Stride Rate and Walking Intensity in Healthy Older Adults

2014 ◽  
Vol 22 (2) ◽  
pp. 276-283 ◽  
Author(s):  
Leslie Peacock ◽  
Allan Hewitt ◽  
David A. Rowe ◽  
Rona Sutherland
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Multilevel Model ◽  
Vigorous Physical Activity ◽  
Treadmill Walking ◽  
Overground Walking ◽  
Healthy Older Adults ◽  
Stride Rate

Purpose:The study investigated (a) walking intensity (stride rate and energy expenditure) under three speed instructions; (b) associations between stride rate, age, height, and walking intensity; and (c) synchronization between stride rate and music tempo during overground walking in a population of healthy older adults.Methods:Twenty-nine participants completed 3 treadmill-walking trials and 3 overground-walking trials at 3 self-selected speeds. Treadmill VO2 was measured using indirect calorimetry. Stride rate and music tempo were recorded during overground-walking trials.Results:Mean stride rate exceeded minimum thresholds for moderate to vigorous physical activity (MVPA) under slow (111.41 ± 11.93), medium (118.17 ± 11.43), and fast (123.79 ± 11.61) instructions. A multilevel model showed that stride rate, age, and height have a significant effect (p < .01) on walking intensity.Conclusions:Healthy older adults achieve MVPA with stride rates that fall below published minima for MVPA. Stride rate, age, and height are significant predictors of energy expenditure in this population. Music can be a useful way to guide walking cadence.

scholarly journals Comparison of College Students’ Energy Expenditure, Physical Activity, and Enjoyment during Exergaming and Traditional Exercise

2018 ◽  
Vol 7 (11) ◽  
pp. 433 ◽  
Author(s):  
Daniel McDonough ◽  
Zachary Pope ◽  
Nan Zeng ◽  
Jung Lee ◽  
Zan Gao
Keyword(s):  
Physical Activity ◽  
College Students ◽  
Energy Expenditure ◽  
Perceived Exertion ◽  
Treadmill Exercise ◽  
Vigorous Physical Activity ◽  
Treadmill Walking ◽  
Rating Of Perceived Exertion ◽  
Intensity Level ◽  
Metabolic Equivalents

This study evaluated the effects of exergaming on college students’ energy expenditure (EE), moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), rating of perceived exertion (RPE), and enjoyment compared to traditional treadmill exercise, and sex differences. Sixty college students (30 female; X ¯ age = 23.6 ± 4.1 years) completed three 20-min exercise sessions on Xbox 360 Kinect Just Dance (Microsoft, Redmond, WA, USA), Xbox 360 Kinect Reflex Ridge (Microsoft, Redmond, WA, USA), and treadmill walking. Their EE and PA were assessed by ActiGraph accelerometers (ActiGraph Co.; Pensacola, FL, USA); RPE every four min; enjoyment via an established scale. Significant exercise-type by sex interaction effects were observed for RPE (p < 0.01): females reported significantly lower RPE during exergaming sessions but significantly higher RPE during treadmill walking. Results revealed significant main effects for all outcomes between exercise sessions (all p < 0.01): treadmill walking resulted in significantly higher metabolic equivalents (METs), MVPA, and EE (p < 0 .01), yet lower LPA (p < 0.01), compared to the two exergaming sessions. Participants’ RPE was significantly higher during treadmill walking than during exergaming sessions, with exergaming eliciting significantly higher enjoyment (all p < 0.01). College students find exergaming more enjoyable and report lower RPE compared to traditional treadmill exercise, though not yet matching the moderate physiological intensity level.

scholarly journals Leisure-Time Physical Activity and Metabolic Syndrome in Older Adults

2019 ◽  
Vol 16 (18) ◽  
pp. 3358 ◽  
Author(s):  
Gallardo-Alfaro ◽  
Bibiloni ◽  
Mateos ◽  
Ugarriza ◽  
Tur
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Metabolic Syndrome ◽  
Energy Expenditure ◽  
Leisure Time ◽  
Leisure Time Physical Activity ◽  
Vigorous Physical Activity ◽  
Cross Sectional Study ◽  
Moderate Physical Activity ◽  
Cross Sectional

