This cross-sectional online survey examined contemporaneous associations among exercise regulation, general self-efficacy, perceived physical activity triggers, and the ZTEx-IP. Theory-informed parallel and ordered indirect-association analyses were used to quantify model-implied indirect associations. Because all variables were measured at the same time, these analyses were not intended to establish temporal or causal mediation. Reporting followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist (Multimedia Appendix 1).

Data were collected through an online questionnaire in China from May to September 2025. The study included participants from 20 of China’s 34 provincial-level administrative divisions, covering eastern, central, and western regions. Recruitment used a multichannel convenience-sampling strategy. Recruitment information was distributed through workplace WeChat (Tencent Holdings Limited) groups, occupation-related online communities, and workplace or institutional contacts. Individual recruitment source was not recorded; therefore, channel-specific recruitment proportions could not be calculated. Self-reported work-unit information indicated participation from public-service institutions, enterprises, and other workplace settings. Organization size and ownership were not systematically recorded. A standardized recruitment notice described the study purpose, eligibility criteria, voluntary participation, confidentiality, approximate completion time, and the right to withdraw before submission. Contacts only forwarded the notice and questionnaire link and had no access to individual responses. The electronic questionnaire was administered through Wenjuanxing and accessed via a web link or QR code. Participants provided electronic informed consent before entering the survey. A standardized definition of ZTEx was presented before the ZTEx-related items. The inclusion criteria required (1) adults aged ≥18 years; (2) employed full-time in sedentary occupations, operationally defined as jobs predominantly involving seated work for ≥6 hours per working day with low energy expenditure (≤1.5 METs), consistent with the Sedentary Behaviour Research Network definition and commonly used cutoffs in occupational sedentary epidemiology; and (3) able to provide informed consent. Exclusion criteria eliminated individuals on long-term leave, with cognitive impairment affecting comprehension, or participating in concurrent behavioral intervention programs.

Of the 820 submitted questionnaires, 30 were excluded because missing data precluded calculation of at least one core scale score or analysis variable, leaving 790 questionnaires for analysis. Thus, 96.3% (790/820) of submitted questionnaires were included; this percentage represents analytic inclusion rather than a response rate because the number of individuals who received or viewed the recruitment information was unavailable. No item-level imputation was performed. Before data collection, sample-size planning used a pragmatic ratio of 10 to 20 participants per anticipated model parameter. Allowing 20% for unusable questionnaires yielded a recruitment target of 325 to 650 participants. Because sample-size requirements for indirect associations depend on path magnitudes and model characteristics [23,24], this heuristic was not regarded as a model-specific power calculation. A subsequent scenario-based Monte Carlo analysis of sample adequacy is reported in Multimedia Appendix 2.

All statistical analyses were conducted using R (version 4.5.1; R Core Team) and IBM SPSS Statistics version 29.0 with the PROCESS macro. Scale-score distributions were assessed using the Shapiro-Wilk test. Categorical variables were summarized as frequencies and percentages, and scale scores as medians and IQRs (25th-75th percentiles). Internal consistency was evaluated using Cronbach α. Differences in the ZTEx-IP across participant characteristics were examined using Mann-Whitney U tests or Kruskal-Wallis H tests, as appropriate. The Benjamini-Hochberg procedure was applied to the 15 comparisons between-group comparisons; unadjusted and adjusted P values are reported in Multimedia Appendix 3. Spearman rank correlations were used to examine associations among the 4 focal scores. Harman’s unrotated single-factor analysis of the 58 core questionnaire items was used as a limited diagnostic for common method variance.

