Is my smartwatch a valid witness? A systematic review and meta-analysis.

Feel lonely? Get active: Exploring the daily relationship between accelerometer-measured physical activity and loneliness.

This study investigated the effects of Transtheoretical Model-based physical activity counseling on physical activity level, physical activity behavior, gait speed, and cardiovascular disease (CVD) risk in kidney transplant recipients (KTRs). Twenty-eight KTRs were assigned to either a physical activity group (n = 14) or a control group (n = 14). The physical activity group participated in a 12-week transtheoretical model-based physical activity counseling that includedmotivational interviews, patient education, physical activity diaries/booklets, and individualized guidance on adopting and maintaining physical activity. Physical activity level was measured with an accelerometer, while physical activity behavior was assessed by the Exercise Stages of Change Questionnaire. Gait speed was evaluated using a wireless inertial sensor system (G-Walk). CVD risk was predicted using a web-based interactive tool (HeartScore). Assessments were conducted at baseline, and at weeks 12 and 24. Baseline number of steps and average metabolic equivalent were higher in the physical activity group (P < .05). At week 12, step count was higher in the physical activity group (P = .004). At week 24, number of steps, average metabolic equivalent, active energy expenditure, and physical activity duration were higher in the physical activity group (P < .05). Number of steps, average metabolic equivalent, active energy expenditure, and physical activity duration did not show any differences within each group (P > .05). Gait speed increased from baseline to the 12th week in the physical activity group (P = .016). At the 12th week, 13 participants were in the action stage and 1 was in the maintenance stage in the physical activity group, whereas in the control group, 3 participants were in the pre-contemplation stage, 2 were in the contemplation stage, and 9 were in the preparation stage (P < .001). There were no significant differences between the groups in HeartScore at any time points (P > .05). Transtheoretical model-based physical activity counseling shows potential as a feasible and acceptable approach to promote physical activity behavioral readiness in KTRs, and could help manage cardiovascular risk factors by addressing physical inactivity.

The ability to engage in physical activity reflects preserved physical function, autonomy, and well-being in older adults, making physical activity a key outcome of healthy aging. Walking is a common form of physical activity in later life, so understanding how walking speed is associated with overall activity is particularly important. This study examined 4‑year changes in physical activity energy expenditure (PAEE) in community‑dwelling older adults and assessed preferred walking speed in relation to PAEE. This study used data from AGNES, a 4-year follow-up study of initially 75-, 80-, and 85-year-old adults. PA was measured using thigh-worn accelerometers and ECG monitors, with PAEE estimated using a branched equation model integrating heart rate and acceleration data. Preferred walking speed was measured using a self-paced six-minute walk test. Associations between PAEE, walking speed, and their 4-year changes were examined using linear mixed models. Among 299 participants, PAEE declined by 16.9kJ·kg- 1·day- 1 over 4 years. Preferred walking speed showed a positive association with PAEE, with each 1km·h- 1 higher speed corresponding to 4.7kJ·kg- 1·day- 1 higher PAEE. There was limited evidence for an association between changes in preferred walking speed and changes in PAEE over the 4‑year follow‑up period. In community-dwelling older adults, PAEE declined substantially with age, more steeply than in total physical activity previously reported in similar cohorts. Walking speed was strongly associated with PAEE, but its change was not consistently associated with changes in PAEE.

Energy expenditure, physical activity and dietary intake among patients with newly diagnosed colorectal cancer.

Negative energy balance (Ebal) and changes in body composition are common in Soldiers during combat training with increased total daily energy expenditure (TDEE) and restricted food intake. Whether sex differences exist in those effects is unknown. Ebal and body composition (dual-energy X-ray absorptiometry; body mass (BM), fat mass (FM), fat-free mass (FFM)) of Soldiers (10 females:27 males) completing US Army Ranger training course (RTC) were compared by sex before baseline and after each training phase (P1-P3). Doubly labeled water was used to estimate TDEE, food wrappers collection to assess dietary energy intake, and daily activity energy expenditure was calculated. Baseline BM and FFM were lower ( p < 0.01) in females than males, while baseline FM was higher in females than males ( p = 0.01). Body composition differences remained at each phase, except at P3 when no sex difference in FM ( p = 0.15) was detected. Males experienced a greater loss in BM from baseline than females at each phase (all, p ≤ 0.05). Females maintained their FFM to a greater extent from baseline than males at each phase (all, p ≤ 0.01). TDEE was higher in males compared to females in P1 and P2 (both, p < 0.01), but not in P3 ( p = 0.37). Females showed a greater increase in dietary intake from P1 to P2 ( p = 0.004) compared to males. No differences in the change from P1 to P2 or P1 to P3 were detected for TDEE, Ebal, resting energy expenditure or activity energy expenditure. Despite differences in absolute Ebal, the magnitude of change in Ebal did not differ by sex across RTC. Males lost more BM throughout the long-term course while females preserved FFM to a greater extent than males.

