implement. We approach this problem by assuming log-transformed outcomes from each treatment arm follow a one-way random effects model. The treatment arm means are functions of multiple parameters for which separate confidence intervals are readily available, suggesting that the method of variance estimates recovery may be applied to obtain closed-form confidence intervals. A simulation study showed that this simple approach performs well in small sample sizes in terms of empirical coverage, relatively balanced tail errors, and interval widths as compared to existing methods. The methods are illustrated using data arising from a cluster randomization trial investigating a critical pathway for the treatment of community acquired pneumonia.

Prevalence of tuberculosis, brucellosis and trypanosomiasis in cattle in Tanzania: a systematic review and meta-analysis

2016 ◽  
Vol 17 (1) ◽  
pp. 16-27 ◽  
Author(s):  
Silvia Alonso ◽  
Ian Dohoo ◽  
Johanna Lindahl ◽  
Cristobal Verdugo ◽  
Isaiah Akuku ◽  
...  
Keyword(s):  
Systematic Review ◽  
Production Systems ◽  
Strong Predictor ◽  
Meta Analysis ◽  
Small Sample ◽  
Skin Tests ◽  
Prevalence Studies ◽  
Prevalence Estimates ◽  
Using Data ◽  
Small Sample Sizes

AbstractA meta-analysis was performed to derive prevalence estimates for Brucella spp., Mycobacterium spp. and Trypanosoma spp. in cattle in Tanzania using data derived from a systematic review of zoonotic hazards in cattle production systems. Articles published before 2012 reporting prevalence and considered at least moderate in quality were included in the analysis. Results showed high heterogeneity between studies, with wide ranges in the reported prevalence: Brucella (0.3–60.8%), Mycobacterium (0.1–13.2%) and Trypanosoma (0.82–33.3%). Overall meta-analytic mean prevalence estimates were 8.2% (95% CI 6.5–10.2), 1.28% (95% CI 0.35–4.58) and 10.3% (95% CI 6.20–16.70) respectively, for Brucella spp., Mycobacterium spp. and Trypanosoma spp. Time and region were predictors of variability of Brucella spp. prevalence, while diagnostic test was a strong predictor of Mycobacterium spp. prevalence, with higher prevalence estimates given by skin tests compared with post-mortem inspection. None of the studied factors were associated with prevalence of Trypanosoma spp. The small sample sizes, range of study locations, study designs and diagnostics used, contributed to high variability among prevalence estimates. Larger and more robust prevalence studies are needed to adequately support risk assessment and management of animal and public health threats.

scholarly journals Exercise and Nutrition Impact on Osteoporosis and Sarcopenia—The Incidence of Osteosarcopenia: A Narrative Review

Nutrients ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 4499
Author(s):  
Sousana K. Papadopoulou ◽  
Konstantinos Papadimitriou ◽  
Gavriela Voulgaridou ◽  
Evridiki Georgaki ◽  
Eudoxia Tsotidou ◽  
...  
Keyword(s):  
Older Adults ◽  
Protective Role ◽  
Scientific Data ◽  
Bone Diseases ◽  
Small Sample ◽  
Narrative Review ◽  
Prevention Of Osteoporosis ◽  
Small Sample Sizes ◽  
Meta Analyses