The adjustment set was defined using a combination of substantive considerations and nominal empirical screening. Candidate variables represented 4 relevant domains. Age and monthly income represented sociodemographic context; incidental physical activity duration and structured exercise frequency and duration represented existing movement patterns; wearable-device use represented potential exposure to activity monitoring and digital cues; and ZTEx familiarity represented prior awareness of the target strategy. The socioeconomic and activity-related rationale was informed by previous literature [32], whereas wearable-device use and ZTEx familiarity were included on substantive grounds because cue exposure and prior knowledge could be related to the focal study variables. To obtain a parsimonious adjustment set for this exploratory analysis, candidate variables meeting the nominal significance criterion (α=.05) in the initial unadjusted comparisons were retained. The resulting set comprised age, monthly income, incidental physical activity duration, structured exercise frequency, structured exercise duration, wearable-device use, and ZTEx familiarity. Monthly income met the nominal screening criterion before multiplicity adjustment (unadjusted P=.04), although the Benjamini-Hochberg–adjusted comparison was not statistically significant (adjusted P=.07). The Benjamini-Hochberg procedure was used only for descriptive multiplicity control and did not redefine the adjustment set. The selected variables were treated as adjustment covariates rather than empirically established confounders. Age, income, incidental physical activity duration, structured exercise frequency and duration, and ZTEx familiarity were entered as ordinal category scores, whereas wearable-device use was entered as a binary indicator. Ordinary least squares regression was used to examine the association between exercise motivation and the ZTEx-IP. Motivation was modeled using sample-specific tertiles, with the lowest tertile as the reference and an ordinal term to test for trend. The continuous association was examined using a restricted cubic spline with knots at motivation scores of 33, 48, 54, and 72 and a reference score of 51; overall and nonlinear associations were tested.

Model-implied indirect associations were estimated using PROCESS Models 4 and 6. Model 4 specified general self-efficacy and perceived physical activity triggers as parallel intermediate variables, whereas Model 6 imposed the prespecified theory-informed ordering of exercise motivation, general self-efficacy, perceived physical activity triggers, and the ZTEx-IP to estimate the corresponding model-implied indirect association. The same covariates were included in every component equation. Direct, specific indirect, total indirect, and total associations were estimated using 5000 nonparametric percentile bootstrap resamples. A 95% bootstrap CI excluding 0 was considered statistical evidence of an indirect association. Unstandardized coefficients were the primary estimates, with standardized coefficients additionally reported for the focal associations. Model R² and overall F statistics were reported. The incremental contribution of each intermediate variable was assessed using ΔR² and Cohen f². Because all variables were measured concurrently, the indirect associations were interpreted as statistical decompositions of cross-sectional associations rather than evidence of temporal or causal mediation. Model assumptions were assessed using variance inflation factors and residual diagnostics, with HC3 heteroscedasticity-consistent SEs examined as a robustness analysis. Three theoretically motivated sensitivity analyses were prioritized: alternative outcome definitions using the behavioral intention subscale and a score excluding perceived behavioral control items, restriction to participants who were at least somewhat familiar with ZTEx, and an alternative ordering of general self-efficacy and perceived physical activity triggers. Additional regression diagnostics and technical robustness analyses are reported in Multimedia Appendices 4 and 5. Bootstrap-based sensitivity analyses used 5000 resamples. All sensitivity analyses were considered exploratory, and no additional multiplicity adjustment was applied.

The study was conducted in accordance with the Declaration of Helsinki, and ethical approval was granted by the Ethics Committee of Hunan Normal University (2025889). Before beginning the online questionnaire, participants received information about the study purpose, voluntary participation, data confidentiality, and their right to decline participation or withdraw before submission without penalty. All participants provided electronic informed consent before proceeding to the survey items. Participants received no financial or material compensation for participation. To protect participant privacy and confidentiality, the analytic dataset was deidentified before analysis by removing information that could identify individual participants or participating institutions. Results were reported in aggregate, data were used solely for the purposes of this study, and access was restricted to the research team.

Of the 820 online questionnaire submissions received, 790 met the completeness requirements described in the “Methods” section and were included in the analyses, corresponding to an analytic inclusion proportion of 96.3% (790/820). Table 1 summarizes participants’ sociodemographic, occupational, and behavioral characteristics and their median ZTEx-IP scores. The sample was relatively balanced by sex, with female participants accounting for 56.6% (447/790), and 75.4% (596/790) of participants were younger than 40 years. Education and research professionals formed the largest occupational group (297/790, 37.6%). Most participants reported middle-income levels and had completed higher education.

The median ZTEx-IP score was 59 (IQR 50-65.75). After Benjamini-Hochberg adjustment, differences remained statistically significant across age groups (adjusted P=.04), incidental physical activity duration (adjusted P=.001), structured exercise frequency (adjusted P<.001), structured exercise duration (adjusted P<.001), wearable-device use (adjusted P=.001), and ZTEx familiarity (adjusted P=.04). No statistically significant differences were observed by sex, educational attainment, occupation, job tenure, daily sedentary duration, musculoskeletal discomfort, health examination in the previous 6 months, or chronic disease status (all adjusted P>.05).