We have previously shown that 16 weeks of combined aerobic–resistance training in older women decreases non-exercise activity thermogenesis (NEAT) when performed 3 times/week, but not when performed once or twice weekly. To examine changes from weeks 16 to 32 in anthropometrics, body composition, fitness, cytokines, mood, and energy expenditure (EE) components among training frequencies in older women. Sedentary women (60–74 y) previously randomized to 1 + 1, 2 + 2, or 3 + 3 sessions/wk of supervised combined aerobic–resistance training continued the program from weeks 16 to 32. Total EE (TEE) (doubly labeled water), resting EE (REE) (indirect calorimetry), training-related EE (TREE) (validated estimates), NEAT = activity EE (AEE) − TREE (AEE = 0.9×TEE − REE), fat mass (FM), and fat-free mass (FFM) (DXA), V̇O₂max (treadmill), mood/fatigue, and serum cytokines were measured at weeks 16 and 32. Two-way repeated-measures ANOVA was used with Tukey posthocs. Fifty-seven women contributed anthropometry/fitness/cytokines; 44 contributed EE components. Weight decreased, whereas FM (kg and %), FFM, V̇O₂max, IL-6, CRP, TNF-α, fatigue, vigor and REE were unchanged. Bench- and leg-press strength increased across groups. Depression varied by group (lower in 3 + 3 vs. 2 + 2; P = 0.044). TEE differed between groups (P = 0.017; 2 + 2 > 1 + 1 and 3 + 3). TREE increased over time (P = 0.002) and differed by group (P < 0.001). NEAT showed a time-by-group interaction (P = 0.049), increasing only in 3 + 3 (P = 0.044). High-frequency (3 + 3) combined aerobic-resistance training was associated with increased NEAT between weeks 16–32, but not lower frequencies. However, the 2 + 2 group continued to have the highest NEAT overall.

Academic motivation plays a critical role in students’ academic engagement, learning persistence, and academic performance during university education. This study aimed to examine the relationships among academic motivation, sleep quality, and physical activity levels in undergraduate students enrolled in the Faculty of Sport Sciences. This cross-sectional correlational study included 230 volunteer undergraduate students (136 males and 94 females) enrolled in the Faculty of Sport Sciences.Data were collected using the Academic Motivation Scale, the International Physical Activity Questionnaire–Short Form, the Sleep Quality Scale, and a personal information form. Statistical analyses included descriptive statistics, independent samples t-tests, one-way ANOVA, and Pearson correlation analysis. The results indicated that physical activity levels differed significantly according to regular exercise participation and weekly exercise frequency, with regularly exercising students demonstrating higher vigorous physical activity and total energy expenditure. Academic motivation differed significantly according to grade point average, with higher GPA students reporting higher intrinsic motivation and lower amotivation. Sleep quality was significantly associated with moderate physical activity and intrinsic motivation to know. However, overall physical activity levels were not significantly correlated with academic motivation. These findings suggest that sleep quality may be linked to certain motivational and lifestyle factors, whereas physical activity alone does not appear to be directly associated with academic motivation in this sample.

