Differential Agreement of Self-Reported Sedentary Behavior and Physical Activity With Accelerometer Measures in Adults With Congenital Heart Disease

Naoto Kawamatsu; Masahiro Matsui; Keisei Kosaki; Yoshihiro Nozaki; Tomoko Machino-Otsuka; Yoshio Nakata; Tomoko Ishizu

doi:10.1253/circrep.CR-26-0010

Abstract

Background: Physical activity influences functional capacity and long-term health in adults with congenital heart disease (ACHD), but the accuracy of self-reported activity remains uncertain.

Methods and Results: In this cross-sectional study, subjects with ACHD underwent physical activity assessment using triaxial accelerometers to quantify sedentary behavior (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Participants were classified into 4 activity profiles based on cohort-specific median values of accelerometer-derived SB time and MVPA. Self-reported activity and SB time were assessed using the International Physical Activity Questionnaire (IPAQ) short form. Agreement between self-reported and accelerometer-derived measures was evaluated, and receiver operating characteristic analysis was performed. Among the 177 participants analyzed, accelerometer-derived SB, LPA, and MVPA, as well as IPAQ-derived SB time, differed significantly across activity profiles (all P<0.001). IPAQ-derived SB time showed good discrimination for identifying the sedentary–inactive profile (area under the curve [AUC]: 0.811). In contrast, IPAQ-derived MVPA showed limited discrimination (AUC: 0.663), largely owing to a pronounced floor effect, with more than half of the participants reporting 0 min of MVPA per week.

Conclusions: IPAQ-reported SB time showed reasonable agreement with objectively measured SB and may be useful for identifying sedentary–inactive individuals with ACHD. However, IPAQ-derived MVPA demonstrated limited agreement with accelerometer-based measures and should be interpreted cautiously.

Central Figure

Congenital heart disease (CHD) occurs in approximately 1% of live births, and with the advances in medical management and surgical techniques that have markedly improved survival, >90% of infants with CHD are now expected to reach adulthood.¹ Therefore, the population of adults with CHD (ACHD) is expanding globally.²^–⁵ Simultaneously, challenges in ACHD have been emerging, including age-related comorbidities and the need for reinterventions.⁶^–⁸ Therefore, in addition to pharmacological and procedural management, optimization of lifestyle behaviors, including physical activity (PA), has become increasingly important for improving the long-term health outcomes in ACHD.

Exercise-based interventions have been shown to improve quality of life and clinical outcomes in patients with chronic heart failure unrelated to CHD.⁹ Likewise, the benefits of exercise training in ACHD have been reported, including improvements in quality of life and prognosis.²^,¹⁰^,¹¹ Recent statements further suggest that in patients with well-managed and clinically stable ACHD, participation in sports may be encouraged, consistent with recommendations for patients with other chronic cardiovascular conditions.¹²^,¹³

The World Health Organization (WHO) recommends that adults accumulate 150–300 min of moderate-intensity aerobic PA, 75–150 min of vigorous-intensity activity, or an equivalent combination per week, and emphasizes reducing sedentary behavior (SB) by incorporating light activities throughout the day.¹⁴^,¹⁵ This guidance aims to prevent lifestyle-related cardiometabolic disease and preserve quality of life. Clinically stable ACHD patients should be also considered an important target population. Reducing SB time and increasing PA are widely recognized as beneficial for cardiovascular disease prevention and improving fitness.¹⁶^,¹⁷ During waking hours, daily activity can be conceptualized as SB, light-intensity PA (LPA), and moderate-to-vigorous PA (MVPA) based on the activity intensity.¹⁴^,¹⁸^,¹⁹ Although increasing PA is clinically important in ACHD, many patients experience exercise restrictions during childhood and may adopt less active lifestyles.²⁰^–²² Indeed, according to accelerometer-derived PA measurements, approximately half of ACHD subjects do not achieve the WHO-recommended level of PA.²³ Our group previously reported that objectively measured PA is associated with exercise capacity in clinically stable ACHD.²⁴ In addition, prolonged SB has been associated with adverse cardiometabolic outcomes and reduced exercise capacity in various cardiovascular populations, independent of overall PA levels.²⁵^,²⁶ Against this background, beyond the total amount of PA, considering SB time together with MVPA may provide additional clinical insight. Specifically, the combination of prolonged SB time and low MVPA may represent a clinically relevant behavioral pattern, underscoring the importance of assessing activity profiles rather than relying on single activity metrics alone.

