Day-level sedentary pattern estimates derived from hip-worn accelerometer cut-points in 8–12-year-olds: Do they reflect postural transitions?

Improving sedentary measurement is critical to understanding sedentary-health associations in youth. This study assessed agreement between the thigh-worn activPAL and commonly used hip-worn ActiGraph accelerometer methods for assessing sedentary patterns in children. Both devices were worn by 8–12-year-olds (N = 195) for 4.6 ± 1.9 days. Two ActiGraph cut-points were applied to two epoch durations: ≤25 counts (c)/15 s, ≤75c/15s, ≤100c/60s, and ≤300c/60s. Bias, mean absolute deviation (MAD), and intraclass correlation coefficients (ICCs) tested agreement between devices for total sedentary time and 11 sedentary pattern variables (usual bout duration, sedentary time accumulated in various bout durations, breaks/day, break rate, and alpha). For most sedentary pattern variables, ActiGraph 25c/15s, 75c/15s, and 100c/60s had poor ICCs, with bias and MAD >20%. ActiGraph 300c/60s had a better agreement than the other cut-points, but all ICCs were <0.587. ActiGraph underestimated sedentary time in longer bouts and usual bout duration, and overestimated sedentary time in shorter bouts, breaks/day, and alpha. For total sedentary time, ActiGraph 25c/15s, 300c/60s, and 75c/15s had good/fair ICCs, with bias and MAD <20%. Sedentary patterns derived from two commonly used ActiGraph cut-points did not appear to reflect postural changes. These differences between measurement devices should be considered when interpreting findings from sedentary pattern studies.     

Validating the Sedentary Sphere method in children: Does wrist or accelerometer brand matter?

This study aimed to validate the Sedentary Sphere posture classification method from wrist-worn accelerometers in children. Twenty-seven 9–10-year-old children wore ActiGraph GT9X (AG) and GENEActiv (GA) accelerometers on both wrists, and activPAL on the thigh while completing prescribed activities: five sedentary activities, standing with a phone, walking (criterion for all 7: observation) and 10-min free-living play (criterion: activPAL). In an independent sample, 21 children wore AG and GA accelerometers on the non-dominant wrist and activPAL for two days of free-living. Per cent accuracy, pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC) analyses were completed. Accuracy was similar, for prescribed activities irrespective of brand (non-dominant wrist: 77–78%; dominant wrist: 79%). Posture estimates were equivalent between wrists within brand (±6%, ICC > 0.81, lower 95% CI ≥ 0.75), between brands worn on the same wrist (±5%, ICC ≥ 0.84, lower 95% CI ≥ 0.80) and between brands worn on opposing wrists (±6%, ICC ≥ 0.78, lower 95% CI ≥ 0.72). Agreement with activPAL during free-living was 77%, but sedentary time was underestimated by 7% (GA) and 10% (AG). The Sedentary Sphere can be used to classify posture from wrist-worn AG and GA accelerometers for group-level estimates in children, but future work is needed to improve the algorithm for better individual-level results.     

Validity of the activPAL and Height-Adjusted Curvilinear Cadence-METs Equations in Healthy Adults

ABSTRACT

The activPAL is a widely-used measure of sedentary time but few studies have evaluated its ability to estimate physical activity intensity. This study determined the accuracy of the algorithm used by the activPAL to predict metabolic equivalents (METs) from cadence and a curvilinear cadence-METs equation individualized for height. Thirty-six healthy adults (25 ± 6 years) completed a progressive walking protocol. Stepping cadence was video recorded and METs were determined via indirect calorimetry. Manually-counted cadence was input into the activPAL and curvilinear equations. The internal activPAL equation overpredicted METs at slower cadences (<120 steps/minute) but underpredicted METs at faster cadences (>120 step/minute) (proportional bias, p < .001). Conversely, the curvilinear equation exhibited neither fixed (p = .37) nor proportional bias (p = .07), and a lower absolute MET difference [0.87 ± 0.65 (range:0.0–3.2) vs. 0.56 ± 0.45 (range:0.0–2.7) METs]. The linear activPAL equation poorly estimates METs from stepping cadence but these inaccuracies may be lessened through the use of an individualized curvilinear equation.     

