Impact of Self-Efficacy and Affective Functioning on Pediatric Concussion Symptom Severity

2021 ◽  
pp. 1-8
Author(s):  
Kesley A. Ramsey ◽  
Christopher Vaughan ◽  
Barry M. Wagner ◽  
Joseph F. McGuire ◽  
Gerard A. Gioia
Keyword(s):  
Symptom Severity ◽  
Self Efficacy ◽  
Regression Models ◽  
Depression Symptoms ◽  
Linear Regression Models ◽  
Pediatric Concussion ◽  
Affective Functioning ◽  
Initial Support ◽  
Concussion Recovery ◽  
The Relationship

Abstract Objective: The purpose of this study was to examine whether self-efficacy predicted pediatric concussion symptom severity and explore whether affective mood states (e.g., depression) influenced this relationship. Method: Children (8–17 years) who were diagnosed with a concussion within 30 days of injury participated in the study (n = 105). Following a clinical assessment, participants and caregivers completed questionnaires that assessed overall concussion symptom severity and current depression symptoms. Participants also completed ratings capturing self-efficacy for managing concussion recovery. Results: Linear regression models revealed that greater levels of self-efficacy predicted lower parent- (R2 = 0.10, p = .001) and youth-rated (R2 = 0.23, p < .001) concussion symptom severity. Interestingly, depression symptoms moderated the relationship between self-efficacy and concussion symptom severity. Conclusions: Findings provide initial support for a relationship between self-efficacy and concussion outcomes and highlight the influence of depressive symptoms. Interventions that optimize youth’s self-efficacy have the potential to increase treatment adherence, reduce concussion symptom severity, and improve recovery prognosis.

scholarly journals The characteristics of anxiety and depression symptom severity in older adults living in public housing

2011 ◽  
Vol 24 (4) ◽  
pp. 614-623 ◽  
Author(s):  
Adam Simning ◽  
Yeates Conwell ◽  
Susan G. Fisher ◽  
Thomas M. Richardson ◽  
Edwin van Wijngaarden
Keyword(s):  
Public Housing ◽  
Older Adult ◽  
Symptom Severity ◽  
Regression Models ◽  
Late Life ◽  
Anxiety And Depression ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
The Social ◽  
Public Housing Residents

ABSTRACTBackground:Anxiety and depression are common in older adult public housing residents and frequently co-occur. To understand anxiety and depression more fully in this socioeconomically disadvantaged population, this study relies on the Social Antecedent Model of Psychopathology to characterize anxiety and depression symptoms concurrently.Methods:190 public housing residents aged 60 years and older in Rochester, New York, participated in a research interview during which they reported on variables across the six stages of the Social Antecedent Model. GAD-7 and PHQ-9 assessed anxiety and depression symptoms, respectively.Results:In these older adult residents, anxiety and depression symptom severity scores were correlated (r = 0.61; p < 0.001). Correlates of anxiety and depression symptom severity were similar for both outcomes and spanned the six stages of the Social Antecedent Model. Multivariate linear regression models identified age, medical comorbidity, mobility, social support, maladaptive coping, and recent life events severity as statistically significant correlates. The regression models accounted for 43% of anxiety and 48% of depression symptom variability.Conclusions:In public housing residents, late-life anxiety and depression symptoms were moderately correlated. Anxiety symptom severity correlates were largely consistent with those found for depression symptom severity. The broad distribution of correlates across demographic, social, medical, and behavioral domains suggests that the context of late-life anxiety and depression symptomatology in public housing is complex and that multidisciplinary collaborative care approaches may be warranted in future interventions.

