Effects of Controlling Versus Autonomy-Supportive Language on Learning a Novel Motor Skill and Cortisol Release

The purpose of this study was to compare how different types of instruction effect the learning of a novel motor skill and how salivary cortisol correlates to learning differences. Participants (N = 44), average age 22.3 years (standard deviation 2.37), were randomly assigned to an autonomy-supportive, controlling-language or neutral language group which was manipulated via instructional video. Saliva was collected before and after each session, and questionnaires were given after pitching was completed during each day. Results showed that there was a significant difference among groups in throwing accuracy on performance and retention. Questionnaire results also showed significant group differences in perceived autonomy and self-efficacy. There was no difference in cortisol on either day between groups. Further analysis showed that the autonomy-supportive group was superior in all domains over the controlling-language group. From these results, we concluded autonomy-supportive language is a beneficial form of instruction for learning a novel motor skill versus controlling language due to its ability to increase self-efficacy and perception of autonomy. Further research should be done on the psychological and hormonal aspect of motor learning

Age and sex effects on SuperG performance are consistent across internet devices

There have been recent advances in the application of online games that assess motor skill acquisition/learning and its relationship to age and biological sex, both of which are associated with dementia risk. While this online motor learning assessment (called Super G), along with other computer-based cognitive tests, was originally developed to be completed on a computer, many people (including older adults) have been shown to access the internet through a mobile device. Thus, to improve the generalizability of our online motor skill learning game, it must not only be compatible with mobile devices but also yield replicable effects of various participant characteristics on performance relative to the computer-based version. It is unknown if age and sex differentially affect game performance as a function of device type (keyboard versus touchscreen control). Thus, the purpose of this study was to investigate if device type modifies the established effects of age and sex on performance. Although there was a main effect of device on performance, this effect did not alter the overall relationship between performance vs. age or sex. This establishes that Super G can now effectively be extended to both computer and mobile platforms to further test for dementia risk factor

Relationships between variance in electroencephalography relative power and developmental status in infants with typical development and at risk for developmental disability: An observational study [version 2; referees: 2 approved]

Background: Electroencephalography (EEG) is a non-invasive tool that has the potential to identify and quantify atypical brain development. We introduce a new measure here, variance of relative power of resting-state EEG. We sought to assess whether variance of relative power of resting-state EEG could predict i) classification of infants as typical development (TD) or at risk (AR) for developmental disability, and ii) Bayley developmental scores at the same visit or future visits. Methods: A total of 22 infants with TD participated, aged between 38 and 203 days. In addition, 11 infants broadly at risk participated (6 high-risk pre-term, 4 low-risk pre-term, 1 high-risk full-term), aged between 40 and 225 days of age (adjusted for prematurity). We used EEG to measure resting-state brain function across months. We calculated variance of relative power as the standard deviation of the relative power across each of the 32 EEG electrodes. The Bayley Scales of Infant Development (3rd edition) was used to measure developmental level. Infants were measured 1-6 times each, with 1 month between measurements. Results: Our main findings were: i) variance of relative power of resting state EEG can predict classification of infants as TD or AR, and ii) variance of relative power of resting state EEG can predict Bayley developmental scores at the same visit (Bayley raw fine motor, Bayley raw cognitive, Bayley total raw score, Bayley motor composite score) and at a future visit (Bayley raw fine motor). Conclusions: This was a preliminary, exploratory, small study. Our results support variance of relative power of resting state EEG as an area of interest for future study as a biomarker of neurodevelopmental status and as a potential outcome measure for early intervention

Expectations about transcranial direct current stimulation for improving motor function

In this project, we surveyed expectations about whether tDCS could enhance motor performance, and explored whether these expectations varied by prior tDCS experience/knowledge, sex, and age

Dietary Strawberries Improve Serum Metabolites of Cardiometabolic Risks in Adults with Features of the Metabolic Syndrome in a Randomized Controlled Crossover Trial

Dietary strawberries have been shown to improve cardiometabolic risks in multiple clinical trials. However, no studies have reported effects on serum metabolomic profiles that may identify the target pathways affected by strawberries as underlying mechanisms. We conducted a 14-week randomized, controlled crossover study in which participants with features of metabolic syndrome were assigned to one of the three arms for four weeks separated by a one-week washout period: control powder, 1 serving (low dose: 13 g strawberry powder/day), or 2.5 servings (high dose: 32 g strawberry powder/day). Blood samples, anthropometric measures, blood pressure, and dietary and physical activity data were collected at baseline and at the end of each four-week phase of intervention. Serum samples were analyzed for primary metabolites and complex lipids using different mass spectrometry methods. Mixed-model ANOVA was used to examine differences in the targeted metabolites between treatment phases, and LASSO logistic regression was used to examine differences in the untargeted metabolites at end of the strawberry intervention vs. the baseline. The findings revealed significant differences in the serum branched-chain amino acids valine and leucine following strawberry intervention (high dose) compared with the low-dose and control phases. Untargeted metabolomic profiles revealed several metabolites, including serum phosphate, benzoic acid, and hydroxyphenyl propionic acid, that represented improved energy-metabolism pathways, compliance measures, and microbial metabolism of strawberry polyphenols, respectively. Thus, dietary supplementation of strawberries significantly improves the serum metabolic profiles of cardiometabolic risks in adults

Participant characteristics.

Participant characteristics.</p

Diffusion tenor model parameter maps.

Fractional anisotropy (top row), radial (second row), mean (third row), and axial (bottom row) diffusivity maps for an example participant that demonstrates the diffusion tensor model fit the data as expected. (TIF)</p

Delayed visuospatial memory test and motor skill task.

A. Participants completed the Rey-Osterrieth Complex Figure Delayed Recall test (measures delayed visuospatial memory). An example drawing from one of the participants is shown. B. Participants used their nondominant hand to perform the motor task that mimicked the upper extremity movements required to feed oneself. This image is adapted from the “Dexterity and Reaching Motor Tasks” by MRL Laboratory that is licensed under CC BY 2.0.</p

Whole-brain fractional anisotropy and radial diffusivity results.

Fractional anisotropy results are shown in Panel A; the first row illustrates the large positive cluster in the right SLF (orange), the second row illustrates the negative cluster in the left CST/ATR/SLF, and the third row illustrates the positive cluster in bilateral CST in the brainstem. Radial diffusivity results are shown in Panel B; the first row shows the negative cluster in the left SLF, the second row shows the positive cluster in the ATR/CST, and the third row illustrates the negative cluster in the bilateral CST in the brainstem. The last row shows the negative cluster in the right ATR.</p