Association Between Acceptable Walking Time and Moderate-intensity Physical Activity Among Japanese Office Workers: A Cross-sectional Validation Study

Association Between Acceptable Walking Time and Moderate-intensity Physical Activity Among Japanese Office Workers: A Cross-sectional Validation Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association Between Acceptable Walking Time and Moderate-intensity Physical Activity Among Japanese Office Workers: A Cross-sectional Validation Study Yoshito Kamiya, Akira Kyan, Kaori Ishii, Noboru Kinjo, Minoru Takakura This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7737707/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Physical inactivity is a global challenge and can be combated by integrating walking for transportation into the lifestyle of working-age populations. Acceptable walking time (AWT) is a psychological predictor of walking for transportation but requires objective validation. We used accelerometry to compare the criterion-related validity of AWT against objectively measured moderate-intensity physical activity (MPA) and step counts in Japanese office workers (n = 95; 65.3% female; mean age, 45.5 years) in Okinawa Prefecture. AWT was assessed by asking “What distance (minutes) would you walk to a destination?” Physical activity was objectively measured using ActiGraph GT3X accelerometers for 7–8 d and analyzed for all-day averages and for workdays and non-workdays. Linear regression was used, adjusting for covariates. Significant linear trends between AWT and physical activity were observed for all-day averages, with MPA increasing by 18.6 min and step counts by 2,207 steps per AWT category. On workdays, MPA increased by 16.2 min and step counts by 1,748 steps per category. No consistent trends were observed on non-workdays. The criterion-related validity of AWT was confirmed, showing linear trends with objectively measured MPA and step counts on workdays. AWT is useful for evaluating physical activity levels through walking for transportation among working-age populations. Health sciences/Health care Health sciences/Health occupations Health sciences/Risk factors Accelerometer ActiGraph Criterion Validity Walking for Transportation Workday Activity Figures Figure 1 Figure 2 Introduction Walking as a mode of transport represents a promising intervention for addressing the global crisis of physical inactivity. The World Health Organization aims to reduce physical inactivity by 15% before 2030 [ 1 ]; however, physical inactivity rates continue to increase in high-income countries [ 2 ]: Only one-quarter of Japanese adults meet the recommended physical activity levels [ 3 ]. For working populations whose occupations are becoming increasingly sedentary [ 4 , 5 ], the primary barrier to physical activity is a lack of time [ 5 ]. Walking for transportation is an ideal solution because it seamlessly integrates into daily routines without demanding additional time [ 6 , 7 ]. Acceptable walking distance and acceptable walking time (AWT) have emerged as crucial psychological predictors of walking for transportation. These concepts represent subjective thresholds at which individuals will undertake walking to specific destinations: Acceptable walking distance is expressed as distance and AWT as time [ 8 ]. Originally conceptualized in urban planning as critical walking distance by Seneviratne in 1985 [ 9 ], similar indicators, such as the reasonable walking distance [ 10 ] and easy walking distance [ 11 ], have been used in public health research. However, the terminology and measurement methods used vary across studies and the conceptual organization is insufficient. A critical need for objectively validating AWT as a behavioral predictor exists. Although several studies have demonstrated consistent associations between AWT and walking behavior across Taiwan [ 12 ], the United States [ 10 ], and Japan [ 8 ], these findings rely on self-reported measurements. Particularly, the Japanese study [ 8 ] used the International Physical Activity Questionnaire, which can overestimate actual activity by 150%–400%, compared with objective measures [ 13 ]. Accelerometry-based validation, particularly that which examines moderate-intensity physical activity (MPA) equivalent to walking (3.0–5.9 metabolic equivalents [METs]) and step counts as appropriate indicators, is essential [ 14 – 16 ]. Office workers represent an ideal population for this investigation because their workday activity reflects regular transportation patterns that include commuting [ 17 , 18 ]: Those who walk or use public transportation demonstrate significantly more moderate-to-vigorous physical activity on workdays than do car commuters [ 18 ]. Therefore, in this study, we aimed to validate AWT as a predictor of walking behavior by examining its criterion-related validity against objectively measured physical activity among Japanese office workers. We hypothesized that higher AWT would show significant linear associations with increased MPA and step counts, particularly on workdays. Methods This study followed the STrengthening the Reporting of OBservational studies in Epidemiology guidelines for reporting observational studies [ 19 ]. Study Sample and Data Collection This was a cross-sectional study. We conducted questionnaire surveys and physical activity assessments of office workers from two companies, A and B, in Okinawa Prefecture, Japan, in March and November 2024, respectively. Paper-based questionnaires were distributed during briefing sessions and the completed questionnaires were collected in sealed envelopes along with accelerometers at the end of the assessment period. The staff of both companies primarily engage in desk work and the company offices are located in walkable areas in central Naha City, Okinawa Prefecture (walk score: Company A, 97; Company B, 91) [ 20 ]. Inclusion criteria were: age of 18–65 years, being a resident of Okinawa Prefecture, and provision of informed consent. Individuals with physical disabilities causing difficulty in walking and pregnant women were excluded. This study was approved by the Research Ethics Committee for Life Science and Medical Research Involving Human Subjects at the University of the Ryukyus (approval number, 