Nutritional interventions often rely on subjective assessments of energy intake (EI), but these are susceptible to measurement error. To introduce an accelerometer-based intake-balance method for assessing EI using data from a time-restricted eating (TRE) trial. Nineteen participants with overweight/obesity (25–63 years old; 16 females) completed a 12-week intervention (NCT03129581) in a control group (unrestricted feeding; n 8) or TRE group (n 11). At the start and end of the intervention, body composition was assessed by dual-energy X-ray absorptiometry (DXA) and daily energy expenditure (EE) was assessed for 2 weeks via wrist-worn accelerometer. EI was back-calculated as the sum of net energy storage (from DXA) and EE (from accelerometer). Accelerometer-derived EI estimates were compared against estimates from the body weight planner of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Mean EI for the control group declined by 138 and 435 kJ/day for the accelerometer and NIDDK methods, respectively (both P ≥ 0·38), v. 1255 and 1469 kJ/day, respectively, for the TRE group (both P < 0·01). At follow-up, the accelerometer and NIDDK methods showed excellent group-level agreement (mean bias of −297 kJ/day across arms; standard error of estimate 1054 kJ/day) but high variability at the individual level (limits of agreement from −2414 to +1824 kJ/day). The accelerometer-based intake-balance method showed plausible sensitivity to change, and EI estimates were biologically and behaviourally plausible. The method may be a viable alternative to self-report EI measures. Future studies should assess criterion validity using doubly labelled water.

Human gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter ( $t_{p}$ ) which is based on minimum standard deviation ( $\sigma$ ). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.

Fe deficiency has negative effects on voluntary physical activity (PA); however, the impact of consuming Fe-biofortified staple foods on voluntary PA remains unclear. This study compared the effects of consuming Fe-biofortified pearl millet or a conventional pearl millet on measures of voluntary PA in Indian schoolchildren (ages 12–16 years) during a 6-month randomised controlled feeding trial. PA data were collected from 130 children using Actigraph GT3X accelerometers for 6 d at baseline and endline. Minutes spent in light and in moderate-to-vigorous PA were calculated from accelerometer counts using Crouter’s refined two-regression model for children. Mixed regression models adjusting for covariates were used to assess relationships between intervention treatment or change in Fe status and PA. Children who consumed Fe-biofortified pearl millet performed 22·3 (95 % CI 1·8, 42·8, P = 0·034) more minutes of light PA each day compared with conventional pearl millet. There was no effect of treatment on moderate-to-vigorous PA. The amount of Fe consumed from pearl millet was related to minutes spent in light PA (estimate 3·4 min/mg Fe (95 % CI 0·3, 6·5, P = 0·031)) and inversely related to daily sedentary minutes (estimate −5·4 min/mg Fe (95 % CI –9·9, −0·9, P = 0·020)). Consuming Fe-biofortified pearl millet increased light PA and decreased sedentary time in Indian schoolchildren in a dose-dependent manner.

Cognitive impairment in heart failure (HF) is believed to in part stem from structural brain alterations, including shrinkage of subcortical regions. Fortunately, neurocognitive dysfunction in HF can be mitigated by physical activity (PA), though mechanisms for this phenomenon are unclear. PA is protective against age-related cognitive decline that may involve improved structural integrity to brain regions sensitive to aging (e.g., subcortical structures). Yet, no study has examined the benefits of PA on the brain in HF and we sought to do so and clarify related cognitive implications. Fifty older adults with HF completed a neuropsychological battery and wore an accelerometer for 7 days. All participants underwent brain MRI. This study targeted subcortical brain volume given subcortical alterations are often observed in HF and the sensitivity of PA to subcortical structures in other patient populations. Participants averaged 4348.49 (SD=2092.08) steps per day and greater daily steps predicted better attention/executive function, episodic memory, and language abilities, p’s<.05. Medical and demographically adjusted regression analyses revealed higher daily steps per day predicted greater subcortical volume, with specific effects for the thalamus and ventral diencephalon, p’s<.05. Greater subcortical volume was associated with better attention/executive function, p<.05. Higher daily PA was associated with increased subcortical brain volume and better cognition in older adults with HF. Longitudinal work is needed to clarify whether daily PA can attenuate brain atrophy in HF to reduce accelerated cognitive decline in this population. (JINS, 2015, 21, 851–860)

