ECSS Paris 2023: CP-MH27
INTRODUCTION: Peer support is a promising approach to promote physical activity. However, evidence on its effects largely relies on self-reported data. In this study, objective measures were used to assess the effects of peer support on physical activity among individuals with type 2 diabetes and/or coronary heart disease in Germany. METHODS: Participants were selected from 1002 individuals included in the main general practice-based RCT for P-SUP (2019-2024) [1] using stratified random sampling ensuring an equal distribution across the two study conditions as in the main RCT. Physical activity was assessed for seven consecutive days prior to the start of the intervention (T0), after six months (T1), and after twelve months (T2) using a hip-worn accelerometer (ActiGraph models GT3X+ and wGT3X-BT). Data were considered valid if the accelerometer was worn for at least 10 hours on at least two weekdays and one weekend day. Time spent in moderate-to-vigorous intensity physical activity (MVPA) was derived from Euclidean norm minus one using established thresholds [2]. Changes in MVPA from baseline were analysed among participants who provided valid data for all three measurement intervals using a mixed ANOVA with time as within- and study condition as between-subject factor. RESULTS: At T0, 143 participants received an accelerometer (67 intervention, 76 control), of whom 48 (21 intervention, 27 control; mean age 65 years, SD=9; 54% women) provided valid data across all three measurements. At T0, weekly MVPA averaged 129 minutes (SD=124) in the intervention group (IG) and 145 minutes (SD=132) in the control group (CG). At T1, MVPA changed by +43 minutes/week (SD=154) in the IG and -16 minutes/week (SD=94) in the CG, with T2 values in both groups being equal to baseline. Inferential analyses revealed no significant effects for time (F(1,46)=0.70, p=.407), study condition (F(1,46)=1.14, p=.292), or interaction between time and study condition (F(1,46)=2.62, p=.112). CONCLUSION: Descriptive analyses suggest a more favourable short-term change in the intervention group compared with the control group. However, there were no statistically significant effects. This might be due to the small sample size, indicating the need for larger samples in future studies. References: [1] Konerding, U., Redaèlli, M., Ackermann, K., Altin, S., Appelbaum, S., Biallas, B., ... & Stock, S. (2021). A pragmatic randomised controlled trial referring to a Personalised Self-management SUPport Programme (P-SUP) for persons enrolled in a disease management programme for type 2 diabetes mellitus and/or for coronary heart disease. Trials, 22, 1-17. [2] Hildebrand, M., Van Hees, V. T., Hansen, B. H., & Ekelund, U. (2014). Age Group Comparability of Raw Accelerometer Output from Wrist- and Hip-Worn Monitors. Medicine & Science in Sports & Exercise, 46(9), 1816–1824. https://doi.org/10.1249/MSS.0000000000000289
Read CV Maximilian SchollECSS Paris 2023: CP-MH27
INTRODUCTION: Cardiorespiratory training in heart failure (HF) improves functional capacity, aerobic fitness, and quality of life, and may reduce hospitalizations. Individualized intensity prescription using cardiopulmonary exercise testing (CPET)-derived ventilatory thresholds (VTs) is recommended, but gas-analysis and expert visual interpretation limit feasibility. Muscle oxygen saturation (SmO₂) may provide practical breakpoints, yet its validity in HF is unclear. Therefore, this study aimed to examine the agreement between visually determined ventilatory thresholds and SmO₂ breakpoints in HF. METHODS: Twenty clinically stable adults with heart failure with reduced ejection fraction (HFrEF; left ventricular ejection fraction [LVEF] ≤40%) and New York Heart Association (NYHA) class II–III performed a symptom-limited incremental CPET on a cycle ergometer (4 min unloaded; ramp +6 W·min⁻¹ for NYHA II or +10 W·min⁻¹ for NYHA III) with breath-by-breath gas exchange and vastus lateralis SmO₂ (Moxy Monitor). Ventilatory threshold 1 (VT1) and ventilatory threshold 2 (VT2) were visually identified by two blind observers, in case of disagreement, third observer analyzed the data to resolve the disagreement. SmO₂ was plotted against oxygen uptake (VO₂) or workload (W) and analyzed in MATLAB using an automated four-knot segmented regression to identify two breakpoints (BP1, BP2). Thresholds were expressed as VO₂ (L·min⁻¹) and workload (W). Agreement was assessed using Bland–Altman analysis and intraclass correlation coefficients (ICC). RESULTS: Between VT1 and BP1 expressed as oxygen uptake, bland–Altman Analysis showed a mean bias of -0,02 L·min⁻¹ with 95% limits of agreement (LOA) from -0.39 L·min⁻¹ to 0.34 L·min⁻¹ and an ICC of 0.67 (0.35 - 0.86). Between VT1 and BP1 expressed workload, bland–Altman Analysis showed a mean bias of 23 W with 95% LOA from -18 to 63 W and an ICC of 0.13 (-0.33 - 0.53). Between VT2 and BP2 expressed as oxygen uptake, bland–Altman Analysis showed a mean bias of 0,04 L·min⁻¹ with 95% LOA from -0.35 L·min⁻¹ to 0.44 L·min⁻¹ and an ICC of 0.80 (0.56 - 0.92). Between VT2 and BP2 expressed workload, bland–Altman Analysis showed a mean bias of 8 W with 95% LOA from -42 to 58 W and an ICC of 0.28 (0 – 0.64). CONCLUSION: Agreement between VT1 and BP1 was low when expressed as VO₂ (L·min⁻¹) or workload (W), suggesting limited interchangeability between BP1 and the first ventilatory threshold in patients with HFrEF. In contrast, agreement between VT2 and BP2 was high when expressed as VO₂, supporting BP2 as a potential surrogate to estimate the second ventilatory threshold. However, when expressed as workload, the strength of agreement decreased, warranting cautious interpretation and further validation.
Read CV Hugo Aceituno PintoECSS Paris 2023: CP-MH27