Search

BackgroundThis study sought to compare three sensor-based wear-time estimation methods to conventional diaries for ActiGraph wGT3X-BT accelerometers worn on the non-dominant wrist in early pregnancy.MethodsPregnant women (n= 108) wore ActiGraph wGT3X-BT accelerometers for 7 days and recorded their device on and off times in a diary (criterion). Average daily wear-time estimates from the Troiano and Choi algorithms and the wGT3X-BT accelerometer wear sensor were compared against the diary. The Hibbing 2-regression model was used to estimate time spent in activity (during periods of device wear) for each method. Wear-time and time spent in activity were compared with multiple repeated measures ANOVAs. Bland Altman plots assessed agreement between methods.ResultsCompared to the diary [825.5 minutes (795.1, 856.0)], the Choi [843.0 (95% CI 812.6, 873.5)] and Troiano [839.1 (808.7, 869.6)] algorithms slightly overestimated wear-time, whereas the sensor [774.4 (743.9, 804.9)] underestimated it, although only the sensor differed significantly from the diary (P < .0001). Upon adjustment for average daily wear-time, there were no statistically significant differences between the wear-time methods in regards to minutes per day of moderate to vigorous physical activity (MVPA), vigorous PA, and moderate PA. Bland Altman plots indicated the Troiano and Choi algorithms were similar to the diary and within ≤ 0.5% of each other for wear-time and MVPA.ConclusionsThe Choi or Troiano algorithms offer a valid and efficient alternative to diaries for the estimation daily wear-time in larger-scale studies of MVPA during pregnancy, and reduce burden for study participants and research staff.

Purpose: To assess changes in criterion validity when modifying cut-points for use in different epoch lengths. Method: Simulated free-living data came from 42 adolescents (2-hr each) and 29 adults (6-hr each) wearing a hip-worn accelerometer and portable indirect calorimeter (Cosmed K4b[superscript 2]). K4b[superscript 2] data were classified as sedentary behavior (SB), light physical activity (LPA), or moderate-to-vigorous physical activity (MVPA), and compared to estimates from accelerometer data processed with three youth and three adult cut-points in six epoch lengths (1, 5, 10, 15, 30, and 60-s). A cut-point of 100 counts per minute was used for all SB estimates. Results: For both adolescents and adults, SB estimates in all but 60-s epochs were significantly higher than the criterion, by 18.4%-78.4% (all p < 0.02). CPS had varied effects on youth LPA, ranging from favorable effects for one cut-point (1.9% underestimation in 1-s epochs, versus 40.2% overestimation in the originally-calibrated epoch length; p < 0.01 and p = 0.91, respectively) to unfavorable effects for another (41.8% underestimation in 1-s epochs, versus 9.8% underestimation in the originally-calibrated epoch length; p < 0.01 and p = 0.39, respectively). Adult LPA estimates in 30-s or 60-s epochs were closest to the criterion (within 5.2%-37.3%, p = 0.0001-0.49). Youth MVPA estimates in 60-s epochs were closest to the criterion (within 9.5%-53.2%, all p < 0.05), whereas adult MVPA estimates in 1-s epochs were closest to the criterion (within 6.6%-34.2%, p = 0.02-0.59). Conclusion: Cut-point modification is not universally beneficial, and thus it is not recommended.

Purpose: The purposes of this article are to: (a) describe the rationale and development of the Youth Compendium of Physical Activities (Youth Compendium); and (b) discuss the utility of the Youth Compendium for audiences in research, education, community, health care, public health, and the private sector. Methods: The Youth Compendium provides a list of 196 physical activities (PA) categorized by activity types, specific activities, and metabolic costs (youth metabolic equivalents of task [METy]) as measured by indirect calorimetry. The utility of the Youth Compendium was assessed by describing ways in which it can be used by a variety of audiences. Results: Researchers can use METy values to estimate PA levels and determine changes in PA in intervention studies. Educators can ask students to complete PA records to determine time spent in physical activities and to identify health-enhancing activities for classroom PA breaks. Community leaders, parents, and health care professionals can identify activity types that promote healthful behaviors. Public health agencies can use the METy values for surveillance and as a resource to inform progress toward meeting national physical activity guidelines. Applications for the private sector include the use of METy in PA trackers and other applications. Conclusion: The National Collaborative on Childhood Obesity Research Web site presents the Youth Compendium and related materials to facilitate measurement of the energy cost of nearly 200 physical activities in children and youth. The Youth Compendium provides a way to standardize energy costs in children and youth and has application for a wide variety of audiences. [This article reflects the content of Barbara Ainsworth's 2018 C. H. McCloy Lecture. Additional funding for this project was provided by the National Collaborative on Childhood Obesity Research--a public-private partnership among the the National Cancer Institute (NCI), Centers for Disease Control and Prevention (CDC), Robert Wood Johnson Foundation, and the U.S. Department of Agriculture.]