ActivPAL™ determined sedentary behaviour,physical activity and academic achievement in college students

The aim of this study was to examine relationships between activPAL?-determined sedentary behavior (SB) and physical activity (PA) with academic achievement. A total of 120 undergraduates (N = 57 female; 20.6 ± 2.3 years) participated in the study. Academic achievement was measured as the grade point average obtained from all completed courses. Participants wore on the right tight an activPAL? for 7 days to determine total sedentary time, total number of sedentary breaks, sedentary bouts, standing time, light and moderate-to-vigorous physical activity (MVPA). Separate multiple linear regression models were performed to examine associations between SB variables and academic achievement. Light PA, MVPA, total sedentary time, total standing time, or total number of sedentary breaks were not related to academic achievement. Independently of PA, the amount of time spent in sedentary bouts of 10-20min during weekdays was positively related to academic achievement. Given that college students spend the majority of their workday in environments that encourage prolonged sitting, these data suggest that interruptions in prolonged periods of sitting time every 10-20min via short breaks may optimize cognitive operations associated with academic performance.     

Comparison of ActiGraph GT3X+ and Actical accelerometer data in 9–11-year-old Canadian children

Accelerometry is the gold standard for field-based physical activity assessment in children; however, the plethora of devices, data reduction procedures, and cut-points available limits comparability between studies. This study aimed to compare physical activity variables from the ActiGraph GT3X+ and Actical accelerometers in children under free-living conditions. A cross-sectional study of 379 children aged 9–11 years from Ottawa (Canada) was conducted. Children wore the ActiGraph GT3X+ and Actical accelerometers on the hip simultaneously for 7 consecutive days (24-h protocol). Moderate-to-vigorous (MVPA), vigorous (VPA), moderate (MPA), and light (LPA) physical activity, as well as sedentary time, (SED) were derived using established data reduction protocols. Excellent agreement between devices was observed for MVPA (ICC = 0.73–0.80), with fair to good agreement for MPA, LPA and SED, and poor agreement for VPA. Bland-Altman plots showed excellent agreement for MVPA, LPA, and SED, adequate agreement for MPA, and poor agreement for VPA. MVPA derived from the Actical was 11.7% lower than the ActiGraph GT3X+. The ActiGraph GT3X+ and Actical are comparable for measuring children’s MVPA. However, comparison between devices for VPA, MPA, LPA, and SED are highly dependent on data reduction procedures and cut-points, and should be interpreted with caution.     

Changes in the school and non-school sedentary time in youth: The UP&DOWN longitudinal study

ABSTRACT

The aims of this study were (i) to examine the sedentary time (ST) during different time periods [i.e., weekend, out-of-school weekdays hours, school hours, recess, physical education classes (PEC)] in children and adolescents; (ii) to identify 2-year longitudinal changes in the ST for these periods; and (iii) to examine if ST at baseline is associated with ST 2 years later. This was a 2-year follow-up study with 826 (51.9% boys) children and 678 (50.7% boys) adolescents. Accelerometers were used to assess ST. Students spent more than 60% of their weekend, out-of-school hours and school hours in ST. During these periods, girls and adolescents were more sedentary than boys and children, respectively (p < 0.05). Over 2-year follow-up, ST increased during the weekend, out-of-school hours, school hours and recess in all subgroups studied (p < 0.001). ST during PEC declined 2% per year in children (p < 0.001) but it increased in adolescents (p < 0.05). ST during the periods analysed at baseline was lowly associated with ST during these periods 2 years later (intraclass correlations from <0.001 to 0.364). Interventions in these settings may be adequate if the intention is to avoid ST increase in students.     

