heterogeneity among older adults with respect to health, physical function, work and leisure activities, and social environment ( Lowsky, Olshansky, Bhattacharya, & Goldman, 2014 ). This is supported by limited evidence from resistance training studies, which suggests that variation in training adaptations may
Mary O. Whipple, Erica N. Schorr, Kristine M.C. Talley, Ruth Lindquist, Ulf G. Bronas and Diane Treat-Jacobson
Sophie Antoine-Jonville, Stéphane Sinnapah, Bruno Laviolle, François Paillard and Olivier Hue
The aim was to examine the relationship between physical activity pattern and dietary profile. Although some clustering of the variables related to these major determinants of cardiovascular risk has been demonstrated, they have not been extensively studied together.
Participants, Design, and Setting:
Two hundred two female university students from the main Guadeloupe (French West Indies) campus participated. They self-administered a validated Food Frequency Questionnaire and the 1-yr recall Modifiable Activity Questionnaire. Principal-component analysis was performed on the scores and the variables related to the physical activity pattern and dietary profile.
A model including 10 variables explained 84.9% of the total variance. The physical activity pattern was not associated with the dietary profile, apart from fruit intake. The physical activity level was homogeneously low (median 1.58, first and last quartile cutoffs 1.54 and 1.66, respectively). There was no correlation between the physical activity level and the Food Frequency Questionnaire score (r = –.005).
The absence of a strong relationship between the food and physical activity profiles is interpreted as a possible reflection of a dysregulation of the quality of food intake in this population with a sedentary lifestyle.
Michael J. Panza, Scott Graupensperger, Jennifer P. Agans, Isabelle Doré, Stewart A. Vella and Michael Blair Evans
relationship between the amount of sport involvement and quality of life, but the authors did not report aggregated findings via meta-analysis ( Evans et al., 2017 ). Although these reviews synthesized evidence, they focus on heterogeneous studies involving an array of psychosocial outcomes. This heterogeneity
Guy C. Wilson, Yorgi Mavros, Lotti Tajouri and Maria Fiatarone Singh
training (RT), and/or balance training is one potential strategy to maintain or increase functional performance. However, heterogeneity in exercise adaptation in both fitness outcomes and associated functional performance is commonly observed, and therefore, further research is necessary to unravel these
Brian M. Moore, Joseph T. Adams, Sallie Willcox and Joseph Nicholson
et al., 2009 ). Further, one may question whether the significant findings reported in these studies are clinically meaningful. We attempted to group these four studies to report the size of the effect of interventions on postural responses; however, because of the heterogeneity of outcomes (see
Alison Keogh, Barry Smyth, Brian Caulfield, Aonghus Lawlor, Jakim Berndsen and Cailbhe Doherty
variety of reported “fit” ranging r 2 values from .10 to .99. It was not possible to identify “the best” equation due to the heterogeneity of participants and the variety of outcomes used. However, runners, coaches, and researchers may use this list of equations contextually depending on the
Renato Sobral Monteiro-Junior, Paulo de Tarso Maciel-Pinheiro, Eduardo da Matta Mello Portugal, Luiz Felipe da Silva Figueiredo, Rodrigo Terra, Lara S. F. Carneiro, Vinícius Dias Rodrigues, Osvaldo J. M. Nascimento, Andrea Camaz Deslandes and Jerson Laks
estimate of the pooled effect size by a difference between standardized mean differences (SMDs) was carried out. The heterogeneity index ( I 2 ) was checked with the aim of analyzing possible discrepancies between studies. Indices were identified between 87% and 94% of heterogeneity. For this reason, we
Judith Jiménez-Díaz, Karla Chaves-Castro and Walter Salazar
, assessment for the violation of the assumption of independence for studies that included multiple ESs was conducted following the guidelines of Borenstein et al. 29 All analyses were conducted using MetaXL and OpenMeta. Nonoverlapping 95% CIs were considered statistically significant. Heterogeneity and
Peter A. van de Hoef, Jur J. Brauers, Maarten van Smeden, Frank J.G. Backx and Michel S. Brink
.3; The Nordic Cochrane Centre, The Cochrane Collaboration, 2014, Copenhagen, Denmark). 16 Heterogeneity was checked using a random effects meta-analysis model; effect sizes and the I 2 statistic were calculated. When different instruments were used to measure the same outcome, performance tests were
Meg E. Letton, Jeanette M. Thom and Rachel E. Ward
based on the baseline experience to account for any factors associated with previous ballet training. There was heterogeneity between the included studies in most study aspects, including participant demographics, intervention characteristics, and reported outcome measures. As such, it was neither