This study evaluated a 4-week program to increase the time families spent engaging in outdoor activity. Parents were provided strategies to increase family outdoor activity and locations to be active. Sixteen families completed the program. Duration and number of family outdoor activity bouts per week, type of activities, locations, and family member attendance were measured using logs. Pre/post surveys were conducted to determine the usefulness of providing educational resources and maps. Compared to baseline (216.1±127.3 min/week), family outdoor activity for weeks 1 (316.1±180.2 min/week), 2 (351.1±209.1 min/week), and 4 (317.5±186.8 min/week) were significantly greater at follow-up. At follow-up, parents reported increased regular exercise and encouragement for their child to be active. Children reported adults engaged in physical activity with them and increased transportation to places to be active. This novel program increased family outdoor activity levels and contributes to limited research on strategies to increase outdoor activity in youth.

Background: The use of mobile phones for physical activity (PA) promotion and assessment represents an attractive research area because this technology is characterized by a widespread reach and dynamic features. Purpose: The purpose of this study was to conduct a systematic review of mobile phone-based approaches for encouraging and assessing PA. Methods: Eligible studies were identified through a search of PubMed and PsycINFO and required to address the usability of mobile phones for PA promotion and/or measurement, be written in English, and be published in a peer-reviewed journal between 2010 and 2013. Results: Fifty-two studies were included in this review. The use of mobile phones for PA promotion, assessment, and both promotion and assessment were addressed by 27, 19, and 6 studies, respectively. Out of 26 studies that reported a PA behavior change outcome, 13 observed favorable PA changes, 3 found mixed results, and 10 found no improvements. Ten studies examined the validity of mobile phones for PA assessment and 9 reported favorable outcomes. Participants found mobile phones to be highly acceptable. Discussion: The findings suggest that mobile phones have great potential as a PA promotion and assessment tool among a variety of individuals. Translation to Health Education Practice: Mobile phones may provide practitioners with efficient ways to promote PA and valid ways to assess PA.

This study was designed to determine the number of days of pedometer monitoring necessary to achieve reliable and valid estimates of a 1-year average of step counts in adults based on either consecutive days (CD) or random days (RD) of data collection. Twenty-three participants (16 women; M age = 38 years, SD = 9.9) wore a Yamax SW 200 pedometer and recorded their step counts for 365 consecutive days. Nine measurement periods of various lengths were selected (2, 3, 4, 5, 6, 7, 14, 21, and 30 days). Each measurement period was randomly selected 10 times each for CD and RD from the larger database. For reliability and validity, two-way intraclass correlation coefficients and mean absolute percentage error (MAPE) were calculated, respectively. The year-round average was considered the criterion measure of the "true" habitual physical activity. Data were analyzed separately by CD and RD. At least 5 CD or 6 RD were necessary to achieve an ICC of 0.80. A minimum of 30 CD or 14 RD were necessary to achieve an MAPE lower than 10%. These findings provide researchers and practitioners with useful information to determine appropriate measurement length and the method of data selection based on a desired level of reliability and validity. (Contains 3 tables and 4 figures.)

Background: Little is known about the daily physical activity (PA) levels of people employed in different occupational categories. Methods: Nine ActiGraph accelerometer-derived daily PA variables are presented and ranked for adults (N = 1465, 20–60 y) working in the 22 occupational categories assessed by NHANES 2005–2006. A composite score was generated for each occupational category by summing the rankings of 3 accelerometer-derived daily PA variables known to have strong associations with health outcomes (total activity counts [TAC], moderate to vigorous PA minutes per week in modified 10-minute bouts [MVPA 10], and percentage of time spent in sedentary activity [SB%]). Results: Classified as high-activity occupational categories, "farming, fishing, forestry," and "building & grounds cleaning, maintenance" occupations had the greatest TAC (461 996 and 449 452), most MVPA 10 (149.6 and 97.8), most steps per day (10 464 and 11 602), and near the lowest SB% (45.2% and 45.4%). "Community, social services" occupations, classified as low-activity occupational categories, had the second lowest TAC (242 085), least MVPA 10 (12.1), fewest steps per day (5684), and near the highest SB% (64.2%). Conclusions: There is a strong association between occupational category and daily activity levels. Objectively measured daily PA permitted the classification of the 22 different occupational categories into 3 activity groupings. [ABSTRACT FROM AUTHOR]