Spearman rank correlations were used to examine associations among exercise motivation, general self-efficacy, perceived physical activity triggers, and the ZTEx-IP (Table 2). All correlations were positive and statistically significant (all P<.001). Exercise motivation was positively correlated with general self-efficacy (ρ=0.347), perceived physical activity triggers (ρ=0.539), and the ZTEx-IP (ρ=0.371). General self-efficacy was positively correlated with perceived physical activity triggers (ρ=0.349) and the ZTEx-IP (ρ=0.378). Perceived physical activity triggers showed the strongest correlation with the ZTEx-IP (ρ=0.420).

Participants were categorized into tertiles according to their exercise motivation scores. In the unadjusted model, compared with participants in the lowest motivation tertile, those in the middle and highest tertiles had higher ZTEx-IP scores, with B coefficients of 5.58 (95% CI 3.60-7.56; P<.001) and 10.60 (95% CI 8.63-12.60; P<.001), respectively. After adjustment for age, income, incidental physical activity duration, structured exercise frequency, structured exercise duration, wearable-device use, and ZTEx familiarity, the corresponding B coefficients were 4.64 (95% CI 2.69-6.60; P<.001) and 8.49 (95% CI 6.43-10.60; P<.001), respectively (Multimedia Appendix 6). A significant positive trend was observed across increasing motivation tertiles (adjusted B for trend=4.25, 95% CI 3.22-5.28; P<.001). When exercise motivation was analyzed as a continuous variable, restricted cubic spline analysis showed a significant overall association with the ZTEx-IP (P overall<.001), with evidence of nonlinearity (P nonlinear<.001; Figure 1). Using an exercise motivation score of 51 as the reference value, the spline curve showed an overall increasing pattern, although the magnitude of the association varied across the motivation score range.

Before the indirect-association analyses, common method variance was assessed using Harman’s single-factor test. All 58 core items from the BREQ-2, GSES, PATQ, and Adult Sedentary Behavior Reduction Intention Questionnaire were included. The first unrotated factor accounted for 31.93% of the total variance. This result did not indicate a predominant single factor, although the test cannot rule out common method bias. Full results are reported in Multimedia Appendix 7.

A parallel indirect-association model was fitted to estimate model-implied indirect associations involving general self-efficacy and perceived physical activity triggers. The model was adjusted for age, income, incidental physical activity duration, structured exercise frequency, structured exercise duration, wearable-device use, and ZTEx familiarity. The adjusted parallel indirect-association model is illustrated in Figure 2. Exercise motivation was positively associated with both general self-efficacy (B=0.214, SE 0.015, 95% CI 0.184-0.245; P<.001) and perceived physical activity triggers (B=0.585, SE 0.031, 95% CI 0.524-0.645; P<.001). In the outcome model, both general self-efficacy (B=0.483, SE 0.075, 95% CI 0.336-0.631; P<.001) and perceived physical activity triggers (B=0.311, SE 0.038, 95% CI 0.237-0.386; P<.001) were positively associated with the ZTEx-IP after adjustment for exercise motivation and covariates. The direct association between exercise motivation and the ZTEx-IP remained significant (B=0.167, SE 0.04, 95% CI 0.088-0.246; P<.001). Bootstrap analysis with 5000 resamples yielded nonzero indirect-association estimates involving general self-efficacy (B=0.104, bootstrap SE (BootSE)=0.026, 95% CI 0.056-0.158) and perceived physical activity triggers (B=0.182, BootSE=0.026, 95% CI 0.129-0.233). The total indirect association was also significant (B=0.286, BootSE=0.034, 95% CI 0.220-0.353). The adjusted outcome model explained 37.6% of the variance in the ZTEx-IP. Detailed adjusted Model 4 results are provided in Multimedia Appendix 8.

