Questionnaire-based scoring system for screening moderate-to-vigorous physical activity in middle-aged Japanese workers.

OBJECTIVES: Currently available questionnaires have limited ability to measure physical activity (PA) using accelerometers as a gold standard. This study aimed to develop a PA questionnaire for middle-aged Japanese workers and propose a PA scoring system for predicting low moderate-to-vigorous PA (MVPA). METHODS: A total of 428 participants (median age 49 years; 75.8% men) participated in a 7-day PA measurement using an accelerometer and a questionnaire. The association between questionnaire responses and low MVPA (<150 min/wk) was assessed by logistic regression analysis. A score was assigned to each response based on the correlation coefficients of the multivariate model. The ability of the sum score to predict low MVPA was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: Five questionnaire items were used for measuring PA scores (range: 0-50; higher scores indicated a higher probability of low MVPA). The AUC was 0.741 (95% CI, 0.689-0.792), and the sensitivity and specificity at the optimal cut-off value were 66.7% and 68.2%, respectively. This predictive ability was slightly increased by body mass index (AUC 0.745 [95% CI, 0.693-0.796]; sensitivity 69.9%; specificity 66.9%). These predictive values were greater than those of conventional questionnaires used in health checkups in Japan (P < .05). CONCLUSIONS: This questionnaire-based PA scoring system showed moderate accuracy in predicting low MVPA. It is useful for screening physically inactive workers and promoting PA.

Density predictive model for an outbreak in adult female Aedes albopictus (Diptera: Culicidae) in Japan.

We developed a predictive model for activities and outbreaks of female Aedes albopictus Skuse, using meteorological data. The number of Ae. albopictus collected from human bait-sweep net collection (h-BNC) surveillance, conducted by the local government between 2010 and 2019 in Japan, was adopted as a mosquito-activity indicator. The best model was composed of the backward cumulative and backward moving mean of meteorological data (parameters that were measured daily include mean, maximum, and minimum temperature, mean humidity, amount of precipitation, maximum wind speed, and sunshine hours). The root mean squared error (RMSE) and the coefficient of determination (R2) of the best model for the test set, which was not included in the training dataset, were 1.33 and 0.74, respectively. The best model was applied to predict the number of Ae. albopictus obtained from our own h-BNC surveillance in Okazaki City, Japan. RMSE and R2 of the results were 1.17 and 0.92, respectively. The present model, using publicly available meteorological values, can predict the collection number of adult Ae. albopictus using h-BNC surveillance thereby providing information to control mosquito activities and outbreaks. Therefore, it may be possible to mitigate the risk of mosquito-borne infections and secondary adverse effects of mosquito bites, such as infectious impetigo and deterioration of the quality of life.