This cross-sectional validation study was conducted at a single cardiology outpatient clinic in Nara Prefecture, Japan, between October 2022 and March 2023.

Fifty-one consecutive outpatients with CAD or heart failure who were enrolled in the outpatient cardiac rehabilitation program were recruited for this study. Inclusion criteria were as follows: (1) diagnosed with ischemic heart disease or heart failure, (2) aged ≥18 years, (3) able to walk independently without assistive devices, (4) willing to wear both activity monitors simultaneously for 7 consecutive days, and (5) capable of providing informed consent. Exclusion criteria included (1) acute decompensated heart failure requiring hospitalization, (2) unstable angina or recent acute coronary syndrome within 4 weeks, (3) severe cognitive impairment precluding informed consent, (4) skin conditions preventing device attachment, and (5) planned hospitalization or major procedure during the monitoring period.

Demographic and clinical data were extracted from medical records, including age, sex, height, weight, BMI, primary cardiac diagnosis (heart failure vs ischemic heart disease), comorbidities (hypertension, dyslipidemia, diabetes mellitus, respiratory disease, renal disease, cerebrovascular disease, musculoskeletal disorders, malignancy), and current medications (beta-blockers, hyperpolarization-activated cyclic nucleotide [HCN]-gated channel blockers, angiotensin-converting enzyme [ACE] inhibitors or angiotensin receptor blockers [ARBs], mineralocorticoid receptor antagonists, angiotensin receptor–neprilysin inhibitor [ARNI], sodium-glucose cotransporter-2 [SGLT2] inhibitors, calcium channel blockers, nitrates, diuretics, statins). New York Heart Association (NYHA) functional class was not collected because the cohort included both patients with heart failure and patients with ischemic heart disease, for whom NYHA classification was not uniformly applicable. Musculoskeletal disorders were extracted from the medical records as an overall comorbidity category. No participant had a documented upper-limb musculoskeletal disorder that was expected to limit arm movement or affect wrist-worn device measurements.

Participants simultaneously wore 2 activity monitors for 7 consecutive days: a research-grade triaxial accelerometer (ActiveStyle Pro HJA-750C) positioned on the waist and a consumer-grade wrist-worn tracker (Fitbit Inspire 3) on the nondominant wrist. The ActiveStyle Pro has been validated for measuring METs against the Douglas bag method [13] and its comparability with other commonly used activity monitors has been examined in free-living contexts [29,30]. The ActiveStyle Pro is widely used in sedentary behavior research [7,31]. The Fitbit Inspire 3 represents the latest generation of consumer wearables with integrated smartphone connectivity.

Participants were instructed to wear both devices continuously during waking hours (from awakening until bedtime) but were permitted to remove them during water-based activities (bathing and swimming). Detailed written and verbal instructions were provided regarding proper device placement and daily charging procedures. Participants maintained a simple daily log recording wake and sleep times and any periods when devices were removed.

Sedentary time was defined as any waking behavior performed in a sitting, reclining, or lying posture with an energy expenditure ≤1.5 METs, consistent with international consensus definitions [1]. For analysis, we extracted sedentary time data from both devices for the period between 9 AM and 9 PM to standardize the measurement window across participants and minimize the influence of sleep-related recordings. Data were recorded at 1-minute epochs. Daily sedentary time was calculated as the sum of all minutes classified as sedentary behavior within the standardized 12-hour measurement window.

To be included in the final analysis, participants were required to have at least 4 valid days of data, defined as days with ≥10 hours of concurrent wear time for both devices within the 9 AM to 9 PM measurement window. Mean sedentary time within the standardized 12-hour window across all valid days was calculated for each participant for both the ActiveStyle Pro and Fitbit devices.

Minute-level activity data were retrieved from the Fitbit Web API (Activity Intraday time series), which returns data at 1-minute intervals. As the intensity index for each epoch, we used the MET value returned with the calories resource. Because the Fitbit API expresses this value as METs multiplied by 10 (eg, a returned value of 15 corresponds to 1.5 METs), we defined an epoch as sedentary when the returned MET value was below 15 (ie, <1.5 METs) and the step count for the same minute was zero. This operationalization aligns the Fitbit-derived classification with the international ≤1.5 MET definition of sedentary behavior applied to the ActiveStyle Pro comparator. We emphasize, however, that this classification is an estimate based on the activity intensity and step information provided by the device and does not constitute a direct measurement of posture; this difference in how “sedentary” is operationalized between the 2 devices is itself a likely contributor to the observed bias.

