Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities

Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction

Oxygen uptake (V˙O2) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate (HR) is a prominent physiological indicator correlated with V˙O2 and is often used for indirect V˙O2 prediction. This study investigates the impact of HR placement on V˙O2 prediction accuracy by analyzing HR data combined with the respiratory rate (RESP) and minute ventilation (V˙E) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on V˙O2, RESP, V˙E, and HR were collected and used to develop a neural network model for V˙O2 prediction. The influence of HR position on prediction accuracy was assessed via Bland-Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE). Our findings indicate that HR combined with RESP and V˙E (V˙O2HR+RESP+V˙E) produces the most accurate V˙O2 predictions (MAE: 165 mL/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V˙O2 prediction decreases, particularly within high-intensity exercise. The substitution of HR with different anatomical sites significantly impacts V˙O2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering HR placement in V˙O2 prediction models, with RESP and V˙E serving as effective compensatory factors. These findings contribute to refining indirect V˙O2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements.

Evaluation of the efficacy of a light ration adapted to cold weather during a 20-day expedition in Greenland

INTRODUCTION

Limiting body mass loss during military expeditions/training in the cold by providing rations containing easy-to-use, highly palatable, and familiar foods is feasible, but the bulk/weight is too high to be realistically used in a military context. We carried out an analysis of lighter rations adapted to cold weather (1,011 g, 15.7 MJ/3750 kcal) during a 20-day expedition in Greenland.

METHODS

Ten French soldiers daily reported all foods and beverages consumed, the reasons they did not consume certain foods, the palatability of each consumed food, the timing of intake, and the sensation of hunger using a diary.

RESULTS

Although energy intake increased in the 3rd week (vs 1st week; p = 0.015), it was insufficient to prevent the loss of body mass (-4.2 ± 1.9 kg, p = 0.002). More extensive analyses showed that 1) energy intake increased only during dinner (p = 0.024) and that hunger levels continued to increase before dinner (p = 0.029), 2) palatability increased during the 3rd week (vs 1st week) especially for savory day foods (p< 0.001), and 3) lack of hunger and lack of appeal (33 % each) were the main reasons for not consuming certain items.

CONCLUSION

Soldiers placed in total autonomy during a 20-day expedition in the cold and provided rations that were slightly undersized but adapted for cold conditions, surprisingly, remained picky, leading to large losses of body mass. Our results suggest a margin for improvement to stimulate spontaneous food intake. For example, more energy-dense and savory foods during the day and the replacement of certain disliked items.

Results of a 12-Month Randomized Controlled Trial Testing the Efficacy of the Diabetes Prevention Program Group Lifestyle Balance (DPP-GLB) for People Post Stroke (GLB-CVA)

BACKGROUND

Experience of stroke is associated with an increased risk for diabetes and metabolic syndrome, yet few interventions exist that have been tailored to the population's unique needs.

PURPOSE

To examine adherence and efficacy of the Diabetes Prevention Program Group Lifestyle Balance program (DPP-GLB) modified for individuals post stroke (GLB-CVA) using a randomized controlled trial.

METHODS

Adults (18-85 years of age), >12 months post stroke, and body mass index ≥25 kg/m2 were included in this study. Sixty-five individuals were assigned to either the GLB-CVA intervention or a 6-month wait-list control. Participants completed the 12-month GLB-CVA intervention, with attendance and assessment of weight, anthropometric, biomarker, functional, and patient reported outcome data collected at baseline, 3, 6, and 12 months.

RESULTS

High attendance (90%) and dietary and activity tracking (71%) suggest high adherence to the 12-month GLB-CVA. Six-month randomized controlled trial data indicate significant weight loss (p = .005) in the GLB-CVA group (7.4 ± 13.6 lbs, 3.65%) compared with the wait-list control (0.1 ± 10.1 lbs, 0%), and improvements in arm circumference (p = .04), high-density lipoprotein (HDL) cholesterol (p = .028), 8-year diabetes risk (p = .011), and pain interference (p < .001). Combined 12-month data showed participants lost 10.1 ± 16.8 lbs (4.88%) and improved waist circumference (p = .001), HbA1c (3.6%), diastolic blood pressure (p < .001), pain (p = .001), social participation (p = .025), and eating practices (p = .01) and habits (p < .001).