To evaluate free-living Total and Physical Activity Energy Expenditures (TEE/PAEE) and to assess the validity of the Daily Activity Questionnaire in Heart Failure (DAQIHF) in chronic heart failure (CHF) patients against the doubly labelled water (DLW) and motion sensors methods. Twenty-nine women/men (12/17) with CHF performed an incremental symptom-limited peak V̇O2 test. Free-living TEE and PAEE were estimated with the DAQIHF (TEEquest), motion sensor (Armband® TEEActi) and measured over 2 weeks using DLW (TEEDLW). Resting metabolic rate (RMR) and body composition were assessed with different methods, and peak V̇O2 with quality of life were correlated to TEE. Bland-Altman and Student's t-test analyses were used to compare methods. Statistical significance was set for P < 0.05. Mean TEE did not significantly differ between TEEDLW and TEEquest (+352.4 kJ.24h-1; +5.3%; P & NS) for the whole group, nor between women or men, NYHA class, or cardiomyopathy: dilated cardiomyopathy/ischaemic cardiomyopathy. Bland-Altman plots revealed no systematic bias for TEE between methods. In a subgroup of women, TEEquest was significantly higher than TEEacti (P < 0.05). RMR estimated from bioelectric impedance overestimated measured RMR (16.4%, P & 0.0028). Patients spent 9.4% of their TEE in activities ≥3 metabolic equivalents. Measured peak V̇O2 and estimated from the questionnaire were similar (14.1 ± 4.7 vs. 14.8 ± 3.4 mL.min-1.kg-1; P < 0.0001) and were correlated to both TEEDLW and TEEquest (R & 0.85 and 0.82, respectively, both P < 0.0001). Free-living TEE and peak V̇O2 can be estimated from the DAQIHF in patients with CHF across all activity domains allowing a complete description/assessment of daily physical activity intensities associated with powerful prognostic risk factors.

All animals perform physical activity, but humans engage in a special kind of physical activity - exercise, defined as discretionary physical activity for health and fitness. However, the effects of physical activity on whole-organism metabolism and health are unresolved, partly because it is difficult to measure the three major components of metabolism: active energy expenditure (AEE), resting energy expenditure (REE) and dietary induced thermogenesis (DIT), which together equal total energy expenditure (TEE). Three competing models make different predictions about the effects of AEE on REE and TEE. Whereas the traditional 'additive' model of energy balance predicts that AEE is independent of REE, the 'stress' model hypothesizes that AEE temporarily increases REE partly because of transient effects of excess post-exercise oxygen consumption (EPOC). In contrast, the 'constrained energy' model predicts that increases in AEE cause compensatory decreases in REE to maintain a constant TEE. Here, we discuss how different analytical models, measurements, experimental designs and statistical methods affect tests of these three models' hypotheses. After accounting for spurious correlations, we find that longitudinal and cross-sectional data provide most support for the additive model. However, more and better data are needed to test these hypotheses rigorously. To conclude, we also review the evidence, mostly from humans, that increased levels of physical activity slow aging and reduce vulnerability to disease by diverting energy away from processes that improve reproductive success at the expense of long-term health and by increasing energy allocation to repair, maintenance and capacity building.

Physical activity and sedentary behavior are closely associated with neuropsychiatric diseases, while previous studies have mainly relied on self-reported data, which have been shown to be inconsistent with objectively measured metrics. Using data from 73,411 participants, objectively quantifying physical activity through energy expenditure and time allocation, we identified longitudinal associations between physical activity, sedentary behavior, and neuropsychiatric diseases using Cox proportional hazards models. Our findings indicated that moderate to vigorous physical activity energy expenditure showed the strongest protective effect on neuropsychiatric diseases (hazard ratios: 0.60-0.86, all FDR-Q < 0.001), while an increased proportion of sedentary time was identified as a risk factor for neuropsychiatric diseases (HRs: 1.05-1.54, FDR-Q < 0.05). Restricted cubic spline analyses demonstrated an L-shaped association linking physical activity and neuropsychiatric diseases and an inverted L-shaped relationship between sedentary behavior and dementia. Linear regression models further linked physical activity and sedentary behavior to cognitive function, physical function, and mental health. Key brain regions related to these behaviors included the lateral occipital cortex, cuneus, pallidum, and accumbens. Proteomics and metabolic analyses identified significant involvement of ITGAV protein and high-intensity lipoprotein. Structural equation modeling elucidated that inflammation and metabolism mediate the relationship between physical activity, sedentary behavior, and neuropsychiatric diseases. Overall, our study provides critical evidence of the link between physical activity, sedentary behavior, and neuropsychiatric diseases, shedding light on potential neurobiological mechanisms underlying these associations.

