Objective

The aim of this study was to develop specific prediction equations based on acceleration data measured at three body sites for estimating energy expenditure (EE) during static and active conditions in middle-aged and older adults with and without type 2 diabetes (T2D).

Research methods

Forty patients with T2D (age: 40–74 yr, body mass index (BMI): 21–29.4 kg·m⁻²) and healthy participants (age: 47–79 yr, BMI: 20.2–29.8 kg·m⁻²) completed trials in both static conditions and treadmill walking. For all trials, gas exchange was monitored using indirect calorimetry and vector magnitude was calculated from acceleration data measured using inertial measurement units placed to the participant's center of mass (CM), hip and ankle. Stepwise multiple regression analyses were conducted to select relevant variables to include in the three EE prediction equations, and three Monte Carlo cross-validation procedures were used to evaluate each separate equation.

Results

Vector magnitude (p < 0.0001) and personal data (gender, diabetes status and BMI; p < 0.0001) were used to develop three linear prediction equations to estimate EE during static conditions and walking. Cross-validation revealed similar robust coefficients of determination (R²: 0.81 to 0.85) and small bias (mean bias: 0.008 to −0.005 kcal·min⁻¹) for all three equations. However, the equation based on CM acceleration exhibited the lowest root mean square error (0.60 kcal·min⁻¹ vs. 0.65 and 0.69 kcal·min⁻¹ for the hip and ankle equations, respectively; p < 0.001).

Conclusion

The three equations based on acceleration data and participant characteristics accurately estimated EE during sedentary conditions and walking in middle-aged and older adults, with or without diabetes.

中文翻译：

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根据健康成人和 2 型糖尿病患者的加速计数据估算能量消耗

 客观的

本研究的目的是根据在三个身体部位测量的加速度数据开发具体的预测方程，用于估计患有和不患有 2 型糖尿病 (T2D) 的中老年人在静态和活动条件下的能量消耗 (EE)。

 研究方法

40 名 T2D 患者（年龄：40–74 岁，体重指数（BMI）：21–29.4 kg·m ⁻² ）和健康参与者（年龄：47–79 岁，BMI：20.2–29.8 kg·m ⁻² ）完成了静态条件和跑步机行走的试验。对于所有试验，均使用间接量热法监测气体交换，并根据放置在参与者质心 (CM)、臀部和脚踝处的惯性测量单元测得的加速度数据计算矢量幅度。进行逐步多元回归分析以选择相关变量以包含在三个 EE 预测方程中，并使用三个蒙特卡罗交叉验证程序来评估每个单独的方程。

 结果

矢量幅度 (p < 0.0001) 和个人数据（性别、糖尿病状况和 BMI；p < 0.0001）用于开发三个线性预测方程，以估计静态条件和步行期间的 EE。交叉验证揭示了所有三个方程的相似稳健确定系数（R ² ：0.81至0.85）和小偏差（平均偏差：0.008至-0.005 kcal·min ^-1 ）。然而，基于 CM 加速度的方程表现出最低的均方根误差（髋部和踝部方程分别为 0.60 kcal·min ^-1与 0.65 和 0.69 kcal·min ^-1 ；p < 0.001）。

 结论

基于加速度数据和参与者特征的三个方程准确地估计了中老年人（无论是否患有糖尿病）久坐和行走时的 EE。