PURPOSE: The Trial for Activity in Adolescent Girls (TAAG) is a group-randomized trial (GRT) to reduce the usual decline in moderate to vigorous physical activity (MVPA) among middle school girls. We report the school-level intraclass correlation (ICC) for MVPA from the TAAG baseline survey of sixth grade girls and describe the relationship between the schedule of data collection and the ICC. METHODS: Each of six sites recruited six schools and randomly selected 60 sixth grade girls from each school; 74.2% participated. Girls were grouped in waves defined by the date measurements began and asked to wear an Actigraph accelerometer for 6 d. Occasional missing data were replaced by imputation, and counts above 1500 per 30 s were treated as MVPA, converted into metabolic equivalents (METs), and summed over 6 a.m.-midnight to provide MET-minutes per 18-h day. Mixed-model regression was used to estimate ICC. RESULTS: The school-level ICC were higher when estimated from a single wave compared with three waves (e.g., 0.057 vs 0.022) and across weekdays compared with weekend days (e.g., 0.024 vs 0.012). Power in a new trial would be greater with some schedules (e.g., 88% given three waves and 6 d) than with others (e.g., 23% given one wave and Tuesday only). CONCLUSIONS: The schedule of data collection can have a dramatic effect on the ICC for MVPA. In turn, this can have a dramatic effect on the standard error for an intervention effect and on power. Investigators will need to consider the expected magnitude of the ICC and the validity of the MVPA estimates associated with their data collection schedule in planning a new study.
RCT Entities:
PURPOSE: The Trial for Activity in Adolescent Girls (TAAG) is a group-randomized trial (GRT) to reduce the usual decline in moderate to vigorous physical activity (MVPA) among middle school girls. We report the school-level intraclass correlation (ICC) for MVPA from the TAAG baseline survey of sixth grade girls and describe the relationship between the schedule of data collection and the ICC. METHODS: Each of six sites recruited six schools and randomly selected 60 sixth grade girls from each school; 74.2% participated. Girls were grouped in waves defined by the date measurements began and asked to wear an Actigraph accelerometer for 6 d. Occasional missing data were replaced by imputation, and counts above 1500 per 30 s were treated as MVPA, converted into metabolic equivalents (METs), and summed over 6 a.m.-midnight to provide MET-minutes per 18-h day. Mixed-model regression was used to estimate ICC. RESULTS: The school-level ICC were higher when estimated from a single wave compared with three waves (e.g., 0.057 vs 0.022) and across weekdays compared with weekend days (e.g., 0.024 vs 0.012). Power in a new trial would be greater with some schedules (e.g., 88% given three waves and 6 d) than with others (e.g., 23% given one wave and Tuesday only). CONCLUSIONS: The schedule of data collection can have a dramatic effect on the ICC for MVPA. In turn, this can have a dramatic effect on the standard error for an intervention effect and on power. Investigators will need to consider the expected magnitude of the ICC and the validity of the MVPA estimates associated with their data collection schedule in planning a new study.
Authors: Margarita S Treuth; Kathryn Schmitz; Diane J Catellier; Robert G McMurray; David M Murray; M Joao Almeida; Scott Going; James E Norman; Russell Pate Journal: Med Sci Sports Exerc Date: 2004-07 Impact factor: 5.411
Authors: D M Murray; B L Rooney; P J Hannan; A V Peterson; D V Ary; A Biglan; G J Botvin; R I Evans; B R Flay; R Futterman Journal: Am J Epidemiol Date: 1994-12-01 Impact factor: 4.897
Authors: Mary Story; Nancy E Sherwood; John H Himes; Marsha Davis; David R Jacobs; Yolanda Cartwright; Mary Smyth; James Rochon Journal: Ethn Dis Date: 2003 Impact factor: 1.847
Authors: Scott Going; Janice Thompson; Stephanie Cano; Dawn Stewart; Elaine Stone; Lisa Harnack; Corleone Hastings; James Norman; Charles Corbin Journal: Prev Med Date: 2003-12 Impact factor: 4.018
Authors: David M Murray; Diane J Catellier; Peter J Hannan; Margarita S Treuth; June Stevens; Kathryn H Schmitz; Janet C Rice; Terry L Conway Journal: Med Sci Sports Exerc Date: 2004-05 Impact factor: 5.411
Authors: Ken Resnicow; Nanhua Zhang; Roger D Vaughan; Sasiragha Priscilla Reddy; Shamagonam James; David M Murray Journal: Am J Public Health Date: 2010-02-18 Impact factor: 9.308
Authors: Lorraine B Robbins; Karin A Pfeiffer; Kimberly S Maier; Yun-Jia Lo; Stacey M Wesolek Ladrig Journal: J Sch Nurs Date: 2012-04-03 Impact factor: 2.835
Authors: Russell R Pate; June Stevens; Larry S Webber; Marsha Dowda; David M Murray; Deborah R Young; Scott Going Journal: J Adolesc Health Date: 2008-10-29 Impact factor: 5.012
Authors: Chris D Baggett; June Stevens; Robert G McMurray; Kelly R Evenson; David M Murray; Diane J Catellier; Ka He Journal: Med Sci Sports Exerc Date: 2008-11 Impact factor: 5.411
Authors: Carly Rich; Marco Geraci; Lucy Griffiths; Francesco Sera; Carol Dezateux; Mario Cortina-Borja Journal: PLoS One Date: 2013-06-24 Impact factor: 3.240
Authors: Lorraine B Robbins; Karin A Pfeiffer; Amber Vermeesch; Kenneth Resnicow; Zhiying You; Lawrence An; Stacey M Wesolek Journal: BMC Public Health Date: 2013-05-15 Impact factor: 3.295
Authors: Rachel Sutherland; Elizabeth Campbell; David R Lubans; Philip J Morgan; Anthony D Okely; Nicole Nathan; Luke Wolfenden; Jannah Jones; Lynda Davies; Karen Gillham; John Wiggers Journal: BMC Public Health Date: 2013-01-22 Impact factor: 3.295