This study investigated the effects of eight weeks of aerobic exercise training on body composition, lipid profiles, organokines (leptin, irisin), inflammatory biomarkers (high-sensitivity C-reactive protein [hs-CRP], interleukin-6 [IL-6]), and intestinal barrier permeability markers (zonulin, lipopolysaccharide-binding protein [LBP]) in overweight and obese women of different age groups. We hypothesized that aerobic exercise would improve cardiorespiratory fitness, body composition, lipid metabolism, and reduce pro-inflammatory responses and intestinal permeability, and that these effects would differ between age groups. A total of 32 participants with a body mass index (BMI) ≥ 23 kg/m² were randomly assigned to one of four groups (n = 8 per group): young exercise (YE), young control (YC), middle-aged exercise (ME), and middle-aged control (MC). The intervention consisted of treadmill running for 50 min per session, four times per week, at an intensity corresponding to 65% of the target heart rate (THR), calculated using the Karvonen formula, for a duration of eight weeks. Body composition variables included body weight, BMI, body fat mass (BFM), percentage body fat (PBF), lean body mass (LBM), and maximal oxygen uptake (VO₂max). Blood samples were analyzed for lipid profiles (total cholesterol [TC], triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C]), organokines, inflammatory markers, and intestinal barrier integrity biomarkers. After the intervention, the YE and ME groups exhibited significant reductions (p < 0.05) in body weight, BMI, BFM, PBF, TC, TG, LDL-C, leptin, hs-CRP, IL-6, zonulin, and LBP. In contrast, LBM and VO₂max significantly increased (p < 0.05) in both exercise groups. No significant changes were observed in irisin concentrations or HDL-C levels (p > 0.05). These results suggest that aerobic exercise training, irrespective of age, is effective in improving cardiorespiratory fitness, body composition, and lipid metabolism, while simultaneously reducing systemic inflammation and is associated with favorable changes in circulating biomarkers of intestinal barrier function in overweight and obese women.

Smartwatches are widely used to monitor physiological responses during exercise; however, their accuracy in measuring heart rate (HR) and energy expenditure (EE) across different exercise modalities remains insufficiently characterized. This study evaluated the accuracy of HR and EE measurements obtained from four commercially available smartwatches in comparison with gold-standard reference methods. Sixty-two healthy adult men performed standardized endurance and resistance exercise protocols while simultaneously wearing four smartwatches (Apple, Galaxy, Fitbit, and Garmin). HR was measured using electrocardiography (ECG), and EE was determined using indirect calorimetry. Measurement accuracy was assessed using repeated-measures analysis of variance, Pearson’s correlation analysis, intraclass correlation coefficients (ICCs), and Bland–Altman analyses. All smartwatches demonstrated high accuracy in HR measurements during both endurance and resistance exercises. During endurance exercise, HR measurements from all smartwatch brands were comparable to those obtained via ECG, whereas during resistance exercise, only the Apple Watch showed no significant difference from the ECG. HRs showed strong correlations with ECG readings (r = 0.64–0.97), excellent reliability (ICC > 0.94), and narrow limits of agreement (approximately ±10 bpm). In contrast, the EE measurements exhibited limited accuracy across all devices. During endurance exercise, EE was consistently underestimated with wide limits of agreement. EE accuracy further deteriorated during resistance exercise, showing weak correlations with indirect calorimetry (r = 0.10–0.34) and poor reliability (ICC < 0.45). Overall, smartwatches provide accurate HR measurements across endurance and resistance exercise modalities, supporting their use in exercise intensity monitoring and HR-based training. However, smartwatch-derived EE estimates do not accurately reflect the metabolic demands, particularly during resistance exercises. Future research should focus on improving EE estimation algorithms through multimodal biosignal integration and machine-learning approaches, and validating these methods across diverse populations and exercise modalities.

