Associations between physical activity and energy and fatigue depend on sleep quality

When experiencing nice interactions at work: Good sleep quality via well-being

Based on the Work-Home Resources Model and Conservation of Resources Theory, we develop dual mechanisms by which nice interactions (patients' compliments and coworkers' informational support) predict sleep quality. Specifically, we expect these nice interactions to help individuals conserve their personal energy in the form of less cognitive depletion (a cognitive process) and diminished physical fatigue (a physical process). Further, we propose employees utilise their energy resources to experience better sleep quality. To test the proposed model, we utilised an experience-sampling method by recruiting 223 female nurses working in a regional university hospital in South Korea. Specifically, we measured nice interactions and personal resources at 3 PM on Day t and sleep quality at 5-6 AM on Day t + 1, and we administered the questionnaire for 10 consecutive days. Overall, after removing 79 invalid observations (not completing questionnaire in a timely manner), we had a final total of two-wave 1997 daily observations from 223 nurses. Receiving more compliments from patients and more information from coworkers positively affects nurses' cognitive energy (less cognitive depletion) and physical energy (less physical fatigue), which predicts better sleep quality. Finally, results supported indirect effects of these nice interactions on sleep quality via cognitive and physical processes.

The effect of sleep deprivation on postural stability among physically active young adults

The study aimed to evaluate the effect of sleep deprivation on postural stability among physically active young adults. The study involved 22 physical education students. Average velocities and spatial distribution of the center of pressure displacements were taken as indicators of postural stability (double and one-leg standing). Two-way ANOVA with two factors of repeated measurements-"session" (control-experimental) and "daytime" (evening-morning)-was used. For indicators of the spatial distribution of the center of pressure in double stance with eyes open and eyes closed, and for average velocities for measurements with eyes closed, statistically significant interaction effects were found (at least p < 0.01, ƞ2 > 0.36, power statistics > 0.90) with the general tendency of higher results in the morning in the session with sleep deprivation than in the control session. In one-leg standing, an increase of average velocities was observed in the control session, and no differences in the session with sleep deprivation (interaction effect: at least p < 0.01, ƞ2 > 0.37, power statistics > 0.90). Besides spatial distribution indicators in double stance, there were no statistical differences between evening-morning tests in the session with sleep deprivation. Despite significant interaction effects, only the results of spatial distribution indicators in double stance were higher in the morning than in the evening in the session with sleep deprivation. So, no clear decline in postural stability after sleep deprivation was observed. This may suggest that sleep deprivation prevents natural regeneration rather than significantly worsening postural stability among physically active adults. It's possible that systematic physical activity might be one of the factors decreasing the risk of accidents among people exposed to sleep deprivation.

Preventive Medicine via Lifestyle Medicine Implementation Practices Should Consider Individuals' Complex Psychosocial Profile

Noncommunicable chronic diseases are associated with lifestyle behaviors. Psychological and social factors may influence the adoption of such behaviors. Being mentally and physically energized or fatigued may influence the intention-behavior gap of healthy lifestyle adoption accordingly. We investigated the associations of age, sex, lifestyle behaviors, mood, and mental and physical energy and fatigue at both the trait and state levels. The participants (N = 670) completed questionnaires assessing their sleep, mood, mental and physical state energy and fatigue, physical activity, mental workload, and diet. The ordinary least squares regression models revealed an overlap between the mental state and trait energy levels for males who consume polyphenols, have a high mental workload, and sleep well. Being younger, having a high stress level, bad sleep habits, and being confused and depressed were associated with high mental fatigue. Physical energy and fatigue shared the same commonalities with the previous results, with greater discrepancies observed between the state and trait indicators compared to that between mental energy and fatigue. Diet and stress management seem to be predictors of high physical energy, and females report higher physical fatigue levels. Health care professionals should consider this psychosocial complex profiling in their differential diagnosis and when one is implementing lifestyle behavioral changes to address the facets of preventive medicine, wellness, and health promotion.

Using Machine Learning to Identify Feelings of Energy and Fatigue in Single-Task Walking Gait: An Exploratory Study

The objective of this study was to use machine learning to identify feelings of energy and fatigue using single-task walking gait. Participants (n = 126) were recruited from a university community and completed a single protocol where current feelings of energy and fatigue were measured using the Profile of Moods Survey–Short Form approximately 2 min prior to participants completing a two-minute walk around a 6 m track wearing APDM mobility monitors. Gait parameters for upper and lower extremity, neck, lumbar and trunk movement were collected. Gradient boosting classifiers were the most accurate classifiers for both feelings of energy (74.3%) and fatigue (74.2%) and Random Forest Regressors were the most accurate regressors for both energy (0.005) and fatigue (0.007). ANCOVA analyses of gait parameters comparing individuals who were high or low energy or fatigue suggest that individuals who are low energy have significantly greater errors in walking gait compared to those who are high energy. Individuals who are high fatigue have more symmetrical gait patterns and have trouble turning when compared to their low fatigue counterparts. Furthermore, these findings support the need to assess energy and fatigue as two distinct unipolar moods as the signals used by the algorithms were unique to each mood.

Trait Energy and Fatigue May Be Connected to Gut Bacteria among Young Physically Active Adults: An Exploratory Study

Recent scientific evidence suggests that traits energy and fatigue are two unique unipolar moods with distinct mental and physical components. This exploratory study investigated the correlation between mental energy (ME), mental fatigue (MF), physical energy (PE), physical fatigue (PF), and the gut microbiome. The four moods were assessed by survey, and the gut microbiome and metabolome were determined from 16 S rRNA analysis and untargeted metabolomics analysis, respectively. Twenty subjects who were 31 ± 5 y, physically active, and not obese (26.4 ± 4.4 kg/m2) participated. Bacteroidetes (45%), the most prominent phyla, was only negatively correlated with PF. The second most predominant and butyrate-producing phyla, Firmicutes (43%), had members that correlated with each trait. However, the bacteria Anaerostipes was positively correlated with ME (0.048, p = 0.032) and negatively with MF (−0.532, p = 0.016) and PF (−0.448, p = 0.048), respectively. Diet influences the gut microbiota composition, and only one food group, processed meat, was correlated with the four moods—positively with MF (0.538, p = 0.014) and PF (0.513, p = 0.021) and negatively with ME (−0.790, p < 0.001) and PE (−0.478, p = 0.021). Only the Firmicutes genus Holdemania was correlated with processed meat (r = 0.488, p = 0.029). Distinct metabolic profiles were observed, yet these profiles were not significantly correlated with the traits. Study findings suggest that energy and fatigue are unique traits that could be defined by distinct bacterial communities not driven by diet. Larger studies are needed to confirm these exploratory findings.