23-2241-01-00-00) and conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines and regulations. Written informed consent was obtained from all participants prior to their participation in the study. Measures Physical Activity and Sedentary Behavior We used ActiGraph GT3X accelerometers (ActiGraph LLC, Pensacola, FL, USA) to evaluate the walking behavior of the participants. Vertical axis acceleration data were calculated as counts/min and used to evaluate the intensity of physical activity. Step counts were measured concurrently. Participants were instructed to wear the device continuously on the right side of their waist from waking to sleeping, except during bathing or engaging in water activities and vigorous sports. The wearing period was set as 7 d for staff at Company A and 8 d for staff at Company B (due to a public holiday during the measurement period) to ensure sufficient workday data were gathered. We used 60-s epoch data [ 21 ]. Non-wear time was defined as ≥ 60 consecutive minutes with zero acceleration signal [ 22 ]. Valid data required ≥ 10 h of wear time per day, and only participants with ≥ 2 valid workdays and ≥ 1 valid non-workdays were included in the final analysis [ 22 ]. Workday information was classified based on data collected from each company's attendance system. Data from every 60 s were classified into sedentary behavior (< 100 steps/min), light-intensity physical activity (100–2,019 steps/min), and moderate-to-vigorous physical activity (≥ 2,020 steps/min) [ 22 ]. These data were aggregated daily and averaged for each participant for workdays and non-workdays [ 22 ], with all-day data calculated as weighted averages. AWT AWT was assessed using a question adapted from Tsunoda et al. [ 23 ] by Kamiya et al. [ 8 ], “What distance (minutes) would you walk to a destination (convenience store/supermarket, restaurant, bus stop/station, hospital, acquaintance’s house, etc.)? (Imagine a pleasant day on flat roads).” Six response options were provided: ≤2, ≤ 5, ≤10, ≤ 15, ≤20, or ≥ 21 min. For analysis, each category was treated as ≤ 2, 3–5, 6–10, 11–15, 16–20, and ≥ 21 min, respectively. Sociodemographic Factors We collected information on sex (male, female), age (continuous), marital status (unmarried, married, widowed/divorced), and educational history (junior high school, high school, vocational school, junior college, university, graduate school) through questionnaires. For analysis, marital status was dichotomized as unmarried or married, and education level was categorized into the following three groups: ≤high school graduate, some college graduate, and ≥ college graduate. Statistical Analysis Associations between AWT, sociodemographic factors, and physical activity were examined using chi-square tests and analysis of variance. The Jonckheere–Terpstra test was used to confirm linear trends between AWT, sedentary behavior, and each physical activity category. Linear associations between AWT and MPA (min/d) and step counts (/d) were examined using multivariate linear regression analysis with orthogonal polynomial contrasts [ 10 ]. Categories corresponding to six levels of AWT were used as explanatory variables, with linear and quadratic contrasts used to statistically verify linearity. To focus on MPA primarily from walking as a mode of transport, detailed analyses were conducted for all-day averages as well as separately for workdays and non-workdays. Data were analyzed for all-day averages (reported in Table 1 ) and separately for workdays and non-workdays (detailed in Supplementary Table S1 ). All analyses were adjusted for sex, age, company, and wear time. The significance level was set at P < 0.05. Table 1 Characteristics of the study participants (N = 95) Characteristic n (%)/mean (SD) Sex Male 33 (34.7) Female 62 (65.3) Age (y) 45.5 (11.1) Company A 62 (65.3) B 33 (34.7) Marital status Unmarried 27 (28.4) Married 68 (71.6) Education level ≤High school graduate 13 (13.7) Some college graduate 31 (32.6) ≥College graduate 51 (53.7) All day a Wear time (min) 1,025.5 (177.9) Physical activity (minutes/d) Sedentary 759.2 (165.3) Light 245.9 (51.0) Moderate 20.2 (13.2) Vigorous 0.17 (1.23) Step count (/d) 5,831.4 (1,651.4) a Data available for all days and non-workdays (n = 72) SD, standard deviation All-day averages were calculated using weighted averages of workdays and non-workdays (workday average × 5 + non-workday average × 2) [ 24 ]. The target sample size was 120; however, the final analysis included 95 participants (power, 75%). Sensitivity analysis was conducted using those participants with data available for both workdays and non-workdays [ 25 ]. Statistical analyses were performed using IBM SPSS Statistics for Windows, version 25.0 J (IBM Corp., Armonk, NY, USA). Results A total of 134 office workers from 2 companies in Okinawa Prefecture, Japan participated in this study (March and November 2024). After excluding 39 participants because of insufficient accelerometer data and incomplete questionnaires, 95 were included in the final analysis. Participant Characteristics The participant characteristics are summarized in Table 1 . The majority were female (65.3%), with a mean age of 45.5 (standard deviation [SD], 11.1) years, and most were married (71.6%), and university graduates (53.7%). The mean MPA was 20.2 (SD, 13.2) min/d for all days. Detailed physical activity data by time period (workdays and non-workdays) are presented in Supplementary Table S1 . AWT, Sociodemographic Factors, Sedentary Behavior, Physical Activity, and Step Counts Tables 2 a and 2 b show the associations between participant characteristics and AWT. Overall, an AWT of 6–10 min was most common (34.7%), followed by 3–5 min (26.3%) and 11–15 min (16.8%). By sex, most commonly, male workers reported an AWT of 6–10 min (42.4%), whereas female workers reported an AWT of 3–5 min (30.6%). In both sexes, > 30% reported an AWT of ≤ 3–5 min (male, 30.3%; female, 32.2%), and > 60% reported an AWT of ≤ 6–10 min (male, 72.7%; female, 62.8%). By age, participants with an AWT of 11–15 min showed the highest mean age, at 49.5 (SD, 11.2) years, with the mean ages reducing as AWT became shorter and longer, showing an inverted U-shaped distribution. Table 2 a. Acceptable Walking Time by Socio-demographic Characteristics