Relationship Between Mental Health, Resilience, and Physical Activity in Older Adults: A 2-Year Longitudinal Study

2021 ◽  
pp. 1-9
Author(s):  
Maria Priscila Wermelinger Ávila ◽  
Jimilly Caputo Corrêa ◽  
Alessandra Lamas Granero Lucchetti ◽  
Giancarlo Lucchetti
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Older Adults ◽  
Regression Models ◽  
Linear Regression Models ◽  
Physically Active ◽  
Effective Interventions ◽  
Self Sufficiency ◽  
The Relationship

The aim of this study was to longitudinally investigate the association between resilience and mental health in older adults and to determine the influence of physical activity on this relationship. A total of 291 older adults were included in a 2-year follow-up study. Adjusted linear regression models evaluated the association between resilience at baseline and mental health after 2 years in sufficiently and insufficiently physically active older adults. A negative correlation was found between resilience at baseline and depression, anxiety, and stress after 2 years for the overall sample. This association changed after stratifying the group. Sufficiently physically active individuals made greater use of the resilience components “Self-Sufficiency” and “Perseverance,” whereas insufficiently physically active individuals made greater use of “Meaning of Life” and “Existential Singularity.” Physical activity can influence the relationship between resilience and mental health. These results can help guide the devising of more effective interventions for this age group.

Illness Uncertainty in Patients Awaiting Liver Transplant

2020 ◽  
pp. 019394592095205
Author(s):  
Donald E. Bailey ◽  
Jia Yao ◽  
Qing Yang
Keyword(s):  
Linear Regression ◽  
Liver Transplant ◽  
Self Efficacy ◽  
Regression Models ◽  
Well Being ◽  
Linear Regression Models ◽  
Illness Uncertainty ◽  
Negatively Associated ◽  
Uncertainty In Illness ◽  
Uncertainty Score

Illness uncertainty is prevalent in patients awaiting liver transplant. We described high levels of illness uncertainty in these patients and examined relationships between uncertainty and person factors and the antecedents of uncertainty. Mishel uncertainty in illness scale was used to measure illness uncertainty. We used modes and interquartile range (IQR) to describe illness uncertainty levels in 115 patients. Multiple logistic and linear regression models estimated the associations of uncertainty with hypothesized antecedents. High total illness uncertainty score was reported by 15.6% of the patients. After adjusting for all variables, illness uncertainty was associated with two antecedents of uncertainty, low social well-being (OR = 0.816; p = .025) and low self-efficacy (OR = 0.931; p = .013). Complexity was negatively associated with social well-being; ambiguity and inconsistency were negatively associated with self-efficacy. One in seven patients experienced high illness uncertainty. Social well-being and self-efficacy were negatively related to illness uncertainty.

scholarly journals Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Linear Regression ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regression Models ◽  
Linear Regression Models ◽  
Winter Snow ◽  
The Relationship

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

scholarly journals Sociodemographic and Psychosocial Factors Associated with Diet Quality Among Native American Adults: Baseline Data from the OPREVENT2 Study (P04-007-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Michelle Estradé ◽  
Angela Trude ◽  
Marla Pardilla ◽  
Joel Gittelsohn
Keyword(s):  
Native American ◽  
Psychosocial Factors ◽  
Healthy Eating ◽  
Diet Quality ◽  
Self Efficacy ◽  
Regression Models ◽  
Nutrition Knowledge ◽  
Food Assistance ◽  
Linear Regression Models ◽  
Dietary Interventions

Abstract Objectives To identify sociodemographic and psychosocial factors associated with diet quality among Native American adults. Methods Cross-sectional data from the baseline assessment of a cluster-randomized obesity prevention trial (OPREVENT2) of 580 Native American adults from six tribal communities in the Midwest and Southwest. The Healthy Eating Index (HEI-2015) was used to define diet quality, calculated from a semi-quantitative food frequency questionnaire (modified Block FFQ). Sociodemographic (age, sex, education, food assistance) and psychosocial factors (nutrition knowledge, self-efficacy, health eating intentions) were assessed via questionnaires administered by trained data collectors. One-way ANOVA, linear regression models, and two-tailed t-tests assessed compared mean total HEI scores among sociodemographic categories. Bivariate linear regression models assessed the relation between psychosocial factors and diet quality. Results Overall diet quality was low, with a mean HEI-2015 score of 49 (SD + 8), which is 10 points lower than in the general U.S. population. The HEI scores of smokers were an average of 3 points lower than those of non-smokers (P < 0.001), and females had better diet quality (2.2 points higher) than males (P < 0.01). Those receiving commodity food assistance had mean total HEI scores 2.7 points lower than those who did not receive commodities (P < 0.005), and no other source of food assistance was associated with HEI. Self-efficacy (b = 0.66; P < 0.001) and healthy eating intentions (b = 0.72; P < 0.001) were positively associated with mean HEI. Conclusions While nutrition knowledge has been a key focus of many dietary interventions, it does not appear to be associated with better diet quality among Native Americans. This finding suggests that it is necessary to focus interventions on factors other than nutrition knowledge that may impact food choice. Because higher self-efficacy and healthy eating intentions were associated with better diet quality, a social-cognitive approach to dietary interventions may be more effective in Native American populations. Funding Sources OPREVENT2 is funded by a grant from the National Heart, Lung, and Blood Institute.

