Anxiety does not always affect balance: the predominating role of cognitive engagement in a video gaming task.

Scientists have predominantly assessed anxiety's impact on postural control when anxiety is created by the need to maintain balance (e.g., standing at heights). In the present study, we investigate how postural control and its mechanisms (i.e., vestibular function) are impacted when anxiety is induced by an unrelated task (playing a video game). Additionally, we compare watching and playing a game to dissociate postural adaptations caused by increased engagement rather than anxiety. Participants [N = 25, female = 8, M (SD) age = 23.5 (3.9)] held a controller in four standing conditions of varying surface compliance (firm or foam) and with or without peripheral visual occlusion across four blocks: quiet standing (baseline), watching the game with a visual task (watching), playing the game (low anxiety), and playing under anxiety (high anxiety). We measured sway area, sway frequency, root mean square (RMS) sway, anxiety, and mental effort. Limited sway differences emerged between anxiety blocks (only sway area on firm surface). The watching block elicited more sway than baseline (greater sway area and RMS sway; lower sway frequency), and the low anxiety block elicited more sway than the watching block (greater sway area and RMS sway; higher sway frequency). Mental effort was associated with increased sway area and RMS sway. Our findings indicate that anxiety, when generated through competition, has minimal impact on postural control. Postural control primarily adapts according to mental effort and more cognitively engaging task constraints (i.e., playing versus watching). We speculate increased sway reflects the prioritization of attention to game performance over postural control.

Local dynamic stability during long-fatiguing walks in people with multiple sclerosis.

BACKGROUND: Altered balance/stability during walking is common in people with multiple sclerosis (PwMS). While dynamic gait stability has been related to falling and localized muscle fatigue, it has rarely been studied in MS. Specifically, the effects of walking-related fatigue on dynamic stability are unclear in PwMS. RESEARCH QUESTIONS: 1) Are temporal changes in dynamic stability during long-walks different among PwMS and healthy controls (HC)? 2) Is there a relationship between stability and walking performance changes in PwMS? METHODS: Twenty-five PwMS and ten HC participated in the six-minute walk test (6MWT) wearing six-wireless inertial sensors. Local dynamic stability (LDS) during gait was quantified by maximum-finite-time Lyapunov exponents (λS), where larger λS indicates less stable dynamics. Linear mixed models were fit to compare changes in LDS and walking performance over time among two groups. Additionally, the percent changes in λS and distance from minute 1 to 6 were recorded as Dynamic Stability Index (DSI6-1) and Distance-Walked Index (DWI6-1) respectively. Finally, Pearson correlation compared the association between DSI6-1 and DWI6-1. RESULTS: A significant group*time interaction was found for LDS. PwMS did not have different LDS than HC until minute-4 of walking, and differences persisted at minute-6. Further, PwMS walked significantly shorter distances and demonstrated a greater decline in walking performance (DWI6-1) during the 6MWT. Finally, DSI6-1 and DWI6-1 were significantly correlated in PwMS. Significance The dynamic stability differences among PwMS and HC were only apparent after 3-minutes of walking and ∼60% of PwMS became less stable over time, supporting the use of long walks in MS to capture stability changes during the motor task performance. A significant relationship between the decline in stability and poor walking performance over time during the 6MWT suggested a possible role of walking-related fatigue in the worsening of balance during long walks in PwMS.

Inflection points in longitudinal models: Tracking recovery and return to play following concussion.

The return to play (RTP) process may occur during longitudinal studies tracking recovery after concussion. This factor, which is often omitted within statistical designs, could affect the fit and overall interpretation of the statistical model. This article demonstrates the difference in results and interpretation between 2 linear mixed-model designs: (1) a between-group longitudinal (GROUP) analysis and (2) a between-group longitudinal model that used an inflection point to account for changes around the time of RTP (RTP analysis). These analyses were conducted on instrumented balance data collected on 23 concussed athletes and 25 controls over 8 weeks following concussion. Total sway area and the range of mediolateral acceleration were used as outcome measures. No significant findings were found in the GROUP design for either outcome measure. In contrast, the RTP analysis revealed significant effects of time (P = .007) and RTP change (P = .007), and group*time (P = .028) and group*RTP change (P = .022) interactions for total sway area, and effects of group (P = .011), time (P = .010), and RTP change (P = .014), and group*time (P = .013) and group*RTP change interactions (P = .013) for range of mediolateral acceleration. For both outcomes, the RTP model fit the data significantly better on comparison of likelihood ratios (P ≤ .027). These results suggest that allowing for an inflection point in the statistical design may assist understanding of what happens around clinically meaningful time points. The choice of statistical model had a considerable effect on the interpretation of findings, and provokes discussion around the best method for analyzing longitudinal datasets when important clinical time points like RTP exist.