PROCESS Model 6 was fitted to estimate model-implied indirect associations under the prespecified theory-informed ordering of exercise motivation, general self-efficacy, perceived physical activity triggers, and the ZTEx-IP. As shown in Table 3, exercise motivation was positively associated with both general self-efficacy (standardized regression coefficient, β=0.460, P<.001) and perceived physical activity triggers (β=0.487, P<.001), and general self-efficacy was positively associated with perceived physical activity triggers (β=0.170, P<.001). These coefficients were compatible with the prespecified statistical ordering, but they do not establish temporal precedence among the constructs. In the full model predicting the ZTEx-IP, exercise motivation (β=0.161, P<.001), general self-efficacy (β=0.216, P<.001), and perceived physical activity triggers (β=0.310, P<.001) all remained statistically significant. The adjusted outcome model explained 37.6% of the variance in the ZTEx-IP (R²=0.376, F_10,779=46.995; P<.001). Compared with reduced models excluding each intermediate variable separately, general self-efficacy contributed an additional 3.3% of explained variance (ΔR²=0.033, Cohen f²=0.053), whereas perceived physical activity triggers contributed an additional 5.4% (ΔR²=0.054, Cohen f²=0.087). Complete model diagnostics and secondary technical robustness analyses are reported in Multimedia Appendix 4.

A percentile bootstrap analysis with 5000 samples was conducted to evaluate model-implied indirect associations under the prespecified ordering. As shown in Table 4, the total indirect association between exercise motivation and the ZTEx-IP was B=0.286 (BootSE=0.034, 95% CI 0.218-0.352). The three specific estimates involved (1) general self-efficacy alone (B=0.104, BootSE=0.025, 95% CI 0.057-0.155), (2) perceived physical activity triggers alone (B=0.157, BootSE=0.024, 95% CI 0.110-0.205), and (3) the prespecified ordered combination of general self-efficacy and perceived physical activity triggers (B=0.025, BootSE=0.007, 95% CI 0.013-0.039). All bootstrap CIs excluded 0. The direct association between exercise motivation and the ZTEx-IP also remained statistically significant (B=0.167, SE=0.040, 95% CI 0.088-0.246). Both the direct association and the model-implied indirect association estimates therefore differed from 0. The observed statistical pattern was consistent with the theory-informed ordering used in PROCESS Model 6. However, the cross-sectional estimates cannot determine whether self-efficacy precedes trigger perception, whether trigger perception enhances self-efficacy, or whether both are influenced by another unmeasured factor. Figure 3 presents standardized coefficients for the prespecified ordered model; its arrows indicate model specification and not temporal sequence.

Analyses using the behavioral intention subscale or an outcome excluding perceived behavioral control items yielded positive direct and model-implied indirect associations, with the corresponding 95% CIs excluding 0. In the subgroup of participants who were at least somewhat familiar with ZTEx (n=187), the bootstrap CIs for both single-intermediate-variable associations and the total indirect association excluded 0, whereas the CIs for the direct and ordered indirect-association estimates included 0. An alternative variable ordering also yielded a nonzero ordered indirect-association estimate, reinforcing that the cross-sectional data could not identify a unique temporal order among the constructs. Complete numerical results and additional technical robustness analyses are reported in Multimedia Appendices 4 and 5.

In this study of 790 Chinese adults in sedentary occupations, higher exercise motivation was associated with higher ZTEx-IP scores. The association was graded and nonlinear across the observed motivation range. In both the parallel and ordered models, general self-efficacy and perceived physical activity triggers showed distinct model-implied indirect associations between exercise motivation and the ZTEx-IP. Perceived triggers had the larger specific indirect estimate and made the greater incremental contribution to explained variance, while the prespecified ordered term involving both intermediate variables yielded a smaller indirect estimate. Together, the adjusted variables explained 37.6% of the variance in the ZTEx-IP. The theoretically motivated sensitivity analyses retained the overall positive association pattern, although estimates in the ZTEx-familiar subgroup were less precise, and the alternative ordering confirmed that the cross-sectional data did not identify a unique temporal sequence. By considering these constructs in one model, the study extends prior work beyond isolated psychosocial correlates and highlights the joint relevance of motivational orientation, perceived agency, and cue-supportive contexts to readiness for brief, lifestyle-integrated movement.