Characteristic ≤ 2 min 3–5 min 6–10 min 11–15 min 16–20 min ≥ 21 min P a Total 5 (5.3) 25 (26.3) 33 (34.7) 16 (16.8) 8 (8.4) 8 (8.4) Sex Male 2 (6.1) 8 (24.2) 14 (42.4) 2 (6.1) 3 (9.1) 4 (12.1) 0.38 Female 3 (4.8) 17 (27.4) 19 (30.6) 14 (22.6) 5 (8.1) 4 (6.5) Age (y) 37.6 (3.8) 43.8 (10.8) 46.1 (11.0) 49.5 (11.2) 45.3 (11.5) 45.3 (13.5) 0.38 Company A 2 (3.2) 12 (19.4) 22 (35.5) 14 (22.6) 6 (9.7) 6 (9.7) 0.11 B 3 (9.1) 13 (39.4) 11 (33.3) 2 (6.1) 2 (6.1) 2 (6.1) Marital status Unmarried 2 (7.4) 5 (18.5) 10 (37.0) 6 (22.2) 1 (3.7) 3 (11.1) 0.67 Married 3 (4.4) 20 (29.4) 23 (33.8) 10 (14.7) 7 (10.3) 5 (7.4) Education level ≤High school graduate 2 (15.4) 1 (7.7) 5 (38.5) 3 (23.1) 2 (15.4) 0 (0.0) < 0.01 Some college graduate 1 (3.2) 10 (32.3) 3 (9.7) 11 (35.5) 2 (6.5) 4 (12.9) ≥College graduate 2 (3.9) 14 (27.5) 25 (49.0) 2 (3.9) 4 (7.8) 4 (7.8) a The chi-square test was used for categorical variables, and one-way analysis of variance for continuous variables. Table 2 b. Acceptable Walking Time by Physical Activity Level Characteristic ≤ 2 min 3–5 min 6–10 min 11–15 min 16–20 min ≥ 21 min P a P for trend b All day c Wear time (min) 1,051 (325) 974 (194) 1,017 (140) 1,088 (190) 1,065 (191) 983 (173) 0.57 0.12 Physical activity (min/d) Sedentary 753 (278) 731 (181) 757 (134) 799 (185) 799 (182) 679 (140) 0.76 0.41 Light 287 (44) 227 (34) 241 (59) 269 (47) 239 (44) 258 (72) 0.17 0.37 Moderate 12 (5) 16 (8) 18 (11) 20 (12) 27 (17) 45 (17) < 0.01 < 0.01 Vigorous 0.0 (0.0) 0.0 (0.0) 0.5 (2.0) 0.0 (0.0) 0.1 (0.3) 0.0 (0.0) 0.84 0.30 Step count (/d) 5,437 (937) 5,174 (1,157) 5,444 (1,454) 6,101 (1,538) 6,559 (1,428) 9,042 (2,189) < 0.01 < 0.01 Workday c Wear time (min) 982 (235) 951 (195) 995 (161) 1,073 (196) 1,101 (199) 971 (175) 0.24 0.06 Physical activity (min/d) Sedentary 710 (193) 717 (180) 744 (153) 777 (197) 820 (188) 708 (158) 0.66 0.29 Light 261 (44) 219 (38) 233 (61) 273 (52) 247 (33) 222 (65) 0.03 0.30 Moderate 11 (5) 15 (10) 18 (12) 22 (13) 33 (21) 41 (23) < 0.01 < 0.01 Vigorous 0.0 (0.0) 0.1 (0.4) 0.1 (0.6) 0.2 (0.5) 0.7 (1.5) 0.0 (0.0) 0.25 0.20 Step count (/d) 5,396 (737) 5,158 (1,446) 5,460 (1,710) 6,697 (1,823) 7,481 (1,895) 8,175 (2,494) < 0.01 < 0.01 a The chi-square test was used for categorical variables, and one-way analysis of variance for continuous variables. b The Jonckheere–Terpstra test was used for trend analysis. c Data were available for all days (n = 72) and workdays (n = 95). Regarding associations of AWT with physical activity and sedentary behavior (Table 2 b), significant linear trends were observed with MPA and step counts in all-day averages (MPA: P for trend < 0.01 and step counts: P for trend 0.05). We found significant positive trends for the associations of AWT with MPA and step counts on workdays (all P for trend 0.05; detailed in Supplementary Table S2). AWT, MPA, and Step Counts After adjusting for sex, age, company, and wear time, estimated MPA (min/d) and AWT showed significant linear associations in the all-day averages (linear contrast: β = 18.6; 95% confidence interval [CI], 5.3 to 31.9; P = 0.007). Further detailed analysis showed this relationship was particularly clear for workdays, with significant linear trends noted (Fig. 1 ). Linear trend testing using polynomial contrasts confirmed that MPA increased by 16.2 min for each increase in the AWT category (linear contrast: β = 16.2; 95% CI, 6.0 to 26.3; P = 0.002). For non-workdays, linear trends were not significant (β = 15.7; 95% CI, − 0.4 to 31.8; P = 0.055). Because the CI included 0, no significant linear trend was observed for non-workdays, unlike the significant trend observed for workdays. For the association between AWT and step counts, significant linear trends were observed in the all-day averages (linear contrast: β = 2,207; 95% CI, 532 to 3,882; P = 0.011), with particularly clear linear trends observed for workdays (Fig. 2 ). Linear trend testing using polynomial contrasts confirmed that step counts increased by 1,748 steps for each increase in AWT category (linear contrast: β = 1,748; 95% CI, 395 to 3,100; P = 0.012). Linear trends were not significant for non-workdays (β = 944; 95% CI, − 1,741 to 3,629; P = 0.484). Sensitivity analysis was limited to participants with data available for both workdays and non-workdays (n = 72); re-analyzing workday AWT–physical activity relationships confirmed the main findings. The linear trends for MPA remained significant (linear contrast: β = 19.8; 95% CI, 4.3 to 35.3; P = 0.013), and similar results were confirmed for step counts (linear contrast: β = 2,613; 95% CI, 655 to 4,571; P = 0.010). The sensitivity analysis showed a tendency toward larger effect sizes, indicating that the associations observed in participants with data for both workdays and non-workdays were more consistent. This confirmed the association between AWT and physical activity under more stringent conditions. Discussion This validation study found that AWT showed criterion-related validity with accelerometer-measured MPA. Particularly for workdays, clear linear trends showed that MPA increased by 16.2 min and step counts increased by 1,748 steps for an increase of one level in AWT, for example, from ≤ 5 to 6–10 min. The substantial difference in MPA (21.2 min/d) between the group with the lowest AWT (≤ 2 min category; MPA: 13.5 min/d) and the group with the highest AWT (≥ 21 min category; MPA: 34.8 min/d) demonstrated that AWT is a useful indicator of individual differences in physical activity levels. Previous research on AWT relied on self-reported measurements, rendering its true predictive power uncertain due to problems related to overestimations of 150–400% [ 13 ]. The present study is the first to objectively verify the criterion-related validity of AWT using accelerometry. This study represents an important step toward organizing, in a uniform manner, AWT-related concepts that previously differed in terminology and measurement methods that differed between studies. By being able to validate objective measurements, the potential exists for AWT to function as a standard indicator in research involving walking as a mode of transport. Previous studies have also reported associations between AWT and walking behavior. Hsia et al. [ 12 ] reported positive associations between