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

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.

How many days of continuous physical activity monitoring reliably represent time in different intensities in cancer survivors

BACKGROUND

Physical activity (PA) monitoring is applied in a growing number of studies within cancer research. However, no consensus exists on how many days PA should be monitored to obtain reliable estimates in the cancer population. The objective of the present study was to determine the minimum number of monitoring days required for reliable estimates of different PA intensities in cancer survivors when using a six-days protocol. Furthermore, reliability of monitoring days was assessed stratified on sex, age, cancer type, weight status, and educational level.

METHODS

Data was obtained from two studies where PA was monitored for seven days using the SenseWear Armband Mini in a total of 984 cancer survivors diagnosed with breast, colorectal or prostate cancer. Participants with ≥22 hours monitor wear-time for six days were included in the reliability analysis (n = 736). The intra-class correlation coefficient (ICC) and the Spearman Brown prophecy formula were used to assess the reliability of different number of monitoring days.

RESULTS

For time in light PA, two monitoring days resulted in reliable estimates (ICC >0.80). Participants with BMI ≥25, low-medium education, colorectal cancer, or age ≥60 years required one additional monitoring day. For moderate and moderate-to-vigorous PA, three monitoring days yielded reliable estimates. Participants with BMI ≥25 or breast cancer required one additional monitoring day. Vigorous PA showed the largest within subject variations and reliable estimates were not obtained for the sample as a whole. However, reliable estimates were obtained for breast cancer survivors (4 days), females, BMI ≥30, and age <60 years (6 days).

CONCLUSION

Shorter monitoring periods may provide reliable estimates of PA levels in cancer survivors when monitored continuously with a wearable device. This could potentially lower the participant burden and allow for less exclusion of participants not adhering to longer protocols.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

The aging human body shape

Body shape and composition are heterogeneous among humans with possible impact for health. Anthropometric methods and data are needed to better describe the diversity of the human body in human populations, its age dependence, and associations with health risk. We applied whole-body laser scanning to a cohort of 8499 women and men of age 40–80 years within the frame of the LIFE (Leipzig Research Center for Civilization Diseases) study aimed at discovering health risk in a middle European urban population. Body scanning delivers multidimensional anthropometric data, which were further processed by machine learning to stratify the participants into body types. We here applied this body typing concept to describe the diversity of body shapes in an aging population and its association with physical activity and selected health and lifestyle factors. We find that aging results in similar reshaping of female and male bodies despite the large diversity of body types observed in the study. Slim body shapes remain slim and partly tend to become even more lean and fragile, while obese body shapes remain obese. Female body shapes change more strongly than male ones. The incidence of the different body types changes with characteristic Life Course trajectories. Physical activity is inversely related to the body mass index and decreases with age, while self-reported incidence for myocardial infarction shows overall the inverse trend. We discuss health risks factors in the context of body shape and its relation to obesity. Body typing opens options for personalized anthropometry to better estimate health risk in epidemiological research and future clinical applications.

Can a contemporary dietary assessment tool or wearable technology accurately assess the energy intake of professional young rugby league players? A doubly labelled water validation study

Accurate quantification of energy intake is imperative in athletes; however traditional dietary assessment tools are frequently inaccurate. Therefore, this study investigated the validity of a contemporary dietary assessment tool or wearable technology to determine the total energy intake (TEI) of professional young athletes. The TEI of eight professional young male rugby league players was determined by three methods; Snap-N-Send, SenseWear Armbands (SWA) combined with metabolic power and doubly labelled water (DLW; intake-balance method; criterion) across a combined ten-day pre-season and seven-day in-season period. Changes in fasted body mass were recorded, alongside changes in body composition via isotopic dilution and a validated energy density equation. Energy intake was calculated via the intake-balance method. Snap-N-Send non-significantly over-reported pre-season and in-season energy intake by 0.21 (2.37) MJ^.day^-1 (p = 0.833) and 0.51 (1.73) MJ^.day^-1 (p = 0.464), respectively. This represented a trivial and small standardised mean bias, and very large and large typical error. SenseWear Armbands and metabolic power significantly under-reported pre-season and in-season TEI by 3.51 (2.42) MJ^.day^-1 (p = 0.017) and 2.18 (1.85) MJ^.day^-1 (p = 0.021), respectively. This represents a large and moderate standardised mean bias, and very large and very large typical error. There was a most likely larger daily error reported by SWA and metabolic power than Snap-N-Send across pre-season (3.30 (2.45) MJ^.day^-1; ES = 1.26 ± 0.68; p = 0.014) and in-season periods (1.67 (2.00) MJ^.day^-1; ES = 1.27 ± 0.70; p = 0.012). This study demonstrates the enhanced validity of Snap-N-Send for assessing athlete TEI over combined wearable technology, although caution is required when determining the individual TEIs of athletes via Snap-N-Send.

Long-term Physical Activity Behavior After Completion of Traditional Versus Fast-track Cardiac Rehabilitation

BACKGROUND

Despite the health benefits associated with regular physical activity (PA), many cardiac patients fail to maintain optimal levels of PA after completing cardiac rehabilitation (CR). The long-term impact of different CR delivery models on the PA habits of cardiac patients is not completely understood.