Under usual living conditions, PA and SB can be objectively assessed using accelerometers, which provide detailed information on habitual behavior. However, the routine use of accelerometers in clinical practice is often limited by cost, logistics, and patient burden. As a result, lifestyle counselling in daily practice frequently relies on self-reported measures. The International Physical Activity Questionnaire (IPAQ) is a widely used and validated instrument in epidemiological studies to assess PA and SB.²⁶ Nevertheless, whether self-reported measures can accurately capture PA and SB in ACHD subjects, who may have unique perceptions of exertion and lifelong adaptations to activity, remains unclear. Therefore, in this study we aimed to examine the agreement between accelerometer-based and self-reported measures of PA and SB, with a particular focus on SB time, and to evaluate the clinical utility of self-reported assessment for classifying activity profiles in ACHD participants.

Methods

Participants

ACHD (age ≥18 years) subjects in a stable condition and being followed up at the outpatient clinics of the University of Tsukuba Hospital were consecutively enrolled in this study, which was conducted according to the ethical principles of the Declaration of Helsinki and approved by the Clinical Research Ethics Review Committee of the University of Tsukuba Hospital (approval number: R03-079). All participants received a detailed explanation of the study protocol during routine outpatient visits and provided written informed consent before enrollment. Formal screening for neurodevelopmental disorders was not performed; however, participation required the ability to understand study instructions, independently handle the accelerometer device, and complete the self-administered questionnaire.

Assessment of SB and PA

SB and PA were objectively assessed using a triaxial accelerometer (Active style Pro HJA-750C; Omron Healthcare Inc., Kyoto, Japan). This device records acceleration signals in 3 orthogonal axes (anteroposterior, mediolateral, and vertical) and estimates activity intensity at 10-s intervals, which are subsequently aggregated on a minute-by-minute basis. The validity and reliability of this accelerometer for estimating PA intensity in free-living conditions have been previously established in multiple studies.²⁷^–²⁹

Participants were instructed to wear the accelerometer on the left side of the waist for 7 consecutive days and to remove it only during water-based activities (e.g., bathing or swimming) and sleeping. To ensure data reliability, only participants with ≥3 valid measurement days, including ≥1 weekend day, were included in the analysis. A valid day was defined as a wear time of ≥10 h/day.²⁴^,³⁰ Activity intensity was classified based on estimated metabolic equivalent (MET) values as follows: SB, ≤1.5 METs; LPA, 1.6–2.9 METs; and MVPA, ≥3.0 METs. For each participant, the average daily time spent in SB and in each PA intensity category was calculated by weighting weekday and weekend data according to the following formula:

Average daily time (min/day) = {(mean weekday time × 5) + (mean weekend time × 2)} / 7.²⁴^,³⁰

Percent time spent in SB, LPA, and MVPA was calculated based on accelerometer-derived activity time and wear time.

Clinical Measurements and Questionnaires

Clinical characteristics including age, sex, CHD diagnosis, age at diagnosis, peripheral oxygen saturation (SpO₂), New York Heart Association (NYHA) functional class, and history of supraventricular arrhythmias, were retrospectively collected from the electronic medical record system. The anatomical complexity of ACHD was classified into 3 categories according to the 2025 American College of Cardiology/American Heart Association /Heart Rhythm Society/International Society for Adult Congenital Heart Disease/Society for Cardiovascular Angiography and Interventions Guideline for the Management of Adults With Congenital Heart Disease: simple, moderate, and complex.⁷

Self-administered questionnaires were distributed to participants at the time of accelerometer placement and were returned by mail together with the device. The questionnaire included the short version of the IPAQ, which was used to estimate self-reported PA and SB time.³¹

Although the IPAQ is commonly applied by scoring the estimated time spent walking, moderate-intensity PA (MPA), vigorous-intensity PA (VPA), and SB, and converting these values into MET-hours per week using standard weighting factors (3.3, 4.0, and 8.0 METs), the present analysis focused specifically on MVPA time (derived from MPA and VPA durations) and SB time only, in order to align with accelerometer-based activity classifications.

