With the revision of the Medical Service Law in April 2005, it has become incumbent on clinical engineers to take charge of medical equipment in their hospital and to run a maintenance check on the equipment at regular intervals.　　However, it is difficult to show what clinical engineers have done as visualized achievements, for no insurance mark to the maintenance and management business itself.　　By using the accumulated maintenance records and a new medical equipment management system - the Arcadia company’s “CEIA system” installed in April 2010. We considered the cost performance of the medical equipment could be enhanced through the efforts of clinical engineers.　　For this purpose, we made the list of technical fees to be paid if a clinical engineer should entrust temporarily the maintenance management to an equipment maker and calculated the cost item by item.　　Moreover, the items which were checked and repaired were divided into those “outsourced” and those taken care of by hospital clinical engineers. When trial calculations were made using “in-house processing-outsourcing＝achievements” formula, we had about 50 million yen or more.

OBJECTIVESTo analyze actual conditions of the quality of life (QOL) in junior high school students, we developed a questionnaire based on the PRECEDE-PROCEED model, and we conducted a survey by using this questionnaire.

METHODSWe conducted a workshop organized with 29 specialists on school health and community health to develop the questionnaire. The QOL outcome was assessed by the QOL Profile-Adolescent Version (QOLPAV). The subjects of the questionnaire surveys were 1600 general students in four junior high schools. To investigate a correlation between QOLPAV, behaviors and three enhancing factors, two different multiple regression models were constructed.

RESULTSThe question battery for QOLPAV was found to be a high value of Chronbach's α. Among present subjects, 16.5% were categorized as "problematic" or "very problematic" classified by QOLPAV scores. In the first multiple regression model, significantly high odds ratios were obtained between the QOLPAV and 4 questions for behaviors, such as "studying with high motivation" (OR 1.64), "getting along well with my friends" (2.72), "having things I am interested in" (1.70), and "making my own decisions" (1.80). In the second model, significantly high odds ratios were obtained commonly between the above 4 questions about behaviors and 2 questions on enabling factors, such as "easy to understand lessons" (1.32-1.71) and "speaking to friends easily" (1.30-3.22).

CONCLUSIONS1) We developed a questionnaire to analyze the actual condition of QOL in junior high school students with sufficient validity and availability. 2) Among the present subjects, 16.5% were found to be problematic QOLPAV, 3) Among the factors of behaviors, those representing positive willing and high coping ability with the elements of each school life contributed significantly to the QOLPAV. And among enhancing factors, "enabling factors" and "reinforcing factors" were stronger contributors to the behaviors strongly related to the QOLPAV than that of "predisposing factors".

Objectives: The relationship between weight-related load and bone mineral density (BMD)/bone microstructure under normal load conditions using high-resolution peripheral quantitative computed tomography (HR-pQCT) remains unconfirmed. The study aims to investigate the differences in effect of body mass index (BMI) on BMD/bone microstructure of loaded and unloaded bones, respectively, in Japanese postmenopausal women. Methods: Fifty-seven postmenopausal women underwent HR-pQCT on the tibia and radius. Correlation analysis, principal component (PC) analysis, and hierarchical multiple regression were performed to examine the relationship between BMI and HR-pQCT parameters. Results: Several microstructural parameters of the tibia and radius correlated with BMI through a simple correlation analysis, and these relationships remained unchanged even with an age-adjusted partial correlation analysis. PC analysis was conducted using seven bone microstructure parameters. The first PC (PC1) reflected all parameters of trabecular and cortical bone microstructures, except for cortical porosity, whereas the second PC (PC2) reflected only cortical bone microstructure. Hierarchical multiple regression analysis indicated that BMI was more strongly related to BMD/bone microstructure in the tibia than in the radius. Furthermore, BMI was associated with trabecular/cortical BMD, and PC1 (not PC2) of the tibia and radius. Thus, BMI was strongly related to the trabecular bone microstructure rather than the cortical bone microstructure. Conclusions Our data confirmed that BMI is associated with volumetric BMD and trabecular bone microstructure parameters in the tibia and radius. However, although BMI may be more related to HRpQCT parameters in the tibia than in the radius, the magnitude of association is modest.

Objectives: The relationship between weight-related load and bone mineral density (BMD)/bone microstructure under normal load conditions using high-resolution peripheral quantitative computed tomography (HR-pQCT) remains unconfirmed. The study aims to investigate the differences in effect of body mass index (BMI) on BMD/bone microstructure of loaded and unloaded bones, respectively, in Japanese postmenopausal women. Methods: Fifty-seven postmenopausal women underwent HR-pQCT on the tibia and radius. Correlation analysis, principal component (PC) analysis, and hierarchical multiple regression were performed to examine the relationship between BMI and HR-pQCT parameters. Results: Several microstructural parameters of the tibia and radius correlated with BMI through a simple correlation analysis, and these relationships remained unchanged even with an age-adjusted partial correlation analysis. PC analysis was conducted using seven bone microstructure parameters. The first PC (PC1) reflected all parameters of trabecular and cortical bone microstructures, except for cortical porosity, whereas the second PC (PC2) reflected only cortical bone microstructure. Hierarchical multiple regression analysis indicated that BMI was more strongly related to BMD/bone microstructure in the tibia than in the radius. Furthermore, BMI was associated with trabecular/cortical BMD, and PC1 (not PC2) of the tibia and radius. Thus, BMI was strongly related to the trabecular bone microstructure rather than the cortical bone microstructure. Conclusions Our data confirmed that BMI is associated with volumetric BMD and trabecular bone microstructure parameters in the tibia and radius. However, although BMI may be more related to HRpQCT parameters in the tibia than in the radius, the magnitude of association is modest.