Limited data exist describing how professional footballers meet their energy requirements during pre-season, a phase characterised by increased training volume and a progressive shift from general conditioning to football-specific preparation. This study quantified total, resting, and activity energy expenditure (AEE), diet-induced thermogenesis, water turnover, and dietary intake in six professional male soccer players (age: 25±1year; height: 182.5±10.1cm; body mass: 77.8±8.2kg). Players were studied across 14 consecutive days, representing training-only and training-plus-match microcycles. Total energy expenditure (TEE) was measured using doubly labelled water, resting energy expenditure (REE) by indirect calorimetry and dietary intake using the remote food photography method. Fourteen-day mean TEE, REE, AEE and water turnover were 13.25±1.31MJ⋅day-1, 7.96±0.89MJ⋅day-1, 4.20±1.03MJ⋅day-1, 5.16±0.66L⋅day-1, respectively. Physical activity level was 1.67±0.16 AU. Energy, carbohydrate, protein, and fat intakes were 10.95±1.52MJ⋅day-1, 2.8±0.6g⋅kg-1⋅day-1, 2.2±0.4g⋅kg-1⋅day-1, and 1.5±0.4g⋅kg-1⋅day-1, respectively. Total energy expenditure was not significantly different between training-only and training-plus-match microcycles (+1.89 ± 1.98 MJ⋅day-1; ES=0.95±1.08; p=0.100). No significant differences were observed in energy or macronutrient intake across weekly microcycles (p>0.068) or between days (p>0.144). Players did not achieve energy balance or align dietary intake with day-to-day training demands, suggesting limited nutrition periodisation during pre-season. These findings highlight the need for practitioners to implement strategies supporting fuelling, recovery and adaptation during this critical phase.

Wearable accelerometer devices are now widely used in both research and daily life settings. This study aimed to compare the accuracy of three commercially available consumer-grade activity monitors with the medical-grade ActiGraph device in a free-living setting in Japan. Thirty-six office workers were enrolled and provided with an ActiGraph. Data were analyzed from participants who also wore Apple Watch (n = 21), Fitbit (n = 22), and Oura Ring (n = 5) over a 3-week period. Step count, physical activity energy expenditure (PAEE), and moderate-to-vigorous physical activity (MVPA) data were collected from all devices. Data were analyzed using correlation coefficients, mean differences, and Bland-Altman plots. ActiGraph data confirmed comparable physical activity levels across the participant subgroups, ensuring a valid basis for the subsequent inter-device comparisons. Step counts were largely consistent across devices, with Apple Watch and Oura Ring measurements within 10% of ActiGraph measurements (mean percentage differences 2.12% and -6.24%, respectively), while the Fitbit overestimated step count by 18.00%. MVPA showed greater variability, with Apple Watch and Oura Ring underestimating by 46.22% and 11.64% respectively, whereas the Fitbit showed minimal mean difference (0.62%). PAEE showed the largest discrepancies, with Apple Watch and Fitbit overestimating by 25.91% and 139.19% respectively, and Oura Ring underestimating by 16.87%. Correlation coefficients were strong for step counts (r = 0.84-0.92) but lower for MVPA and PAEE across all devices. Bland-Altman analysis revealed proportional bias in the Fitbit's PAEE and the Apple Watch's MVPA, with errors increasing at higher activity levels. Step counts were largely consistent with the ActiGraph for most devices; however, the Fitbit showed a notable overestimation. However, the ability of those devices to accurately measure MVPA and PAEE appeared to be more limited, particularly at higher activity levels. These findings underscore that the selection of a consumer-grade wearable for research or clinical use must be carefully guided by the specific metric of interest. However, the findings for the Oura Ring should be interpreted with caution due to the small sample size.

Providing high-quality and balanced nutrition for seagoing vessel crews is a critical element of maritime safety and maintaining personnel performance. This study focuses on the significant challenge of supplying dairy products to ship crews. While these products are highly nutritious, they require specific logistics and storage conditions during long voyages, which may be an issue of particular relevance in the current geopolitical context. The article offers a comprehensive analysis of the opportunities and constraints within the national system for supplying dairy products to maritime shipping. The research objective was to identify the factors affecting these processes, as well as to determine possible improvement directions. The research objects included regulatory acts used to compile ship crew diets and some relevant documents on Russia’s dairy industry statistics. The methodology combined an interdisciplinary approach with comparative legal, economic, and statistical analyses. The study demonstrated a discrepancy between the energy value of the diet stipulated by regulations (4,000 kcal) and the actual energy expenditure of various categories of seafarers (3,300–4,500 kcal). Their current daily ration contains not enough dairy products. While the macroeconomic constraints of the domestic dairy industry are related to low cattle population, the promising development directions include freeze-dried dairy products, logistics chains, and new digital platforms that would integrate suppliers and consumers. Three groups of problems (methodological, logistical, and organizational) are currently affecting food supply efficiency. The current dairy product consumption norms need to be revised taking into account professional and climatic stressors. Freeze-drying methods may expand the product range and prolong the shelf life of dairy products during long ship voyages. The proposed measures hold high practical significance for state governance bodies, shipowners, and dairy industry enterprises. A more sustainable supply system may improve the maritime labor performance.