Cardiopulmonary exercise testing (CPX) data were retrospectively obtained from the electronic medical records when available. Because CPX was not performed as part of the present study protocol, only tests conducted during clinically stable periods without significant changes in clinical status were considered valid for analysis.

Statistical Analysis

Continuous variables are presented as median and interquartile range, and categorical variables as counts and percentages. Differences in continuous variables among activity profile groups were assessed using the Kruskal–Wallis test, followed by post hoc pairwise comparisons with appropriate adjustment when applicable. Categorical variables were compared using the chi-square or Fisher’s exact test, as appropriate.

Because SB and MVPA represent physiologically and clinically distinct constructs rather than opposite ends of a single continuum, participants were classified into 4 activity profiles based on accelerometer-derived SB time and MVPA using cohort-specific median values as cutoff points. This cross-classification approach enabled the identification of combined activity–sedentary phenotypes (e.g., sedentary–inactive or active but sedentary), which may not be captured when relying on a single activity dimension.

To examine the associations between accelerometer-derived PA measures and self-reported PA assessed by the IPAQ, Spearman’s rank correlation coefficients were calculated. Correlations were evaluated between accelerometer-derived SB time and MVPA (min/day) and corresponding IPAQ-derived SB time (min/day) and MVPA (min/week). Two-tailed P values are reported, and 95% confidence intervals (CIs) for Spearman’s rho were estimated using Fisher’s z transformation.

The discriminative ability of IPAQ-derived SB time and MVPA for identifying a sedentary–inactive activity profile was evaluated using receiver operating characteristic (ROC) curve analysis. Optimal cutoff values were determined using the Youden index. Area under the curve (AUC) values with 95% CIs were calculated.

As a sensitivity analysis, we evaluated whether accelerometer wear time influenced the association between activity profiles and accelerometer-derived PA variables. For each outcome (%SB, %LPA, and %MVPA), analysis of variance models were constructed with activity profile (4 categories) and wear time included as explanatory variables. Details of this analysis are provided in Supplementary Table 1. In addition, activity profiles were redefined using proportions of accelerometer wear time, and ROC analyses were repeated to confirm the robustness of the discriminative performance of IPAQ-derived measures.

All statistical analyses were performed using SPSS Statistics (version 30.0; IBM Japan, Tokyo, Japan) and R software (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria). A two-sided P<0.05 was considered statistically significant.

Results

Patient Classification Based on Sedentary Time and MVPA Data Obtained From Accelerometers

Figure 1 shows the distribution of participants according to accelerometer-derived SB and MVPA times. Using the cohort-specific median values of SB time (568.7 min/day) and MVPA time (45.0 min/day) as cutoffs, participants were classified into 4 distinct activity profiles: active (n=58), low MVPA and low SB time (n=31), sedentary–inactive (n=57), and active but sedentary (n=31). The sedentary–inactive group was characterized by prolonged SB time combined with low MVPA, whereas the active but sedentary group exhibited higher MVPA despite substantial SB time.

Figure 1.

Activity profiles based on accelerometer-derived sedentary time and moderate-to-vigorous physical activity. Scatter plot showing individual participants according to daily sedentary behavior (SB, min/day) and moderate-to-vigorous physical activity (MVPA, min/day) measured by accelerometry (n=177). Dashed vertical and horizontal lines indicate the cohort-specific median values of MVPA (45.0 min/day) and sedentary behavior (SB; 568.7 min/day), respectively, which were used to define 4 activity profiles: active, active but sedentary, low MVPA and low SB, and sedentary–inactive.

The Table summarizes the clinical characteristics and PA measures according to accelerometer-derived activity profiles. Age, age at diagnosis, sex distribution, and ACHD anatomical complexity did not differ significantly among the 4 groups. In contrast, functional status differed across activity profiles, with higher NYHA classes more frequently observed in the sedentary–inactive group (P=0.047). Accelerometer-derived SB time, LPA, and MVPA differed significantly among the 4 profiles (all P<0.001). The distribution of underlying CHD diagnoses across activity profiles is shown in Supplementary Table 2, and additional clinical characteristics are provided in Supplementary Table 3. β-blocker use differed significantly across activity profiles (P=0.007), whereas atrial fibrillation, surgical history, and other medications did not. CPX data were available in a subset of participants (n=95). Peak V̇O₂ and %predicted peak V̇O₂ differed significantly across activity profiles, with lower values observed in the sedentary–inactive group (Supplementary Table 4).