Osteoporosis and sarcopenia are diseases which affect the myoskeletal system and often occur in older adults. They are characterized by low bone density and loss of muscle mass and strength, factors which reduce the quality of life and mobility. Recently, apart from pharmaceutical interventions, many studies have focused on non-pharmaceutical approaches for the prevention of osteoporosis and sarcopenia with exercise and nutrition to being the most important and well studied of those. The purpose of the current narrative review is to describe the role of exercise and nutrition on prevention of osteoporosis and sarcopenia in older adults and to define the incidence of osteosarcopenia. Most of the publications which were included in this review show that resistance and endurance exercises prevent the development of osteoporosis and sarcopenia. Furthermore, protein and vitamin D intake, as well as a healthy diet, present a protective role against the development of the above bone diseases. However, current scientific data are not sufficient for reaching solid conclusions. Although the roles of exercise and nutrition on osteoporosis and sarcopenia seem to have been largely evaluated in literature over the recent years, most of the studies which have been conducted present high heterogeneity and small sample sizes. Therefore, they cannot reach final conclusions. In addition, osteosarcopenia seems to be caused by the effects of osteoporosis and sarcopenia on elderly. Larger meta-analyses and randomized controlled trials are needed designed based on strict inclusion criteria, in order to describe the exact role of exercise and nutrition on osteoporosis and sarcopenia.

Effectiveness of Outdoor Exercise Parks on Health Outcomes in Older Adults—A Mixed-Methods Systematic Review and Meta-Analysis

2020 ◽  
pp. 1-13
Author(s):  
Yoke Leng Ng ◽  
Keith D. Hill ◽  
Pazit Levinger ◽  
Elissa Burton
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Older Adults ◽  
Meta Analysis ◽  
Small Sample ◽  
Activity Levels ◽  
Single Leg Stance ◽  
Small Sample Sizes ◽  
Meta Analyses

The objective of this systematic review was to examine the effectiveness of outdoor exercise park equipment on physical activity levels, physical function, psychosocial outcomes, and quality of life of older adults living in the community and to evaluate the evidence of older adults’ use of outdoor exercise park equipment. A search strategy was conducted from seven databases. Nine articles met the inclusion criteria. The study quality results were varied. Meta-analyses were undertaken for two physical performance tests: 30-s chair stand test and single-leg stance. The meta-analysis results were not statistically significant. It was not possible to conclude whether exercise parks were effective at improving levels of physical activity. The review shows that older adults value the benefits of health and social interaction from the use of exercise parks. Findings should be interpreted with caution due to the small sample sizes and the limited number of studies.

scholarly journals The Effectiveness of Combining Visual Art Activities and Physical Exercise for Older Adults on Well-Being or Quality of Life and Mood: A Scoping Review

2017 ◽  
Vol 38 (12) ◽  
pp. 1784-1804 ◽  
Author(s):  
Roswiyani Roswiyani ◽  
Linda Kwakkenbos ◽  
Jan Spijker ◽  
Cilia L.M. Witteman
Keyword(s):  
Quality Of Life ◽  
Older Adults ◽  
Physical Exercise ◽  
Visual Art ◽  
Low Cost ◽  
Well Being ◽  
Small Sample ◽  
Art Activities ◽  
Small Sample Sizes

Visual art activities and physical exercise are both low-intensity and low-cost interventions. The present study aims to comprehensively describe published literature on the effectiveness of a combination of these interventions on well-being or quality of life (QoL) and mood of older adults. Embase, CINAHL, Ovid Medline (R), PsycINFO, and Web of Science databases were searched for studies published between 1990 and 2015 that evaluated interventions combining visual art therapy and exercise for people aged 50 years or older with at least one resultant well-being or QoL or mood outcome. We found 10 studies utilizing different combination programs and outcome measures, and most had small sample sizes. Seventy percent of the studies reported that combining both interventions was effective in improving well-being or QoL and mood in older adults. Future studies are, however, requisite to investigate whether in the respective population such a combination is more effective than either of the interventions alone.

scholarly journals Returning to the Fight: Addressing the Drivers and Likelihood of Terrorist Disengagement and Re-Engagement

Proceedings ◽  
2021 ◽  
Vol 77 (1) ◽  
pp. 19
Author(s):  
Mary Beth Altier
Keyword(s):  
Small Sample ◽  
Sunk Costs ◽  
Investment Model ◽  
Terrorist Group ◽  
Pull Factors ◽  
Role Exit ◽  
Terrorist Groups ◽  
Traditional Work ◽  
Using Data ◽  
Small Sample Sizes

