was estimated with the Veterans Specific Activity Questionnaire, a 13-item, self-administered questionnaire that determines which daily activities typically cause fatigue, shortness of breath, chest discomfort, or claudication. 15 , 16 Body mass (measured using weighing scales: Model 762; Seca GmbH
Nicolas Aguilar-Farias, Wendy J. Brown, Tina L. Skinner and G.M.E.E. (Geeske) Peeters
Erwin Huiszoon, Paul L. de Vreede, Inge Bramsen, Chris H.Z. Kuiper and Harald S. Miedema
The Assessment of Daily Activity Performance (ADAP) test has been developed to measure the physical capacity of older adults to carry out instrumental activities of daily living (ADL). The present study explores the option to create a less time-consuming short version of the ADAP that can be completed in the individual’s home environment and that imposes less of a physical burden. Data from 141 independently living women aged 70 and older were analyzed using principal components analysis (PCA). PCA identified two factors, on which 10 of the original 21 items had loaded sufficiently to be eligible for inclusion in a short version. The ADAP short version is considerably shorter than the original test and provides a good representation of the constructs being measured. More research is necessary to develop a short version of the ADAP that is easily applicable in the home environment of older adults.
Yuki Hikihara, Shigeho Tanaka, Kazunori Ohkawara, Kazuko Ishikawa-Takata and Izumi Tabata
The current study evaluated the validity of 3 commercially-available accelerometers to assess metabolic equivalent values (METs) during 12 activities.
Thirty-three men and thirty-two women were enrolled in this study. The subjects performed 5 nonlocomotive activities and 7 locomotive movements. The Douglas bag method was used to gather expired air. The subjects also wore 3 hip accelerometers, a Lifecorder uniaxial accelerometer (LC), and 2 triaxial accelerometers (ActivTracer, AT; Actimarker, AM).
For nonlocomotive activities, the LC largely underestimated METs for all activities (20.3%–55.6%) except for desk work. The AT overestimated METs for desk work (11.3%) and hanging clothes (11.7%), but underestimated for vacuuming (2.3%). The AM underestimated METs for all nonlocomotive activities (8.0%–19.4%) except for hanging clothes (overestimated by 16.7%). The AT and AM errors were significant, but much smaller than the LC errors (23.2% for desk work and –22.3 to –55.6% for the other activities). For locomotive movements, the 3 accelerometers significantly underestimated METs for all activities except for climbing down stairs.
We conclude that there were significant differences for most activities in 3 accelerometers. However, the AT, which uses separate equations for nonlocomotive and locomotive activities, was more accurate for nonlocomotive activities than the LC.
Stephen D. Herrmann, Tiago V. Barreira, Minsoo Kang and Barbara E. Ainsworth
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.
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.
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).
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.
Benjamin C. Guinhouya, Mohamed Lemdani, Géoffroy K. Apété, Alain Durocher, Christian Vilhelm and Hervé Hubert
This study was designed to model the relationship between an ActiGraph-based “in-school” physical activity (PA) and the daily one among children and to quantify how school can contribute to the daily PA recommendations.
Fifty boys and 43 girls (aged 8 to 11 years) wore ActiGraph for 2 schooldays of no structured PA. The daily moderate-to-vigorous PA (MVPAd) was regressed on the school time MVPA (MVPAs). Then, a ROC analysis was computed to define the required MVPAs.
Children spent 57% of their awaking time at school. School time PA opportunities (ie, recesses: ~18% of a child’s awaking time) accounted for >70% of the MVPAd among children. Then, MVPAd (Y) could be predicted from MVPAs (X) using the equation: Y = 2.06 X 0.88; R 2 = .889, P < .0001. Although, this model was sex-specifically determined, cross-validations showed valid estimates of MVPAd. Finally, with a sensitivity of 100% and a specificity of 90%, MVPAs, a 34 min.d−1 was required to prompt the daily recommendation.
The current study shows the contribution of MVPA at school to recommended activity levels and suggests the value of activity performed during recesses. It also calls for encouraging both home- and community-based interventions, predominantly directed toward girls.
Glen Nielsen, Anna Bugge, Bianca Hermansen, Jesper Svensson and Lars Bo Andersen
This study investigates the influence of school playground facilities on children’s daily physical activity.
Participants were 594 school children measured at preschool (age 6 to 7 years) and 3 years later in third grade (518 children age 9 to 10 years) from 18 schools in 2 suburban municipalities in Denmark. Physical activity data were obtained using accelerometers. These were related to the number of permanent play facilities in school grounds and the school playground area (m2).
The number of play facilities in the school grounds was positively associated with all measures of children’s activity. In preschool every 10 additional play facilities the children had access to was associated with an increase in the average accelerometer counts of 14% (r = .273, P < .001) in school time and 6.9% (r = .195, P < .001) overall. For the children in third grade, access to 10 additional play facilities was associated with an increase in school time activity level of 26% (r = .364, P < .001) and an increase in overall activity level of 9.4% (r = .211, P < .001). School playground area did not affect activity levels independently of the number of permanent play facilities.
Increasing the number of play facilities in primary school playgrounds may increase the level of children’s daily physical activity.
Allison Naber, Whitney Lucas Molitor, Andy Farriell, Kara Honius and Brooke Poppe
, Harvey, Skelton, & Chastin, 2018 ; Terada & Sexsmith, 2015 ). Older adults residing in assisted living facilities may be even less active than those described above due to medical conditions resulting in mobility restrictions and an increased need for caregiver assistance during daily activities. A
Mieko Yokozuka, Chie Miki, Makoto Suzuki and Rieko Katsura
not the difference in the life space level (home, outside home, neighborhood, town, etc.) that is important; we think that the difference in the frequency of daily activity (less than once/week, one to three times per week, and four to six times per week) chiefly affects the life space level. The
Kyle R. Lynch, Michael Fredericson, Bruna Turi-Lynch, Ricardo R. Agostinete, Igor H. Ito, Rafael Luiz-de-Marco, Mario A. Rodrigues-Junior and Rômulo A. Fernandes
on TF related to participation in sports, the objective of this study was to investigate the effects of different sports on the incidence of TF (sport-related fractures and those occurring in daily activities) among adolescents during the 9-month follow-up period. We hypothesized that adolescents
Afshin Moghadasi, Gholamali Ghasemi, Ebrahim Sadeghi-Demneh and Masoud Etemadifar
. The program was designed and modified on the basis of earlier studies. 10 , 12 – 15 , 17 , 18 The control group received their usual routine care and daily activities. Table 1 TRX Suspension Training Program Exercises Week Sessions 1 and 2 Sessions 3 and 4 Sessions 5 and 6 (1) TRX 45° rowing Level 1