Table.

Patients’ Characteristics According to Accelerometer-Defined Activity Profiles

Variable	All (n=177)	Active (n=58)	Low MVPA & Low SB (n=31)	Sedentary– inactive (n=57)	Active but sedentary (n=31)	P value
Age, years	33 [25–48]	33 [26–49]	33 [26–52]	36 [25–48]	28 [23–38]	0.265
Age at diagnosis, years	0 [0–6]	0 [0–19]	0 [0–7]	0 [0–2]	0 [0–0]	0.526
Male sex, n (%)	103 (58.2)	33(56.9)	20 (64.5)	36 (63.2)	14 (45.2)	0.349
SpO₂, %	98 [97–98]	98 [98–99]	98 [97–98]	98 [97–98]	98 [98–98]	0.160
SB time, min/day	566 [461–666]	439 [377–480]	517 [449–539]	677 [632–741]	637 [587–674]	<0.001
LPA time, min/day	338 [254–448]	457 [386–486]	395 [323–466]	248 [204–296]	278 [220–334]	<0.001
MVPA time, min/day	45 [29–72]	76 [57–94]	35 [25–42]	25 [19–33]	56 [49–73]	<0.001
Accelerometer wear time, min/day	957 [919–1,007]	955 [922–988]	905 [858–966]	975 [933–1,010]	973 [947–1,018]	<0.001
%SB time	59.7 [47.4–68.8]	45.0 [40.9–50.9]	56.0 [47.9–60.1]	70.8 [66.2–76.2]	64.4 [61.0–68.4]	<0.001
%LPA time	35.4 [26.5–45.6]	46.8 [40.2–50.3]	40.9 [36.3–49.0]	26.4 [20.8–30.4]	27.5 [24.3–31.5]	<0.001
%MVPA time	4.7 [3.1–7.3]	8.2 [6.3–9.7]	3.9 [2.9–4.4]	2.7 [2.0–3.5]	5.8 [5.0–7.2]	<0.001
IPAQ sedentary time, min/day	390 [240–600]	300 [180–360]	310 [195–532]	600 [480–720]	360 [300–600]	<0.001
IPAQ MVPA, min/week	0 [0–90]	11 [0–225]	0 [0–98]	0 [0–0]	0 [0–155]	<0.001
NYHA class I/II/III, n	102/56/17	41/16/1	16/11/3	25/21/10	20/8/3	0.047
ACHD complexity (simple/moderate/ complex), n	80/58/39	31/17/10	13/9/9	21/24/12	15/9/7	0.522

Activity profiles were defined based on accelerometer-derived sedentary time and moderate-to-vigorous physical activity time (min/day). Continuous variables are presented as median [interquartile range]. Categorical variables are presented as number (%). P values were calculated using the Kruskal–Wallis test for continuous variables and the chi-square test for categorical variables. %SB, %LPA, and %MVPA represent the proportion of accelerometer wear time spent in SB, LPA, and MVPA, respectively. ACHD, adult congenital heart disease; BMI, body mass index; IPAQ, International Physical Activity Questionnaire; LPA, light-intensity physical activity; MVPA, moderate-to-vigorous physical activity; NYHA, New York Heart Association functional class; SB, sedentary behavior; SpO₂, peripheral oxygen saturation.

The distribution of accelerometer-derived activity composition across the 4 profiles is illustrated in Figure 2, showing a clear stepwise increase in %SB and reciprocal decreases in %LPA and %MVPA from the active to the sedentary–inactive profile. The sedentary–inactive group showed the longest SB time and the lowest MVPA, whereas the active group demonstrated higher MVPA and shorter SB time. Although wear time differed across the groups, the absolute differences were small (median values from 905 to 975 min/day). To account for potential residual confounding by wear time, sensitivity analyses adjusting for accelerometer wear time, activity profile remained significantly associated with all accelerometer-derived PA variables (Supplementary Table 1), supporting the internal consistency of the classification. SB time assessed using the IPAQ also differed significantly across activity profiles (P<0.001). IPAQ-derived MVPA, expressed as minutes per week, also showed a significant difference among groups (P<0.001). Because a substantial proportion of participants reported 0 min of MVPA on the IPAQ, IPAQ-derived MVPA was analyzed and presented as minutes per week rather than minutes per day. Given the distinct distribution of functional status and SB across profiles, subsequent analyses focused on identifying factors associated with the sedentary–inactive activity profile.