Effects of breaking up sedentary time with “chair squats” on postprandial metabolism

Prolonged sitting induces adverse metabolic changes. We aimed to determine whether breaking up prolonged sedentary time with short periods of repeated sit-to-stand transitions (“chair squats”) every 20 minutes influences postprandial metabolic responses. Fourteen participants (11 men, 3 women), age 37 ± 16 years, BMI 30.5 ± 3.8 kg.m^?2 (mean ± SD) each participated in two experimental trials in random order, in which they arrived fasted, then consumed a test breakfast (8 kcal.kg^?1 body weight, 37% energy from fat, 49% carbohydrates, 14% protein) and, 3.5 hours later, an identical test lunch. Expired air and blood samples were taken fasted and for 6.5 hours postprandially. In one trial (SIT) participants sat continuously throughout the observation period; in the “Chair squat” trial (SIT/STAND), participants performed “chair squats” (10 × standing and sitting over 30 seconds, every 20 minutes). Compared to SIT, energy expenditure was 409.7 ± 41.6 kJ (16.6 ± 1.7%) higher in SIT/STAND (p < 0.0001). Postprandial insulin concentrations over the post-breakfast period were 10.9 ± 8.4% lower in SIT/STAND than SIT (p = 0.047), but did not differ between trials in the post-lunch period. Glucose and triglyceride concentrations did not differ significantly between trials. These data demonstrate that a simple, unobtrusive intervention to break up sedentary time can induce some favourable metabolic changes.     

Estimating physical activity in youth using an ankle accelerometer

This study developed and validated a vector magnitude (VM) two-regression model (2RM) for use with an ankle-worn ActiGraph accelerometer. For model development, 181 youth (mean ± SD; age, 12.0 ± 1.5 yr) completed 30 min of supine rest and 2–7 structured activities. For cross-validation, 42 youth (age, 12.6 ± 0.8 yr) completed approximately 2 hr of unstructured physical activity (PA). PA data were collected using an ActiGraph accelerometer, (non-dominant ankle) and the VM was expressed as counts/5-s. Measured energy expenditure (Cosmed K4b²) was converted to youth METs (MET_y; activity VO₂ divided by resting VO₂). A coefficient of variation (CV) was calculated for each activity to distinguish continuous walking/running from intermittent activity. The ankle VM sedentary behavior threshold was ≤10 counts/5-s, and a CV≤15 counts/5-s was used to identify walking/running. The ankle VM2RM was within 0.42 MET_y of measured MET_y during the unstructured PA (P > 0.05). The ankle VM2RM was within 5.7 min of measured time spent in sedentary, LPA, MPA, and VPA (P > 0.05). Compared to the K4b², the ankle VM2RM provided similar estimates to measured values during unstructured play and provides a feasible wear location for future studies.     

A cross-sectional and prospective analyse of reallocating sedentary time to physical activity on children’s cardiorespiratory fitness

Cardiorespiratory fitness (CRF) is associated with health benefits in children, improving cardiac morphology, cardiovascular disease risk factors, and biological outcomes. This study aimed to examine the substitution effects of displaying a fixed duration of sedentary time with a fixed duration of physical activity (PA) at different intensities on children’s CRF. A total of 315 children (136 boys) were assessed (age: 10.6 ± 0.6 years old). Outcomes at baseline and follow-up (16-months) were CRF determined using a maximal cycle test and sedentary time and PA measured with accelerometers. Data were analysed by isotemporal substitution analyses estimating the effect of reallocating 30 min/day of sedentary time by light (LPA), moderate (MPA) and vigorous physical activity (VPA) on CRF. VPA was positively and significantly associated with CRF cross-sectional (β = 0.026, p < 0.001) and prospectively (β = 0.010, p < 0.001). Reallocating 30 min/day of sedentary time into VPA was positively cross-sectionally (β = 0.780, p < 0.001) and prospectively (β = 0.303, p < 0.05) associated with CRF. Conversely, relocating 30-minutes of sedentary time into 30 minutes of LPA and MPA was not associated with CRF. These results suggest that reallocating an equal amount of time from sedentary into VPA is cross-sectional and prospectively associated with a favourable CRF.     

