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Alex Griffiths, Calum Mattocks, Andy Robert Ness, Kate Tilling, Chris Riddoch and Sam Leary

Background:

A study deriving a threshold for moderate- to vigorous-intensity physical activity (MVPA) in terms of accelerometer counts in 12-year-old children was repeated with a subset of the same children at 16 years.

Methods:

Fifteen girls and thirty boys took part in 6 activities (lying, sitting, slow walking, walking, hopscotch and jogging) while wearing an Actigraph 7164 accelerometer and a Cosmed K4b2 portable metabolic unit. Random intercepts modeling was used to estimate cut points for MVPA (defined as 4 METs).

Results:

Using a single model, the sex-specific thresholds derived for MVPA at 16 years were some way below the 3600 counts/minute used for both sexes at age 12, particularly for girls. However graphical examination suggested that a single model might be inadequate to describe both higher- and lower-intensity activities. Models using only lower-intensity activities close to the 4 METs threshold supported retention of the 3600 counts/minute cut point for both sexes.

Conclusions:

When restricting to lower-intensity activities only, these data do not provide sufficient evidence to change the previously established cut point of 3600 counts/minute to represent MVPA. However, further data and more sophisticated modeling techniques are required to confirm this decision.

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Leon Straker, Amity Campbell, Svend Erik Mathiassen, Rebecca Anne Abbott, Sharon Parry and Paul Davey

Background:

Capturing the complex time pattern of physical activity (PA) and sedentary behavior (SB) using accelerometry remains a challenge. Research from occupational health suggests exposure variation analysis (EVA) could provide a meaningful tool. This paper (1) explains the application of EVA to accelerometer data, (2) demonstrates how EVA thresholds and derivatives could be chosen and used to examine adherence to PA and SB guidelines, and (3) explores the validity of EVA outputs.

Methods:

EVA outputs are compared with accelerometer data from 4 individuals (Study 1a and1b) and 3 occupational groups (Study 2): seated workstation office workers (n = 8), standing workstation office workers (n = 8), and teachers (n = 8).

Results:

Line graphs and related EVA graphs highlight the use of EVA derivatives for examining compliance with guidelines. EVA derivatives of occupational groups confirm no difference in bouts of activity but clear differences as expected in extended bouts of SB and brief bursts of activity, thus providing evidence of construct validity.

Conclusions:

EVA offers a unique and comprehensive generic method that is able, for the first time, to capture the time pattern (both frequency and intensity) of PA and SB, which can be tailored for both occupational and public health research.

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Melissa Raymond, Adele Winter and Anne E. Holland

Background:

Older adults undergoing rehabilitation may have limited mobility, slow gait speeds and low levels of physical activity. Devices used to quantify activity levels in older adults must be able to detect these characteristics.

Objective:

To investigate the validity of the Positional Activity Logger (PAL2) for monitoring position and measuring physical activity in older inpatients (slow stream rehabilitation).

Methods:

Twelve older inpatients (≥65 years) underwent a 1-hour protocol (set times in supine, sitting, standing; stationary and moving). Participants were video-recorded while wearing the PAL2. Time spent in positions and walking (comfortable and fast speeds) were ascertained through video-recording analysis and compared with PAL2 data.

Results:

There was no difference between the PAL2 and video recording for time spent in any position (P-values 0.055 to 0.646). Walking speed and PAL2 count were strongly correlated (Pearson’s r = .913, P < .01). The PAL2 was responsive to within-person changes in gait speed: activity count increased by an average of 52.47 units (95% CI 3.31, 101.63). There was 100% agreement for transitions between lying to sitting and < 1 transition difference between siting to standing.

Conclusion:

The PAL2 is a valid tool for quantifying activity levels, position transitions, and within-person changes in gait speed in older inpatients.

Open access

Jeffer Eidi Sasaki, Cheryl A. Howe, Dinesh John, Amanda Hickey, Jeremy Steeves, Scott Conger, Kate Lyden, Sarah Kozey-Keadle, Sarah Burkart, Sofiya Alhassan, David Bassett Jr and Patty S. Freedson

Background:

Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth.

Methods:

Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities.

Results:

Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured.