Figure 2.

Distribution of accelerometer-derived activity composition across activity profiles. Box plots show the proportion of accelerometer wear time spent in sedentary behavior (%SB, A), light physical activity (%LPA, B), and moderate-to-vigorous physical activity (%MVPA, C) across the 4 activity profiles. Boxes indicate the interquartile range, the horizontal line indicates the median, and whiskers indicate 1.5-fold the interquartile range. Activity profiles were defined based on cohort-specific median values of sedentary time and MVPA time derived from accelerometry.

Correlation Between Accelerometer-Derived and IPAQ-Derived Measures

Spearman correlation analyses showed statistically significant but variable associations between accelerometer-derived and IPAQ-derived PA measures (Supplementary Table 5). Accelerometer-derived SB time was moderately correlated with IPAQ-reported SB time, whereas the correlation between accelerometer-derived MVPA and IPAQ-derived MVPA was modest. In contrast, the correlation between accelerometer-derived SB time and IPAQ-derived MVPA was weak. These findings indicate partial agreement between objective and self-reported measures, with greater concordance for SB than for MVPA.

ROC Analysis of IPAQ-Derived PA Measures

ROC analyses were performed to evaluate the ability of IPAQ-derived SB time and MVPA to identify a sedentary–inactive activity profile defined by accelerometry (Figure 3). IPAQ-derived SB time showed good discriminatory ability, with an AUC of 0.811 (95% CI, 0.744–0.879). The optimal cutoff value was 405 min/day, yielding a sensitivity of 84.2% and a specificity of 69.0% (n=177).

Figure 3.

Receiver operating characteristic curve for sedentary time (A) and moderate-to-vigorous physical activity time (B) derived from the International Physical Activity Questionnaire (IPAQ) in identifying a sedentary–inactive activity profile defined by accelerometry. (A) IPAQ-derived sedentary time demonstrates good discriminatory ability (AUC 0.811, 95% CI 0.744–0.879). The optimal cutoff value was 405 min/day, yielding a sensitivity of 84.2% and a specificity of 69.0% (n=177). The diagonal line indicates the reference line (AUC=0.5). (B) IPAQ-derived MVPA shows modest discriminatory ability (AUC 0.663, 95% CI 0.583–0.744; n=177). No clinically meaningful cutoff could be identified due to a marked floor effect, with a substantial proportion of participants reporting 0 min/week of MVPA. AUC, area under the curve; CI, confidence interval.

In contrast, IPAQ-derived MVPA showed only a modest discriminatory performance (AUC: 0.663, 95% CI, 0.583–0.744). A pronounced floor effect was observed for IPAQ-derived MVPA, with 61.9% of participants reporting 0 min/week. In addition, several participants reported extremely high MVPA values, resulting in a highly skewed distribution with substantial upper outliers (Supplementary Table 6). These distributional characteristics likely limited the utility of IPAQ-derived MVPA as a continuous discriminative measure in this cohort.

Sensitivity analyses using activity profiles defined as proportions of accelerometer wear time yielded results consistent with the primary analysis based on absolute time.