Minutes for which the Fitbit returned no detected heart rate were treated as nonwear and excluded from analysis. For the ActiveStyle Pro, a period of 60 or more consecutive minutes of zero counts was treated as nonwear. Valid days required at least 10 hours of concurrent wear time for both devices within the 9 AM to 9 PM window, and participants with at least 4 valid days were retained, as described above.

Participants were instructed to record any daytime napping in their paper daily log. No participant reported a daytime nap during the monitoring period; therefore, daytime sleep did not contribute to the sedentary tallies of either device, and any device-specific differences in nap handling could not have confounded the present comparison.

The wrist-placement setting in the Fitbit companion app was configured to match the actual placement of the device on the participant’s nondominant wrist, ensuring that the device’s motion-processing sensitivity corresponded to the physical wear location and removing altered handedness settings as a potential source of systematic error.

Continuous variables are presented as mean (SD) or median (IQR) according to the normality of their distribution, assessed with the Shapiro-Wilk test. Categorical variables are presented as frequencies and percentages. Because n <100, percentages are reported without decimal places, and absolute numbers are reported alongside percentages.

Agreement between the ActiveStyle Pro comparator and the Fitbit was evaluated with several complementary analyses. Pearson correlation quantified the strength of the linear relationship. Bland-Altman analysis was used to assess systematic bias and to calculate the 95% limits of agreement [21]. Because the construction of parametric limits of agreement assumes that the paired differences are normally distributed, we first verified this assumption with the Shapiro-Wilk test applied to the differences. Fixed bias was tested with a one-sample t test of the paired differences, and proportional bias was examined by regressing the differences on the means of the two measurements.

Lin CCC was calculated to evaluate overall agreement, combining precision and accuracy, with 95% CIs obtained by bootstrap resampling [22]. The CCC ranges from −1 to +1, with values closer to 1 indicating better agreement. Categorical agreement was assessed by classifying each participant into distribution-based tertiles (lower, middle, and upper third) of sedentary time separately for each device and computing the Cohen kappa coefficient for the resulting categories [23].

Where Bland-Altman analysis indicated significant fixed bias, we conducted a secondary analysis subtracting the mean bias from the Fitbit values and recomputing agreement. As an internal consistency check on the stability of the sample mean bias, we recomputed the correction using leave-one-out cross-validation (LOOCV): for each participant, the mean bias was estimated from the remaining participants and applied to that held-out participant, and the concordance of the cross-validated, bias-corrected Fitbit values with the comparator was then assessed. As a sensitivity analysis, we repeated this resampling procedure using repeated random 50/50 splits (Monte Carlo cross-validation, 2000 repetitions).

As an exploratory, hypothesis-generating analysis of whether the bias was moderated by participant characteristics, we fitted a single multivariable linear regression of the individual difference (Fitbit−ActiveStyle Pro) on sex, age, primary cardiac diagnosis (heart failure vs ischemic heart disease), and presence of a musculoskeletal disorder. Because the study was not powered for subgroup detection, a single adjusted model was used rather than multiple stratified tests.

All analyses were performed in R version 4.1.3 (R Foundation for Statistical Computing); statistical significance was set at P<.05.

This study was approved by the Ethics Review Committee of Nagoya Aoi University (formerly Nagoya Women’s University; approval number 2022‐33) and was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants prior to enrollment. To protect privacy and confidentiality, data collection and data analysis were carried out by different personnel, and analyses were performed on deidentified data. No compensation was provided to participants. No images or other materials permitting identification of individual participants are included in this manuscript or its supplementary files.

A total of 51 outpatients (25/51, 49% male; mean age 78.4, SD 9.0 years) were enrolled in the study. Participant characteristics are summarized in Table 1. The mean BMI was 23.8 (SD 4.6) kg/m². The primary cardiac diagnosis was heart failure in 55% (28/51) of participants and ischemic heart disease in 45% (23/51). The cohort had a high burden of cardiovascular risk factors and comorbidities: hypertension (44/51, 86%), dyslipidemia (36/51, 71%), diabetes mellitus (12/51, 24%), renal disease (20/51, 39%), and musculoskeletal disorders (18/51, 35%). Most participants were receiving evidence-based cardiovascular medications, including statins (34/51, 67%), ARNI (31/51, 61%), beta-blockers (24/51, 47%), calcium channel blockers (21/51, 41%), and SGLT2 inhibitors (31%, 16/51; Table 1).