scholarly journals Association between Cross-Cultural Social Adaptation and Overseas Life Satisfaction among Chinese Medical Aid Team Members (CMATMs) in Africa

2019 ◽  
Vol 16 (9) ◽  
pp. 1572 ◽  
Author(s):  
Xiaochang Chen ◽  
Xiaojun Liu ◽  
Wei Yu ◽  
Anran Tan ◽  
Chang Fu ◽  
...  
Keyword(s):  
Life Satisfaction ◽  
Regression Models ◽  
Cross Cultural ◽  
Social Adaptation ◽  
Linear Regression Models ◽  
Team Members ◽  
Medical Aid ◽  
Electronic Questionnaires ◽  
The Relationship ◽  
Overall Life Satisfaction

This study evaluated the relationship between cross-cultural social adaptation and overseas life satisfaction among Chinese medical aid team members (CMATMs) in Africa. A revised Chinese version of the Sociocultural Adaptation Scale (CSCAS) was used to measure participants’ cross-cultural social adaptation. The self-designed survey of the CMATMs’ overseas life satisfaction includes the following five aspects: food, housing, transportation, entertainment, and security. Electronic questionnaires were distributed non-randomly. Linear regression models were established to explore the association between cross-cultural social adaptation and all dimensions of overseas life satisfaction. After adjusting all the confounders, compared with moderate adaptation, poor adaptation was negatively correlated with all dimensions of overseas life satisfaction (B for food = −0.71, B for housing = −0.76, B for transportation = −0.70, B for entertainment = −0.53, B for security = −0.81, B for overall satisfaction = −0.71, all p < 0.001), whereas good adaptation was positively associated with all dimensions of overseas life satisfaction (B for food = 1.23, B for housing = 1.00, B for transportation = 0.84, B for entertainment = 0.84, B for security = 0.76, B for overall life satisfaction = 0.94, all p < 0.001). This study shows that a better cross-cultural social adaptation was positively connected to a higher level of overseas life satisfaction in general, and more specifically to higher levels of satisfaction with food, housing, transportation, entertainment, and security. This knowledge can be utilized in promoting cross-cultural social adaptation and overseas life satisfaction among CMATMs in Africa.

BAYESIAN IDENTIFICATION OF OUTLIERS AND CHANGE-POINTS IN MEASUREMENT ERROR MODELS

2005 ◽  
Vol 08 (04) ◽  
pp. 433-449 ◽  
Author(s):  
FERNANDO A. QUINTANA ◽  
PILAR L. IGLESIAS ◽  
HELENO BOLFARINE
Keyword(s):  
Linear Regression ◽  
Outlier Detection ◽  
Change Point ◽  
Regression Models ◽  
Point Problem ◽  
Linear Regression Models ◽  
Outlier Identification ◽  
Change Point Problem ◽  
Error In Variables ◽  
The Relationship