Background: Metabolic syndrome (MetS) is a cluster of risk factors for cardiovascular disease, atherosclerosis and diabetes mellitus type 2 which may be reduced by practicing regular physical activity. Objective: To assess the leisure-time physical activity (LTPA) of older adults with MetS and without MetS. Methods: Cross-sectional study of older adults (55–80 years old) from Balearic Islands (Spain) with MetS (n = 333; 55% men) and without MetS (n = 144; 43.8% men). LTPA was assessed with the validated Spanish version of the Minnesota LTPA Questionnaire. Two criteria of physically active were used: >150 min/week of moderate physical activity or >75 min/week of vigorous physical activity or a combination of both, and total leisure-time energy expenditure of >300 MET·min/day. Sociodemographic and lifestyle characteristics, anthropometric variables, MetS components, and adherence to the Mediterranean diet (MD) were also measured. Results: MetS subjects showed lower energy expenditure in LTPA, lower adherence to the MD, higher obesity and waist circumference, and were less active than non-MetS peers. LTPA increased as participants got older and there was higher LTPA intensity as educational level increased. Adherence to MD was as high as LTPA was. Conclusions: MetS is associated with physical inactivity and unhealthy diet. To increase LTPA recommendations and raise awareness in the population about the health benefits of PA and high adherence to MD is highly recommended.

Validation of the Kenz Lifecorder EX and ActiGraph GT1M accelerometers for walking and running in adults

2008 ◽  
Vol 33 (6) ◽  
pp. 1155-1164 ◽  
Author(s):  
Mark G. Abel ◽  
James C. Hannon ◽  
Katie Sell ◽  
Tia Lillie ◽  
Geri Conlin ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Walking Speed ◽  
Treadmill Walking ◽  
Activity Monitors ◽  
Step Counts ◽  
Equation Analysis ◽  
Walking Speeds ◽  
Activity Energy

Accelerometer-based activity monitors are commonly used by researchers and clinicians to assess physical activity. Recently, the Kenz Lifecorder EX (KL) and ActiGraph GT1M (AG) accelerometers have been made commercially available, but there is limited research on the validity of these devices. Therefore, we sought to validate step count, activity energy expenditure (EE), and total EE output from the KL and AG during treadmill walking and running. Ten male and 10 female participants performed 10 min treadmill walking and running trials, at speeds of 54, 80, 107, 134, 161, and 188 m·min–1. Step counts were hand tallied by 2 observers, and indirect calorimetry was used to validate the accelerometers’ estimates of EE. AG total EE was calculated using the Freedson equation. Analysis of variance (ANOVA) and Pearson’s correlations were used to analyze the data. At the slowest walking speed, the AG and KL counted 64% ± 15% and 92% ± 6% of the observed steps, respectively. At all other treadmill speeds, both activity monitors undercounted, compared with observed steps, by ≤3%. The KL underestimated activity EE at faster running speeds (p < 0.01), overestimated total EE at some walking speeds, and underestimated total EE at some running speeds (p < 0.01). The Freedson equation inaccurately measured total EE at most walking and running speeds. The KL and the AG are moderately priced accelerometers that provide researchers and clinicians with accurate estimates of step counts and activity EE at most walking and running speeds.

scholarly journals Morning fatigue and structured exercise interact to affect non-exercise physical activity of fit and healthy older adults

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tomas Vetrovsky ◽  
Dan Omcirk ◽  
Jan Malecek ◽  
Petr Stastny ◽  
Michal Steffl ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Exercise Training ◽  
Training Program ◽  
Vigorous Physical Activity ◽  
Text Messages ◽  
Daily Physical Activity ◽  
Exercise Session ◽  
Healthy Older Adults ◽  
Low Volume