All participants successfully completed the 7-day monitoring period with both devices. The median number of valid measurement days was 7 (IQR 7‐7), with all participants achieving at least 4 valid days of concurrent device wear. Mean daily wear time within the standardized measurement window (9 AM to 9 PM) was 11.8 (SD 0.4) hours, indicating high compliance with the monitoring protocol.

Mean sedentary time within the standardized 12-hour window (9 AM to 9 PM) was 497.9 (SD 86.5) minutes for the ActiveStyle Pro comparator and 559.9 (SD 84.3) minutes for the Fitbit. A strong positive correlation was found between the two devices (Pearson r=0.905, 95% CI 0.838‐0.945; P<.001; Figure 1).

The paired differences were normally distributed (Shapiro-Wilk W=0.97; P=.16), supporting the use of parametric limits of agreement. Bland-Altman analysis revealed a significant fixed bias of 62.0 minutes (95% CI 51.5‐72.5; P<.001), indicating that the Fitbit systematically overestimated sedentary time relative to the comparator. The 95% limits of agreement were wide, ranging from −11.3 to 135.2 minutes, indicating substantial interindividual variability in the magnitude of disagreement. Regression of the differences on the means showed a significant intercept of 76.4 minutes (95% CI 7.7‐145.2; P=.03) and a nonsignificant slope of −0.03 (95% CI −0.16 to 0.10; P=.67), confirming fixed bias without proportional bias; that is, the degree of overestimation was approximately constant across the range of sedentary time (Figure 2).

When sedentary time was classified into distribution-based tertiles computed separately for each device (ActiveStyle Pro boundaries: lower third <458 minutes, middle third 458‐549 minutes, upper third >549 minutes; Fitbit boundaries: lower third <533 minutes, middle third533‐593 minutes, upper third >593 minutes), the Cohen kappa coefficient was 0.588 (95% CI 0.405‐0.772), indicating moderate categorical agreement. The overall agreement rate was 72.5% (37/51 participants), with an agreement of 82.4% (14/17), 58.8% (10/17), and 76.5% (13/17) in the lower, middle, and upper tertiles, respectively. All 14 misclassifications (27.5%) occurred between adjacent tertiles, with no participant misclassified by two or more categories. The middle tertile showed the lowest agreement, indicating that classification was most difficult near category boundaries. The mean absolute difference in measured sedentary time among misclassified cases was 70.3 (SD 49.7) minutes.

After subtracting 62 minutes from the Fitbit values to correct for the fixed bias, regression of the corrected differences on the means showed no significant fixed bias (intercept=13.6 minutes, 95% CI −51.3 to 78.4; P=.68) while proportional bias remained absent (slope=−0.03; P=.67). The in-sample CCC improved from 0.71 (95% CI 0.57‐0.80) to 0.904 (95% CI 0.84‐0.94). To assess the internal stability of the in-sample correction, we used LOOCV: the out-of-sample CCC was 0.901, essentially identical to the in-sample value (shrinkage=0.004), and the per-fold bias estimate was highly stable (range 60.5‐63.4 minutes; SD 0.7). Repeated random 50/50 splits (Monte Carlo cross-validation) yielded a mean out-of-sample concordance of 0.89 (95% interval 0.81‐0.94). This reflects the stability of the sample mean within the present dataset and does not by itself establish that the correction factor generalizes to other samples (see Study limitations). Notwithstanding this group-level improvement, the limits of agreement for the cross-validated corrected values remained wide (approximately −74.7 to 74.7 minutes), indicating that correction reduces systematic bias at the group level but does not resolve individual-level imprecision (Figure 2B).

In the exploratory multivariable model of the individual difference (Fitbit−ActiveStyle Pro), none of the candidate characteristics significantly moderated the bias. The adjusted estimates were −13.9 minutes for male versus female sex (95% CI −34.4 to 6.5; P=.18), +0.7 minutes per additional year of age (95% CI −0.5 to 1.9; P=.25), +17.4 minutes for heart failure versus ischemic heart disease (95% CI −3.7 to 38.5; P=.10), and −6.7 minutes for the presence of a musculoskeletal disorder (95% CI −28.0 to 14.5; P=.53). The overall model explained little of the variance and was not statistically significant (R²=0.15; model P=.12). Although heart failure versus ischemic heart disease and older versus younger age appeared associated with the bias when examined in isolation, neither remained significant after mutual adjustment, consistent with confounding between age and diagnosis and with multiple comparisons. However, this model was underpowered (N=51; model P=.12; R²=0.15), and these null results should not be read as evidence that clinically relevant subgroup differences are absent. In particular, the adjusted estimate for heart failure versus ischemic heart disease (+17.4 minutes, 95% CI −3.7 to 38.5 minutes) remains compatible with a difference large enough to matter relative to the overall 62-minute bias. We therefore regard these analyses as hypothesis-generating only and do not conclude that a single population-level correction is definitively adequate across clinically important subgroups (see Study limitations).