CONCLUSIONS

Engagement in the GLB-CVA can result in weight loss and improved health for individuals who are overweight or obese following stroke. Future efforts should examine effectiveness in real-world settings and focus on knowledge translation efforts.

Assessment of Physical Activity in Adults using Wrist Accelerometers

The health benefits of physical activity have been widely recognized, yet traditional measures of physical activity including questionnaires and category-based assessments of volume and intensity provide only broad estimates of daily activities. Accelerometers have advanced epidemiologic research on physical activity by providing objective and continuous measurement of physical activity in free-living conditions. Wrist-worn accelerometers have become especially popular due to low participant burden. However, the validity and reliability of wrist-worn devices for adults have yet to be summarized. Moreover, accelerometer data provide rich information on how physical activity is accumulated throughout the day, but only a small portion of these rich data have been utilized by researchers. Lastly, new methodological developments that aim to overcome some of the limitations of accelerometers are emerging. The purpose of this review is to provide an overview of accelerometry research, with a special focus on wrist-worn accelerometers. We describe briefly how accelerometers work, summarize the validity and reliability of wrist-worn accelerometers, discuss the benefits of accelerometers including measuring light-intensity physical activity, and discuss pattern metrics of daily physical activity recently introduced in the literature. A summary of large-scale cohort studies and randomized trials that implemented wrist-worn accelerometry is provided. We conclude the review by discussing new developments and future directions of research using accelerometers, with a focus on wrist-worn accelerometers.

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Background

Inertial sensors, such as accelerometers, serve as convenient devices to predict the energy expenditures (EEs) during physical activities by a predictive equation. Although the accuracy of estimate EEs especially matter to athletes receive physical training, most EE predictive equations adopted in accelerometers are based on the general population, not athletes. This study included the heart rate reserve (HRR) as a compensatory parameter for physical intensity and derived new equations customized for sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults.

Methods

With indirect calorimetry as the criterion measure (CM), the EEs of participants on a treadmill were measured, and vector magnitudes (VM), as well as HRR, were simultaneously recorded by a waist-worn accelerometer with a heart rate monitor. Participants comprised a sedentary group (SG), an exercise-habit group (EHG), a non-endurance group (NEG), and an endurance group (EG), with 30 adults in each group.

Results

EE predictive equations were revised using linear regression with cross-validation on VM, HRR, and body mass (BM). The modified model demonstrates valid and reliable predictions across four populations (Pearson correlation coefficient, r: 0.922 to 0.932; intraclass correlation coefficient, ICC: 0.919 to 0.930).

Conclusion

Using accelerometers with a heart rate monitorcan accurately predict EEs of athletes and non-athletes with an optimized predictive equation integrating the VM, HRR, and BM parameters.

Daily energy balance and eating behaviour during a 14-day cold weather expedition in Greenland

We assessed energy compensation, appetite, and reward value of foods during a 14-day military expedition in Greenland realized by 12 male French soldiers, during which energy compensation was optimized by providing them with easy-to-eat palatable foods in excess. Although daily energy expenditure (estimated by accelerometry) stayed relatively constant throughout the expedition (15 ± 9 MJ·day−1), energy intake (EI; estimated by self-reported diaries) was 17% higher during the D8–D14 period compared with the D1–D7 period, leading to a neutral energy balance (EB). Body fat mass (BFM) significantly decreased (–1.0 ± 0.7 kg, p < 0.001) but not body mass (BM). Neither hunger scores (assessed by visual analog scales) nor components of the reward value of food (explicit liking (EL) and food preference) were significantly altered. However, changes in EL at D10 were positively correlated with changes in BM (r = 0.600, p < 0.05) and BFM (r = 0.680, p < 0.05) and changes in hunger in the EI of the relevant period (r = 0.743, p < 0.01 for D1–D7, r = 0.652, p < 0.05 for D8–14). This study shows that the negative EB and BM loss can be attenuated by an appropriate food supply and that subjective components of eating behaviour, such as hunger and EL, may be useful to predict the magnitude of energy compensation.

Novelty Energy intake increases during of a 14-day expedition in the cold. Energy compensation was likely facilitated by providing participants with easy-to-eat palatable and familiar foods. Hunger scores and EL for energy-dense foods were associated with high EIs and low BM changes.

