Background: Although fall predictions using motor ability have been well reported in elderly people, there are few reports on physical cognitive ability. Objective: To examine the relationship of the results of motor function tests that include physical cognitive ability on the ability to predict falls and to determine which test is the most appropriate. Methods: We studied 174 community-dwelling elderly adults (mean age 75.7 ± 5.7, 41 males and 133 females), and measured grip strength, one-leg standing time (OLS), timed up and go test (TUG), functional reach test, sit and reach test, and maximal step length (MSL). The estimation error (EE), which was defined as the difference between the predicted and actual values, was calculated in all motor ability tests. Other assessments included the number of falls in the previous year, BMI, frequency of going out, Mini-Mental State Examination score, and Falls Efficacy Scale. In the baseline study, we divided the subjects into a fall group (n = 33) and a nonfall group (n = 141) and compared motor ability and EE for the two groups. During a 1-year follow-up, the nonfall group (baseline study) was assessed for the same measurements by using the same methods. Results: In the baseline study, the fall group had significantly lower values of OLS and MSL. Furthermore, the fall group significantly overestimated their OLS, TUG, and MSL. In logistic regression analysis, EE of TUG (OR = 1.27) and EE of MSL (OR = 1.08) were detected as risk factors for falls. During follow-up, 11 subjects (7.8%) experienced falls. In logistic regression analysis, TUG (OR = 1.89) and EE of MSL (OR = 1.06) were detected as significant risk factors for falls. Since EE of MSL had higher values of both the area under the receiver operating characteristic curve and the sum of sensitivity and specificity than EE of TUG, the nonfall group was divided into two groups with a cutoff value of 2 cm for EE of MSL. A significant distribution disparity in falls between the two groups was found during follow-up and showed a relative risk of 18.78 for EE of MSL. Conclusions: We suggest that EE of MSL is a potent predictor for falls among healthy elderly adults.

Background Many studies have already reported on the relationship between exercise habits and health among schoolchildren. However, few have examined social and/or family factors as determinants of exercise habits. Methods This study’s participants included 1721 schoolchildren aged between 6 and 13 who were involved in the Super Shokuiku School Project in January 2016. A survey was conducted to assess gender, grade level, physical activity, lifestyle, overall health, enrichment of school life, social background, and parental lifestyles. Both dislike and lack of physical activity were used to measure poor exercise habits; correlates were analyzed using logistic regression. Results “Lack of close friends” had the strongest links with both dislike (adjusted odds ratio [OR] 5.30; 95% confidence interval [CI], 2.78–10.1) and lack of (adjusted OR 5.40; 95% CI, 2.81–10.4) physical activity. Further, children who engaged in long periods of screen time and lacked parental communication also tended to dislike and lack physical activity. Children with mothers who were unemployed (housewives) and had unhealthy lifestyles, as well as those with poor health, were also more likely to lack physical activity. Conclusion Social and family factors (e.g., having close friends) may be determinants of exercise habits among schoolchildren, independent of their own lifestyle factors. Although a longitudinal study is needed to determine causality, substantial attention may thus be required to these factors when promoting physical activity in children.

The relationship between certain lifestyle habits and schoolchildren’s health has previously been reported on, but the exact pathway of the effects lifestyle habits have on physical/psychosocial health (PPH) has not been investigated nor has the relative influence of different habits on schoolchildren’s health. In this study, schoolchildren were recruited from a primary school in Toyama Prefecture, Japan ( n = 576), and the relevant data were collected in June/July 2017. Path analysis was used to examine the relationships of lifestyle habits and physical fitness with PPH among schoolchildren in grades 1–4 and 5–6. Body weight and total fitness scores were found to be not related to the children’s PPH. The pathway via which lifestyle habits influenced PPH was determined successfully. Among children in grades 1–4, sex ( p < .05), age ( p < .01), and breakfast intake ( p < .05) were related to PPH. Among schoolchildren in grades 5–6, the duration of sleep ( p < .05) was related to PPH. Thus, factors related to schoolchildren’s PPH vary by school grade. The identification of the predictors of the PPH of schoolchildren should inform the design of tailored, grade-specific health promotion interventions in Japanese elementary schools.

This study explored the associations of lifestyle, familial, and social factors with sleep habits in 1882 elementary school children, aged 6–13 years, from the Super Shokuiku School Project in January 2016. A survey assessed sex, grade, sleep habits, lifestyle, social background, and parental lifestyle. Bedtime “≥22:00,” wake-up time “≥07:00,” sleep duration “<8 h,” and “daytime sleepiness” were defined as poor sleep habits; correlates were analyzed using logistic regression. Skipping breakfast was consistently significantly associated with poor sleep, especially among children with late wake-up times (adjusted odds ratio 5.45; 95% confidence interval 3.20–9.30). Excessive screen time was associated with late bed and wake-up times. Physical inactivity was significantly associated with daytime sleepiness. Children of mothers with poor lifestyle habits were likely to go to bed late and feel sleepy the next day. Social and family factors were associated with children’s sleep habits. Several behaviors, including skipping breakfast, excessive screen time, and physical inactivity, were associated with poor sleep habits, manifesting as a night-oriented lifestyle. Although a longitudinal study is needed to determine causality, in addition to sleep education for children, sleep education for parents and society at large may be necessary to improve children’s sleep habits.

No abstract