This study evaluated the validity of a consumer-grade wrist-worn activity tracker (Fitbit Inspire 3) for measuring sedentary time in Japanese outpatients with cardiac issues, using a research-grade waist-worn accelerometer (ActiveStyle Pro) as the comparator. We found that the Fitbit systematically overestimated sedentary time by approximately 62 minutes within a standardized 12-hour daytime window (9 AM to 9 PM; values are therefore not full-day estimates), with clear evidence of fixed bias but no proportional bias across the range of sedentary behavior. Although uncorrected agreement was only moderate, applying a simple constant correction substantially improved concordance between devices. Taken together, these findings indicate that consumer-grade wrist-worn devices can provide clinically meaningful information on sedentary behavior in cardiac outpatients with cardiac issues when their systematic measurement error is explicitly acknowledged and appropriately calibrated.

Our finding that the Fitbit overestimated sedentary time by approximately 62 minutes within the standardized 12-hour daytime window (9 AM to 9 PM) contrasts with some previous validation studies [24,25] that reported underestimation of sedentary time by wrist-worn devices. This discrepancy warrants careful consideration and may be explained by several factors related to our specific study population and context, while also revealing important insights into the mechanisms underlying device measurement error.

Population characteristics likely play a crucial role in explaining the direction of bias. Our study enrolled older outpatients with cardiac issues (mean age 78.4, SD 9.0 years) with heart failure or ischemic heart disease, whereas many previous validation studies examined younger, healthier adults. The mean sedentary time observed with the ActiveStyle Pro comparator was 498 minutes within the standardized 12-hour window (9 AM to 9 PM; approximately 8.3 hours). Extrapolated across typical waking hours, this is broadly consistent with the approximately 11 hours per day reported in recent Japanese data on rehabilitation patients with cardiac issues, although our measurement was confined to the standardized window. This underscores both the clinical relevance of accurate sedentary behavior measurement in this population and the distinctive activity profile that may influence device performance.

Older patients with cardiac issues may exhibit distinct movement patterns during daily activities. Specifically, even when standing or performing light activities such as cooking, housework, or slow walking, these patients may have minimal arm displacement due to age-related changes in gait, cardiovascular limitations restricting vigorous arm swing, or cautious movement patterns to avoid symptoms. Consequently, periods of standing or light activity with stationary arms may be misclassified by the wrist-worn device as sedentary time, leading to overestimation.

In contrast, previous studies [24,25] reporting underestimation may have examined populations where wrist movements during sedentary activities (eg, typing, using smartphones or tablets while sitting, and hand gestures during conversation) were sufficient to trigger activity classification, resulting in sedentary time being counted as active time. Furthermore, participants in these studies had mean ages of approximately 20 and 68 years, which are younger than those in the current study, suggesting that age-related differences in movement patterns may partly explain this discrepancy.

Taken together, these considerations suggest that the overestimation observed in older outpatients with cardiac issues reflects predictable interactions between age-related movement patterns and the limitations of wrist-based sensing, underscoring the need for population-specific validation when applying consumer wearables in clinical cardiac care.

The consistent 62-minute overestimation reflects fundamental differences between wrist-worn and waist-worn device designs. Wrist placement cannot directly measure postural transitions (sitting-to-standing and lying-to-sitting) that are the defining feature of sedentary behavior. Instead, wrist devices must infer posture from arm movement patterns—an indirect approach prone to misclassification [19,25]. When individuals stand still or perform light activities with minimal arm displacement, the wrist device interprets the lack of movement as sedentary behavior, regardless of actual posture. Conversely, waist-worn devices like the ActiveStyle Pro capture whole-body movement and postural changes more directly through detection of trunk position and acceleration patterns.

Proprietary algorithms in consumer devices are optimized primarily for step counting and moderate-to-vigorous activity tracking, the metrics most valued by general consumers, rather than sedentary time classification. The Fitbit Inspire 3 represents a newer generation of consumer wearables with updated algorithms compared to devices examined in earlier studies. The threshold and decision rules for classifying sedentary versus active states may have been modified across device generations and may not be optimized for populations with limited mobility or distinct movement signatures.