Discussion

In this study, we compared self-reported PA assessed using the IPAQ with accelerometer-derived PA profiles to evaluate the clinical utility of the IPAQ for ACHD subjects. The main findings were that IPAQ-derived SB time showed good discriminatory performance for identifying a sedentary–inactive activity profile, but the IPAQ-derived MVPA time showed limited performance. Specifically, self-reported SB times were significantly higher in participants classified as sedentary–inactive, and ROC analysis demonstrated good discriminative ability (AUC: 0.811). This result suggested that self-reported SB time reasonably reflects objectively assessed prolonged SB in this population. In contrast, the IPAQ-derived MVPA time showed only modest discrimination (AUC: 0.663) and only marginal differences across accelerometer-defined profiles, despite clear separation of accelerometer-derived MVPA between groups. Notably, the IPAQ-derived MVPA time showed a pronounced floor effect, with more than half of participants reporting 0 min per week of MVPA. In addition, several participants reported extremely high MVPA values, resulting in a markedly skewed distribution with substantial upper outliers. These findings underscore the inherent limitations of questionnaire-based MVPA assessment, particularly in populations with a high prevalence of low PA. Misclassification of activity intensity, recall bias, and overestimation of activity duration are well-recognized challenges in self-report-based assessment.³¹^,³² In ACHD subjects, Larsson et al. previously demonstrated that self-reported PA tends to overestimate accelerometer-measured activity levels, highlighting the limited agreement between subjective and objective assessments.³³ Our findings are directionally consistent with this observation, but extend previous work by demonstrating that the degree of agreement differs substantially between activity domains, with SB showing better discriminative performance than MVPA. ACHD subjects, among whom a less active lifestyle is common,²⁰^–²³ may also misperceive or misreport their true PA levels. The validity of self-reporting may vary according to participant characteristics and background factors.³⁴ Importantly, similar limitations have been described in clinical populations such as those with chronic heart failure, in which self-report instruments, including the IPAQ short-form, are susceptible to recall and intensity misclassification and demonstrate poor agreement with device-based measures.³⁵ Taken together, our results indicate that in ACHD subjects, IPAQ-derived MVPA should be interpreted cautiously as a continuous quantitative measure, particularly when attempting to distinguish clinically meaningful PA profiles. Importantly, the cohort-specific classification applied in this study was not intended to redefine guideline-based thresholds for health risk, such as the recommended ≥150 min/week of MVPA derived from general population studies,¹⁴^,¹⁵ but rather to identify relatively inactive individuals within the ACHD population for phenotypic characterization. This classification strategy was intended to capture clinically relevant behavioral phenotypes rather than to create purely statistical categories. In this context, exploratory analyses using guideline-based cutoffs suggested limited discriminatory performance, supporting the use of median-based classifications in this cohort. Although median-based categorization may lead to clustering around the median, our primary analyses were conducted using continuous variables, and the cross-classification into 4 activity profiles was used primarily for phenotypic illustration rather than hypothesis testing.

Consistent with the clinical relevance of this phenotypic classification, NYHA functional class differed across accelerometer-defined activity profiles, whereas ACHD anatomical complexity did not. This finding suggests that habitual activity behavior may be more closely related to functional capacity than to structural disease severity in ACHD.

Beta-blocker use also differed across activity profiles, being more frequent in the sedentary–inactive and low-MVPA/low-SB groups. Because β-blockers may influence exercise tolerance and perceived exertion, medication use may partially contribute to differences in habitual activity behavior in ACHD subjects. However, the primary analyses of this study focused on the agreement between self-reported and accelerometer-derived activity measures rather than causal determinants of activity behavior. Interestingly, the active but sedentary group demonstrated the highest peak V̇O₂, supporting the notion that SB and MVPA represent distinct behavioral dimensions rather than opposite ends of a single continuum.

Although the IPAQ provides concrete examples of moderate and vigorous activities, a methodological discrepancy exists between perceived intensity-based self-reporting and absolute MET-based accelerometer classification, and perceived intensity may vary substantially depending on individual exercise capacity. In some ACHD subjects, exercise capacity and the anaerobic threshold may be markedly reduced.³⁶^,³⁷ Consequently, activities classified as LPA (≤3 METs) in the general population may represent moderate or even vigorous physiological intensity at an individual level in this population. This discrepancy highlights a limitation of absolute MET-based classifications when interpreting PA and providing individualized lifestyle counseling for ACHD patients. Future research may benefit from incorporating relative measures of exercise intensity, rather than relying solely on absolute MET-based classifications, to better reflect physiological load in individual patients with ACHD. Combining objective activity monitoring with patient-reported assessments may also enhance individualized lifestyle counselling in this population. This conceptual gap between perceived relative intensity and absolute MET-based classification may partly explain the overestimation of MVPA reported in previous studies, including that by Larsson et al.,³³ and the limited the discriminative performance of IPAQ-derived MVPA observed in the present analysis.