Abstract Background Exercise training is crucial for maintaining physical and mental health in aging populations. However, as people participate in structured exercise training, they tend to behaviorally compensate by decreasing their non-exercise physical activity, thus potentially blunting the benefits of the training program. Furthermore, physical activity of older adults is substantially influenced by physical feelings such as fatigue. Nevertheless, how older people react to day-to-day fluctuations of fatigue and whether fatigue plays a role in non-exercise physical activity compensation is not known. Thus, the purpose of this study was twofold: (1) To explore whether the volume and intensity of habitual physical activity in older adults were affected by morning fatigue. (2) To investigate the effect of attending power and resistance exercise sessions on the levels of non-exercise physical activity later that day and the following day. Methods Twenty-eight older adults wore an accelerometer during a 4-week low-volume, low-intensity resistance and power training program with three exercise sessions per week and for 3 weeks preceding and 1 week following the program. During the same period, the participants were prompted every morning, using text messages, to rate their momentary fatigue on a scale from 0 to 10. Results Greater morning fatigue was associated with lower volume (p = 0.002) and intensity (p = 0.017) of daily physical activity. Specifically, one point greater on the fatigue scale was associated with 3.2 min (SE 1.0) less moderate-to-vigorous physical activity. Furthermore, attending an exercise session was associated with less moderate-to-vigorous physical activity later that day by 3.7 min (SE 1.9, p = 0.049) compared to days without an exercise session. During the next day, the volume of physical activity was greater, but only in participants with a body mass index up to 23 (p = 0.008). Conclusions Following low-volume exercise sessions, fit and healthy older adults decreased their non-exercise physical activity later that day, but this compensation did not carry over into the next day. As momentary morning fatigue negatively affects daily physical activity, we suggest that the state level of fatigue should be monitored during intensive exercise programs, especially in less fit older adults with increased fatigability.

scholarly journals Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort (Preprint)

2018 ◽  
Author(s):  
Salvatore Tedesco ◽  
Marco Sica ◽  
Andrea Ancillao ◽  
Suzanne Timmons ◽  
John Barton ◽  
...  
Keyword(s):  
Older Adults ◽  
Energy Expenditure ◽  
Vigorous Physical Activity ◽  
Living Environment ◽  
Percentage Error ◽  
Free Living ◽  
Healthy Older Adults ◽  
Activity Trackers ◽  
Sleep Parameters ◽  
Highly Correlated

BACKGROUND Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings. OBJECTIVE This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment. METHODS Overall, 20 participants (>65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered. RESULTS For step counting, all trackers were highly correlated with one another (ICCs>0.89). Although the Fitbit tended to overcount steps (MPE=12.36%), the Garmin and ActiGraph undercounted (MPE 9.36% and 11.53%, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25%), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13%. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7%. CONCLUSIONS The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes.

scholarly journals Accelerometer Assessment of Physical Activity in Active, Healthy Older Adults

2009 ◽  
Vol 17 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Jennifer L. Copeland ◽  
Dale W. Esliger
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Healthy Aging ◽  
Vigorous Physical Activity ◽  
Activity Patterns ◽  
Healthy Older Adults ◽  
Cut Point ◽  
Rich Information ◽  
The Rich ◽  
Older Populations

Despite widespread use of accelerometers to objectively monitor physical activity among adults and youth, little attention has been given to older populations. The purpose of this study was to define an accelerometer-count cut point for a group of older adults and to then assess the group’s physical activity for 7 days. Participants (N= 38, age 69.7 ± 3.5 yr) completed a laboratory-based calibration with an Actigraph 7164 accelerometer. The cut point defining moderate to vigorous physical activity (MVPA) was 1,041 counts/min. On average, participants obtained 68 min of MVPA per day, although more than 65% of this occurred as sporadic activity. Longer bouts of activity occurred in the morning (6 a.m. to 12 p.m.) more frequently than other times of the day. Almost 14 hr/day were spent in light-intensity activity. This study demonstrates the rich information that accelerometers provide about older adult activity patterns—information that might further our understanding of the relationship between physical activity and healthy aging.

Energy Expenditure in Vinyasa Yoga Versus Walking

2017 ◽  
Vol 14 (8) ◽  
pp. 597-605 ◽  
Author(s):  
Sally A. Sherman ◽  
Renee J. Rogers ◽  
Kelliann K. Davis ◽  
Ryan L. Minster ◽  
Seth A. Creasy ◽  
...  
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Energy Expenditure ◽  
Energy Cost ◽  
Vigorous Physical Activity ◽  
Treadmill Walking ◽  
Moderate Intensity ◽  
Moderate Intensity Physical Activity ◽  
Metabolic Equivalents ◽  
Intensity Physical Activity