Device and algorithm differences across validation studies must be considered. Our comparator was the ActiveStyle Pro, a waist-worn triaxial accelerometer validated against metabolic measurements (Douglas bag method), whereas previous studies often used the ActiGraph or activPAL (a thigh-worn inclinometer). Different comparator devices use distinct classification approaches, posture-based (activPAL, using inclinometry) versus movement-based (ActiGraph and ActiveStyle Pro, using acceleration thresholds), which may influence the magnitude and direction of observed bias. Direct comparison across validation studies is complicated by this methodological heterogeneity.

Additionally, activity context and measurement protocol may contribute to differences across studies. We measured sedentary time during a standardized 12-hour daytime window (9 AM to 9 PM) in free-living conditions. In older patients with cardiac issues, this window likely captures prolonged periods of sitting while watching television, reading, or resting—activities performed with relatively stationary arms. If healthy adults in previous studies engaged in more dynamic activities with frequent arm movements during nonsedentary time, wrist devices might better distinguish active from sedentary periods in those populations.

Importantly, the absence of proportional bias in our data, confirmed by the nonsignificant regression slope (P=.67), indicates that the magnitude of overestimation remains constant regardless of whether individuals have low or high sedentary time. This finding has both mechanistic and practical implications.

Mechanistically, the consistent bias suggests that overestimation stems from systematic misclassification of specific activity types (low-movement standing and light activities) rather than from device calibration errors that would vary with activity volume. If the bias were due to gain or sensitivity errors, we would expect proportional bias where the error increases with higher sedentary time. Instead, the fixed bias pattern indicates that a relatively constant duration of nonsedentary activities (approximately 62 minutes within the 12-hour window; 9 AM to 9 PM) is being misclassified as sedentary across all participants.

Practically, this finding means that a simple constant correction can substantially improve agreement, as evidenced by the improvement in CCC from 0.71 to 0.904 after subtracting 62 minutes. This correction approach would not be appropriate if proportional bias were present.

The mixed findings across validation studies, including our own, underscore a critical point: wearable device validity is population-specific and context-dependent. A device validated in one population cannot be assumed to perform equivalently in another, particularly when movement patterns and activity repertoires differ substantially. This context-dependency extends beyond population characteristics to include cultural factors (eg, floor-sitting in some Asian populations), occupational demands, and the specific activities that comprise daily routines.

For research applications, our results suggest that Fitbit devices should not be used interchangeably with research-grade accelerometers for primary outcome assessment without applying appropriate corrections or acknowledging measurement limitations. The moderate uncorrected CCC (0.71) indicates insufficient agreement for studies requiring precise absolute sedentary time measurements. However, the excellent agreement after constant correction (CCC=0.904) demonstrates that simple calibration may render consumer devices suitable for some research contexts.

Population-specific calibration should be performed before using consumer devices in research. Our 62-minute correction factor applies specifically to older Japanese outpatients with cardiac issues and should not be assumed to apply to other populations without validation. Researchers planning to use Fitbit or similar devices should consider conducting preliminary validation studies in their target population to determine appropriate correction factors.

The moderate categorical agreement (κ=0.588) when classifying participants into sedentary time tertiles suggests acceptable performance for epidemiological studies examining associations between categorical sedentary behavior levels and health outcomes. Importantly, all misclassifications occurred between adjacent categories with no extreme misclassifications, indicating that the device maintains relative ordering of individuals. This property may be sufficient for observational studies where precise quantification is less critical than rank-ordering or categorical stratification.

For intervention trials, where detecting within-person change is paramount, our cross-sectional data provide only indirect evidence of suitability. The absence of proportional bias suggests that changes in sedentary time should be measured with similar accuracy regardless of baseline levels, but longitudinal validation is needed to confirm this hypothesis and quantify measurement error for within-person change.

Consumer wearables may offer particular value in large-scale studies where research-grade devices are cost-prohibitive. With appropriate calibration and acknowledgment of limitations, devices like the Fitbit could enable sedentary behavior research in larger, more diverse samples, potentially improving external validity and generalizability of findings.

Several limitations warrant consideration and point to important directions for future research.

Several avenues for future research emerge from this work.

Longitudinal validation studies should evaluate test-retest reliability, minimal detectable change, and responsiveness to behavioral interventions in cardiac populations. Studies should include repeated measurements over weeks to months to characterize within-person measurement error and determine whether devices can reliably detect clinically meaningful changes.

