Identifying the most proximal multi-level factors associated with meeting each of the 24-h movement behavior recommendations in a sample of autistic adults.

BACKGROUND: Autistic adults have poorer 24-h movement behaviors, including lower levels of physical activity, more time spent being sedentary, and shorter sleep duration than neurotypical adults. Social ecological frameworks posit that 24-h movement behaviors are determined by multi-level domains; however, not known is which multi-level factors are most important to meeting each of the 24-h movement behavior guidelines among autistic adults. OBJECTIVE: This study examined the relative importance of a range of multi-level determinants on meeting guidelines for the 24-h movement behaviors of aerobic physical activity, sedentary behavior, and sleep. METHODS: We administered at cross-sectional electronic survey to a national self-selecting, convenience sample of autistic adults and caregivers of autistic adults residing in the USA. We used machine learning to examine the relative variable importance (VIMP) of 55 multi-level variables with meeting recommendations for physical activity, sedentary behavior, and sleep duration. VIMPs >0 indicate predictive variables/domains. RESULTS: A greater number of group activities attended in the last 3-months, and greater independence in completing activities of daily living were most important to meeting aerobic physical activity guidelines. Group activity participation and marital status were important to meeting sedentary behavior guidelines while having a fewer number of comorbidities was most important to achieving adequate sleep. CONCLUSIONS: These data support hypotheses about the role of family and social level interventions targeting movement behaviors in autistic adults.

Brief Report: Perceived Barriers to Physical Activity Among a National Sample of Autistic Adults.

Improving physical activity (PA) levels in autistic adults is an important population health goal. Limiting efforts to achieve this goal is an incomplete understanding of the barriers to PA in this high-risk group. This study utilized cross-sectional data collected via an electronic survey from 253 autistic adults aged 18-50 years to examine their perceived barriers to PA, how PA barriers differed by demographic factors, and the relationship between PA barriers and meeting PA guidelines. The Barriers to Physical Activity scale assessed the independent variable. Lack of motivation to exercise, perceiving exercise as boring, and lack of transportation were the most strongly endorsed barriers to PA. Participants who reported these barriers were significantly less likely (≤ 50%) to meet PA guidelines.

24-h movement behaviors among autistic adults: Differences by sex, age, and level of independence.

BACKGROUND: Autistic adults have low levels of physical activity [PA], high levels of sedentary behavior [SB], and insufficient sleep. Not known is the extent to which engagement in these movement behaviors vary by sex, age, and level of independence in activities of daily living (ADLs). OBJECTIVE: To characterize movement behaviors in a national sample of autistic adults by sex, age, and level of independence in ADLs. METHODS: A national sample of autistic adults and caregivers of autistic adults self-reported PA, SB and sleep behaviors as well as demographic variables using an electronic survey. Levels of engagement in movement behaviors were described, and compared by sex, age (young-adult versus middle-age), and level of independence in ADLs. RESULTS: Data were collected on 361 autistic adults (60.3% male, n = 217, mage = 30.82 years, SD = 10.24). Overall, 44% did not meet the PA guideline; PA guideline adherence was lowest among males and those who were dependent on others in ADLs. Overall, the SB guideline was not met by 43% of the sample at weekdays and 48% at weekends. SB guideline adherence was lowest among adults who were middle-aged, and those who were fully independent in ADLs. Overall, 35.2% did not meet the total sleep time (TST) guideline. Middle-aged autistic adults had the lowest adherence to the TST guideline. CONCLUSIONS: These finding should prompt researchers to consider these demographic differences, and tailor research and programmatic efforts to account for the unique movement behavior profiles of different segments of this heterogenous population.

Sweat, Sit, Sleep: A Compositional Analysis of 24-hr Movement Behaviors and Body Mass Index among Children with Autism Spectrum Disorder.

This study (a) examined the daily composition of 24-hr movement behaviors in children with ASD using objective measures, and (b) applied compositional analysis to examine the associations of the time spent in moderate-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behavior (SB), and sleep duration (SD) with body mass index (BMI), relative to the time spent in the other movement behaviors in a sample of children (aged 7-19 years) with ASD. Time spent in MVPA, LPA, SB, and SD were measured using accelerometers over a 7-day period. BMI was calculated from measured height and weight. Participants (n = 46) spent 40% of time in LPA (M = 9.6 hr), 30.6% (M = 7.34 hr) in SB, 24.9% (M = 5.98 hr) asleep, and 4.5% (M = 64.8 min) in MVPA. Reallocating 30 min from LPA to SD decreased BMI by 0.471 kg/m2 (P = 0.003). Reallocating 30 min from MVPA to SD decreased BMI by 0.658 kg/m2 (P = 0.051). Reallocation of 60 min in equal proportions from SB, MVPA, and SD to LPA increased BMI by 0.418 kg/m2 (P = 0.021), and reallocation of 60 min in equal proportions from LPA, MVPA, and SD to SB increased BMI by 0.295 kg/m2 (P = 0.052). Finally, reallocation of 60 min in equal proportions from SB, LPA, and MVPA to SD decreased BMI by -0.845 kg/m2 (P = 0.001). LAY SUMMARY: Data was collected on time spent in light physical activity (LPA), moderate-vigorous physical activity (MVPA), sedentary behavior (SB), and sleep in 46 children with autism. The sample had insufficient sleep (a mean of 6 hr/night). We showed that replacing 30 min of LPA or MVPA with sleep decreased BMI. Also, moving 60 min to LPA or SB from the remaining movement behaviors (i.e., 20 min from each) increased BMI, and moving 60 min to sleep from the remaining behaviors decreased BMI.

Feline disseminated cutaneous phaeohyphomycosis due to Exophiala spinifera.

A six-year-old domestic shorthair cat was presented for a subcutaneous digital nodular lesion on the right forelimb. On physical examination a similar lesion was identified on the right hindlimb. Disseminated cutaneous phaeohyphomycosis was diagnosed from histopathological changes in representative tissue biopsies and fungal culture. The isolate was identified by sequencing of ITS rDNA as Exophiala spinifera. This is the first report of disseminated cutaneous disease caused by E. spinifera in the cat.

Cluster Analysis of Tumor Suppressor Genes in Canine Leukocytes Identifies Activation State.

Cells of the immune system undergo activation and subsequent proliferation in the normal course of an immune response. Infrequently, the molecular and cellular events that underlie the mechanisms of proliferation are dysregulated and may lead to oncogenesis, leading to tumor formation. The most common forms of immunological cancers are lymphomas, which in dogs account for 8%-20% of all cancers, affecting up to 1.2% of the dog population. Key genes involved in negatively regulating proliferation of lymphocytes include a group classified as tumor suppressor genes (TSGs). These genes are also known to be associated with progression of lymphoma in humans, mice, and dogs and are potential candidates for pathological grading and diagnosis. The aim of the present study was to analyze TSG profiles in stimulated leukocytes from dogs to identify genes that discriminate an activated phenotype. A total of 554 TSGs and three gene set collections were analyzed from microarray data. Cluster analysis of three subsets of genes discriminated between stimulated and unstimulated cells. These included 20 most upregulated and downregulated TSGs, TSG in hallmark gene sets significantly enriched in active cells, and a selection of candidate TSGs, p15 (CDKN2B), p18 (CDKN2C), p19 (CDKN1A), p21 (CDKN2A), p27 (CDKN1B), and p53 (TP53) in the third set. Analysis of two subsets suggested that these genes or a subset of these genes may be used as a specialized PCR set for additional analysis.