The graded association between exercise motivation and the ZTEx-IP is most plausibly interpreted as a relationship between a more self-determined regulatory profile and readiness to reorganize sedentary routines. The BREQ-2 assesses exercise regulation along an internalization continuum, capturing the quality and self-endorsement of motivation rather than the intensity of a momentary impulse [27,28]. This interpretation is consistent with evidence that perceived competence and more autonomous forms of regulation are recurrent correlates of physical activity [12]. The Chinese working-age context makes the finding particularly relevant. Half of Chinese adults aged 18-44 years report sitting for at least 6 hours per day [5], and longer sitting duration has been associated with a higher risk of major cardiovascular events among both manual and nonmanual workers [33]. For workers who find it difficult to set aside dedicated exercise time, an internalized value placed on movement may make brief bouts incorporated into existing routines more acceptable. The nonlinear pattern adds nuance but does not identify a threshold or plateau. Instead, it suggests that the strength of the motivation–ZTEx-IP association varies across the observed motivation range, providing a reason to test support tailored to workers’ motivational profiles without imposing arbitrary cutoffs. The 37.6% of variance explained by the adjusted model falls within the broad range reported for theory-based models of behavioral and physical activity intentions [34]. Direct comparison should nevertheless be cautious because the present outcome includes several Theory of Planned Behavior domains. The unexplained variance may reflect factors not captured in the model, including job control, workload, habit, time availability, organizational norms, and access to feasible opportunities for movement [35,36].

The model-implied indirect associations show that exercise motivation, generalized perceived agency, and recognizable movement opportunities covaried within the prespecified statistical model. General self-efficacy reflects confidence in handling challenges across situations rather than efficacy for a particular ZTEx task [30]. From an affordance perspective, workers reporting greater agency may also be more likely to regard a task transition, reminder, or scheduled break as a usable opportunity for movement [37]. This is one theoretically plausible interpretation, not evidence that self-efficacy temporally precedes trigger perception. The larger trigger-related estimate is also plausible for a low-threshold strategy such as ZTEx, which requires little time, space, equipment, or technical skill. A cue is useful only when movement is feasible within the task and acceptable in the workplace. This point is especially relevant in Chinese workplaces, where time pressure and limited social support have been reported as barriers to sustained activity, while locally tested programs increasingly combine digital delivery with managerial, team-based, or interactive support [38-41]. Perceived triggers may therefore reflect both cue exposure and the perceived legitimacy of responding to a cue. The co-occurrence of motivation, perceived agency, and salient opportunity is compatible with COM-B and the Fogg behavior model, but neither framework determines temporal order in these data. Trigger perception might enhance self-efficacy; both constructs might reflect unmeasured influences such as job control or workplace culture, or reciprocal relations might operate. The observed statistical pattern was consistent with the theory-informed ordering used in PROCESS Model 6; however, longitudinal or intensive repeated-measures research is required to determine whether and how these constructs develop over time.

The relative estimates identify perceived trigger exposure, exercise motivation, and general self-efficacy as constructs for hypothesis-driven intervention research rather than as intervention components ready for implementation. In the prespecified ordered model, the specific indirect estimate involving perceived triggers exceeded that involving general self-efficacy (B=0.157 vs B=0.104), and triggers made the larger incremental contribution to explained variance (ΔR²=0.054 vs 0.033). Because these observational estimates relate perceived triggers to the ZTEx-IP, they do not demonstrate that delivered prompts or changes in workplace practices affect actual ZTEx behavior. A primary hypothesis for prospective evaluation is that context-sensitive prompts may help translate intention into objectively measured sitting interruptions and ZTEx performance. Future trials could also examine whether any such effect varies according to baseline motivation, self-efficacy, or occupational context. Evidence that workplace prompts have modest and heterogeneous effects [42-44] and that the effects of team communication vary according to message type and frequency [38] underscores the need to evaluate, rather than assume, the added value of particular delivery or organizational components. The present data cannot determine which components, delivery modes, or combinations are effective. Future workplace trials should prespecify occupation-specific feasibility and safety criteria and assess objectively measured sitting interruptions, actual ZTEx frequency, adherence over time, and acceptability to workers and employers rather than intention alone [45].