While increasing moderate-to-vigorous physical activity (MVPA) enhances muscular health through metabolic and vascular adaptations, its association with muscle strength improvement remains unclear, especially in real-world contexts. This study therefore aimed to investigate the longitudinal relationship between changes in MVPA and handgrip strength (HGS) to inform lifestyle guidelines and promote healthy ageing. A pragmatic intervention study enrolled 4344 Chinese community-dwelling adults aged rld contexts. This study therefore aimed to investigate the longitudinal physical activity energy expenditure (PAEE, kJ/kg/day) were continuously monitored via wearable devices. HGS was measured upon enrollment and at 3-, 6- and 12-month follow-ups. During each follow-up, we performed latent class growth analysis (LCGA) to characterize longitudinal patterns of MVPA change (ΔMVPA), and robust linear regression models to evaluate the associations between ΔMVPA trajectories and HGS changes. At the 12-month endpoint, we employed restricted cubic spline analysis to characterize a dose-response association. We also analysed the relationship between PAEE and HGS after accounting for MVPA. Participants wore the activity tracker for 75, 151 and 282 valid days and achieved 9.5%, 30.9% and 65.1% increase in MVPA during each follow-up, respectively. LCGA identified distinct ΔMVPA trajectories over time and further revealed the temporal pattern of association between ΔMVPA trajectories and HGS changes, with non-significant directional changes at 6months, and significant differences by 12months, where increasers demonstrating a 1.88kg gain (95% CI: 1.29-2.47) and accelerators a 3.51kg gain (95% CI: 2.58-4.45) compared to maintainers. Moreover, we established a dose-response relationship as 0.66kg (95%CI: 0.51-0.82) HGS improvement per 10min/day MVPA increase, with stronger associations observed in females and those with lower baseline HGS or MVPA levels. PAEE showed no independent association with HGS improvement. This study revealed time- and dose-dependent associations of MVPA promotion with muscle strength improvement, without additional association from PAEE, highlighting the necessity of engaging and maintaining MVPA despite age-related barriers, particularly for females, inactive individuals and those with low strength.

Disproportionate declines in physical activity energy expenditure relative to energy intake among Chinese adults: an 18-Year longitudinal study

To examine whether educational attainment modifies the effectiveness of personalized lifestyle recommendations for improving cardiovascular risk factors. This post-hoc analysis used data from a population-based randomized controlled trial in Girona, Spain, including adults aged 35-74years without cardiovascular disease at baseline. Participants (n = 759; 48.7% men) were randomized to an intervention group (n = 380), which received personalized recommendations on diet and physical activity, or a control group (n = 379), which received a standard report of baseline results. Changes in systolic and diastolic blood pressure, LDL cholesterol, and physical activity energy expenditure from baseline to 12months were analyzed. Multivariable linear regression models adjusted for age included an interaction term defined as group × educational attainment × time (1year) to assess effect modification. Analyses were stratified by sex. Among women in the intervention group, significant interactions by educational attainment were observed. Compared with women with lower educational attainment, those with higher attainment showed more favorable changes in diastolic blood pressure [beta-coefficient (95% confidence interval): - 1.98 (- 4.23; 0.27) vs. 1.63 (- 0.21; 3.48)], LDL cholesterol [- 4.61 (- 11.40; 2.18) vs. 5.71 (0.25; 11.17)], and physical activity energy expenditure [0.52 (0.04; 1.23) vs. - 0.01 (- 0.36; 0.26)]. No significant interactions were found among men in the intervention group or among participants in the control group. Overall, within-group changes in these outcomes did not reach statistical significance. Personalized lifestyle interventions may provide greater benefits for women with higher educational attainment, although overall improvements were modest.