Recent interest in terrorist risk assessment and rehabilitation reveals that the likelihood and risk factors for terrorist disengagement, re-engagement, and recidivism are poorly understood. In this presentation, I review related literature on criminal desistance, disaffiliation from new religious movements, commitment, and turnover in traditional work organizations, role exit, and the investment model to develop a series of theoretical starting points for gauging the likelihood and predictors of risk, which can help inform evaluation efforts. I then highlight key findings from the existing literature on terrorist disengagement and re-engagement/recidivism as well as key differences across samples and the methodological challenges associated with such research—mainly the absence of control groups, relatively small sample sizes, the need for a lengthy time horizon, and inconsistencies in what constitutes re-engagement and recidivism. Then, using data collected on 185 terrorist engagement events for 85 individuals representing over 70 unique terrorist groups, I present my and my colleagues’ findings on the drivers of terrorist disengagement and re-engagement. We find that terrorist disengagement is a lengthy process more commonly driven by “push” rather than “pull” factors, specifically disillusionment with the strategy or actions of the terrorist group, disillusionment with leaders or other members, disillusionment with one’s day-to-day tasks, burnout, difficulty living a clandestine lifestyle, difficulty coping with attacks, and psychological distress. Importantly, “de-radicalization” is only cited as playing a “large role” in just 16% of disengagement events in our sample. I then discuss how one’s role within a terrorist group offers insight into the disengagement process. Our research shows that leaders and violent operatives have a harder time disengaging than those in logistical or support roles because of the sunk costs associated with their involvement and/or the fewer opportunities available to them. We also find that individuals in certain roles are more/less likely to experience certain push/pull factors for disengagement. I conclude by discussing our research on terrorist re-engagement, which shows that in the short term, a deep commitment to the ideology, maintaining ties to individuals still involved in terrorism, and being young increase the likelihood one will return to terrorism.

scholarly journals What Do We Know about The Use of Virtual Reality in the Rehabilitation Field? A Brief Overview

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1042
Author(s):  
Antonino Naro ◽  
Rocco Salvatore Calabrò
Keyword(s):  
Virtual Reality ◽  
Low Cost ◽  
Assessment Method ◽  
Small Sample ◽  
Cognitive Domains ◽  
Rehabilitation Outcomes ◽  
The Past ◽  
Pros And Cons ◽  
Small Sample Sizes ◽  
Near Future

Over the past two decades, virtual reality technology (VRT)-based rehabilitation has been increasingly examined and applied to assist patient recovery in the physical and cognitive domains. The advantages of the use of VRT in the neurorehabilitation field consist of the possibility of training an impaired function as a way to stimulate neuron reorganization (to maximize motor learning and neuroplasticity) and restoring and regaining functions and abilities by interacting with a safe and nonthreatening yet realistic virtual reality environment (VRE). Furthermore, VREs can be tailored to patient needs and provide personalized feedback on performance. VREs may also support cognitive training and increases patient motivation and enjoyment. Despite these potential advantages, there are inconclusive data about the usefulness of VRT in neurorehabilitation settings, and some issues on feasibility and safety remain to be ascertained for some neurological populations. The present brief overview aims to summarize the available literature on VRT applications in neurorehabilitation settings, along with discussing the pros and cons of VR and introducing the practical issues for research. The available studies on VRT for rehabilitation purposes over the past two decades have been mostly preliminary and feature small sample sizes. Furthermore, the studies dealing with VRT as an assessment method are more numerous than those harnessing VRT as a training method; however, the reviewed studies show the great potential of VRT in rehabilitation. A broad application of VRT is foreseeable in the near future due to the increasing availability of low-cost VR devices and the possibility of personalizing VR settings and the use of VR at home, thus actively contributing to reducing healthcare costs and improving rehabilitation outcomes.

scholarly journals Characterizing the Urban Mine—Simulation-Based Optimization of Sampling Approaches for Built-in Batteries in WEEE