The problem of outlier and change-point identification has received considerable attention in traditional linear regression models from both, classical and Bayesian standpoints. In contrast, for the case of regression models with measurement errors, also known as error-in-variables models, the corresponding literature is scarce and largely focused on classical solutions for the normal case. The main object of this paper is to propose clustering algorithms for outlier detection and change-point identification in scale mixture of error-in-variables models. We propose an approach based on product partition models (PPMs) which allows one to study clustering for the models under consideration. This includes the change-point problem and outlier detection as special cases. The outlier identification problem is approached by adapting the algorithms developed by Quintana and Iglesias [32] for simple linear regression models. A special algorithm is developed for the change-point problem which can be applied in a more general setup. The methods are illustrated with two applications: (i) outlier identification in a problem involving the relationship between two methods for measuring serum kanamycin in blood samples from babies, and (ii) change-point identification in the relationship between the monthly dollar volume of sales on the Boston Stock Exchange and the combined monthly dollar volumes for the New York and American Stock Exchanges.

Amendment of traditional assessment measures for the negative symptoms of schizophrenia

2018 ◽  
Vol 49 ◽  
pp. 50-55 ◽  
Author(s):  
Aida Farreny ◽  
Judith Usall ◽  
Jorge Cuevas-Esteban ◽  
Susana Ochoa ◽  
Gildas Brébion
Keyword(s):  
Verbal Memory ◽  
Regression Models ◽  
Negative Symptoms ◽  
Linear Regression Models ◽  
Illness Duration ◽  
Traditional Assessment ◽  
Syndrome Scale ◽  
Assessment Measures ◽  
Negative Syndrome ◽  
The Relationship

AbstractSchizophrenia research based on traditional assessment measures for negative symptoms appears to be, to some extent, unreliable. The limitations of the Positive and Negative Syndrome Scale (PANSS) and the Scale for the Assessment of Negative Symptoms (SANS) have been extensively acknowledged and should be taken into account. The aim of this study is to show how the PANSS and the SANS conflate negative symptoms and cognition and to offer alternatives for the limitations found.MethodsA sample of 117 participants with schizophrenia from two independent studies was retrospectively investigated. Linear regression models were computed to explore the effect of negative symptoms and illness duration as predictors of cognitive performance.ResultsFor the PANSS, the item “abstract thinking” accounted for the association between negative symptoms and cognition. For the SANS, the “attention” subscale predicted the performance in verbal memory, but illness duration emerged as a stronger predictor than negative symptoms for outcomes of processing speed, verbal and working memory.ConclusionUtilizing alternative models to the traditional PANSS and SANS formats, and accounting for illness duration, provide more precise evidence on the relationship between negative symptoms and cognition. Since these measures are still extensively utilized, we recommend adopting more rigorous approaches to avoid misleading results.

scholarly journals Loneliness and COVID-19 preventive behaviours among Japanese adults

2020 ◽  
Author(s):  
Andrew Stickley ◽  
Tetsuya Matsubayashi ◽  
Michiko Ueda
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Odds Ratio ◽  
Regression Models ◽  
Online Survey ◽  
Health Behaviours ◽  
Regression Analyses ◽  
Linear Regression Models ◽  
Japanese Adults ◽  
The Relationship

Abstract Background There is some evidence that loneliness may be linked to poorer health behaviours. Despite this, there has been little research to date on the relationship between loneliness and COVID-19 preventive behaviours. We studied these associations in a sample of the Japanese population. Methods Data were analysed from an online survey of 2000 adults undertaken in April and May 2020. Loneliness was assessed with the Three-Item Loneliness Scale. Information was also collected on 13 COVID-19 preventive behaviours. Regression analyses were used to examine associations. Results In linear regression models adjusted for demographic and mental health variables, both dichotomous and continuous loneliness measures were negatively associated with engaging in COVID-19 preventive behaviours. Logistic regression analyses further showed that loneliness was also associated with reduced odds for a variety of individual preventive behaviours including wearing a mask (odds ratio [OR]: 0.77, 95% confidence interval [CI]: 0.62–0.95), disinfecting hands (OR: 0.80, 95% CI: 0.67–0.94) and social distancing when outdoors (OR: 0.75, 95% CI: 0.61–0.92). Conclusions Loneliness is associated with lower engagement in COVID-19 preventive behaviours. Interventions to prevent or ameliorate loneliness during the ongoing pandemic may be important in combating the spread of the coronavirus.

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