This cross-sectional study examined associations between sleep, body weight, body composition, appetite, and food reward after bariatric surgery. A single 7-day study period in 22 female adults (age, 53.5 ± 9.3 years; BMI, 35.5 ± 8.5 kg/m2; body fat: 44.9% ± 8.6%) who underwent bariatric surgery ≥ 1 year prior to enrollment assessed: actigraphy-measured sleep (duration, efficiency, midpoint, and variability [coefficient of variation]) and activity energy expenditure (AEE); sleep architecture and apnea-hypopnea index (AHI) via in-home polysomnography; energy intake via food diaries; resting EE via indirect calorimetry; fasting appetite via visual analog scales; and food reward via Leeds Food Preference Questionnaire. Weight regain was 10.6% relative to nadir postsurgery weight. Rapid eye movement (REM) sleep duration was associated with lower body fat percentage (r = -0.52, p = 0.02). Participants with AHI ≥ 5 had a greater waist to hip ratio compared to those with AHI < 5 (mean difference = 0.09, p = 0.01). Sleep timing and duration variability was associated with fasting prospective food consumption (r = 0.44, p = 0.05 and r = 0.47, p = 0.03, respectively). Greater sleep duration was associated with lower AEE (r = -0.62, p < 0.01) and explicit liking for sweet foods (r = 0.45, p = 0.04). Our exploratory results underscore the need to evaluate whether sleep behaviors, including total and REM sleep duration, AHI, and sleep regularity, predict long-term weight loss maintenance after bariatric surgery.

Nutrition guidelines specific to short stature athletes are lacking. Achondroplasia, the most common cause for disproportionate short stature (dwarfism), potentially increases resting energy requirements. Therefore, for the first time, this case study aimed to establish the resting metabolic rate (RMR), physical activity energy expenditure (PAEE), and total energy expenditure (TEE) of a dwarf para-powerlifter across rest and training days. A second aim was to determine the suitability of RMR prediction equations recommended for use in athletic populations. RMR was measured using indirect calorimetry on four occasions (two days post-competition, one day post-training, one- and three-days post-training camps). PAEE was monitored using a wrist-worn accelerometer for two 7-day periods. The thermic effect of feeding was estimated; therefore, TEE was inferred. RMR was greater 1- and 3-days post-training camps (1896 and 1992 kcal∙day−1, respectively) than post-training (1613 kcal∙day−1) and post-competition (1409 kcal∙day−1). PAEE and TEE was similar between rest and training days (PAEE, 957 and 948; TEE, 2715 and 2705 kcal∙day−1, respectively). Accuracy of RMR prediction equations utilised ranged from − 580 to + 189 kcal∙day−1 when compared to measured RMR post competition and training. Underestimation was greater (−412 ± 107 kcal∙day−1) post training camps. Results highlight the importance of establishing the energy requirements of dwarf para-athletes, particularly because prediction equations were not accurate in this case study. Further investigation of energy demands during training camps, particularly the relationship of workload on energy expenditure, are now warranted in a larger population.

IntroductionInsufficient physical activity and prolonged sedentary behavior remain common among university students, yet conventional self-reports suffer from recall bias. The key idea of this research is to establish a unified, single-sensor framework that bridges engineering-based activity recognition with public health-oriented energy assessment by mapping raw inertial signals directly to metabolic intensity levels.MethodsThis study developed and evaluated a waist-worn IMU-based framework for daily physical activity recognition and energy expenditure level classification. A purpose-built dataset was collected from 96 healthy university students (mean age 20.4 ± 1.7 years), yielding 106,376 valid signal windows across six representative activities: sitting, standing, level walking, stair ascent, stair descent, and jogging. Triaxial acceleration and angular velocity signals were processed through filtering, normalization, and sliding-window segmentation.ResultsUnder leave-one-subject-out (LOSO) validation, the best-performing CNN-LSTM model achieved an accuracy of 93.8% (macro-F1: 0.932) for activity recognition and 89.6% (macro-F1: 0.883) for three-level energy expenditure classification.DiscussionThe primary contribution lies in validating single-sensor feasibility within a specific campus movement ecology, demonstrating that the system can simultaneously deliver behavioral and metabolic insights without post hoc label mapping. This study fills a critical knowledge gap by quantifying the accuracy trade-offs of a joint pipeline under rigorous subject-independent conditions, providing a feasible basis for large-scale campus health surveillance.