these variables in Taiwanese adults, with individuals who tolerated longer acceptable walking distance showing longer actual walking times. Watson et al. [ 10 ] conducted a national survey of 3,653 adults in the United States and noted that, although > 90% considered walking for transportation reasonable, < 43% considered walking ≥ 1 mile (approximately 20 min) reasonable; the researchers reported that individuals who considered longer distances and or times reasonable had a higher frequency of actually walking for transportation. Furthermore, a recent survey of 881 Japanese adults [ 8 ] found that an AWT of 6–10 min was most common (male, 29.0%; female, 26.3%). In that study, clear linear trends (P < 0.001) between AWT categories and weekly walking time were consistently confirmed across different types of transportation environments: public transportation-oriented metropolitan areas (Greater Tokyo) and car-dependent rural areas (Okinawa). The linear trends between AWT and MPA observed in the present study are consistent with previous findings and objectively demonstrate that AWT functions as a psychological factor that predicts the use of walking as a mode of transport [ 8 , 26 ]. Importantly, AWT was associated only with MPA and showed no associations with physical activity of other intensities or with sedentary behavior. This specificity indicates that AWT is a predictor specific to walking-equivalent physical activity. This specific predictive power for walking for transportation has important implications for practical health assessment. Individuals with an AWT ≥ 15 min exceeded an average of 7,000 steps on workdays, a value approaching 8,000 steps/d, which is recommended for maintaining health [ 3 , 27 ]. Notably, when the weekly workday MPA was calculated, the group with an AWT ≥ 15 min reached an MPA of approximately 128 min (average, 25.6 min × 5 d), achieving 85% of the 150 min/wk target recommended by the World Health Organization [ 28 ]. Reaching this level on weekdays alone suggests the possibility of achieving the recommended levels by engaging in additional physical activity on their days off. These findings suggest that AWT, despite being a simple single-question assessment, could serve as a practical tool for effectively screening individual physical activity levels. AWT may function as a tool for easily estimating daily levels of physical activity engaged in walking for transportation, similar to that way in which the Rating of Perceived Exertion predicts exercise intensity [ 29 ]. A notable finding of the current study is that, despite participants working in areas that were walkable, 66.3% reported relatively short AWT ≤ 10 min. In Okinawa Prefecture, a region in which people are highly dependent on cars—the modal share for cars is 71.3% [ 30 ]—few residents accepted walking for ≥ 11 min. This may reflect a phenomenon whereby dependence on cars makes people “too lazy to walk” [ 31 ] and raises psychological barriers to walking long distances. However, because the participants worked in walkable areas, appropriate health education approaches could potentially promote behavioral change toward walking for transportation. In the context of walkable community policies, such as that of 15-min cities [ 32 ], AWT may serve as a useful indicator for evaluating the potential that residents show for walking for transportation. From an implementation perspective, the simplicity of AWT is a major advantage. Adding a single AWT question to large-scale travel surveys, such as person–trip surveys [ 33 ], could enable the walking for transportation MPA levels of residents to be estimated. This approach could provide cost-effective and practical information for decision-making for urban planning and public health policy. It is also a much more feasible approach, compared with conventional detailed surveys of physical activity. This study has some limitations. First, due to constraints in wearing the accelerometer, swimming and high-intensity sports may not have been evaluated. However, because the present study targeted walking-equivalent MPA as the outcome, with exercise or sports activities of ≥ 6 METs being outside the scope of the analysis, this impact was limited. Second, seasonal differences in physical activity levels may have influenced the results. Although this impact was minimized by statistically adjusting for company variables, residual seasonal effects cannot be completely ruled out. Third, participants were workers in Naha City, Okinawa Prefecture, a region with high car dependency (private vehicle modal share: 71.3%). Caution is required in generalizing findings to different transportation environments. Fourth, the extreme categories of AWT (≤ 2 and ≥ 21 min) included small numbers of participants (7 and 8, respectively), resulting in wide CIs for estimates in these groups. Despite the relatively small sample, the detection of statistically significant and consistent linear associations between AWT and physical activity suggests that the association is strong, and supports the criterion-related validity of AWT. Larger studies that more precisely examine the relationships in the extreme categories of AWT could further clarify its application range. Finally, the detailed psychological mechanisms through which AWT predicts walking as a mode of transport remain insufficiently elucidated. Future research should construct comprehensive models of behavioral science [ 34 ] clarifying how AWT, as an individual psychological factor, interacts with environmental factors and individual capabilities. Elucidating such mechanisms could enable tailored interventions to be developed according to individual levels of AWT [ 35 ]. Conclusion AWT showed clear associations with MPA, which was measured using accelerometers. Thus, AWT was confirmed as a useful tool for evaluating physical activity in terms of walking for transportation, particularly on workdays. This simple assessment method shows potential as an effective marker for evaluating individual physical activity levels and may find applications in promoting policies for walking for transportation among working populations. Declarations Acknowledgements The authors would like to thank the participants for their cooperation in the study. We would like to thank Editage (www.editage.com) for English language editing. Author contributions Y.K., A.K. and N.K. conceived the idea and designed the study. Y.K. analyzed the data and drafted the paper. Y.K., A.K., K.I., and M.T. contributed to the writing and assisted with the interpretation. All authors reviewed and approved the final manuscript. Funding Y.K. is supported by a grant from the President's Discretionary Fund of Meio University. Data availability Anonymized data are available from the corresponding author on reasonable request. Competing Interests The authors declare no competing interests. References World Health Organization. Global Action Plan on Physical Activity 2018–2030: More Active People for a Healthier World https://iris.who.int/bitstream/handle/10665/272722/9789241514187-eng.pdf (2018). Guthold, R., Stevens, G. A., Riley, L. M. & Bull, F. 