This study had several strengths, including a relatively large and geographically diverse sample, standardized Chinese-language instruments with satisfactory internal consistency in the present sample, and extensive sensitivity analyses. Several limitations should nevertheless be considered. First, all variables were measured at one time point, so temporal precedence and causal relationships among exercise motivation, general self-efficacy, perceived physical activity triggers, and the ZTEx-IP could not be established. The model-implied indirect associations should therefore be understood as theory-informed decompositions of contemporaneous associations, not evidence of causal mediation. Reverse or bidirectional associations and alternative variable orderings remain possible [46-48]. Although the candidate variables were substantively motivated, the final adjustment set partly relied on nominal associations with the ZTEx-IP and was not derived from a prespecified causal graph. Outcome-based screening cannot identify confounding and may omit relevant variables with weak unadjusted associations. Residual confounding by workload, job control, workplace culture, or other excluded or unmeasured factors therefore remains possible. Second, the Adult Sedentary Behavior Reduction Intention Questionnaire has not been validated as a ZTEx-specific intention measure. It captures general willingness and perceived control regarding sedentary behavior reduction rather than intention to perform a specified ZTEx movement, and it does not directly assess ZTEx-specific knowledge, task feasibility, workplace appropriateness, or action planning [26]. This limitation is particularly important because 76.3% (603/790) of participants were unfamiliar with ZTEx and may have responded with reference to standing, walking, or other ways of interrupting sitting. Familiarity was measured with a single self-report item, and neither the standardized definition nor the sensitivity analyses could confirm ZTEx-specific understanding or construct validity. The ZTEx-IP should therefore be interpreted as a broad indicator of intention relevant to ZTEx adoption rather than a direct measure of ZTEx-specific intention. Future studies should develop and validate ZTEx-specific intention and readiness instruments. Third, although the instruments were reliable and theoretically relevant, they were not one-to-one operationalizations of COM-B or Fogg constructs. The BREQ-2 assesses the regulation and internalization of exercise motivation, the GSES assesses generalized perceived agency rather than confidence in performing ZTEx under specific work conditions, and the PATQ assesses perceived exposure to broad activity-related cues rather than prompts immediately preceding behavior [31]. Their differing behavioral and temporal referents further limit their correspondence with the proposed frameworks. Future research should use behavior-specific measures with aligned behavioral and temporal referents. Fourth, all principal variables were self-reported by the same participants at the same time. Common method variance, recall error, social desirability, and acquiescent responding may therefore have contributed to the observed associations. Although the first factor accounted for only 31.93% of the variance in Harman’s single-factor analysis, this diagnostic cannot exclude common method bias [49]. Temporally separated or objective assessments would provide stronger protection against this bias. Fifth, the study assessed an intention proxy rather than actual ZTEx uptake, the frequency of sitting interruptions, or behavioral maintenance. Higher ZTEx-IP scores therefore cannot be assumed to translate into ZTEx adoption or sustained practice [45]. Future studies should combine ZTEx-specific intention measures with objective sitting assessments, ZTEx performance records, and longitudinal follow-up. Finally, multichannel online convenience sampling did not provide a population-based sampling frame or a response-rate denominator, and the recruitment source was not recorded at the individual level. Individuals with greater interest in health, exercise, wearable devices, or digital health, as well as those more active in workplace WeChat groups or occupation-related online communities, may have been more likely to participate. This self-selection may have yielded more favorable psychosocial score profiles and may limit the transportability of the observed score distributions, reliability estimates, and associations. Individuals with limited digital access or less engagement with online workplace networks may also have been underrepresented. Generalizability is further limited by the inclusion of only 14 adults aged 60 years or older and by differences in occupational constraints on sitting interruption, particularly for drivers. Nonsignificant subgroup differences should not be interpreted as evidence of equivalence because some estimates were imprecise. Occupation-specific heterogeneity should be examined in adequately represented, independently recruited samples.

Among Chinese adults in sedentary occupations, higher exercise motivation, general self-efficacy, and more frequent perceived physical activity triggers were jointly associated with higher scores on the ZTEx-IP. Perceived triggers had the largest specific indirect estimate and incremental contribution, but the findings do not establish a unique causal or temporal sequence or demonstrate that modifying these factors would increase actual ZTEx behavior. More broadly, intention relevant to ZTEx adoption may reflect the alignment of motivational orientation, perceived agency, and recognizable opportunities within everyday work routines rather than motivation alone. By integrating these theoretically distinct factors within an occupational context, this study identifies an empirically supported pattern that can inform the formulation and prioritization of hypotheses for prospective intervention research. Specifically, the findings support testing whether multicomponent strategies combining autonomy-supportive motivational content, graded movement options, context-sensitive prompts, and appropriate organizational support can facilitate the translation of intention into behavior. Workplace trials should determine whether these components affect objectively measured sitting interruptions, actual ZTEx frequency, adherence and maintenance over time, and workplace acceptability across desk-based and safety-sensitive occupations. Thus, rather than providing evidence of intervention effectiveness, the present study offers an empirically grounded and context-sensitive framework for advancing ZTEx research from intention-related associations to behavioral and intervention evaluation in Chinese occupational settings.