| PurposeThe purpose of this study was to establish energy expenditure prediction equations of accelerometer, to explore convenient and accurate method for monitoring physical activity, to establish physical activity volume prediction equations using pedometer data as the independent variable so as to expand the function of pedometer in physical activity monitoring, to use pedometer to objectively investigate the physical activity status of college students and analyze the relationship of physical activity and physical fitness, and to use three-axis accelerometer to monitor the intensity of short-distance campus orienteering.Methods1 Participants were 80 college students whose major were not PE, they were randomly divided into development group(n=60) and validation group(n=20). Participants wore Actigraph GT3X accelerometer, K4b2 portable indirect calorimetry (IC) system and polar heart rate(HR) monitor while performing physical activities. Subjects of development group performed 7 activities including sitting, reading, cleaning desk, sweeping, slow walk(4km/h), fast walk(6km/h) and slow run(8km/h). Using the energy expenditure (EE) measured by IC as standard, accelerometry counts of axis Z (ACz), vector magnitude (VM) and HRnet as independent variables, we developed EE prediction equations based on the 7 activities, we also developed the combined prediction equations of VM and HRnet, as well as the intensity cut-points of ACz and VM. Subjects of validation group performed 7 activities including sitting, using computer, wiping the table, dragging, slow walk(4km/h), fast walk(self-selected speed) and run(self-selected speed), they should also attend another 4h daily physical activity assessment, the proportion of the activities in which is the same as the proportion in daily life. The equations and cut-points from this research and other references were validated by the data of validation group. Individual EE prediction equations of VM and VM-HR were developed based on the 7 activities'data of validation group and validated by the 4h daily physical activity.2 The same 80 subjects above-mentioned were randomly re-divided into development group(n=60) and validation group(n=20). 3?4 days'physical activities of development group and 7 days'physical activities of validation group were monitored by GT3X accelerometer and Omron HJ-113 pedometer simultaneously. Time of cycling, walking upstairs and bathing were recorded by questionnaire. Individual VM physical activity energy prediction equations based on the data of 7 activities above-mentioned were established and used to calculate the EE and time of physical activity. The relationship between pedometer data and physical activity was analyzed and physical activity volume prediction equations were established using pedometer data.3 302 college students'physical activity of 7 consecutive days were monitored by pedometer, time of cycling, walking upstairs and bathing were recorded by questionnaire. EE and time of physical activities were calculated by pedometer prediction equations and further adjusted by questionnaire. Fitness tests including height, weight, vital capacity, step test and standing broad jump were also done. Then the relationship between pedometer data, physical activity volume and physical fitness were analyzed and physical activity volume reference standard for college students were developed.4 39 college students whose major were not PE attended a short-distance campus orienteering test, the distance was 2500m for male and 2000m for female, the check point number was 12 for male and 10 for female. Actitrainer accelerometer was used to monitor the VM and HR during the test, exercise intensity per minute was calculated using the VM EE prediction equation.Results1 The correlation between ACz ,VM and EE(r=0.93?0.92,p<0.01)were the highest in walk/run activities, in other activities the highest correlation was found between VM and EE(r=0.86,p<0.01). Correlation between VM and EE was high throughout low, moderate and vigorous activities, correlation between HRnet and EE were the highest among all the variables of HR.2 Using the data of ADL, walk/run and“ADL+ walk/run”, using VM?ACz and HRnet as independent variable, we developed 8 EE prediction group equations, a flex-ACz equation and a VM-HRnet equation, 6 intensity cut-points were also developed based on these equations. The equations based on“ADL+ walk/run”data worked well across 7 activities. After been adjusted by the EE of cycling and walking upstairs, flex-ACz equation, two group equations of VM and all individual equations worked well in predicting the PAEE4h. The flex-ACz equation and two group equations of VM were shown below:(1)METs=0.000721*VM+1.399(2)Kcals/min=0.000784*VM+0.054*W-1.947(3)flex-ACz equation: ACz<1630, METs=1.419+0.005644* ACz-5.927*10-6* ACz2+1.993*10-9* ACz3 ACz?1630, METs=1.818+0.000752* ACz3 Individual equations were confirmed to be valid in measuring physical activity time, while no single group equation could measure the time of light, moderate and vigorous physical activity correctly and simultaneously. After analyzing, we set 1463 counts/ min and 4945 counts/min as the ACz cut-points of 3METs and 6METs, 2491counts/min and 5866counts/min as the VM cut-points of 3METs and 6METs.4 During the physical activity survey, total steps/day was negative correlated with light physical activity (LPA) time (r =-0.303, p <0.05), moderately correlated with the total physical activities volume (TPA) and the volume and time of moderate-to- vigorous physical activities (MVPA) (r = 0.530?0.675, p <0.01). Aerobic walking time were moderately correlated with the volume and time of MVPA10 (r = 0.698?0.757, p <0.01). The data of pedometer can be used to predict TPA and the volume and time of MVPA and MVPA10. The most valuable equations were shown below:(1)TPA volume=0.031764*steps/day+1457.58(2)MVPA 10 volume=2.234*aerobic walking time+54.75 (3)MVPA 10 time=0.641* aerobic walking time+12.33 5 College students walked 11528.9±3188.4 steps/day, the aerobic steps was 3618.0±2191.8 steps/day, aerobic walking time was 34.3±20.6 min/day, with the characteristics of "PE day> regular school day> weekend" in daily steps. TPA volume of college students was 1841.7±95.3 METs?min/day, MVPA10 volume was 133.6±53.6 METs?min/day, MVPA10 time was 34.3±14.7 min/day. Overall, college students'physical activity level was relatively high.6 College Students'fitness test score was 66.7±10.4, 25.2% of which were failed (<60), the fitness test score of male was lower than female (p=0.046). Vital capacity, standing long jump and step test scores were relatively low in failed students. BMI, step test index, vital capacity/weight index and fitness test score were lightly correlated with steps/day and aerobic walking time (r =- 0.17 ~ 0.37, p <0.05).7 ROC curve analysis showed that 11,000 steps/day can be used as the steps reference standard of college students, further calculation showed that MVPA10 reach 250 min per week or 900 METs?min per week can be used as physical activity reference standards of college students.8 More than 90% of the test time was above 3METs during short-distance campus orienteering; The average intensity was 6.52±0.67METs for male and 5.67±0.49METs for female, average HR was 144.2±7.1beats/min for male and 145.3±8.5 beats/min for female, HR was above 130beats/min during 70% of the test time.Conclusion1 Group EE prediction equations of accelerometer based on "ADL+walk/run" have high validity and can be applied to monitor daily physical activity of college students, individual EE prediction equations work best. In practice, three-axis accelerometer and questionnaire used in combination can further improve the accuracy in monitoring physical activity.2 Pedometer data can be used to calculate the variables such as total physical activity column, MVPA volume and MVPA time during daily physical activity assessment of college students.3 There is light correlation between physical activity level and fitness level of college students. 11000 steps/day can be used as the walking reference standard of college students.4 Short-distance campus orienteering is a MVPA with mainly aerobic content, the intensity is mainly above 3METs. |
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