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Walking cadence (steps/min) as a practical estimate of intensity in adults: a narrative review. Br. J. Sports Med. 52 , 776-788 (2018). World Health Organization. WHO Guidelines on Physical Activity and Sedentary Behaviour https://www.who.int/publications/i/item/9789240015128 (2020). Borg, G. & Borg, E. A new generation of scaling methods: level-anchored ratio scaling. Psychologica 28 , 15-45 (2001). Statistics Bureau, Ministry of Internal Affairs and Communications. 2020 Population Census. e-Stat https://www.e-stat.go.jp/stat-search/files?page=1&query=%E4%BA%A4%E 9%80%9A%E6%89%8B%E6%AE%B5&am p;layout =dataset&toukei=00200521&am p;tstat=000001136464&stat_infid =000032214689&metadata=1&data=1 (2020). Loukopoulos, P. & Gärling, T. Are car users too lazy to walk? The relationship of distance thresholds for driving to the perceived effort of walking. Transp. Res. Rec. 1926 , 206-211 (2005). Moreno, C., Allam, Z., Chabaud, D., Gall, C. & Pratlong, F. Introducing the “15-minute city”: sustainability, resilience and place identity in future post-pandemic cities. Smart Cities 4 , 93-111 (2021). Kubota, A., Abe, T., Hadgraft, N., Owen, N. & Sugiyama, T. Prevalence of physically active and sedentary travel in a regional area of Japan: geographic and demographic variations. J. Transp. Health 24 , 101318 (2022). Michie, S., Van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6 , 42 (2011). Ghanvatkar, S., Kankanhalli, A. & Rajan, V. User models for personalized physical activity interventions: scoping review. JMIR Mhealth Uhealth 7 , e11098 (2019). Additional Declarations No competing interests reported. Supplementary Files submissionfileAWTMPATableFIgure20250929.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor invited by journal 08 Oct, 2025 Editor assigned by journal 06 Oct, 2025 Submission checks completed at journal 03 Oct, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Kyan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACHhBRAecmIAvj03KGZC2MbRha8AD+njOGnwvn2dkzSCSwfeZtS0vccID54QcGmTs4tUic7TGWnrktObFBIoF5Nm9bDlALm7EEA88z3Nac5zGQ5t12IIFBIv8zM29bBVALgxnQvYdx6pA/z2P8m3fOAZDDmKFa2L/h1WJwtsdMmrfhAGMDRAvIYTz4bTE8c6zMmudYcmIbzwNmxjnn0oxnHuYplkjA4xe5M8mbb/PU2NnzsycwM7wpS5btO96+8cPHHtwhxsDAYQCm2ICYCRhJjg3MQFZizwE8WtgfwJmMPxgY7CHMH/i0jIJRMApGwQgDAN1JTpJ7f03fAAAAAElFTkSuQmCC","orcid":"","institution":"University of the Ryukyus","correspondingAuthor":true,"prefix":"","firstName":"Akira","middleName":"","lastName":"Kyan","suffix":""},{"id":546078798,"identity":"7296374e-451c-46ec-9e7b-da19c9940ac0","order_by":2,"name":"Kaori Ishii","email":"","orcid":"","institution":"Waseda 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15:27:22","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114802,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7737707/v1/7735a4c298aaa8175c7fec34.html"},{"id":96483264,"identity":"d6d652a8-2f69-4827-9854-8540cbbf5fdf","added_by":"auto","created_at":"2025-11-21 15:27:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57348,"visible":true,"origin":"","legend":"\u003cp\u003eDaily moderate physical activity time across acceptable walking time categories for all days and workdays, adjusted for sex, age, company, and wear time. Data points show mean values with 95% confidence intervals. Significant linear trends are observed for all days (linear contrast: β=18.6; 95% confidence interval, 5.3–31.9; P=0.007) and workdays (linear contrast: β=16.2; 95% confidence interval, 6.0–26.3; P=0.002).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7737707/v1/1025f458b71fb1f7a283a1fd.png"},{"id":96483265,"identity":"6d3c59df-5824-4143-8e34-0ac87f621112","added_by":"auto","created_at":"2025-11-21 15:27:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75469,"visible":true,"origin":"","legend":"\u003cp\u003eDaily step counts across acceptable walking time categories for all days and workdays, adjusted for sex, age, company, and wear time. Data points show mean values with 95% confidence intervals. Significant linear trends are observed for all days (linear contrast: β=2,207; 95% confidence interval: 532–3,882; P=0.011) and workdays (linear contrast: β=1,748; 95% confidence interval: 395–3,100; P=0.012).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7737707/v1/efb1cdf30306376400c5750e.png"},{"id":96607834,"identity":"3b9b737b-d793-470d-94b8-7a1a23907b7c","added_by":"auto","created_at":"2025-11-24 09:27:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":980030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7737707/v1/c614d75b-9035-45ad-af51-ebb9109968f1.pdf"},{"id":96603691,"identity":"77dbdd16-ff8e-4b86-9c5f-8d60256cc6e3","added_by":"auto","created_at":"2025-11-24 09:11:07","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":147162,"visible":true,"origin":"","legend":"","description":"","filename":"submissionfileAWTMPATableFIgure20250929.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7737707/v1/b70c51ffbd9a39ffcc3a8c70.