Validation of the SenseWear armband in circuit resistance training with different loads

Methods to Assess Energy Expenditure of Resistance Exercise: A Systematic Scoping Review

BACKGROUND

Nutrition guidance for athletes must consider a range of variables to effectively support individuals in meeting energy and nutrient needs. Resistance exercise is a widely adopted training method in athlete preparation and rehabilitation and therefore is one such variable that will influence nutrition guidance. Given its prominence, the capacity to meaningfully quantify resistance exercise energy expenditure will assist practitioners and researchers in providing nutrition guidance. However, the significant contribution of anaerobic metabolism makes quantifying energy expenditure of resistance exercise challenging.

OBJECTIVE

The aim of this scoping review was to investigate the methods used to assess resistance exercise energy expenditure.

METHODS

A literature search of Medline, SPORTDiscus, CINAHL and Web of Science identified studies that included an assessment of resistance exercise energy expenditure. Quality appraisal of included studies was performed using the Rosendal Scale.

RESULTS

A total of 19,867 studies were identified, with 166 included after screening. Methods to assess energy expenditure included indirect calorimetry (n = 136), blood lactate analysis (n = 25), wearable monitors (n = 31) and metabolic equivalents (n = 4). Post-exercise energy expenditure was measured in 76 studies. The reported energy expenditure values varied widely between studies.

CONCLUSIONS

Indirect calorimetry is widely used to estimate energy expenditure. However, given its limitations in quantifying glycolytic contribution, indirect calorimetry during and immediately following exercise combined with measures of blood lactate are likely required to better quantify total energy expenditure. Due to the cumbersome equipment and technical expertise required, though, along with the physical restrictions the equipment places on participants performing particular resistance exercises, indirect calorimetry is likely impractical for use outside of the laboratory setting, where metabolic equivalents may be a more appropriate method.

ACCELEROMETRY UNDERESTIMATES ENERGY EXPENDITURE IN CIRCUIT-BASED RESISTANCE TRAINING

ABSTRACT Introduction: Accelerometry is a very accurate method for determining energy expenditure (EE) in endurance training. However, further studies are needed to prove its accuracy in resistance training. Objective: To compare the EE obtained by accelerometry and indirect calorimetry in three different circuit resistance training circuits. Methods: Six overweight volunteers performed three sets in three resistance training circuits: machine circuit (MC), free-weight circuit (FWC) and resistance + aerobic circuit (RAC). EE was measured by indirect calorimetry using an Oxycon Mobile® and by the accelerometers SenseWear® Armband Pro2 and ActiTrainer®. Results: ActiTrainer® and SenseWear® underestimated EE in all circuits when compared to indirect calorimetry (p<0.05). The difference was greater in the FWC: 44.4% METs and 81.4% Kcal for ActiTrainer® and 32.3% METs and 24.9% Kcal for SenseWear® compared to indirect calorimetry. Conclusion: Both ActiTrainer® and SenseWear® underestimated EE when compared to indirect calorimetry in three different resistance training circuits. Level of evidence II; Diagnostic studies - Investigating a diagnostic test.

Physical Capacity and Energy Expenditure of Cavers

Caves are an extreme environment for humans because of the high humidity, mud, darkness, and slippery conditions. Explorations can last many hours or even days, and require extensive climbing and ropework. Very little is known about the physical capacity of cavers and their energy expenditure (EE) during caving. The physical capacity of 17 (7 females) expert cavers (age 43.9 ± 7.3 years) was assessed during an incremental cycle-ergometer test (IET) with gas exchange analysis. Moreover, a wearable metabolic band (Armband Fit Core) was used to estimate their EE during caving. In terms of physical capacity, the IET showed that cavers had a maximum oxygen uptake (VO_2max) of 2,248.7 ± 657.8 ml·min⁻¹ (i.e., 32.4 ± 6.4 ml·kg⁻¹·min⁻¹), while anaerobic threshold (AT) occurred on average at 74.5% of VO_2max. Results from caving sessions provided an average time spent in cave of 9.4 ± 1.2 h while the average EE was 268.8 ± 54.8 kcal·h⁻¹, which corresponded to about 40% of VO_2max measured during IET. A mean distance of 10.6 ± 2.2 km was covered by subjects. Data from the present investigation provide evidence that cavers have a level of aerobic physical capacity only slightly higher than that of sedentary people, thereby suggesting that a high aerobic fitness is not needed by cavers. Moreover, during caving the EE was on average well below the level of AT. However, in absolute terms, the total EE was elevated (i.e., 2,672.3 ± 576 kcal in total) due to the long time spent in caving.

