Accuracy of two Generic Prediction Equations and One Population-Specific Equation for Resting Energy Expenditure in Individuals with Spinal Cord Injury

Validation of basal metabolic rate equations in persons with innervated and denervated chronic spinal cord injury

Basal metabolic rate (BMR) measurement is time consuming and requires specialized equipment. Prediction equations allow clinicians and researchers to estimate BMR; however, their accuracy may vary across individuals with chronic spinal cord injury (SCI). The objective of this study was to investigate the validity of SCI-specific equations as well as able-bodied (AB) prediction equations in individuals with upper motor neuron (UMN), lower motor neuron (LMN), and females with SCI. Twenty-six men and women with chronic SCI (n = 12 innervated males, n = 6 innervated females, n = 8 denervated males) participated in this cross-sectional study. BMR values were measured by indirect calorimetry. Body composition (dual-energy X-ray absorptiometry and anthropometrics) assessment was conducted. AB-prediction equations [Cunningham, Nelson, Owen, Harris and Benedict, Mifflin, Schofield, Henry] and SCI-specific equations [Chun and Nightingale & Gorgey] were used to estimate and validate BMR. The accuracy of AB-specific FFM equations in predicting BMR was evaluated using Bland-Altman plots, paired t-tests, and error metrics. Measured BMR for innervated males, females, and denervated males was 1436 ± 213 kcal/day, 1290 ± 114, and 1597 ± 333 kcal/day, respectively. SCI-specific equations by Chun et al., Nightingale & Gorgey, and AB-specific FFM equations accurately predicted BMR for innervated males. For the denervated males, Model 4 equation by Nightingale & Gorgey was not different (p = 0.18), and Bland-Altman analyses showed negative mean bias but similar limits of agreement between measured and predicted BMR for the SCI-specific equations and AB-specific FFM equations. We demonstrated that SCI-specific equations accurately predicted BMR for innervated males but underpredicted it for denervated males. The Model 4 equation by Nightingale & Gorgey accurately estimated BMR in females with SCI. Findings from the current study will help to determine caloric needs in different sub-groups of SCI.

Wearable Sensors in Ambulatory Individuals With a Spinal Cord Injury: From Energy Expenditure Estimation to Activity Recommendations

Inappropriate physical inactivity is a global health problem increasing the risk of cardiometabolic diseases. Wearable sensors show great potential to promote physical activity and thus a healthier lifestyle. While commercial activity trackers are available to estimate energy expenditure (EE) in non-disabled individuals, they are not designed for reliable assessments in individuals with an incomplete spinal cord injury (iSCI). Furthermore, activity recommendations for this population are currently rather vague and not tailored to their individual needs, and activity guidelines provided for the non-disabled population may not be easily translated for this population. However, especially in iSCI individuals with impaired abilities to stand and walk, the assessment of physical activities and appropriate recommendations for a healthy lifestyle are challenging. Therefore, the study aimed at developing an EE estimation model for iSCI individuals able to walk based on wearable sensor data. Additionally, the data collected within this study was used to translate common activity recommendations for the non-disabled population to easily understandable activity goals for ambulatory individuals with an iSCI. In total, 30 ambulatory individuals with an iSCI were equipped with wearable sensors while performing 12 different physical activities. EE was measured continuously and demographic and anthropometric variables, clinical assessment scores as well as wearable-sensor-derived features were used to develop different EE estimation models. The best EE estimation model comprised the estimation of resting EE using the updated Harris-Benedict equation, classifying activities using a k-nearest neighbor algorithm, and applying a multiple linear regression-based EE estimation model for each activity class. The mean absolute estimation error of this model was 15.2 ± 6.3% and the corresponding mean signed error was −3.4 ± 8.9%. Translating activity recommendations of global health institutions, we suggest a minimum of 2,000–3,000 steps per day for ambulatory individuals with an iSCI. If ambulatory individuals with an iSCI targeted the popular 10,000 steps a day recommendation for the non-disabled population, their equivalent would be around 8,000 steps a day. The combination of the presented dedicated EE estimation model for ambulatory individuals with an iSCI and the translated activity recommendations is an important step toward promoting an active lifestyle in this population.