Validation of a method for estimating energy expenditure during walking in middle-aged adults

Real-Time Tracking of Human Neck Postures and Movements

Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a digital platform for analyzing the impact of human neck movements on the neck musculoskeletal system. The secondary objective is to design a rehabilitation monitoring system that brings accountability in the treatment prescribed, which is shown in the use-case model. To record neck movements effectively, a Smart Neckband integrated with the Inertial Measurement Unit (IMU) was designed. The initial task was to find a suitable position to locate the sensors embedded in the Smart Neckband. IMU-based real-world kinematic data were captured from eight research subjects and were used to extract kinetic data from the OpenSim simulation platform. A Random Forest algorithm was trained using the kinetic data to predict the neck movements. The results obtained correlated with the novel idea proposed in this paper of using the hyoid muscles to accurately detect neck postures and movements. The innovative approach of integrating kinematic data and kinetic data for analyzing neck postures and movements has been successfully demonstrated through the efficient application in a rehabilitation use case with about 95% accuracy. This research study presents a robust digital platform for the integration of kinematic and kinetic data that has enabled the design of a context-aware neckband for the support in the treatment of neck musculoskeletal disorders.

Estimating energy expenditure from accelerometer data in healthy adults and patients with type 2 diabetes

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.

Accelerometry-based method for assessing energy expenditure in patients with diabetes during walking

BACKGROUND

Monitoring activity-related energy expenditure (EE) is essential in the management of daily activity and the dietary programme in patients with type 2 diabetes (T2D) and must be estimated accurately. Accelerometry-based equations have frequently used to estimate EE, although the validity of these methods has not been confirmed in patients with T2D. The present study aimed to test the validity of an accelerometry-based method (Bouten's method) to assess EE during walking in patients with T2D.

METHODS

The study included 20 patients with controlled T2D [mean (SD) duration: 10.6 (6.1) years; age: 57.5 (8.4) years; body mass index: 26.4 (2.6) kg m- ²]. All participants performed five 6-min periods of walking at different speeds (0.5-1.5 m s-1 ) on a treadmill. Mechanical data were recorded using an inertial measurement unit placed on the lower back with gas exchange being simultaneously monitored.

RESULTS

Values of EE during walking estimated by the accelerometer method did not differ significantly from those measured by indirect calorimetry. Bias and root mean square error were -1.17 and 2.93 kJ min-1 , respectively, on average across speeds.

CONCLUSIONS

Our results suggest that EE during walking may be accurately estimated in patients with diabetes mellitus using an accelerometer.