Monitoring Energy Expenditure Using a Multi-Sensor Device-Applications and Limitations of the SenseWear Armband in Athletic Populations

In order to monitor their energy requirements, athletes may desire to assess energy expenditure (EE) during training and competition. Recent technological advances and increased customer interest have created a market for wearable devices that measure physiological variables and bodily movement over prolonged time periods and convert this information into EE data. This mini-review provides an overview of the applicability of the SenseWear armband (SWA), which combines accelerometry with measurements of heat production and skin conductivity, to measure total daily energy expenditure (TDEE) and its components such as exercise energy expenditure (ExEE) in athletic populations. While the SWA has been shown to provide valid estimates of EE in the general population, validation studies in athletic populations indicate a tendency toward underestimation of ExEE particularly during high-intensity exercise (>10 METs) with an increasing underestimation as exercise intensity increases. Although limited information is available on the accuracy of the SWA during resistance exercise, high-intensity interval exercise, or mixed exercise forms, there seems to be a similar trend of underestimating high levels of ExEE. The SWA, however, is capable of detecting movement patterns and metabolic measurements even at high exercise intensities, suggesting that underestimation may result from limitations in the proprietary algorithms. In addition, the SWA has been used in the assessment of sleep quantity and quality as well as non-exercise activity thermogenesis. Overall, the SWA provides viable information and remains to be used in various clinical and athletic settings, despite the termination of its commercial sale.

Effects of supervised and unsupervised physical activity programmes for weight loss

Objectives

Physical activity is important for weight management. However, it remains unclear what type of physical activity prescription/programme is optimal for increasing physical activity during a standard behavioural weight loss intervention. This study examined changes in physical activity after a 12‐week supervised programme prescribed in minutes per week (SUP‐PA), an unsupervised programme prescribed in minutes per week (UNSUP‐PA) and an unsupervised programme prescribed in steps per day (STEP).

Methods

Fifty‐two adults who were overweight or obese (age: 43.5 ± 10.1 years, BMI: 31.5 ± 3.5 kg·m⁻²) were randomized to STEP (n = 18), UNSUP‐PA (n = 17) and SUP‐PA (n = 17). Subjects attended weekly in‐person group intervention sessions and were prescribed a calorie‐restricted diet (1,200–1,800 kcals·day⁻¹) combined with increased physical activity (150 min·week⁻¹ or 10,000 steps·day⁻¹ with 2,500 brisk steps·day⁻¹).

Results

All three groups significantly increased moderate‐to‐vigorous physical activity (STEP: 80.6 ± 218.5 min·week⁻¹, UNSUP‐PA: 112.9 ± 180.4 min·week⁻¹ and SUP‐PA: 151.1 ± 174.0 min·week⁻¹, p < 0.001) with no differences between groups (p = 0.94) or group by time interaction (p = 0.81). In addition, there were no significant differences in weight loss between the groups (p = 0.81).

Conclusions

In this short‐term study, all three physical activity programmes increased physical activity and elicited modest weight loss when combined with a standard behavioural weight loss intervention.

Cardiovascular Fitness and Energy Expenditure Response during a Combined Aerobic and Circuit Weight Training Protocol

OBJECTIVES

The present study describes the oxygen uptake and total energy expenditure (including both aerobic and anaerobic contribution) response during three different circuit weight training (CWT) protocols of equivalent duration composed of free weight exercises, machine exercises, and a combination of free weight exercises intercalating aerobic exercise.

DESIGN

Controlled, randomized crossover designs.

METHODS

Subjects completed in a randomized order three circuit weight training protocols of the same duration (3 sets of 8 exercises, 45min 15s) and intensity (70% of 15 repetitions maximum). The circuit protocols were composed of free weight exercises, machine exercises, or a combination of free weight exercises with aerobic exercise. Oxygen consumption and lactate concentration were measured throughout the circuit to estimate aerobic and anaerobic energy expenditure respectively.

RESULTS

Energy expenditure is higher in the combined exercise protocol (29.9±3.6 ml/kg/min), compared with Freeweight (24.2±2.8ml/kg/min) and Machine (20.4±2.9ml/kg/min). The combined exercise protocol produced the highest total energy expenditure but the lowest lactate concentration and perceived exertion. The anaerobic contribution to total energy expenditure was higher in the machine and free weight protocols compared with the combined exercise protocol (6.2%, 4.6% and 2.3% respectively).