Development of raw acceleration cut-points for wrist and hip accelerometers to assess sedentary behaviour and physical activity in 5–7-year-old children

ABSTRACT

This study validated sedentary behaviour (SB), moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) accelerometer cut-points in 5–7-year-old children. Participants (n = 49, 55% girls) wore an ActiGraph GT9X accelerometer, recording data at 100 Hz downloaded in 1 s epochs, on both wrists and the right hip during a standardised protocol and recess. Cut-points were generated using ROC analysis with direct observation as a criterion. Subsequently, cut-points were optimised using Confidence intervals equivalency analysis and then cross-validated in a cross-validation group. SB cut-points were 36 mg (Sensitivity (Sn) = 79.8%, Specificity (Sp) = 56.8%) for non-dominant wrist, 39 mg (Sn = 75.4%, Sp = 70.2%) for dominant wrist and 20 mg (Sn = 78%, Sp = 50.1%) for hip. MVPA cut-points were 189 mg (Sn = 82.6%, Sp = 78%) for non-dominant wrist, 181 mg (Sn = 79.1%, Sp = 76%) for dominant wrist and 95 mg (Sn = 79.3%, Sp = 75.6%) for hip. VPA cut-points were 536 mg (Sn = 75.1%, Sp = 68.7%) for non-dominant wrist, 534 mg (Sn = 67.6%, Sp = 95.6%) for dominant wrist and 325 mg (Sn = 78.2%, Sp = 96.1%) for hip. All placements demonstrated adequate levels of accuracy for SB and PA assessment.     

Evaluating the Responsiveness of Accelerometry to Detect Change in Physical Activity

The responsiveness to change of the Actical and ActiGraph accelerometers was assessed in children and adolescents. Participants (N = 208) aged 6 to 16 years completed two simulated free-living protocols, one with primarily light-to-moderate physical activity (PA) and one with mostly moderate-to-vigorous PA. Time in sedentary, light, moderate, and vigorous PA was estimated using 8 previously developed cut-points (4 for Actical and 4 for ActiGraph) and 5-sec, 15-sec, and 30-sec epochs. Accelerometer responsiveness for detecting differences in PA between protocols was assessed using standardized response means (SRMs). SRM values ≥.8 represented high responsiveness to change. Both accelerometers showed high responsiveness for all PA intensities (SRMs = 1.2–4.7 for Actical and 1.1–3.3 for ActiGraph). All cut-points and epoch lengths yielded high responsiveness, and choice of cut-points and epoch length had little effect on responsiveness. Thus, both the Actical and ActiGraph can detect change in PA in a simulated free-living setting, irrespective of cut-point selection or epoch length.     

Validity of Activity Trackers in Estimating Energy Expenditure During High-Intensity Functional Training

ABSTRACT

Purpose: The purpose of this study was to evaluate the agreement of five commercially available accelerometers in estimating energy expenditure while performing an acute bout of high-intensity functional training (HIFT). Methods: Participants (n = 47; average age: 28.5 ± 11.6 years) consisted of recreationally active, healthy adults. Each participant completed a session of HIFT: a 15-minute workout consisting of 12 repetitions each of air-squats, sit-ups, push-ups, lunges, pull-ups, steps-ups, and high-knees; performed circuit-style by completing as many rounds as possible. During this session, each participant wore the Cosmed K4b2 portable metabolic analyzer (PMA) and five different accelerometers (ActiGraph GT3X, Nike Fuelband, Fitbit One, Fitbit Charge HR, and Jawbone UP Move). Results: Four of the five activity trackers reported lower (p < .05) total EE values compared to the PMA during the acute bout of HIFT. The waist-mounted device (ActiGraph, 182.55 ± 37.93 kcal) was not significantly different from, and most closely estimated caloric expenditure compared to the PMA (144.99 ± 37.13 kcal) (p = .056). A repeated-measures ANOVA showed that all activity trackers were significantly different from the reference measure (PMA) (p < .05). Systematic relative agreement between the activity trackers was calculated, exhibiting a significant ICC = 0.426 (F [46,230] = 5.446 [p < .05]). Conclusion: The wrist- and hip-mounted activity trackers did not accurately assess energy expenditure during HIFT exercise. With the exception of the ActiGraph GT3X, the remaining four activity trackers showed inaccurate estimates of the amount of kilocalories expended during the HIFT exercise bout compared to the PMA.     