The ultimate goal of upper-limb rehabilitation after stroke is to promote real-world use, that is, use of the paretic upper-limb in everyday activities outside the clinic or laboratory. Although real-world use can be collected through self-report questionnaires, an objective indicator is preferred. Accelerometers are a promising tool. The current paper aims to explore the feasibility of accelerometers to measure upper-limb use after stroke and discuss the translation of this measurement tool into clinical practice. Accelerometers are non-invasive, wearable sensors that measure movement in arbitrary units called activity counts. Research to date indicates that activity counts are a reliable and valid index of upper-limb use. While most accelerometers are unable to distinguish between the type and quality of movements performed, recent advancements have used accelerometry data to produce clinically meaningful information for clinicians, patients, family and care givers. Despite this, widespread uptake in research and clinical environments remains limited. If uptake was enhanced, we could build a deeper understanding of how people with stroke use their arm in real-world environments. In order to facilitate greater uptake, however, there is a need for greater consistency in protocol development, accelerometer application and data interpretation.

Activity monitors are increasingly being used to quantify the activity of pet dogs. The aim of the present study was to investigate associations between signalment and activity of free-living pet dogs. Healthy pet dogs were recruited to wear an Actical activity monitor on their collars continuously for 2 weeks in their home environment. At least fifteen dogs were enrolled in each of the following weight ranges: <10, 10–20, 21–30, 31–40, >40 kg and their age, sex and reproductive status recorded. Each dog's intensity of activity for each minute of recording was classified using the total counts for that minute and our pre-established cut-points. The percentage of time dogs spent in sedentary, light or moderate/vigorous activity each day was calculated. Median total daily activity counts and median percentages of time dogs spent in activities of differing intensity were used for the analysis. Associations between signalment characteristics and activity parameters were evaluated with the Mann–Whitney test, the Kruskal–Wallis test and Spearman rank correlations. Ninety-eight dogs were enrolled with ≥17 dogs in each weight category. Time that dogs were sedentary correlated positively with age (r 0·50, uncorrected P < 0·001), while the median total daily activity count (r −0·47, uncorrected P < 0·001), time spent in light (r −0·46, uncorrected P < 0·001) and more vigorous activity (r −0·50, uncorrected P < 0·001) were negatively associated with age. No other significant associations between signalment and activity parameters were found. The lack of differences in activity across weight categories may reflect the impact of lifestyle negating any potential behavioural differences across breeds.

Physical activity tends to be lower in school-age children with congenital heart disease than in healthy controls. To the best of our knowledge, objectively measured physical activity levels of preschool-age children with congenital heart disease have not been studied. Methods: A total of 10 children with either coarctation of the aorta (n=6; age 3.8±0.9) or tetralogy of Fallot (n=4, age 4.3±0.9) were recruited from the cardiology unit of McMaster Children’s Hospital. Height (103.7±8.2 cm) and weight (17.3±2.7 kg) measurements were recorded, and physical activity was determined using accelerometry over 7 consecutive days. Patients were compared with age-, sex-, and season of data acquisition-matched controls. Parents completed a questionnaire regarding the child’s physical activity and sedentary behaviours. Results: Patients spent on average 219.4±39.9 minutes engaged in total physical activity per day at the following intensities: light, 147.5±22.3; moderate, 44.0±11.8; moderate-to-vigorous, 71.9±22.6; and vigorous, 27.9±11.7. No significant differences were observed between patients and controls for total physical activity (p=0.80) or any of the intensities (p=0.71, 0.46, 0.43, and 0.45, respectively). Only 40% of patients and controls met the new Canadian Physical Activity Guidelines for the Early Years of at least 180 minutes of physical activity at any intensity every day. Of the patients’ parents, 90% believed that their child was as active, if not more active, than his/her siblings, and 80% of parents reported their child spending 1–3 hours in screen time activities daily. Conclusion: Children aged 3–5 years old with congenital heart disease have comparable physical activity levels to age-, sex-, and season-matched controls, and many do not meet Canadian Physical Activity Guidelines.