Recycling ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 19
Author(s):  
Paul Martin Mählitz ◽  
Nathalie Korf ◽  
Kristine Sperlich ◽  
Olivier Münch ◽  
Matthias Rösslein ◽  
...  
Keyword(s):  
Small Sample ◽  
Data Sets ◽  
Distributed Data ◽  
Sample Sizes ◽  
Representative Sampling ◽  
Comprehensive Knowledge ◽  
Simulation Based ◽  
Using Data ◽  
Small Sample Sizes ◽  
Almost All

Comprehensive knowledge of built-in batteries in waste electrical and electronic equipment (WEEE) is required for sound and save WEEE management. However, representative sampling is challenging due to the constantly changing composition of WEEE flows and battery systems. Necessary knowledge, such as methodologically uniform procedures and recommendations for the determination of minimum sample sizes (MSS) for representative results, is missing. The direct consequences are increased sampling efforts, lack of quality-assured data, gaps in the monitoring of battery losses in complementary flows, and impeded quality control of depollution during WEEE treatment. In this study, we provide detailed data sets on built-in batteries in WEEE and propose a non-parametric approach (NPA) to determine MSS. For the pilot dataset, more than 23 Mg WEEE (6500 devices) were sampled, examined for built-in batteries, and classified according to product-specific keys (UNUkeys and BATTkeys). The results show that 21% of the devices had battery compartments, distributed over almost all UNUkeys considered and that only about every third battery was removed prior to treatment. Moreover, the characterization of battery masses (BM) and battery mass shares (BMS) using descriptive statistical analysis showed that neither product- nor battery-specific characteristics are given and that the assumption of (log-)normally distributed data is not generally applicable. Consequently, parametric approaches (PA) to determine the MSS for representative sampling are prone to be biased. The presented NPA for MSS using data-driven simulation (bootstrapping) shows its applicability despite small sample sizes and inconclusive data distribution. If consistently applied, the method presented can be used to optimize future sampling and thus reduce sampling costs and efforts while increasing data quality.

scholarly journals MISSING VALUE IMPUTATION VIA GRAPH COMPLETION IN QUESTIONNAIRE SCORES FROM PERSONS WITH DEMENTIA

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S972-S972
Author(s):  
Chen Kan ◽  
Won Hwa Kim ◽  
Ling Xu ◽  
Noelle L Fields
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Small Sample Size ◽  
Small Sample ◽  
Small Scale ◽  
Assessment And Treatment ◽  
Using Data ◽  
Small Sample Sizes ◽  
Clinical Conditions

Abstract Background: Questionnaires are widely used to evaluate cognitive functions, depression, and loneliness of persons with dementia (PWDs). Successful assessment and treatment of dementia hinge on effective analysis of PWDs’ answers. However, many studies, especially pilot ones, are with small sample sizes. Further, most of them contain missing data as PWDs skip some study sessions due to their clinical conditions. Conventional imputation strategies are not well-suited as bias will be introduced because of insufficient samples. Method: A novel machine learning framework was developed based on harmonic analysis on graphs to robustly handle missing values. Participants were first embedded as nodes in the graph with edges derived by their similarities based on demographic information, activities of daily living, etc. Then, questionnaire scores with missing values were regarded as a function on the nodes, and they were estimated based on spectral analysis of the graph with a smoothness constraint. The proposed approach was evaluated using data from our pilot study of dementia subjects (N=15) with 15% data missing. Result: A few complete variables (binary or ordinal) were available for all participants. For each variable, we randomly removed 5 scores to mimic missing values. With our approach, we could recover all missing values with 90% accuracy on average. We were also able to impute the actual missing values in the dataset within reasonable ranges. Conclusion: Our proposed approach imputes missing values with high accuracy despite the small sample size. The proposed approach will significantly boost statistical power of various small-scale studies with missing data.

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