Are habitual runners physically inactive?

The World Health Organisation’s (WHO) physical activity guidelines recommend 150min/week of moderate- to vigorous-intensity physical activity (MVPA) accumulated in 10 min bouts. To see whether people performing habitual exercise for recreation meet these guidelines, 25 long-distance runners [mean 67 km/wk], 25 joggers [mean 28 km/wk], and 20 sedentary adults wore an ActiGraph GT3X+ accelerometer for 7 days. Sedentary time and bouts were similar in runners and sedentary adults (p > 0.46). Sedentary adults performed 20 ± 16 min/day of MVPA (usual bout duration (W50%): 9.53 ± 3.45min), with joggers and runners performing 45 ± 31min (W50%: 16.92 ± 9.53min) and 83 ± 58min (W50%: 20.35 ± 8.85min), respectively (p ≤ 0.001 versus sedentary group). Data showed that 65% of the sedentary group, 32% of joggers and 4% of long-distance runners did not meet the WHO guideline for MVPA. Failure to meet the guideline was most prominent in, but not restricted to, runners who reported ≤50km running per week. Self-reported running does not ensure adults meet physical activity guidelines or offset daily sedentary behaviours. On the other hand, the sedentary group was very close in accumulating recommended bouts of MVPA in incidental activities. Future studies should assess whether modification of work and leisure physical activity would be more fruitful than encouraging recreational exercise per se in meeting physical activity guidelines.     

Accuracy of activPAL Self-Attachment Methods

This study examined the accuracy of self-attachment of the activPAL activity monitor. A convenience sample of 50 participants self-attached the monitor after being presented with written material only (WMO) and then written and video (WV) instructions; and completed a questionnaire regarding the acceptability of the instructional methods. Participants positioned the monitor lower than the instructed position on the thigh (WMO ?5.15 ± 2.75 cm, WV ?4.16 ± 2.15 cm; p = .008 difference) and approximately 2 cm laterally from the thigh midline (WMO 1.90 ± 0.92 cm; WV 2.08 ± 1.24 cm). The orientation of the device was positioned correctly along the midline (within < 1° of vertical). Acceptability was high for both instructional methods although preference was shown for the WV instruction. In conclusion, participants consistently self-attached the activPAL close to the intended placement with either instructional method. The addition of video instruction produced a slightly more accurate attachment and was preferred by the participants.     

Sedentary time and vigorous physical activity are independently associated with cardiorespiratory fitness in middle school youth

Abstract

The purpose of this cross-sectional study was to examine the relationship between objectively measured physical activity, sedentary time, and cardiorespiratory fitness in a diverse sample of youth. Participants were recruited from three middle schools and completed assessments of height, weight, cardiorespiratory fitness, and wore an accelerometer for a minimum of four days. Hierarchical general linear models controlling for age, body mass index (BMI) percentile, and sex were used to evaluate the association of time (minutes per day) spent sedentary, and in moderate physical activity and vigorous physical activity with cardiorespiratory fitness (i.e., heart rate response [beats per minute], dependent variable). Results indicated age (β = –0.16, P < 0.05), BMI percentile (β = 0.33, P <0.05), being male (β = 0.17, P < 0.05), sedentary time (β = 0.11, P <0.05), moderate (β = –0.03, P > 0.05) and vigorous (β = –0.22, P < 0.05) physical activity explained 29% of the variance in cardiorespiratory fitness. Evaluation of fitness among high sedentary/high vigorous, high sedentary/low vigorous, low sedentary/low vigorous, and low sedentary/high vigorous groups (defined by the median split) showed that high levels of vigorous activity removed the detrimental effect of high levels of sedentary time on cardiorespiratory fitness. These analyses suggest that the negative impact of sedentary time can be mitigated by engaging in vigorous activity.     

