We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P,0·001) and locomotive (r 0·961, P, 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cutoff classification models. This method may be useful for field investigations as well as for self-monitoring by general users.

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The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220, R2 = 0.772) and locomotive (METs = 0.005×synthetic acceleration +0.944, R2 = 0.880) activities, except for climbing down and up. The mean differences between the values predicted by our model and measured METs were −0.50 to 0.23 for moderate to vigorous intensity (>3.5 METs) PAs like running, ball throwing and washing the floor, which were regarded as unpredictable PAs. In addition, the difference was within 0.25 METs for sedentary to mild moderate PAs (<3.5 METs). Our specific calibration model that discriminates between nonlocomotive and locomotive activities for children can be useful to evaluate the sedentary to vigorous PAs intensity of both nonlocomotive and locomotive activities.

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Objectives To examine the effects of telephone-delivered lifestyle coaching on preventing the development of type 2 diabetes mellitus (T2DM) in participants with impaired fasting glucose (IFG). Design Cluster randomised trial. Setting 40 groups from 17 healthcare divisions in Japan: companies (31), communities (6) and mixed settings (3). Participants Participants aged 20–65 years with fasting plasma glucose (FPG) of 5.6–6.9 mmol/L were invited from the 17 healthcare divisions. Randomisation The groups were then randomly assigned to an intervention or a control arm by independent statisticians according to a computer-generated list. Intervention The intervention arm received a 1-year telephone-delivered intervention provided by three private lifestyle support centres (at different frequencies: low-frequency (3 times), middle-frequency (6 times) and high-frequency (10 times) support calls). The intervention and control arms both received self-help devices such as a weight scale and pedometer. Outcomes Participants were followed up using data from annual health check-ups and a questionnaire regarding lifestyle. The primary outcome was the development of T2DM defined as FPG ≥7.0 mmol/L, the diagnosis of diabetes, or use of an antidiabetic drug, confirmed by referring to medical cards. Results Of 14 473 screened individuals, participants were enrolled in either the intervention (n=1240) arm or control (n=1367) arm. Overall, the HR for the development of T2DM in the intervention arm during 5.5 years was 1.00 (95% CI 0.74 to 1.34). In the subanalysis, the HR was 0.59 (95% CI 0.42 to 0.83) in the subgroup that received phone calls the most frequently, compared with the control arm. A limitation of the study includes a lack of blinding. Conclusions High-frequency telephone-delivered lifestyle support could effectively prevent T2DM in participants with IFG in a primary healthcare setting, although low-frequency and middle-frequency phone calls did not. Trial registration number This trial has been registered with the University Hospital Medical Information Network (UMIN000000662).