Feasibility of the Energy Expenditure Prediction for Athletes and Non-Athletes from Ankle-Mounted Accelerometer and Heart Rate Monitor

Due to the nature of micro-electromechanical systems, the vector magnitude (VM) activity of accelerometers varies depending on the wearing position and does not identify different levels of physical fitness. Without an appropriate energy expenditure (EE) estimation equation, bias can occur in the estimated values. We aimed to amend the EE estimation equation using heart rate reserve (HRR) parameters as the correction factor, which could be applied to athletes and non-athletes who primarily use ankle-mounted devices. Indirect calorimetry was used as the criterion measure with an accelerometer (ankle-mounted) equipped with a heart rate monitor to synchronously measure the EE of 120 healthy adults on a treadmill in four groups. Compared with ankle-mounted accelerometer outputs, when the traditional equation was modified using linear regression by combining VM with body weight and/or HRR parameters (modified models: Model A, without HRR; Model B, with HRR), both Model A (r: 0.931 to 0.972; ICC: 0.913 to 0.954) and Model B (r: 0.933 to 0.975; ICC: 0.930 to 0.959) showed the valid and reliable predictive ability for the four groups. With respect to the simplest and most reasonable mode, Model A seems to be a good choice for predicting EE when using an ankle-mounted device.

The impact of a patient-directed activity program on functional outcomes and activity participation after stroke during inpatient rehabilitation—a randomized controlled trial

Objective:

Individuals post stroke are inactive, even during rehabilitation, contributing to ongoing disability and risk of secondary health conditions. Our aims were to (1) conduct a randomized controlled trial to examine the efficacy of a “Patient-Directed Activity Program” on functional outcomes in people post stroke during inpatient rehabilitation and (2) examine differences three months post inpatient rehabilitation discharge.

Design:

Randomized control trial.

Setting:

Inpatient rehabilitation facility.

Subjects:

Patients admitted to inpatient rehabilitation post stroke.

Interventions:

Patient-Directed Activity Program (PDAP) or control (usual care only). Both groups underwent control (three hours of therapy/day), while PDAP participants were prescribed two additional 30-minute activity sessions/day.

Main measures:

Outcomes (Stroke Rehabilitation Assessment of Movement Measure, Functional Independence Measure, balance, physical activity, Stroke Impact Scale) were collected at admission and discharge from inpatient rehabilitation and three-month follow-up.

Results:

Seventy-three patients (PDAP ( n = 37); control ( n = 36)) were included in the primary analysis. Patients in PDAP completed a total of 23.1 ± 16.5 sessions (10.7 ± 8.5 upper extremity; 12.4 ± 8.6 lower extremity) during inpatient rehabilitation. No differences were observed between groups at discharge in functional measures. PDAP completed significantly more steps/day (PDAP = 657.70 ± 655.82, control = 396.17 ± 419.65; P = 0.022). The Stroke Impact Scale showed significantly better memory and thinking (PDAP = 86.2 ± 11.4, control = 80.8 ± 16.7; P = 0.049), communication (PDAP = 93.6 ± 8.3, control = 89.6 ± 12.4; P = 0.042), mobility (PDAP = 62.2 ± 22.5, control = 53.8 ± 21.8; P = 0.038), and overall recovery from stroke (PDAP = 62.1 ± 19.1, control = 52.2 ± 18.7; P = 0.038) for PDAP compared to control. At three months post discharge, PDAP ( n = 11) completed significantly greater physical activity ( P = 0.014; 3586.5 ± 3468.5 steps/day) compared to control ( n = 10; 1760.9 ± 2346.3 steps/day).

Conclusion:

Functional outcome improvement was comparable between groups; however, PDAP participants completed more steps and perceived greater recovery.

Correction of estimation bias of predictive equations of energy expenditure based on wrist/waist-mounted accelerometers

Background

Using wearable inertial sensors to accurately estimate energy expenditure (EE) during an athletic training process is important. Due to the characteristics of inertial sensors, however, the positions in which they are worn can produce signals of different natures. To understand and solve this issue, this study used the heart rate reserve (HRR) as a compensation factor to modify the traditional empirical equation of the accelerometer EE sensor and examine the possibility of improving the estimation of energy expenditure for sensors worn in different positions.

Methods

Indirect calorimetry was used as the criterion measure (CM) to measure the EE of 90 healthy adults on a treadmill (five speeds: 4.8, 6.4, 8.0, 9.7, and 11.3 km/h). The measurement was simultaneously performed with the ActiGraph GT9X-Link (placed on the wrist and waist) with the Polar H10 Heart Rate Monitor.