Conclusion:

This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

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Evelin Lätt, Jarek Mäestu and Jaak Jürimäe

Background: Little is known about the impact of sedentary and moderate-to-vigorous physical activity (MVPA) bouts on cardiometabolic health. The aim was to examine how the accumulation of bouts of sedentary time and MVPA associates to cardiometabolic health in children independently of total sedentary and MVPA time. Methods: In a cross-sectional study with 123 boys (10–13 y), sedentary and MVPA bouts were determined using 7-day accelerometry. Each bout was compared with cardiometabolic risk factors and with the risk score that was calculated using standardized values of body mass index, waist circumference, homeostasis model assessment for insulin resistance, triglycerides, and total cholesterol/high-density cholesterol ratio. Results: Time in 10- to 14-minute sedentary bouts was negatively associated with continuous cardiometabolic risk score in weekdays and weekend days and with triglycerides in a weekend (P < .05). Time accumulated in ≥30-minute sedentary bouts was associated with higher insulin and homeostasis model assessment for insulin resistance values in weekend (P < .05). Weekday total MVPA and time accumulated in ≥10-minute MVPA bouts were negatively associated with continuous cardiometabolic risk score and body mass index in weekdays (P < .05). No associations were found between total sedentary time and metabolic health. Conclusion: Significant associations between sedentary and MVPA bouts with cardiometabolic risk factors suggest the need of the more detailed analysis for sedentary behavior and its effects on health risks.

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John R. Sirard, Ann Forsyth, J. Michael Oakes and Kathryn H. Schmitz

Background:

The purpose of this study was to determine 1) the test-retest reliability of adult accelerometer-measured physical activity, and 2) how data processing decisions affect physical activity levels and test-retest reliability.

Methods:

143 people wore the ActiGraph accelerometer for 2 7-day periods, 1 to 4 weeks apart. Five algorithms, varying nonwear criteria (20 vs. 60 min of 0 counts) and minimum wear requirements (6 vs. 10 hrs/day for ≥ 4 days) and a separate algorithm requiring ≥ 3 counts per min and ≥ 2 hours per day, were used to process the accelerometer data.

Results:

Processing the accelerometer data with different algorithms resulted in different levels of counts per day, sedentary, and moderate-to-vigorous physical activity. Reliability correlations were very good to excellent (ICC = 0.70−0.90) for almost all algorithms and there were no significant differences between physical activity measures at Time 1 and Time 2.

Conclusions:

This paper presents the first assessment of test-retest reliability of the Actigraph over separate administrations in free-living subjects. The ActiGraph was highly reliable in measuring activity over a 7-day period in natural settings but data were sensitive to the algorithms used to process them.

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Karyn Tappe, Ellen Tarves, Jayme Oltarzewski and Deirdra Frum

Background:

Predictive modeling for physical activity behavior has included many different psychological components, including planning, motivation, personality, and self-efficacy. However, habit formation in exercise maintenance has not been well explored and lacks reliable measurement tools. The current study explores novel survey questions that examine behavioral components of exercise habit, including frequency, environmental cuing, and temporal constancy of behavior. We then relate these concepts to an established psychological measure of habit, the Self-Report Habit Inventory (SRHI).

Methods:

One hundred and seventy-four exercisers were surveyed at 2 private fitness clubs. A single questionnaire was administered that included the SRHI and the novel behavioral questions developed from habit formation concepts.

Results:

Habit formation was reported by many of the exercisers. Participants scoring higher on the SRHI also reported higher frequency of physical activity and a higher probability of environmental cuing. Exercise frequency did not correlate well with environmental cuing.

Conclusions:

Habit formation appears relevant to the physical activity patterns of many regular exercisers. However, wide variation in response styles was evident suggesting further development and exploration of the novel questionnaire is warranted. The ultimate goals are to include habit in predictive models of physical activity, and then to inform interventions to increase exercise adherence.

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Sarah M. Camhi, Susan B. Sisson, William D. Johnson, Peter T. Katzmarzyk and Catrine Tudor-Locke

Background:

Objective physical activity data analyses focus on moderate-to-vigorous physical activity (MVPA) without considering lower intensity lifestyle-type activities (LA). We describe 1) quantity of LA (minutes and steps per day) across demographic groups, 2) proportion of LA to total physical activity, and 3) relationships between LA and MVPA using NHANES 2005−2006 accelerometer adult data (n = 3744).