Background: To date, no physical activity (PA) questionnaires intended for primary care have been compared against a criterion measure of PA and current (2008) aerobic PA recommendations of the American College of Sports Medicine/American Heart Association (ACSM/AHA). Aim: This study evaluated preliminary evidence for criterion validity of two brief (<1 min) PA questionnaires with accelerometry, and their ability to identify if individuals meet ACSM/AHA PA recommendations. Methods: 45 health clinic staff wore an accelerometer for seven consecutive days and afterwards completed two brief PA questionnaires, the Physical Activity Vital Sign (PAVS), and the Speedy Nutrition and Physical Activity Assessment (SNAP). Agreement and descriptive statistics were calculated between the PAVS or SNAP and accelerometry in order to measure each questionnaire’s ability to quantify the number of days participants achieved ⩾30 min of moderate–vigorous PA (MVPA) performed in bouts of ⩾10 continuous minutes. Participants with <5 days of ⩾30 bout-min of MVPA were considered insufficiently active according to PA recommendations. Findings: There was a significant positive correlation between number of days with ⩾30 bout-min MVPA and the PAVS (r=0.52, P<0.001), and SNAP (r=0.31, P<0.05). The PAVS had moderate agreement with accelerometry for identifying if individuals met or did not meet PA recommendations (κ=0.46, P<0.001), whereas SNAP had poor agreement (κ=0.12, P<0.05). Conclusions: This study provides preliminary evidence of criterion validity of the PAVS and SNAP with accelerometry and agreement identifying if respondents meet current (2008) ACSM/AHA aerobic PA recommendations. The PAVS and SNAP should be evaluated further for repeatability, and in populations varying in PA levels, age, gender, and ethnicity.

The objective of the present study was to investigate whether a previously reported apparent negative relationship between fat mass and daily physical activity in Japanese adult women would also be observed in Japanese adult men. The subjects were grouped into quartiles of BMI and body fat percentage (%BF). The number of steps walked each day and the duration of light- to vigorous-intensity physical activity were assessed by an accelerometer over the same period of time as for the doubly labelled water experiment. The results showed that BMI negatively correlated with the number of steps and time spent in moderate-intensity physical activity, whereas %BF showed a negative relationship with physical activity-related energy expenditure (PAEE)/body weight (BW) and physical activity level. The analysis of data using %BF quartiles revealed that PAEE/BW decreased from the second quartile in which the BMI was < 25 kg/m2. These observations are similar to those reported in our previous study in Japanese adult women. These cross-sectional studies cannot prove causality, and that obesity causes physical inactivity may be the case. However, the results of the present study provide information regarding which physical activity variables should be used in longitudinal studies.

Objective estimates of activity patterns and energy expenditure (EE) are important for the measurement of energy balance. The Intelligent Device for Energy Expenditure and Activity (IDEEA) can estimate EE from the thirty-five postures and activities it can identify and record. The present study evaluated the IDEEA system's estimation of EE using whole-body indirect calorimetry over 24 h, and in free-living subjects using doubly-labelled water (DLW) over 14 d. EE was calculated from the IDEEA data using calibration values for RMR and EE while sitting and standing, both as estimated by the IDEEA system (IDEEAest) and measured by indirect calorimetry (IDEEAmeas). Subjects were seven females and seven males, mean age 38·1 and 39·7 years, mean BMI 25·2 and 26·2 kg/m2, respectively. The IDEEAest method produced a similar estimate of EE to the calorimeter (10·8 and 10·8 MJ, NS), while the IDEEAmeas method underestimated EE (9·9 MJ, P < 0·001). After removing data from static cycling, which the IDEEA was unable to identify as an activity, both the IDEEAest and IDEEAmeas methods overestimated EE compared to the calorimeter (9·9 MJ, P < 0·001; 9·1 MJ, P < 0·05 and 8·6 MJ, respectively). Similarly, the IDEEA system overestimated EE compared to DLW over 14 d; 12·7 MJ/d (P < 0·01), 11·5 MJ/d (P < 0·01) and 9·5 MJ/d for the IDEEAest, IDEEAmeas and DLW, respectively. The IDEEA system overestimated EE both in the controlled laboratory and free-living environments. Using measured EE values for RMR, sitting and standing reduced, but did not eliminate, the error in estimated EE.