CONCLUSIONS

In the proposed protocols, the combined exercise protocol results in the highest oxygen consumption. Total energy expenditure is related to the type of exercise included in the circuit. Anaerobic contributions to total energy expenditure during circuit weight training may be modest, but lack of their estimation may underestimate total energy expenditure.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01116856.

Validity of SenseWear® Armband v5.2 and v2.2 for estimating energy expenditure

We compared SenseWear Armband versions (v) 2.2 and 5.2 for estimating energy expenditure in healthy adults. Thirty-four adults (26 women), 30.1 ± 8.7 years old, performed two trials that included light-, moderate- and vigorous-intensity activities: (1) structured routine: seven activities performed for 8-min each, with 4-min of rest between activities; (2) semi-structured routine: 12 activities performed for 5-min each, with no rest between activities. Energy expenditure was measured by indirect calorimetry and predicted using SenseWear v2.2 and v5.2. Compared to indirect calorimetry (297.8 ± 54.2 kcal), the total energy expenditure was overestimated (P < 0.05) by both SenseWear v2.2 (355.6 ± 64.3 kcal) and v5.2 (342.6 ± 63.8 kcal) during the structured routine. During the semi-structured routine, the total energy expenditure for SenseWear v5.2 (275.2 ± 63.0 kcal) was not different than indirect calorimetry (262.8 ± 52.9 kcal), and both were lower (P < 0.05) than v2.2 (312.2 ± 74.5 kcal). The average mean absolute per cent error was lower for the SenseWear v5.2 than for v2.2 (P < 0.001). SenseWear v5.2 improved energy expenditure estimation for some activities (sweeping, loading/unloading boxes, walking), but produced larger errors for others (cycling, rowing). Although both algorithms overestimated energy expenditure as well as time spent in moderate-intensity physical activity (P < 0.05), v5.2 offered better estimates than v2.2.

Analysis of energy metabolism in humans: A review of methodologies

Background

Obesity is a consequence of chronic energy imbalance. We need accurate and precise measurements of energy intake and expenditure, as well as the related behaviors, to fully understand how energy homeostasis is regulated in order to develop interventions and evaluate their effectiveness to combat the global obesity epidemic.

Scope of review

We provide an in-depth review of the methodologies currently used to measure energy intake and expenditure in humans, including their principles, advantages, and limitations in the clinical research setting. The aim is to provide researchers with a comprehensive guide to conduct obesity research of the highest possible quality.

Major conclusions

An array of methodologies is available to measure various aspects of energy metabolism and none is perfect under all circumstances. The choice of methods should be specific to particular research questions with practicality and quality of data the priorities for consideration. A combination of complementary measurements may be preferable. There is an imperative need to develop new methodologies to improve the accuracy and precision of energy intake assessments.

•

Image-based technology is a significant step to improve energy intake measurement.

•

Physical activity informs patterns but not absolute energy expenditure.

•

Combining complementary measurements overcomes shortfalls of individual methods.

Comparison of Consumer and Research Monitors under Semistructured Settings

PURPOSE

This study evaluated the relative validity of different consumer and research activity monitors during semistructured periods of sedentary activity, aerobic exercise, and resistance exercise.

METHODS

Fifty-two (28 male and 24 female) participants age 18-65 yr performed 20 min of self-selected sedentary activity, 25 min of aerobic exercise, and 25 min of resistance exercise, with 5 min of rest between each activity. Each participant wore five wrist-worn consumer monitors [Fitbit Flex, Jawbone Up24, Misfit Shine (MS), Nike+ Fuelband SE (NFS), and Polar Loop] and two research monitors [ActiGraph GT3X+ on the waist and BodyMedia Core (BMC) on the arm] while being concurrently monitored with Oxycon Mobile (OM), a portable metabolic measuring system. Energy expenditure (EE) on different activity sessions was measured by OM and estimated by all monitors.

RESULTS

Mean absolute percent error (MAPE) values for the full 80-min protocol ranged from 15.3% (BMC) to 30.4% (MS). EE estimates from ActiGraph GT3X+ were found to be equivalent to those from OM (± 10% equivalence zone, 285.1-348.5). Correlations between OM and the various monitors were generally high (ranged between 0.71 and 0.90). Three monitors had MAPE values lower than 20% for sedentary activity: BMC (15.7%), MS (18.2%), and NFS (20.0%). Two monitors had MAPE values lower than 20% for aerobic exercise: BMC (17.2%) and NFS (18.5%). None of the monitors had MAPE values lower than 25% for resistance exercise.