OBJECTIVE

The purpose of this study is to use a multisensor accelerometer to compare the long-term impact of a traditional versus fast-track CR on the PA of patients with coronary artery disease 6 months after CR entry.

METHODS

Forty-four participants attended either traditional (twice a week, 12 weeks; n = 24) or fast-track (once a week, 8 weeks; n = 20) CR. Exercise capacity (ie, 6-minute walk test distance) and PA were assessed at baseline and at 12 weeks and 6 months after CR entry.

RESULTS

At 12 weeks, exercise capacity increased significantly in both groups and remained elevated by the 6-month follow-up. Sedentary time decreased from baseline to 12 weeks. However, at 6 months, it was comparable with the baseline level. There was no significant change in any other PA marker (ie, steps/day, time in light and moderate-vigorous PA) over the course of the study.

CONCLUSIONS

Findings support the long-term effectiveness of CR on exercise capacity irrespective of the delivery model. However, participation in CR program, whether it be a traditional or fast-track CR exercise program, may not lead to long-term PA behavior change. Thus, CR participants may benefit from structured strategies that promote long-term PA adherence in addition to facilitating exercise capacity improvement.

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

BACKGROUND

Automatically attenuating the postprandial rise in the blood glucose concentration without manual meal announcement is a significant challenge for artificial pancreas (AP) systems. In this study, a meal module is proposed to detect the consumption of a meal and to estimate the amount of carbohydrate (CHO) intake.

METHODS

The meals are detected based on qualitative variables describing variation of continuous glucose monitoring (CGM) readings. The CHO content of the meals/snacks is estimated by a fuzzy system using CGM and subcutaneous insulin delivery data. The meal bolus amount is computed according to the patient's insulin to CHO ratio. Integration of the meal module into a multivariable AP system allows revision of estimated CHO based on knowledge about physical activity, sleep, and the risk of hypoglycemia before the final decision for a meal bolus is made.

RESULTS

The algorithm is evaluated by using 117 meals/snacks in retrospective data from 11 subjects with type 1 diabetes. Sensitivity, defined as the percentage of correctly detected meals and snacks, is 93.5% for meals and 68.0% for snacks. The percentage of false positives, defined as the proportion of false detections relative to the total number of detected meals and snacks, is 20.8%.

CONCLUSIONS

Integration of a meal detection module in an AP system is a further step toward an automated AP without manual entries. Detection of a consumed meal/snack and infusion of insulin boluses using an estimate of CHO enables the AP system to automatically prevent postprandial hyperglycemia.

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.

Energy Expenditure, Availability, and Dietary Intake Assessment in Competitive Female Dragon Boat Athletes

Dragon boat racing requires high physical activity levels during competition and training. The female athletic triad refers to a number of negative health consequences (e.g., amenorrhoea, low bone mineral density, and low energy availability) that may result from high physical activity in female athletes in parallel with inadequate dietary intake. This study aimed to estimate energy expenditure and dietary adequacy in female competitive dragon boat athletes. Following ethical approval, energy expenditure was assessed by use of Sensewear^TM armbands (which measure movement as well as galvanic heat loss) on nine dragon boat athletes preparing for the Southeast Asian Games 2013. The mean estimated energy expenditure for the athletes was 2226 ± 711 kJ/day. Mean total energy, recorded using three-day food diaries (6715 ± 2518 kJ/day) and energy availability (99 ± 56 kJ/kg/day), were low. Estimated micronutrient intake (calcium 699.3 ± 328.7 mg/day and iron 10.6 ± 4.7 mg/day) did not meet recommended daily allowances of 800 mg/day and 19 mg/day, respectively. The low intake of energy, calcium, and iron noted within this study could have negative effects on performance and short- and long-term health in female dragon boat athletes.

Carrying loads: Validating a portable tri-axial accelerometer during frequent and brief physical activity

OBJECTIVES

The aim of this study is to evaluate the validity of the SenseWear Armband™ (SWA, Model MF-SW, BodyMedia Inc.) as a tool to assess daily brief, light- to moderate-intensity activities.

DESIGN

A total of 41 volunteers were recruited (27 males, 14 females) to perform several trials, including a resting metabolic rate test and a number of walking trials while carrying different loads in a backpack.

METHODS

Energy expenditure during trials was measured using both the SWA™ mini device, with the indirect calorimetry method (MasterScreen CPX and Oxycon Mobile JAEGER™ devices) used for validated comparative measurement.

RESULTS

The SWA™ mini shows agreement with indirect calorimetry in all trials. However, the SWA™ mini over-estimated expenditure in all participants. Individual assessment estimates with the SWA™ mini also exhibited random errors. The variations in energy expenditure (EE) resulting from increased carried loads during the trials were not statistically significant when EE was measured with the SWA™ mini. Furthermore, Metabolic Equivalent of Task (MET) calculation, highly dependent on estimated energy expenditure per unit time, also was likely overestimated. In contrast, the SWA™ mini provided estimates of the resting metabolic rate with a small error.

CONCLUSIONS

The SWA™ mini is not a valid device for estimating energy expenditure in brief light- or moderate activities.