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  • Author: Maria Giné-Garriga x
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Myriam Guerra, Maria Giné-Garriga and Bo Fernhall

The Wingate anaerobic test (WAnT) has not been used to assess individuals with Down syndrome (DS) and it is unknown if it is reliable in this population. We investigated the reliability of the WAnT in 19 adolescents with DS (age = 14.8 yrs; weight = 52.7 kg; height = 146.3 cm). Participants completed, on separate days, two standards WAnT using a resistance of 0.7 Nm × body weight (kg) in individuals ≥ 14 years old, and 0.5 Nm × body weight (kg) in participants < 14 years of age. Data were analyzed using intraclass correlation coefficient (ICC), dependent t tests and Bland-Altman plots. There was a significant difference between days for peak power (210.37 W vs. 236.26 W; ICC = 0.93), but not for mean power (158.72 vs. 168.71 W; ICC = 0.86), time to peak power (6.67 vs. 6.28 s; ICC = 0.69), or the fatigue index (9.33 vs. 5.43 W/sec; ICC = 0.09). Adolescents with DS exhibit low WAnT performance compared with previously published data on adolescents without DS and the reliability of WAnT is questionable in this population.

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Maria Giné-Garriga, Míriam Guerra, Esther Pagès, Todd M. Manini, Rosario Jiménez and Viswanath B. Unnithan

The purpose of this study was to evaluate whether a 12-wk functional circuit-training program (FCT) could alter markers of physical frailty in a group of frail community-dwelling adults. Fifty-one individuals (31 women, 20 men), mean age (± SD) 84 (± 2.9) yr, met frailty criteria and were randomly assigned into groups (FCT = 26, control group [CG] = 25). FCT underwent a 12-wk exercise program. CG met once a week for health education meetings. Measures of physical frailty, function, strength, balance, and gait speed were assessed at Weeks 0, 12, and 36. Physical-frailty measures in FCT showed significant (p < .05) improvements relative to those in CG (Barthel Index at Weeks 0 and 36: 73.41 (± 2.35) and 77.0 (± 2.38) for the FCT and 70.79 (± 2.53) and 66.73 (± 2.73) for the CG. These data indicate that an FCT program is effective in improving measures of function and reducing physical frailty among frail older adults.

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Jason J. Wilson, Mathias Skjødt, Ilona McMullan, Nicole E. Blackburn, Maria Giné-Garriga, Oriol Sansano-Nadal, Marta Roqué i Figuls, Jochen Klenk, Dhayana Dallmeier, Emma McIntosh, Manuela Deidda, Mark A. Tully, Paolo Caserotti and On behalf of the SITLESS Group

Accurately measuring older adults’ physical activity (PA) and sedentary behavior (SB) using accelerometers is essential, as both are important markers of health. This study aimed to highlight how steps taken during data processing may affect key hip-based accelerometry outcomes in older adults, using a selection of baseline accelerometry data (n = 658) from the SITLESS study. Different analytical parameters tested included wear-time algorithms, use of low-frequency extension (LFE) filter, epoch length, and minimum and maximum daily wear-time thresholds. These were compared against vertical axis counts per minute (CPM), vector magnitude (VM) CPM, SB, light PA, moderate-to-vigorous PA, step counts, and wear-time percentage. Differences in settings across the analytical parameters were assessed using paired sample t-tests and repeated measures ANOVAs using Bonferroni correction. Using the “Choi” versus “Troiano” wear-time algorithm resulted in a higher percentage wear-time. Most SB and PA outcomes were significantly different across wear-time algorithms (p < .001). This was similar when using the LFE filter versus normal filter (p < .001). Using 10-second epoch length increased daily SB time (between +75.7 and +79.2 minutes) compared to 60-second. Most SB and PA outcomes significantly changed comparing minimum-wear-time thresholds of 360, 480, 600, and 720 minutes per day (p < .001). Applying a log-diary with a ≥1140-minute threshold had a significant impact on vertical axis CPM, VM CPM, SB, and light PA outcomes (p < .001). This study demonstrates the potential variability in the number of participants being included in studies and reported SB and PA levels when processing older adults’ accelerometry data dependent on the analytical procedures utilized.