Commercial smartwatches are increasingly used to monitor physiological responses during exercise; however, their validity for resistance exercise remains uncertain. This study evaluated the criterion validity of four commercial smartwatches (Apple Watch Series 8, Samsung Galaxy Watch 5, Fitbit Charge 5, and Garmin Vivosmart 5) for measuring heart rate (HR) and energy expenditure (EE) during resistance exercise. Seventy healthy men aged 20–39 years performed two sets each of the bench press, T-bar row, squat, and deadlift at individualized 10-repetition maximum loads. Smartwatch-derived HR was evaluated against electrocardiography, whereas smartwatch-derived workout-session EE was evaluated against indirect calorimetry. The Apple Watch showed the lowest measurement error for mean HR across the protocol (bias = −0.36 bpm; MAE = 1.81 bpm; ICC = 0.969) and for overall dynamic HR (bias = −0.72 bpm; MAE = 4.95 bpm; ICC = 0.904). HR agreement deteriorated markedly immediately after each set, particularly for the Samsung and Fitbit devices, which underestimated ECG-derived HR by 25.60 and 19.59 bpm, respectively. In contrast, EE estimates showed poor agreement with indirect calorimetry across all devices (ICC = 0.021–0.033). These findings demonstrate that the agreement of smartwatch-derived HR with electrocardiography measurements varies across time points during resistance exercise and indicate that smartwatch-derived workout-session EE should be interpreted cautiously when accurate metabolic assessment is required.

Smartwatch heart-rate (HR) displays may be inaccurate during rapid post-exercise transitions. This study evaluated timepoint-specific smartwatch–electrocardiography (ECG) error and its reproducibility across three resistance-exercise sessions. Sixty healthy young men completed three identical sessions five days apart using the Apple Watch Series 7, Galaxy Watch 4, Fitbit Charge 5, and Garmin Vivosmart 5. Displayed HR was recorded before each set and at 5, 30, and 60 s afterward. The primary outcome was participant-level session-mean error across eight 5 s observations. Mean absolute error increased in the range of 1.94–2.32 bpm before exercise to 8.71–25.43 bpm at 5 s and decreased to a range of 3.58–10.06 bpm at 30 s. Session-mean 5 s error intraclass correlation coefficients were 0.892, 0.771, 0.879, and 0.834 for Apple, Galaxy, Fitbit, and Garmin, respectively, indicating moderate-to-good through good-to-excellent reliability. Session effects were not statistically significant. Raw single-observation limits of agreement were substantially wider than session-mean limits, and all devices exhibited proportional bias. Participant-level variance estimates remained similar in exploratory models including ECG-response and wrist-position covariates. Therefore, early post-set error contained a reproducible session-level component, but individual 5 s readings were not interchangeable with ECG measurements. These findings support cautious interpretation of post-set HR displays without establishing a stable participant-specific error trait.

Background: Body mass index (BMI) screening may fail to identify normal-weight obesity (NWO) despite excess adiposity and unfavorable metabolic profiles. Methods: In this randomized trial, 30 women (aged 20–39 years; BMI 18.5–24.9 kg/m², body fat ≥ 30%) were assigned to supervised exercise or non-exercise control groups. The exercise group trained three days per week for eight weeks. Body fat percentage was the primary outcome. Secondary outcomes included body mass, BMI, fat mass, fat-free mass, skeletal muscle mass, VO₂max, fasting glucose and insulin, homeostatic model assessment of insulin resistance (HOMA-IR), and lipid profile. High-sensitivity C-reactive protein (hs-CRP) and tumor necrosis factor alpha (TNF-α) were exploratory outcomes. Results: Baseline-adjusted analysis of covariance showed lower post-intervention body fat percentage (adjusted difference, −2.15 percentage points; 95% confidence interval [CI], −2.75 to −1.56; p < 0.001) and fat mass (−1.38 kg; 95% CI, −1.73 to −1.04; p < 0.001) in the exercise group. Bioimpedance-estimated fat-free mass and skeletal muscle mass and VO₂max were higher. Secondary analyses favored exercise for insulin, HOMA-IR, triglycerides, and high- and low-density lipoprotein cholesterol, without multiplicity adjustment. Raw-scale hs-CRP and TNF-α differences were nonsignificant; log-transformed findings were not fully robust to leave-one-out sensitivity analyses. Attendance was 90.0% (324/360 sessions), with no dropouts or prespecified exercise-related adverse events. Conclusions: Combined aerobic and resistance exercise program improved body composition and cardiorespiratory fitness in young women with NWO, with favorable changes in selected secondary cardiometabolic outcomes. These benefits warrant assessment beyond body weight and BMI, considering body composition, cardiorespiratory fitness, and cardiometabolic markers.