Background:Whether the energy cost of vinyasa yoga meets the criteria for moderate-to-vigorous physical activity has not been established.Purpose:To compare energy expenditure during acute bouts of vinyasa yoga and 2 walking protocols.Methods:Participants (20 males, 18 females) performed 60-minute sessions of vinyasa yoga (YOGA), treadmill walking at a self-selected brisk pace (SELF), and treadmill walking at a pace that matched the heart rate of the YOGA session (HR-Match). Energy expenditure was assessed via indirect calorimetry.Results:Energy expenditure was significantly lower in YOGA compared with HR-Match (difference = 79.5 ± 44.3 kcal; P < .001) and SELF (difference = 51.7 ± 62.6 kcal; P < .001), but not in SELF compared with HR-Match (difference = 27.8 ± 72.6 kcal; P = .054). A similar pattern was observed for metabolic equivalents (HR-Match = 4.7 ± 0.8, SELF = 4.4 ± 0.7, YOGA = 3.6 ± 0.6; P < .001). Analyses using only the initial 45 minutes from each of the sessions, which excluded the restorative component of YOGA, showed energy expenditure was significantly lower in YOGA compared with HR-Match (difference = 68.0 ± 40.1 kcal; P < .001) but not compared with SELF (difference = 15.1 ± 48.7 kcal; P = .189).Conclusions:YOGA meets the criteria for moderate-intensity physical activity. Thus, YOGA may be a viable form of physical activity to achieve public health guidelines and to elicit health benefits.

scholarly journals Validity of the iPhone M7 Motion Coprocessor to Estimate Physical Activity During Structured and Free-Living Activities in Healthy Adults

2021 ◽  
pp. 1-8
Author(s):  
Nicola K. Thomson ◽  
Lauren McMichan ◽  
Eilidh Macrae ◽  
Julien S. Baker ◽  
David J. Muggeridge ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Body Movement ◽  
Primary Objective ◽  
Physical Activities ◽  
Healthy Adults ◽  
Treadmill Walking ◽  
Step Count ◽  
Free Living

Modern smartphones such as the iPhone contain an integrated accelerometer, which can be used to measure body movement and estimate the volume and intensity of physical activity. Objectives: The primary objective was to assess the validity of the iPhone to measure step count and energy expenditure during laboratory-based physical activities. A further objective was to compare free-living estimates of physical activity between the iPhone and the ActiGraph GT3X+ accelerometer. Methods: Twenty healthy adults wore the iPhone 5S and GT3X+ in a waist-mounted pouch during bouts of treadmill walking, jogging, and other physical activities in the laboratory. Step counts were manually counted, and energy expenditure was measured using indirect calorimetry. During two weeks of free-living, participants (n = 17) continuously wore a GT3X+ attached to their waist and were provided with an iPhone 5S to use as they would their own phone. Results: During treadmill walking, iPhone (703 ± 97 steps) and GT3X+ (675 ± 133 steps) provided accurate measurements of step count compared with the criterion method (700 ± 98 steps). Compared with indirect calorimetry (8 ± 3 kcal·min−1), the iPhone (5 ± 1 kcal·min−1) underestimated energy expenditure with poor agreement. During free-living, the iPhone (7,990 ± 4,673 steps·day−1) recorded a significantly lower (p < .05) daily step count compared with the GT3X+ (9,085 ± 4,647 steps·day−1). Conclusions: The iPhone accurately estimated step count during controlled laboratory walking but recorded a significantly lower volume of physical activity compared with the GT3X+ during free-living.

The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation (Preprint)

2020 ◽  
Author(s):  
Anis Davoudi ◽  
Mamoun T. Mardini ◽  
Dave Nelson ◽  
Fahd Albinali ◽  
Sanjay Ranka ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Energy Expenditure ◽  
Activity Recognition ◽  
Recognition Accuracy ◽  
Body Position ◽  
Computer Work ◽  
Accelerometer Data ◽  
Upper Arm ◽  
Lifestyle Activities

BACKGROUND Research shows the feasibility of human activity recognition using Wearable accelerometer devices. Different studies have used varying number and placement for data collection using the sensors. OBJECTIVE To compare accuracy performance between multiple and variable placement of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. METHODS Participants (n=93, 72.2±7.1 yrs) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary vs. non-sedentary, locomotion vs. non-locomotion, and lifestyle vs. non-lifestyle activities (e.g. leisure walk vs. computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on five different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used in developing Random Forest models to assess activity category recognition accuracy and MET estimation. RESULTS Model performance for both MET estimation and activity category recognition strengthened with additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03 to 0.09 MET increase in prediction error as compared to wearing all five devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for detection of locomotion (0-0.01 METs), sedentary (0.13-0.05 METs) and lifestyle activities (0.08-0.04 METs) compared to all five placements. The accuracy of recognizing activity categories increased with additional placements (0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. CONCLUSIONS Additional accelerometer devices only slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

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