Objectives: This study explored the relationships among physical activity (PA), Body Mass Index (BMI), and physical fitness in junior high school students in Shanghai. Methods: A nationwide online survey was conducted in Shanghai between February and June 2023, using a randomly stratified sampling design. A total of 403 questionnaires were administered to 10 ninth-grade classes in 10 schools in Shanghai. A total of 372 students completed the questionnaire, and detailed responses were included in the final analysis. The corresponding numbers matched their physical fitness scores. Using Smart-PLS 4.0, structural equation modeling techniques were employed to analyze the collected data. Results: Light physical activity (LPA) does not influence BMI, 800/1000 meters (800/1000M), sitting forward bend (FB), standing long jump (SLJ), 50 meters (50M), or vital capacity (VC). The results of H2 (β=-0.106, t=1.961, p=0.05), H10a (β=0.118, t=2.426, p=0.015), H10c (β=0.14, t=2.472, p=0.013), H11a (β=0.155, t=3.255, p=0.001), H3 (β=0.134, t=2.353, p=0.019), H11c (β=-0.123, t=2.413, p=0.016), H4 (β=-0.301, t=4.963, p=0.000), H5 (β=-0.368, t=5.571, p=0.000), H7 (β=-0.480, t=9.023, p=0.000), and H8 (β=0.279, t=5.812, p=0.000) supported our hypothetical model and explained that BMI can affect 50M, 800/1000M, SLJ, and VC.

Objectives: This study aims to explore parents’ exercise habits (PEH), exercise motivation (EMO), and exercise behavior (EB) in Shanghai junior high school students. Methods: A nationwide online survey was conducted across Shanghai from February to June 2023 using a randomly stratified sampling design. In total, 803 questionnaires were distributed across Shanghai via online platforms. A total of 777 complete and detailed responses were included in the final analysis. Structural equation modeling techniques were employed to analyze the collected data using Smart-PLS 4.0. Results: It has been statistically proven that all the scales used in this inquiry to determine the mean scores, standard deviation, excess kurtosis, and skewness values were reliable and produced satisfactory results except H2 and H3. The results (H1: β=0299, t=4.358, p=0.000; H4: β=0.129, t=2.713, p=0.007; H5: β=-0.198, t=3.502, p=0.000; H6 : β=0.208, t=5.482, p=0.007; H7: β=0.140, t=3.735, p=0.007; H8: β=0.143, t=3.801, p=0.007; H9: β=0.132, t=3.175, p=0.000; H10: β=0.106, t=2.891, p=0.000; H11: β=0.366, t=9.110, p=0.000) supported our hypothetical model and explained that parents’ exercise habits (PEH), social motivation (SMO), ability motivation (AMO), and healthy motivation (HMO) benefits students’ exercise behavior (EB). Although fun motivation (FMO) and appearance motivation (APMO) have no effect on students’ exercise behavior, parents’ exercise habits (PEH) have effect on all types of exercise motivation (EMO). Conclusions: PEH, SMO, AMO, and HMO affect EB in adolescents. The SMO and AMO play mediating roles in the relationship between PEH and EB.