Results

At the same exercise intensity, the EE measurements of the GT9X on the wrist and waist had significant differences from those of the CM (p < 0.05). By using multiple regression analysis—utilizing values from vector magnitudes (VM), body weight (BW) and HRR parameters—accuracy of EE estimation was greatly improved compared to traditional equation. Modified models explained a greater proportion of variance (R²) (wrist: 0.802; waist: 0.805) and demonstrated a good ICC (wrist: 0.863, waist: 0.889) compared to Freedson’s VM3 Combination equation (R²: wrist: 0.384, waist: 0.783; ICC: wrist: 0.073, waist: 0.868).

Conclusions

The EE estimation equation combining the VM of accelerometer measurements, BW and HRR greatly enhanced the accuracy of EE estimation based on data from accelerometers worn in different positions, particularly from those on the wrist.

Weight Loss After Stroke Through an Intensive Lifestyle Intervention (Group Lifestyle Balance-Cerebrovascular Accident): Protocol for a Randomized Controlled Trial

BACKGROUND

Weight gain can be a consequence of stroke, or cerebrovascular accident (CVA), because of impaired mobility, behavioral and emotional disorders, and sensory losses. Weight gain increases the patient's risk of recurrent stroke and chronic diseases, such as diabetes, metabolic syndrome, and pulmonary and heart disease. Approaches to weight loss in this population are lacking, although necessary because of the unique physiological and cognitive needs of persons after a stroke. Evidence shows that intensive behavioral therapy interventions that address both physical activity and diet offer the greatest potential for weight loss. The Group Lifestyle Balance (GLB) intervention is a 12-month, evidence-based weight loss program that has been used extensively with the general population; this program was modified to meet the needs of people who have had a stroke (GLB-CVA).

OBJECTIVE

This randomized controlled trial (RCT) aims to examine the efficacy of the GLB-CVA on weight and secondary outcomes, compared with that of a waitlist control group.

METHODS

This RCT will enroll and randomize 64 patients over an 18-month period.

RESULTS

Currently, 51 people are waitlisted, with 23 out of 51 screened and 16 out of 23 eligible.

CONCLUSIONS

It is anticipated that the findings from this RCT will contribute to the evidence base regarding weight loss strategies for people living with stroke.

CLINICAL TRIAL

ClinicalTrials.gov NCT03873467; https://clinicaltrials.gov/ct2/show/NCT03873467.

Quantification of energy expenditure during daily living activities after stroke by multi-sensor

Objective: To explore the validity of energy expenditure estimates using the SenseWear Armband during a sequence of four daily living activities in patients post-stroke. Method: Patients with stroke who were able to walk during 6 min without human assistance were asked to wear the SenseWear Armband on the non-paretic arm while performing transfers, a manual task, walking, and walking up and down stairs. The energy expenditure estimated using the SenseWear Armband was compared to the energy expenditure calculated from oxygen consumption, measured by a portable indirect calorimeter (Metamax 3B). The mean of energy expenditure was pooled for each task. Accuracy was explored by mean bias (MB) of Bland-Altman analysis and root mean square error (RMSE), agreement by 95% of limits of agreement (95%LoA) and coefficient of correlation (r). Results: Thirty-eight participants (65.7 ± 13.5 years) were included. The SenseWear Armband globally underestimated energy expenditure, MB = 9.77 kcal for the whole sequence. RMSE were large, accounting for 15% to 41% of the measured energy expenditure. Agreement was low with r < 0.70 and 95%LoA from 42% to 93% of the measured energy expenditure. Conclusions: This study reported a global underestimation and a low level of agreement of the energy expenditure estimated by SenseWear Armband in four daily living activities in patients after stroke. Abbreviations: EE: Energy Expenditure; NIHSS: National Institute of Health Stroke Score.

Accuracy of the energy expenditure during uphill exercise measured by the Waist-worn ActiGraph

Background/objective

The application of Micro-Electro-Mechanical Sensors (MEMS) as measurements of energy expenditure (EE) has certain disadvantages. For example, the inertial sensors cannot easily distinguish changes in ground slope during walking/running conditions, so the accuracy of EE calculation is biased. To resolve this issue, heart rate (HR) and heart rate reserve (HRR) were used as compensatory factors respectively to correct the classical empirical formula of the accelerometer analyzer for EE in this study.