Combinations of Epoch Durations and Cut-Points to Estimate Sedentary Time and Physical Activity Among Adolescents

The purpose of the current study was to investigate how combinations of different epoch durations and cut-points affect the estimations of sedentary time and physical activity in adolescents. Accelerometer data from 101 adolescents were derived and 30 combinations were used to estimate sedentary time, light, moderate, vigorous, and combined moderate-to-vigorous physical activity. Data were analyzed with repeated measurement analyses of variance. Large differences of sedentary time and times of different physical activity intensities were observed between 1 s and longer epoch durations using virtually all cut-points. Generally, sedentary time, moderate physical activity, vigorous physical activity, and combined moderate-to-vigorous physical activity progressively decreased, whereas light physical activity increased with longer epoch durations. The extreme differences between cut-points were large and increased with longer epoch durations for sedentary time and for all physical activity intensities except for vigorous physical activity per epoch duration. Caution is required when cross-comparing studies using different epoch durations and cut-points. To accurately register adolescents’ spontaneous intermittent physical activity behavior, short epoch durations are recommended.     

Evaluation of Actical equations and thresholds to predict physical activity intensity in young children

This study examined the validity of current Actical activity energy expenditure (AEE) equations and intensity cut-points in preschoolers using AEE and direct observation as criterion measures. Forty 4–6-year-olds (5.3 ± 1.0 years) completed a ~150-min room calorimeter protocol involving age-appropriate sedentary behaviours (SBs), light intensity physical activities (LPAs) and moderate-to-vigorous intensity physical activities (MVPAs). AEE and/or physical activity intensity were calculated using Actical equations and cut-points by Adolph, Evenson, Pfeiffer and Puyau. Predictive validity was examined using paired sample t-tests. Classification accuracy was evaluated using weighted kappas, sensitivity, specificity and area under the receiver operating characteristic curve. The Pfeiffer equation significantly overestimated AEE during SB and underestimated AEE during LPA (P < 0.0125 for both). There was no significant difference between measured and predicted AEEs during MVPA. The Adolph cut-point showed significantly higher accuracy for classifying SB, LPA and MVPA than all others. The available Actical equation does not provide accurate estimates of AEE across all intensities in preschoolers. However, the Pfeiffer equation performed reasonably well for MVPA. Using cut-points of ≤6 counts · 15 s^?1, 7–286 counts · 15 s^?1 and ≥ 287 counts · 15 s^?1 when classifying SB, LPA and MVPA, respectively, is recommended.     

The Effects of Varying Structured Physical Activity Duration on Young Children’s and Parents’ Activity Levels