Methods:

LA was defined as 760 to 2019 counts per minute (cpm) and MVPA as ≥2020 cpm. LA was compared within gender, ethnicity, age, and BMI groups. Regression analyses examined independent effects. Correlations were evaluated between LA and MVPA. All analyses incorporated sampling weights to represent national estimates.

Results:

Adults spent 110.4 ± 1.6 minutes and took 3476 ± 54 steps per day in LA. Similar to MVPA, LA was highest in men, Mexican Americans, and lowest in adults ≥60 years or obese. When LA was held constant, ethnic differences no longer predicted MVPA minutes, and age no longer predicted MVPA steps. LA and MVPA minutes (r = .84) and steps per day (r = .72) were significantly correlated, but attenuated with MVPA modified bouts (≥10 minutes sustained activity).

Conclusions:

LA accumulation differs between demographic subgroups and is related to MVPA: adults who spend more minutes and steps in MVPA also spend them in LA.

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Kurusart Konharn, Wichai Eungpinichpong, Kluaymai Promdee, Paramaporn Sangpara, Settapong Nongharnpitak, Waradanai Malila and Jirachai Karawa

Background:

The suitability of smartphone applications (apps) currently used to track walking/running may differ depending on a person’s weight condition. This study aimed to examine the validity and reliability of apps for both normal-weight and overweight/obese young adults.

Methods:

Thirty normal-weight (aged 21.7 ± 1.0 years, BMI 21.3 ± 1.9 kg/m2) and 30 overweight/ obese young adults (aged 21.0 ± 1.4 years, BMI 28.6 ± 3.7 kg/m2) wore a smartphone and pedometer on their right hip while walking/running at 3 different intensities on treadmills. Apps was randomly assigned to each individual for measuring average velocity, step count, distance, and energy expenditure (EE), and these measurements were then analyzed.

Results:

The apps were not accurate in counting most of the measured variables and data fell significantly lower in the parameters than those measured with standard-reference instruments in both light and moderate intensity activity among the normal-weight group. Among the overweight and obese group, the apps were not accurate in detecting velocity, distance, or EE during either light or vigorous intensities. The percentages of mean difference were 30.1% to 48.9%.

Conclusion:

Apps may not have sufficient accuracy to monitor important physical parameters of human body movement. Apps need to be developed that can, in particular, respond differently based on a person’s weight status.

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Stephen D. Herrmann, Tiago V. Barreira, Minsoo Kang and Barbara E. Ainsworth

Background:

There is little consensus on how many hours of accelerometer wear time is needed to reflect a usual day. This study identifies the bias in daily physical activity (PA) estimates caused by accelerometer wear time.

Methods:

124 adults (age = 41 ± 11 years; BMI = 27 ± 7 kg·m-2) contributed approximately 1,200 days accelerometer wear time. Five 40 day samples were randomly selected with 10, 11, 12, 13, and 14 h·d-1 of wear time. Four semisimulation data sets (10, 11, 12, 13 h·d-1) were created from the reference 14 h·d-1 data set to assess Absolute Percent Error (APE). Repeated-measures ANOVAs compared min·d-1 between 10, 11, 12, 13 h·d-1 and the reference 14 h·d-1 for inactivity (<100 cts·min-1), light (100−1951 cts·min-1), moderate (1952−5724 cts·min-1), and vigorous (≥5725 cts·min-1) PA.

Results:

APE ranged from 5.6%−41.6% (10 h·d-1 = 28.2%−41.6%; 11 h·d-1 = 20.3%−36.0%; 12 h·d-1 = 13.5%−14.3%; 13 h·d-1 = 5.6%−7.8%). Min·d-1 differences were observed for inactivity, light, and moderate PA between 10, 11, 12, and 13 h·d-1 and the reference (P < .05).

Conclusions:

This suggests a minimum accelerometer wear time of 13 h·d-1 is needed to provide a valid measure of daily PA when 14 h·d-1 is used as a reference.