In contrast, IPAQ-derived SB time appeared to be a more robust and clinically informative self-reported indicator, showing closer alignment with objectively measured SB. The closer agreement observed for SB may, at first glance, appear intuitive, as SB time is directly queried and less influenced by perceived exertion. However, our results suggested that this domain-specific difference is clinically meaningful rather than trivial, as it highlights that disagreement between subjective and objective measures in the ACHD population is not uniform but depends on the behavioral domain being assessed.

As the number of ACHD cases continues to increase, and is projected to age over time,³⁸ this population nonetheless remains a comparatively young cardiovascular population for whom participation in work or school constitutes a major component of daily functioning. Participation in supervised exercise-based rehabilitation can be difficult for many patients. Moreover, outpatient cardiac rehabilitation participation in Japan remains low, at approximately 7%,³⁹ and reports specifically focusing on ACHD are scarce, suggesting that supervised rehabilitation is uncommon among ACHD patients. In this context, individualized lifestyle counselling for unsupervised activity during routine outpatient visits becomes especially important. From a practical standpoint, in settings where accelerometer-based monitoring is not feasible, self-reported SB time may be more useful than self-reported MVPA for the initial screening of high-risk behavioral patterns.

From a practical perspective, a brief assessment focused on daily SB time may provide more clinically actionable information than an attempt to quantify MVPA in detail by using self-reporting among ACHD patients. For patients reporting prolonged SB time, counselling may benefit from focusing not only on increasing exercise volume but also on reducing and interrupting SB time throughout the day. Taken together, our findings suggested that the IPAQ-derived SB time may serve as a pragmatic first-step screening tool for identifying sedentary–inactive behavioral patterns in ACHD patients, whereas IPAQ-derived MVPA should be interpreted with caution when used as a continuous quantitative measure of PA.

Study Limitations

Certain limitations of this study should be acknowledged. First, the IPAQ short-form relies on self-reported PA and is therefore susceptible to recall bias and misclassification. This limitation also applies to the assessment of life-course factors, including self-reported participation in elementary school physical education classes. In particular, IPAQ-derived MVPA showed a pronounced floor effect, with a substantial proportion of participants reporting 0 min/week, which limited its discriminatory performance. Second, formal screening for neurodevelopmental disorders was not performed. Although participants were required to independently manage the accelerometer device and complete the questionnaire, the potential influence of neurodevelopmental factors on PA reporting cannot be completely excluded. Third, accelerometer-derived estimates of PA intensity are based on MET algorithms developed and validated in healthy adults. In patients with ACHD, altered cardiovascular and skeletal muscle physiology may affect the relationship between movement and metabolic demand. As a result, absolute classification of activity intensity based on MET thresholds may not accurately reflect individual physiological load. Nevertheless, because our analyses relied on relative comparisons within the cohort rather than absolute MET values, this limitation is unlikely to undermine the main conclusions of the study. Fourth, the cross-sectional design precluded causal inference regarding the relationship between SB and clinical status. Fifth, accelerometer data were obtained over a limited monitoring period, which may not fully capture long-term habitual activity patterns. Sixth, CPX data were available only in a subset of participants and were retrospectively obtained from clinical records, which may introduce selection bias. Finally, this was a single-cohort study, and the generalizability of the findings to other ACHD populations requires further validation.

Conclusions

In ACHD patients, accelerometer-derived activity profiling identified a substantial proportion of patients with a sedentary–inactive behavior pattern associated with worse functional status. Although the IPAQ-derived MVPA showed limited utility owing to pronounced floor effects, self-reported SB time demonstrated good agreement with objective measures, and may serve as a practical screening tool in routine clinical practice. These findings highlight the importance of focusing on SB, in addition to exercise promotion, when assessing and counseling for PA in this population.

Acknowledgments

None.

Conflict of Interest

The authors declare they have no conflicts of interest.

Funding

This work was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JSPS KAKENHI; grant number: 23K15124).

IRB Information

This study was approved by the Clinical Research Ethics Review Committee of the University of Tsukuba Hospital (approval number: R03-079).

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical restrictions.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circrep.CR-26-0010

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