ABSTRACT

Purpose: The purpose of this study was to examine whether structured physical activity (PA) in a family-based community exercise program affects PA of young children and parents. Method: Twenty-two children (mean ± SD; age, 4.9 ± 2.1 years) and their parents (age, 34.3 ± 7.6 years) participated in unstructured PA sessions followed by either short- or long-duration structured PA sessions, while wearing an ActiGraph GT9X activity monitor on their right hip to estimate PA. Independent t-tests compared children’s and parents’ PA during short- and long-structured PA sessions. Paired t-tests compared short- versus long-structured PA sessions. A mixed model ANOVA compared PA during unstructured versus structured sessions and between children and parents. Results: Children spent proportionately more time in moderate-to-vigorous PA (MVPA) and had higher accelerometer counts/min than parents during short-structured PA (children:60.9 ± 18.8% vs. parents:17.7 ± 6.8%, children:3870 ± 742 vs. parents:1836 ± 556 counts/min, p < .05) and long-structured PA (children:61.1 ± 20.1% vs. parents:12.6 ± 4.9%, children:3415 ± 758 vs. parents:1604 ± 633 counts/min, p < .05). No statistical differences were found between short- and long-structured PA sessions for proportion of time spent in MVPA or counts/min for children or parents (all, p > .05). Children spent proportionally more time in MVPA and had higher counts/min during unstructured PA compared to structured PA (unstructured MVPA:54.4 ± 3.9% vs. structured MVPA:38.2 ± 4.2%, unstructured counts/min:3830 ± 222 vs. structured counts/min:2768 ± 239 counts/min; p < .05). Conclusions: Children were more active than parents during both the unstructured and structured PA sessions. However, unstructured PA sessions resulted in 63–77% and 10–11% of PA recommendations for children and adults, respectively. Family-based exercise programming can provide an opportunity for children and their parents to attain MVPA during the week.     

Comparison of children’s free-living physical activity derived from wrist and hip raw accelerations during the segmented week

This study assessed children’s physical activity (PA) levels derived from wrist-worn GENEActiv and hip-worn ActiGraph GT3X+ accelerometers and examined the comparability of PA levels between the two devices throughout the segmented week. One hundred and twenty-nine 9–10-year-old children (79 girls) wore a GENEActiv (GAwrist) and ActiGraph GT3X+ (AGhip) accelerometer on the left wrist and right hip, respectively, for 7 days. Mean minutes of light PA (LPA) and moderate-to-vigorous PA (MVPA) per weekday (whole-day, before-school, school and after-school) and weekend day (whole-day, morning and afternoon–evening) segments were calculated, and expressed as percentage of segment time. Repeated measures analysis of variance examined differences in LPA and MVPA between GAwrist and AGhip for each time segment. Bland–Altman plots assessed between-device agreement for LPA and MVPA for whole weekday and whole weekend day segments. Correlations between GAwrist and AGhip were weak for LPA (r = 0.18–0.28), but strong for MVPA (r = 0.80–0.86). LPA and MVPA levels during all weekday and weekend day segments were significantly higher for GAwrist than AGhip (p < 0.001). The largest inter-device percent difference of 26% was observed in LPA during the school day segment. Our data suggest that correction factors are needed to improve raw PA level comparability between GAwrist and AGhip.     

Differences in body composition and risk of lifestyle-related diseases between young and older male rowers and sedentary controls

Abstract

The aim of this cross-sectional study was to compare body composition and risk factors of lifestyle-related diseases between young and older male rowers and sedentary controls. Healthy males aged 19–73 years participated in the study, and were divided into four groups: 26 young rowers, 24 senior rowers, 23 young sedentary controls, and 22 senior sedentary controls. Total and regional lean soft tissue, fat mass, and bone mineral density were measured using dual-energy X-ray absorptiometry. The HDL-cholesterol of senior rowers (67.4 ± 13.4 mg · dl^?1) was significantly (P < 0.05) higher than that of senior sedentary controls (59.2 ± 11.9 mg · dl^?1), while HDL-cholesterol was similar in senior rowers and young rowers (66.1 ± 10.8 mg · dl^?1). Arm, leg, and trunk lean soft tissue mass were significantly higher in senior rowers (5.6 ± 0.6 kg, 18.2 ± 1.8 kg, and 27.3 ± 3.2 kg respectively) than in senior sedentary controls (5.1 ± 0.4 kg, 16.3 ± 1.4 kg, and 24.6 ± 1.7 kg respectively; P < 0.05). Bone mineral density was also significantly higher in senior rowers than in senior sedentary controls (ribs, lumbar spine, and pelvic segments; P < 0.05). We conclude that age-related increases in the risk of lifestyle-related diseases, such as osteoporosis and sarcopenia, are attenuated in male rowers. These results suggest that regular rowing exercise may have a positive influence in the prevention of lifestyle-related diseases in older Japanese people.