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Acceptable Walking Time and Moderate-intensity Physical Activity Among Japanese Office Workers: A Cross-sectional Validation Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWalking as a mode of transport represents a promising intervention for addressing the global crisis of physical inactivity. The World Health Organization aims to reduce physical inactivity by 15% before 2030 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]; however, physical inactivity rates continue to increase in high-income countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]: Only one-quarter of Japanese adults meet the recommended physical activity levels [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For working populations whose occupations are becoming increasingly sedentary [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the primary barrier to physical activity is a lack of time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Walking for transportation is an ideal solution because it seamlessly integrates into daily routines without demanding additional time [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAcceptable walking distance and acceptable walking time (AWT) have emerged as crucial psychological predictors of walking for transportation. These concepts represent subjective thresholds at which individuals will undertake walking to specific destinations: Acceptable walking distance is expressed as distance and AWT as time [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Originally conceptualized in urban planning as \u003cem\u003ecritical walking distance\u003c/em\u003e by Seneviratne in 1985 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], similar indicators, such as the \u003cem\u003ereasonable walking distance\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and \u003cem\u003eeasy walking distance\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], have been used in public health research. However, the terminology and measurement methods used vary across studies and the conceptual organization is insufficient.\u003c/p\u003e\u003cp\u003eA critical need for objectively validating AWT as a behavioral predictor exists. Although several studies have demonstrated consistent associations between AWT and walking behavior across Taiwan [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the United States [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and Japan [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], these findings rely on self-reported measurements. Particularly, the Japanese study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] used the International Physical Activity Questionnaire, which can overestimate actual activity by 150%\u0026ndash;400%, compared with objective measures [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Accelerometry-based validation, particularly that which examines moderate-intensity physical activity (MPA) equivalent to walking (3.0\u0026ndash;5.9 metabolic equivalents [METs]) and step counts as appropriate indicators, is essential [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Office workers represent an ideal population for this investigation because their workday activity reflects regular transportation patterns that include commuting [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]: Those who walk or use public transportation demonstrate significantly more moderate-to-vigorous physical activity on workdays than do car commuters [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, in this study, we aimed to validate AWT as a predictor of walking behavior by examining its criterion-related validity against objectively measured physical activity among Japanese office workers. We hypothesized that higher AWT would show significant linear associations with increased MPA and step counts, particularly on workdays.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study followed the STrengthening the Reporting of OBservational studies in Epidemiology guidelines for reporting observational studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStudy Sample and Data Collection\u003c/p\u003e\u003cp\u003eThis was a cross-sectional study. We conducted questionnaire surveys and physical activity assessments of office workers from two companies, A and B, in Okinawa Prefecture, Japan, in March and November 2024, respectively. Paper-based questionnaires were distributed during briefing sessions and the completed questionnaires were collected in sealed envelopes along with accelerometers at the end of the assessment period. The staff of both companies primarily engage in desk work and the company offices are located in walkable areas in central Naha City, Okinawa Prefecture (walk score: Company A, 97; Company B, 91) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Inclusion criteria were: age of 18\u0026ndash;65 years, being a resident of Okinawa Prefecture, and provision of informed consent. Individuals with physical disabilities causing difficulty in walking and pregnant women were excluded. This study was approved by the Research Ethics Committee for Life Science and Medical Research Involving Human Subjects at the University of the Ryukyus (approval number, 23-2241-01-00-00) and conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines and regulations. Written informed consent was obtained from all participants prior to their participation in the study.\u003c/p\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePhysical Activity and Sedentary Behavior\u003c/h2\u003e\u003cp\u003eWe used ActiGraph GT3X accelerometers (ActiGraph LLC, Pensacola, FL, USA) to evaluate the walking behavior of the participants. Vertical axis acceleration data were calculated as counts/min and used to evaluate the intensity of physical activity. Step counts were measured concurrently. Participants were instructed to wear the device continuously on the right side of their waist from waking to sleeping, except during bathing or engaging in water activities and vigorous sports. The wearing period was set as 7 d for staff at Company A and 8 d for staff at Company B (due to a public holiday during the measurement period) to ensure sufficient workday data were gathered. We used 60-s epoch data [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Non-wear time was defined as \u0026ge;\u0026thinsp;60 consecutive minutes with zero acceleration signal [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Valid data required\u0026thinsp;\u0026ge;\u0026thinsp;10 h of wear time per day, and only participants with \u0026ge;\u0026thinsp;2 valid workdays and \u0026ge;\u0026thinsp;1 valid non-workdays were included in the final analysis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Workday information was classified based on data collected from each company's attendance system. Data from every 60 s were classified into sedentary behavior (\u0026lt;\u0026thinsp;100 steps/min), light-intensity physical activity (100\u0026ndash;2,019 steps/min), and moderate-to-vigorous physical activity (\u0026ge;\u0026thinsp;2,020 steps/min) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These data were aggregated daily and averaged for each participant for workdays and non-workdays [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], with all-day data calculated as weighted averages.