Prospective intervention trials should assess whether Fitbit-detected changes in sedentary time correspond to changes measured by research-grade devices and whether these changes predict clinical outcomes (cardiovascular events, hospital readmissions, functional capacity, and quality of life). Such studies would establish the clinical utility of consumer devices for outcome assessment.

Machine learning and algorithm development studies could explore whether using raw Fitbit accelerometer data (rather than proprietary processed outputs) with population-specific classification algorithms can improve sedentary time accuracy in clinical populations. Recent advances in machine learning may enable personalized calibration approaches that account for individual movement signatures.

Multidevice validation studies would clarify relative performance and inform device selection for different research contexts. Such studies should incorporate a posture-based reference standard such as the activPAL, movement-based research accelerometers such as the ActiGraph and the ActiveStyle Pro, and multiple consumer devices such as the Fitbit, Apple Watch, and Garmin (Garmin Ltd).

Moderator analyses should systematically investigate sources of individual variability in measurement error, including patient characteristics (age, sex, body composition, cardiovascular functional capacity, and comorbidities), device factors (wear location and band tightness), and activity patterns (occupational demands and activity repertoire). Identifying moderators could enable stratified correction approaches or inform patient-device matching.

Pattern-based validation should assess device accuracy for sedentary bout duration, breaks in sedentary time, and temporal distribution patterns—metrics potentially more important than total volume for cardiovascular health.

Implementation research should examine patient and clinician perspectives on wearable-based sedentary behavior monitoring in real-world cardiac care settings, addressing questions of feasibility, acceptability, data interpretation, integration into clinical workflows, and impact on behavior change counseling effectiveness.

Cross-cultural validation should extend this work to diverse geographic and ethnic populations, recognizing that cultural practices (floor-sitting and traditional activities), built environments, and social norms around activity may influence device performance.

These findings have important implications for both clinical practice and research in digital cardiovascular care. From a clinical perspective, the strong correlation and absence of proportional bias indicate that consumer-grade wrist-worn activity trackers can be used to monitor relative changes in sedentary behavior over time in outpatients with cardiac issues. This property is particularly relevant for remote monitoring, lifestyle counseling, and secondary prevention programs, where trends and behavioral patterns are often more informative than absolute values. However, clinicians should interpret absolute sedentary time estimates with caution, as wrist-worn devices may systematically overestimate sedentary behavior in older patients with cardiac issues. Applying a simple population-specific correction, as demonstrated in this study, may help contextualize tracker-derived metrics when setting behavioral targets or evaluating intervention effects.

From a research perspective, our results suggest that consumer-grade wrist-worn devices should not be used interchangeably with research-grade accelerometers for precise quantification of sedentary time without appropriate calibration. The moderate uncorrected concordance observed in this study indicates that such devices may be suboptimal as primary outcome measures when absolute sedentary time is required. Nevertheless, the substantial improvement in agreement after constant correction and the acceptable categorical agreement support their potential utility for large-scale observational studies, ranking-based analyses, and intervention trials focused on within-person change. Importantly, these findings underscore the necessity of population-specific validation and calibration when deploying consumer wearables in clinical research, particularly in older or functionally limited populations.

In older Japanese outpatients with cardiac issues, the Fitbit Inspire 3 systematically overestimated sedentary time by approximately 62 minutes within a standardized 12-hour daytime window compared with the waist-worn ActiveStyle Pro comparator, with significant fixed bias but no proportional bias. The between-device correlation was strong (r=0.905), and a simple constant correction improved concordance from moderate (CCC=0.71) to high; cross-validation indicated that this in-sample improvement was internally stable (out-of-sample CCC=0.90). In an exploratory analysis, the bias was not significantly moderated by sex, age, primary diagnosis, or musculoskeletal comorbidity, although this analysis was underpowered and a clinically relevant difference, particularly by primary diagnosis, cannot be excluded. Of greater practical importance, the wide limits of agreement indicate substantial interindividual variability that constrains the device’s precision for individual-level measurement, and categorical agreement was only moderate (κ=0.59). These findings indicate that, in this population, a bias-corrected consumer wrist-worn tracker can provide group-level estimates and relative comparisons of sedentary time, but should not be treated as interchangeable with a research-grade comparator for precise individual quantification. Because the correction factor is specific to this population, device generation, and 12-hour measurement window, it should not be transferred to other settings without independent validation.