Methods

To explore the improvement of the accuracy of EE during uphill exercise and compare the correction levels between HR and HRR, oxygen uptake was used as a criterion measure (CM). Thirty healthy adult males wore an ActiGraph GT3X with the Polar HR monitor and Vmax indirect calorimeter during twelve treadmill activities (3 gradients and 4 speeds).

Results

When the slopes were increased by 0%, 3%, and 6%, the measurement accuracy of the accelerometers, calculated by intraclass correlation coefficient (ICC), decreased by 0.877, 0.755, and 0.504, respectively (p < 0.05). The HR and HRR parameters of linear regression were used to correct the classical formula. The results showed that HR had higher coefficients of determination (R2) (0.801, 0.700, and 0.642 respectively) and ICCs (0.887, 0.825, and 0.785 respectively) than did the accelerometer outputs. HRR showed the highest coefficients of determination (R2) (0.821, 0.728, and 0.656 respectively) and ICCs (0.901, 0.844, and 0.795 respectively).

Conclusions

Through adding HRR parameters, the accuracy of the classical prediction formula EE was significantly improved during walking/running on sloping ground.

Wrist-specific accelerometry methods for estimating free-living physical activity

OBJECTIVES

To compare accelerometry-derived estimates of physical activity from 9 wrist-specific predictive models and a reference hip-specific method.

DESIGN

Prospective cohort repeated measures study.

METHODS

110 participants wore an accelerometer at wrist and hip locations for 1 week of free-living. Accelerometer data from three axes were used to calculate physical activity estimates using existing wrist-specific models (3 linear and 6 artificial neural network models) and a reference hip-specific method. Estimates of physical activity were compared to reference values at both epoch (≤60-s) and weekly levels.

RESULTS

9044h were analysed. Physical activity ranged from 7 to 96min per day of moderate-to-vigorous physical activity (MVPA). Method of analysis influenced determination of sedentary behaviour (<1.5 METs), light physical activity (1.5 to <3 METs) and MVPA (>3 METs) (p<0.001, respectively). All wrist-specific models produced total weekly MVPA values that were different to the reference method. At the epoch level, Hildebrand et al. (2014) produced the strongest correlation (r=0.69, 95%CI: 0.67-0.71) with tightest ratio limits of agreement (95%CI: 0.53-1.30) for MVPA, and highest agreement to predict MVPA (94.1%, 95%CI: 94.0-94.1%) with sensitivity of 63.1% (95%CI: 62.6-63.7%) and specificity of 96.0% (95%CI: 95.9-96.0%).

CONCLUSIONS

Caution is required when comparing results from studies that use inconsistent analysis methods. Although a wrist-specific linear model produced results that were most similar to the hip-specific reference method when estimating total weekly MVPA, modest absolute and relative agreement at the epoch level suggest that additional analysis methods are required to improve estimates of physical activity derived from wrist-worn accelerometers.

Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers

The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from (N=152) adult participants; age 18–64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings.

Validity of the Actigraph GT3x and influence of the sensor positioning for the assessment of active energy expenditure during four activities of daily living in stroke subjects

Objective:

To explore the validity of the Actigraph and the influence of the placement of the sensor when estimating the active energy expenditure in four common daily activities (transfers, manual task, walking in overground and walking up and down stairs) in stroke survivors.

Design:

Cross-sectional study.

Setting:

Department of Physical Medicine and Rehabilitation.

Subjects:

A total of 46 subjects affected with stroke sequelae were wearing the Actigraph on three different locations—ankle, hip and wrist on the non-affected side—and performed four tasks: transfers, manual task, walking on flat ground and walking up and down stairs.

Main measures:

The values of active energy expenditure estimated by the Actigraph were compared to those measured by a portable breathing gas exchange analyzer Metamax3B. The accuracy and agreement between Actigraph and Metamax values were analyzed with mean bias, root mean square error, correlation coefficient and Bland–Altman plots for each task and each sensor location.

Results:

The mean bias between the Metamax and Actigraph placed on wrist, hip and ankle were, respectively, MD = 1.16 kcal (%MD = 3%), MD =−20.44 kcal (%MD =−58%) and MD = 17.64 kcal (%MD = 50%). The agreement with the Metamax was poor in general regardless of the sensor location and type of task ( r = 0.12–0.58).

Conclusion:

This study found large differences and a poor agreement between the active energy expenditure as measured by the Actigraph and the Metamax according to the location of the sensor and the type of task performed by the subject.