The objective of the present study was to investigate the relationship between the indices of body size such as BMI, fat-free mass index (FFMI, FFM/height2), fat mass index (FMI, FM/height2), and body fat percentage (%BF), and physical activities assessed by the doubly-labelled water (DLW) method and an accelerometer in free-living Japanese adult women. We conducted a cross-sectional study in 100 female subjects ranging in age from 31 to 69 years. Subjects were classified in quartiles of BMI, FFMI, FMI and %BF. Daily walking steps and the duration of light to vigorous physical activity were simultaneously assessed by an accelerometer for the same period as the DLW experiment. Only physical activity-related energy expenditure (PAEE)/FFM and PAEE/body weight (BW) decreased in the highest quartile of BMI. Physical activity level, PAEE/FFM and PAEE/BW decreased in the highest quartile of FMI and %BF, whereas they were not different among quartiles of FFMI. Daily walking steps and the duration of moderate- and vigorous-intensity physical activities decreased or tended to decrease in the highest quartile of FMI and %BF, but did not differ among quartiles of FFMI and BMI. These results clearly showed that Japanese adult women with higher fat deposition obviously had a low level of physical activities assessed by both the DLW method and accelerometry, but those with larger BMI had lower PAEE/FFM and PAEE/BW only. Our data suggest that the relationship between obesity and daily physical activities should be discussed using not only BMI but also FMI or %BF.

We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P < 0·001) and locomotive (r 0·961, P < 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cut-off classification models. This method may be useful for field investigations as well as for self-monitoring by general users.

Determining total energy expenditure (EE) in children under free-living conditions has become of increasingly clinical interest. The aim of this study was to compare three different methods to assess EE triaxial accelerometry (TriTrac-R3D; Professional Products, Division of Reining International, Madison, WI, USA), activity diary and heart-rate (HR) monitoring combined with indirect calorimetry (IC). Twenty non-obese children and adolescents, aged 5.5 to 16.0 years, participated in this study. Results from the three methods were collected simultaneously under free-living conditions during the same 24 h schoolday period. Neither activity diary (5904 (SD 1756) KJ) NOR THE TRITRAC-R3D (6389 (sd 979) kJ) showed statistical differences in 24 h total EE compared with HR monitoring (5965 (sd 1911) kJ). When considering different physical activity (PA) periods, compared with HR monitoring, activity diary underestimates total EE during sedentary periods (P<0·001) and overestimates total EE and PA-EE during PA periods (P<0·001) because of the high energy cost equivalence of activity levels. The TriTrac-R3D, compared with HR monitoring, shows good agreement for assessing PA-EE during PA periods (mean difference +0·25 (sd 1·9) kJ/min; 95 % CI for the bias -0·08, 0·58), but underestimates PA-EE and it does not show good precision during sedentary periods (-0·87 (sd 1·4) kJ/min, P<0·001). Correlation between the vector magnitude generated by the TriTrac-R3D accelerometer and EE of activities derived from HR monitoring is high. When compared with the HR method, the TriTrac-R3D and activity diary are not systematically accurate and must be carefully used for the assessment of children's EE depending on the purpose of each study.

The objective of the study was to assess the validity of an activity monitor (AM) within a psychophysiological study. The AM was based on four body-fixed accelerometers and discriminated postures, transitions, and dynamic activities. Three subjects participated in each of two 4-hr sessions. During each session, consisting of two protocols, ambulatory accelerometer and heart rate measurements were made. The output of the AM was compared with simultaneously recorded video tapes. An overall agreement between AM and video of 88 and 96% was found. The number of transitions and dynamic periods, and the duration of activities were well determined. Posture-related heart rate changes were demonstrated. A three-sensor configuration hardly influenced the validity scores. The AM appeared to be a valid instrument to quantify aspects of physical activity, and offers new possibilities for ambulatory psychophysiological research.