. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine . Sports Med . 1998 ; 26 ( 4 ): 217 – 238 . PubMed ID: 9820922 doi:10.2165/00007256-199826040-00002 9820922 10.2165/00007256-199826040-00002 17. Bland J , Altman D . Measuring agreement in method
Jason Lake, Peter Mundy, Paul Comfort, John J. McMahon, Timothy J. Suchomel and Patrick Carden
Carlo Castagna, Matthew Varley, Susana C.A. Póvoas and Stefano D’Ottavio
To test the interchangeability of 2 match-analysis approaches for external-load detection considering arbitrary selected speeds and metabolic power (MP) thresholds in male top-level soccer.
Data analyses were performed considering match physical performance of 60 matches (1200 player cases) of randomly selected Spanish, German, and English first-division championship matches (2013–14 season). Match analysis was performed with a validated semiautomated multicamera system operating at 25 Hz.
During a match, players covered 10,673 ± 348 m, of which 1778 ± 208 m and 2759 ± 241 m were performed at high intensity, as measured using speed (≥16 km/h, HI) and metabolic power (≥20 W/kg, MPHI) notations. High-intensity notations were nearly perfectly associated (r = .93, P < .0001). A huge method bias (980.63 ± 87.82 m, d = 11.67) was found when considering MPHI and HI. Very large correlations were found between match total distance covered and MPHI (r = .84, P < .0001) and HI (r = .74, P < .0001). Player high-intensity decelerations (≥–2 m/s2) were very largely associated with MPHI (r = .73, P < .0001).
The speed and MP methods are highly interchangeable at relative level (magnitude rank) but not absolute level (measure magnitude). The 2 physical match-analysis methods can be independently used to track match external load in elite-level players. However, match-analyst decisions must be based on use of a single method to avoid bias in external-load determination.
Lucia Andrea Leone and Dianne S. Ward
Obese women have lower levels of physical activity than nonobese women, but it is unclear what drives these differences.
Mixed methods were used to understand why obese women have lower physical activity levels. Findings from focus groups with obese white women age 50 and older (N = 19) were used to develop psychosocial items for an online survey of white women (N = 195). After examining the relationship between weight group (obese vs. nonobese) and exercise attitudes, associated items (P < .05) were tested for potential mediation of the relationship between weight and physical activity.
Obese women were less likely than nonobese women to report that they enjoy exercise (OR = 0.4, 95% CI 0.2−0.8) and were more likely to agree their weight makes exercise difficult (OR = 10.6, 95% CI 4.2−27.1), and they only exercise when trying to lose weight (OR = 3.8, 95% CI 1.6−8.9). Enjoyment and exercise for weight loss were statistically significant mediators of the relationship between weight and physical activity.
Exercise interventions for obese women may be improved by focusing on exercise enjoyment and the benefits of exercise that are independent of weight loss.
Bryan L. Riemann and George J. Davies
objective strength measures, the purpose of this investigation was to determine the relationship between concentric isokinetic pushing force and SSASP performance. A secondary purpose was to conduct a method comparison analysis of limb symmetry indices (LSIs; dominant to nondominant limbs) between the SSASP
Simon Roberts, Grant Trewartha and Keith Stokes
To assess the validity of a digitizing time–motion-analysis method for field-based sports and compare this with a notational-analysis method using rugby-union match play.
Five calibrated video cameras were located around a rugby pitch, and 1 subject completed prescribed movements within each camera’s view. Running speeds were measured using photocell timing gates. Two experienced operators digitized video data (operator 1 on 2 occasions) to allow 2-dimensional reconstruction of the prescribed movements.
Accuracy for total distance calculated was within 2.1% of the measured distance. For intraoperator and interoperator reliability, calculated distances were within 0.5% and 0.9%, respectively. Calculated speed was within 8.0% of measured photocell speed with intraoperator and interoperator reliability of 3.4% and 6.0%, respectively. For the method comparison, two 20-minute periods of rugby match play were analyzed for 5 players using the digitizing method and a notational time–motion method. For the 20-minute periods, overall mean absolute differences between methods for percentage time spent and distances covered performing different activities were 3.5% and 198.1 ± 138.1 m, respectively. Total number of changes in activity per 20 minutes were 184 ± 24 versus 458 ± 48, and work-to-rest ratios, 10.0%:90.0% and 7.3%:92.7% for notational and digitizing methods, respectively.
The digitizing method is accurate and reliable for gaining detailed information on work profiles of field-sport participants and provides applied researchers richer data output than the conventional notational method.