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAWT\u003c/h3\u003e\n\u003cp\u003eAWT was assessed using a question adapted from Tsunoda et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] by Kamiya et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], \u0026ldquo;What distance (minutes) would you walk to a destination (convenience store/supermarket, restaurant, bus stop/station, hospital, acquaintance\u0026rsquo;s house, etc.)? (Imagine a pleasant day on flat roads).\u0026rdquo; Six response options were provided: \u0026le;2, \u0026le;\u0026thinsp;5, \u0026le;10, \u0026le;\u0026thinsp;15, \u0026le;20, or \u0026ge;\u0026thinsp;21 min. For analysis, each category was treated as \u0026le;\u0026thinsp;2, 3\u0026ndash;5, 6\u0026ndash;10, 11\u0026ndash;15, 16\u0026ndash;20, and \u0026ge;\u0026thinsp;21 min, respectively.\u003c/p\u003e\u003cp\u003eSociodemographic Factors\u003c/p\u003e\u003cp\u003eWe collected information on sex (male, female), age (continuous), marital status (unmarried, married, widowed/divorced), and educational history (junior high school, high school, vocational school, junior college, university, graduate school) through questionnaires. For analysis, marital status was dichotomized as unmarried or married, and education level was categorized into the following three groups: \u0026le;high school graduate, some college graduate, and \u0026ge;\u0026thinsp;college graduate.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAssociations between AWT, sociodemographic factors, and physical activity were examined using chi-square tests and analysis of variance. The Jonckheere\u0026ndash;Terpstra test was used to confirm linear trends between AWT, sedentary behavior, and each physical activity category.\u003c/p\u003e\u003cp\u003eLinear associations between AWT and MPA (min/d) and step counts (/d) were examined using multivariate linear regression analysis with orthogonal polynomial contrasts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Categories corresponding to six levels of AWT were used as explanatory variables, with linear and quadratic contrasts used to statistically verify linearity. To focus on MPA primarily from walking as a mode of transport, detailed analyses were conducted for all-day averages as well as separately for workdays and non-workdays. Data were analyzed for all-day averages (reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and separately for workdays and non-workdays (detailed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All analyses were adjusted for sex, age, company, and wear time. The significance level was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the study participants (N\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)/mean (SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33 (34.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62 (65.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (y)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.5 (11.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCompany\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62 (65.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33 (34.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27 (28.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68 (71.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;High school graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (13.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31 (32.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;College graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (53.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAll day\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWear time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,025.5 (177.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical activity (minutes/d)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedentary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e759.2 (165.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e245.9 (51.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.2 (13.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVigorous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.17 (1.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep count (/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,831.4 (1,651.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003ea\u003c/sup\u003eData available for all days and non-workdays (n\u0026thinsp;=\u0026thinsp;72)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eSD, standard deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll-day averages were calculated using weighted averages of workdays and non-workdays (workday average \u0026times; 5\u0026thinsp;+\u0026thinsp;non-workday average \u0026times; 2) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The target sample size was 120; however, the final analysis included 95 participants (power, 75%). Sensitivity analysis was conducted using those participants with data available for both workdays and non-workdays [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Statistical analyses were performed using IBM SPSS Statistics for Windows, version 25.0 J (IBM Corp., Armonk, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 134 office workers from 2 companies in Okinawa Prefecture, Japan participated in this study (March and November 2024). After excluding 39 participants because of insufficient accelerometer data and incomplete questionnaires, 95 were included in the final analysis.