KaKi Tse1, Rebecca H Neiberg2, Daniel P Beavers2, Stephen B Kritchevsky3, Barbara J Nicklas3, Dalane W Kitzman4, W Jack Rejeski1, Stephen P Messier1, Kristen M Beavers1. 1. Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina, USA. 2. Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA. 3. Section on Gerontology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA. 4. Section on Cardiovascular Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Abstract
BACKGROUND: The purpose of this study was to examine whether select baseline characteristics influenced the likelihood of an overweight/obese, older adult experiencing a clinically meaningful gait speed response (±0.05 m/s) to caloric restriction (CR). METHODS: Individual level data from 1 188 older adults participating in 8, 5/6-month, weight loss interventions were pooled, with treatment arms collapsed into CR (n = 667) or no CR (NoCR; n = 521) categories. Exercise assignment was equally distributed across groups (CR: 65.3% vs NoCR: 65.4%) and did not interact with CR (p = .88). Poisson risk ratios (95% confidence interval [CI]) were used to examine whether CR assignment interacted with select baseline characteristic subgroups: age (≥65 years), sex (female/male), race (Black/White), body mass index (BMI; ≥35 kg/m2), comorbidity (diabetes, hypertension, cardiovascular disease) status (yes/no), gait speed (<1.0 m/s), or inflammatory burden (C-reactive protein ≥3 mg/L, interleukin-6 ≥2.5 pg/mL) to influence achievement of ±0.05 m/s fast-paced gait speed change. Main effects were also examined. RESULTS: The study sample (69.5% female, 80.1% White) was 67.6 ± 5.3 years old with a BMI of 33.8 ± 4.4 kg/m2. Average weight loss achieved in the CR versus NoCR group was -8.3 ± 5.9% versus -1.1 ± 3.8%; p < .01. No main effect of CR was observed on the likelihood of achieving a clinically meaningful gait speed improvement (risk ratio [RR]: 1.09 [95% CI: 0.93, 1.27]) or gait speed decrement (RR: 0.77 [95% CI: 0.57, 1.04]). Interaction effects were nonsignificant across all subgroups. CONCLUSION: The proportion of individuals experiencing a clinically meaningful gait speed change was similar for CR and NoCR conditions. This finding is consistent across several baseline subgroupings.
BACKGROUND: The purpose of this study was to examine whether select baseline characteristics influenced the likelihood of an overweight/obese, older adult experiencing a clinically meaningful gait speed response (±0.05 m/s) to caloric restriction (CR). METHODS: Individual level data from 1 188 older adults participating in 8, 5/6-month, weight loss interventions were pooled, with treatment arms collapsed into CR (n = 667) or no CR (NoCR; n = 521) categories. Exercise assignment was equally distributed across groups (CR: 65.3% vs NoCR: 65.4%) and did not interact with CR (p = .88). Poisson risk ratios (95% confidence interval [CI]) were used to examine whether CR assignment interacted with select baseline characteristic subgroups: age (≥65 years), sex (female/male), race (Black/White), body mass index (BMI; ≥35 kg/m2), comorbidity (diabetes, hypertension, cardiovascular disease) status (yes/no), gait speed (<1.0 m/s), or inflammatory burden (C-reactive protein ≥3 mg/L, interleukin-6 ≥2.5 pg/mL) to influence achievement of ±0.05 m/s fast-paced gait speed change. Main effects were also examined. RESULTS: The study sample (69.5% female, 80.1% White) was 67.6 ± 5.3 years old with a BMI of 33.8 ± 4.4 kg/m2. Average weight loss achieved in the CR versus NoCR group was -8.3 ± 5.9% versus -1.1 ± 3.8%; p < .01. No main effect of CR was observed on the likelihood of achieving a clinically meaningful gait speed improvement (risk ratio [RR]: 1.09 [95% CI: 0.93, 1.27]) or gait speed decrement (RR: 0.77 [95% CI: 0.57, 1.04]). Interaction effects were nonsignificant across all subgroups. CONCLUSION: The proportion of individuals experiencing a clinically meaningful gait speed change was similar for CR and NoCR conditions. This finding is consistent across several baseline subgroupings.
Authors: Kristen M Beavers; Beverly A Nesbit; Jessica R Kiel; Jessica L Sheedy; Linda M Arterburn; Amy E Collins; Sherri A Ford; Rebecca M Henderson; Christopher D Coleman; Daniel P Beavers Journal: J Gerontol A Biol Sci Med Sci Date: 2019-05-16 Impact factor: 6.053
Authors: Stephanie Studenski; Subashan Perera; Kushang Patel; Caterina Rosano; Kimberly Faulkner; Marco Inzitari; Jennifer Brach; Julie Chandler; Peggy Cawthon; Elizabeth Barrett Connor; Michael Nevitt; Marjolein Visser; Stephen Kritchevsky; Stefania Badinelli; Tamara Harris; Anne B Newman; Jane Cauley; Luigi Ferrucci; Jack Guralnik Journal: JAMA Date: 2011-01-05 Impact factor: 56.272
Authors: Barbara J Nicklas; Elizabeth Chmelo; Osvaldo Delbono; J Jeffrey Carr; Mary F Lyles; Anthony P Marsh Journal: Am J Clin Nutr Date: 2015-03-11 Impact factor: 7.045
Authors: Stephen P Messier; Richard F Loeser; Gary D Miller; Timothy M Morgan; W Jack Rejeski; Mary Ann Sevick; Walter H Ettinger; Marco Pahor; Jeff D Williamson Journal: Arthritis Rheum Date: 2004-05
Authors: Dalane W Kitzman; Peter Brubaker; Timothy Morgan; Mark Haykowsky; Gregory Hundley; William E Kraus; Joel Eggebeen; Barbara J Nicklas Journal: JAMA Date: 2016-01-05 Impact factor: 56.272
Authors: Michael E Miller; Jay Magaziner; Anthony P Marsh; Roger A Fielding; Thomas M Gill; Abby C King; Stephen Kritchevsky; Todd Manini; Mary M McDermott; Rebecca Neiberg; Denise Orwig; Adam J Santanasto; Marco Pahor; Jack Guralnik; W Jack Rejeski Journal: J Am Geriatr Soc Date: 2018-04-02 Impact factor: 5.562