Michael W. Beets and John T. Foley
Much of the research conducted to date implies overweight youth exhibit uniform active and sedentary behavioral patterns. This approach negates the possibility that multiple co-occurring, and seemingly contrasting, behaviors may manifest within the same individual. We present a substantive dialogue on alternative analytical approaches to identifying risk-related active/sedentary behavioral patterns associated with overweight in adolescents.
Comparisons were made among latent profile analysis (LPA), cluster analysis (CA), and multinomial logistic regression (MLR). A cross sectional sample of youth (N = 6603; 12−18 yrs) completed a questionnaire assessing: physical activity (PA); competing activities (COMP); and sedentary activities (SED). Demographics associated with PA (age, sex, BMI) were used as covariates/predictors.
Comparisons among methods revealed that LPA and CA detected subgroupings of behavioral patterns associated with overweight, each unique in regards to behaviors and demographic characteristics, whereas MLR results followed established associations of low PA and high SED without subgroup separation.
Use of LPA and CA provides a rich understanding of behavioral patterns and the related demographic characteristics. Decisions guiding the selection of analytical techniques are discussed.
Lindsay B. Baker, John R. Stofan, Henry C. Lukaski and Craig A. Horswill
Simultaneous whole-body wash-down (WBW) and regional skin surface sweat collections were completed to compare regional patch and WBW sweat calcium (Ca), magnesium (Mg), copper (Cu), manganese (Mn), iron (Fe), and zinc (Zn) concentrations. Athletes (4 men, 4 women) cycled in a plastic open-air chamber for 90 min in the heat. Before exercise, the subjects and cycle ergometer (covered in plastic) were washed with deionized water. After the onset of sweating, sterile patches were attached to the forearm, back, chest, forehead, and thigh and removed on saturation. After exercise, the subjects and cycle ergometer were washed with 5 L of 15-mM ammonium sulfate solution to collect all sweat minerals and determine the volume of unevaporated sweat. Control trials were performed to measure mineral contamination in regional and WBW methods. Because background contamination in the collection system was high for WBW Mn, Fe, and Zn, method comparisons were not made for these minerals. After correction for minimal background contamination, WBW sweat [Ca], [Mg], and [Cu] were 44.6 ± 20.0, 9.8 ± 4.8, and 0.125 ± 0.069 mg/L, respectively, and 5-site regional (weighted for local sweat rate and body surface area) sweat [Ca], [Mg], and [Cu] were 59.0 ± 15.9, 14.5 ± 4.8, and 0.166 ± 0.031 mg/L, respectively. Five-site regional [Ca], [Mg], and [Cu] overestimated WBW by 32%, 48%, and 33%, respectively. No individual regional patch site or 5-site regional was significantly correlated with WBW sweat [Ca] (r = –.21, p = .65), [Mg] (r = .49, p = .33), or [Cu] (r = .17, p = .74). In conclusion, regional sweat [Ca], [Mg], and [Cu] are not accurate surrogates for or significantly correlated with WBW sweat composition.
Jacob A. Goldsmith, Cameron Trepeck, Jessica L. Halle, Kristin M. Mendez, Alex Klemp, Daniel M. Cooke, Michael H. Haischer, Ryan K. Byrnes, Robert F. Zoeller, Michael Whitehurst and Michael C. Zourdos
KL . Folded empirical distribution function curves—mountain plots . Am Stat . 1995 ; 49 ( 4 ): 342 – 345 . doi:10.2307/2684570 10. Altman DG , Bland JM . Measurement in medicine: the analysis of method comparison studies . Statistician . 1983 ; 32 ( 3 ): 307 – 317 . doi:10.2307/2987937 10
Stefan Altmann, Steffen Ringhof, Benedikt Becker, Alexander Woll and Rainer Neumann
. 2017 ; 31 : 1994 – 1999 . PubMed ID: 28277431 doi:10.1519/JSC.0000000000001889 10.1519/JSC.0000000000001889 28277431 6. Bland JM , Altman DG . Measuring agreement in method comparison studies . Stat Methods Med Res . 1999 ; 8 : 135 – 160 . PubMed ID: 10501650 doi:10
; 11 ( 1 ): 135 – 140 . PubMed doi:10.1123/ijspp.2014-0582 10.1123/ijspp.2014-0582 26114855 4. Hunter F , Bray J , Towlson C , et al . Individualisation of time-motion analysis: a method comparison and case report series . Int J Sports Med . 2014 ; 36 ( 1 ): 41 – 48 . PubMed doi:10.1055/s