Parkself6626
Lifestyle factors may individually protect against the development of mild cognitive impairment. We investigate the relationships between both self-reported physical activity and measured physical function with cognition in a population of elderly adults, more than half of whom follow vegetarian dietary patterns. Otherwise healthy adults (n = 127, mean age 74.9 ± 7.9 years, 61.3% current vegetarians) were assessed using a comprehensive neuropsychological battery. A principal components analysis derived processing speed, executive function, and memory/language factors. Participants reported current levels of vigorous physical activity on questionnaires, and physical function and mobility were measured with the Physical Performance Test (PPT) and Timed Up and Go (TUG) Test. Generalized linear models estimated β coefficients for cross-sectional associations between cognitive factors and indicators of physical abilities and self-reported physical activity. Better physical function indicated by PPT was associated with higher scores on the processing speed factor (β = 0.21 SDs for each 4.4-point increase in PPT score; p = 0.02). Faster TUG times were also associated with higher processing speed factor scores (β = 0.21 SDs increase for each 2.8 second less TUG time; p = 0.02). Rilematovir purchase Self-reported levels of vigorous physical activity were not associated with any area of cognitive function; the association between PPT, TUG and processing speed was independent of physical activity. Associations between PPT and TUG and processing speed were stronger among participants who followed vegetarian dietary patterns. Better physical function may have an effect on cognition in a context of healthy lifestyles.
This study examined the relationship among social support, leisure time physical activity (LTPA), and mental health among people with cancer.
Cross-sectional study.
Using the 2017 Health Information National Trends Survey, we extracted data of 504 respondents who had been diagnosed with any of the 22 types of cancer listed in the survey questionnaire.
As independent variables, we assessed 3 different types of support emotional, informational, and tangible support. As mediating and outcome variables, we measured LTPA and mental health, respectively.
Using AMOS version 22, a path analysis was conducted to measure model fit. A mediation test was then conducted using bootstrapping procedures.
The hypothesized model provided an acceptable fit to the data. Specifically, emotional support (
= .15, p = .005), informational support (
= .13, p = .008), tangible support (
= .12, p = .010), and LTPA (
= .14, p = .001) were significantly associated with mental health. We revealed a significant mediating effect of LPTA on the relationship between emotional support and mental health (Estimate = .037, 95% CI = .001-.098, p < .05).
Social support and LTPA played a significant role in promoting mental health among people with cancer. In particular, the results confirmed that individuals with cancer who reported receiving emotional support tended to engage in LTPA and thus reported better mental health.
Social support and LTPA played a significant role in promoting mental health among people with cancer. In particular, the results confirmed that individuals with cancer who reported receiving emotional support tended to engage in LTPA and thus reported better mental health.
Ultrasonography is used to evaluate muscle quality (i.e. echo intensity [EI]), but an attenuation of ultrasound waves occurs in deeper tissues, potentially affecting these measures.
To determine whether muscle thickness (MT) affects EI and if EI varies between the superficial and deep portions of the muscle.
MT, EI, subcutaneous adipose tissue thickness (SAT), tissue depth (DIS
), and EI of the overall (EI
) as well as deep (EI
) and superficial (EI
) portions of the vastus lateralis (VL) were assessed in 33 resistance-trained males using ultrasonography. The difference (EI
) between EI
and EI
was calculated. Mean differences between EI
, EI
and EI
were analyzed using a repeated-measures analysis of variance (ANOVA). Relationships between measures of muscle depth/ thickness and EI were examined using Pearson's
.
EI
was greater than EI
(
< 0.001) and EI
(
< 0.001). MT was negatively correlated with EI
(
< 0.001) and positively correlated with EI
(
< 0.001).h is necessary to determine if changes in EI following resistance training are driven by increases in MT.
Determine prevalence of overweight and obesity as reported in Head Start Program Information Reports.
Serial cross-sectional census reports from 2012-2018.
Head Start programs countrywide, aggregated from program level to state and national level.
Population of children enrolled in Head Start with reported weight status data.
Prevalence of overweight (body mass index [BMI] ≥85th percentile to <95th percentile) and obesity (BMI ≥95th percentile).
Used descriptive statistics to present the prevalence of overweight and obesity by state. Performed unadjusted regression analysis to examine annual trends or average annual changes in prevalence.
In 2018, the prevalence of overweight was 13.7% (range 8.9% in Alabama to 20.4% in Alaska). The prevalence of obesity was 16.6% (range 12.5% in South Carolina to 27.1% in Alaska). In the unadjusted regression model, 34 states and the District of Columbia did not have a linear trend significantly different from zero. There was a statistically significant positive trend in obesity prevalence for 13 states and a negative trend for 3 states.
The prevalence of obesity and overweight in Head Start children remained stable but continues to be high. Head Start reports may be an additional source of surveillance data to understand obesity prevalence in low-income young children.
The prevalence of obesity and overweight in Head Start children remained stable but continues to be high. Head Start reports may be an additional source of surveillance data to understand obesity prevalence in low-income young children.