CONCLUSION

Overall, the research monitors and Fitbit Flex, Jawbone Up24, and NFS provided reasonably accurate total EE estimates at the individual level. However, larger error was evident for individual activities, especially resistance exercise. Further research is needed to examine these monitors across various activities and intensities as well as under real-world conditions.

Validation and reliability of two activity monitors for energy expenditure assessment

OBJECTIVES

This study explores the reliability and validity of the SenseWear Armband (SWA) and Actical (ACT) for free-living total energy expenditure, and energy expenditure during rest and light-to-moderate exercises (walking, ergocycling).

DESIGN

Participants wore the 2 devices during 7 days (free-living) and then participated to 3 days of testing in our laboratory.

METHODS

SWA and ACT estimates of total energy expenditure was compared to the doubly labeled water technique (7 days), and energy expenditure during rest (60min), treadmill (45min; intensities ∼22% to ∼41% VO2peak) and ergocycling (45min; ∼50% VO2peak) were compared to indirect calorimetry over the following 3 days. Paired T-tests and intra-class correlation coefficient (ICC) with 95% confidence interval (CI95) were computed.

RESULTS

Twenty adults were recruited (BMI 23.1±2.3kg/m(2)). Compared to doubly labelled water, SWA overestimated energy expenditure by 94kcal/d (±319; P=0.2) and ACT underestimated by -244kcal/d (±258; P=0.001). Energy expenditure during rest (SWA 210±116, ACT 124±133kcal/d; p<0.05) and treadmill (according on intensity: SWA 54±46 to 67±38, ACT 68±25 to 84±40kcal; p<0.05) were overestimated and underestimated during ergocycling (SWA -93±65, ACT -269±111kcal; p<0.05) compared to indirect calorimetry. High ICC were observed at rest (SWA 0.994 CI95 0.987-0.997; ACT 0.998 CI95 0.996-0.999) and during ergocycling (SWA 0.941 CI95 0.873-0.975; ACT 0.854 CI95 0.687-0.939).

CONCLUSION

Acceptable estimation of total energy expenditure was observed with the SWA. Both devices were reliable but not accurate for energy expenditure's estimations during rest and for specific exercises.

Low validity of the Sensewear Pro3 activity monitor compared to indirect calorimetry during simulated free living in patients with osteoarthritis of the hip

Background

To validate physical activity estimates by the Sensewear Pro3 activity monitor compared with indirect calorimetry during simulated free living in patients diagnosed with osteoarthritis of the hip pre or post total hip arthroplasty.

Methods

Twenty patients diagnosed with hip osteoarthritis (10 pre- and 10 post total hip arthroplasty; 40% female; age: 63.3 ± 9.0; BMI: 23.7 ± 3.7). All patients completed a 2 hour protocol of simulated free living with 8 different typical physical activity types. Energy consumption (kcal/min) was estimated by the Sense Wear pro3 Armband activity monitor and validated against indirect calorimetry (criterion method) by means of a portable unit (Cosmed K4b²). Bias and variance was analyzed using functional ANOVA.

Results

Mean bias during all activities was 1.5 Kcal/min 95%CI [1.3; 1.8] corresponding to 72% (overestimation). Normal gait speed showed an overestimation of 2.8 Kcal/min, 95%CI [2.3; 3.3] (93%) while an underestimation of -1.1 Kcal/min, 95%CI [-1.8; -0.3] (-25%) was recorded during stair climb. Activities dominated by upper body movements showed large overestimation with 4.37 Kcal/min, 95%CI [3.8; 5.1] (170%) being recorded during gardening. Both bias and variance appeared to be dependent on activity type.

Conclusion

The activity monitor generally overestimated the energy consumption during common activities of low to medium intensity in the patient group. The size and direction of the bias was highly dependent on the activity type which indicates the activity monitor is of limited value in patients with hip osteoarthritis and that the results do not express the real energy expenditure.

Accuracy of the SenseWear Armband Mini and the BodyMedia FIT in resistance training

OBJECTIVES

To examine the validity and reliability of the SenseWear Armband Mini and BodyMedia FIT in comparison to the Cosmed K4b(2) portable for assessing energy expenditure during resistance training.

DESIGN

Lab based validation and reliability study.

METHODS

Eleven males and 7 females (age = 22.7 ± 4.5 years) completed two resistance-training sessions. Total energy expenditure was simultaneously assessed by the SenseWear Armband Mini, BodyMedia FIT and Cosmed K4b(2) portable. The resistance-training sessions involved 3 sets of 10 repetitions for 9 different exercises. The weight lifted was 70% of the participants 1 repetition max with 90-s rest intervals. Validity and re-test reliability was assessed using Pearson correlations and mean change (%) ± 90% confidence limits and log transformed typical error measurements (TEMCV%).

RESULTS

A very large correlation was identified for total energy expenditure (Kcal) from both the SenseWear Armband Mini and BodyMedia FIT in comparison to the Cosmed K4b(2) portable (r = 0.77 and r = 0.78, respectively) and an almost perfect correlation was observed for total energy expenditure (Kcal) between the SenseWear Armband Mini and BodyMedia FIT (r = 0.97). A small percent mean change for the SenseWear Armband Mini (23.7%) and a trivial percent mean change for the BodyMedia FIT (13.8%) was observed in comparison to the Cosmed K4b(2) portable with a tendency to underestimate total energy expenditure. The SenseWear Armband Mini and BodyMedia FIT displayed an almost perfect correlation for total energy expenditure (Kcal) between sessions (r = 0.96 and r = 0.97, respectively) whilst TEMCV% of 5.1 and 4.3, respectively were observed.

CONCLUSIONS

The SenseWear Armband Mini and BodyMedia FIT provide a valid and reliable measure of energy expenditure during resistance training. There was no significant difference in validity or reliability observed between the SenseWear Armband Mini and BodyMedia FIT.

Nutrition and physical activity programs for obesity treatment (PRONAF study): methodological approach of the project

BACKGROUND

At present, scientific consensus exists on the multifactorial etiopatogenia of obesity. Both professionals and researchers agree that treatment must also have a multifactorial approach, including diet, physical activity, pharmacology and/or surgical treatment. These two last ones should be reserved for those cases of morbid obesities or in case of failure of the previous ones. The aim of the PRONAF study is to determine what type of exercise combined with caloric restriction is the most appropriate to be included in overweigth and obesity intervention programs, and the aim of this paper is to describe the design and the evaluation methods used to carry out the PRONAF study.

METHODS/DESIGN

One-hundred nineteen overweight (46 males) and 120 obese (61 males) subjects aged 18-50 years were randomly assigned to a strength training group, an endurance training group, a combined strength + endurance training group or a diet and physical activity recommendations group. The intervention period was 22 weeks (in all cases 3 times/wk of training for 22 weeks and 2 weeks for pre and post evaluation). All subjects followed a hypocaloric diet (25-30% less energy intake than the daily energy expenditure estimated by accelerometry). 29-34% of the total energy intake came from fat, 14-20% from protein, and 50-55% from carbohydrates. The mayor outcome variables assesed were, biochemical and inflamatory markers, body composition, energy balance, physical fitness, nutritional habits, genetic profile and quality of life. 180 (75.3%) subjects finished the study, with a dropout rate of 24.7%. Dropout reasons included: personal reasons 17 (28.8%), low adherence to exercise 3 (5.1%), low adherence to diet 6 (10.2%), job change 6 (10.2%), and lost interest 27 (45.8%).

DISCUSSION

Feasibility of the study has been proven, with a low dropout rate which corresponds to the estimated sample size. Transfer of knowledge is foreseen as a spin-off, in order that overweight and obese subjects can benefit from the results. The aim is to transfer it to sports centres. Effectiveness on individual health-related parameter in order to determine the most effective training programme will be analysed in forthcoming publications.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01116856.

Unfavorable influence of structured exercise program on total leisure-time physical activity

In randomized controlled trials (RCTs), with customized structured physical exercise activity (SPEA) interventions, the dose of leisure-time physical activity (LTPA) should exceed the LTPA dose of the nonexercising control (C) group. This increase is required to substantiate health improvements achievable by exercise. We aimed to compare the dose of SPEA, LTPA, and total LTPA (SPEA + LTPA) between a randomized Nordic walking (NW) group, a power-type resistance training (RT) group, and a C group during a 12-week exercise intervention in obese middle-aged men (n = 144) with impaired glucose regulation. The dose of physical activity was measured with diaries using metabolic equivalents. No significant difference (P > 0.107) between the groups was found in volume of total LTPA. The volume of LTPA was, however, significantly higher (P < 0.050) in the C group than in the NW group, but not compared with the RT group. These results indicate that structured exercise does not automatically increase the total LTPA level, possibly, as a result of compensation of LTPA with structured exercise or spontaneous activation of the C group. Thus, the dose of total LTPA and the possible changes in spontaneous LTPA should be taken into account when implementing a RCT design with exercise intervention.