\u003c/p\u003e\u003cp\u003eParticipant Characteristics\u003c/p\u003e\u003cp\u003eThe participant characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The majority were female (65.3%), with a mean age of 45.5 (standard deviation [SD], 11.1) years, and most were married (71.6%), and university graduates (53.7%). The mean MPA was 20.2 (SD, 13.2) min/d for all days. Detailed physical activity data by time period (workdays and non-workdays) are presented in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAWT, Sociodemographic Factors, Sedentary Behavior, Physical Activity, and Step Counts\u003c/p\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb show the associations between participant characteristics and AWT. Overall, an AWT of 6\u0026ndash;10 min was most common (34.7%), followed by 3\u0026ndash;5 min (26.3%) and 11\u0026ndash;15 min (16.8%). By sex, most commonly, male workers reported an AWT of 6\u0026ndash;10 min (42.4%), whereas female workers reported an AWT of 3\u0026ndash;5 min (30.6%). In both sexes, \u0026gt;\u0026thinsp;30% reported an AWT of \u0026le;\u0026thinsp;3\u0026ndash;5 min (male, 30.3%; female, 32.2%), and \u0026gt;\u0026thinsp;60% reported an AWT of \u0026le;\u0026thinsp;6\u0026ndash;10 min (male, 72.7%; female, 62.8%). By age, participants with an AWT of 11\u0026ndash;15 min showed the highest mean age, at 49.5 (SD, 11.2) years, with the mean ages reducing as AWT became shorter and longer, showing an inverted U-shaped distribution.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ea. Acceptable Walking Time by Socio-demographic Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;2 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u0026ndash;5 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u0026ndash;10 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u0026ndash;15 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16\u0026ndash;20 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;21 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33 (34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14 (42.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (y)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.6 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.8 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.1 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49.5 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.3 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e45.3 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCompany\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22 (35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20 (29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;High school graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11 (35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;College graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25 (49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e The chi-square test was used for categorical variables, and one-way analysis of variance for continuous variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eb. Acceptable Walking Time by Physical Activity Level\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;2 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u0026ndash;5 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u0026ndash;10 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u0026ndash;15 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16\u0026ndash;20 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;21 min\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP for trend\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAll day\u003c/b\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWear time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,051 (325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e974 (194)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,017 (140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,088 (190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,065 (191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e983 (173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical activity (min/d)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedentary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e753 (278)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e731 (181)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e757 (134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e799 (185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e799 (182)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e679 (140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e287 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e227 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e241 (59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e269 (47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e239 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e258 (72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVigorous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep count (/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,437 (937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,174 (1,157)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,444 (1,454)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,101 (1,538)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,559 (1,428)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9,042 (2,189)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWorkday\u003c/b\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWear time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e982 (235)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e951 (195)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e995 (161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,073 (196)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,101 (199)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e971 (175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical activity (min/d)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedentary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e710 (193)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e717 (180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e744 (153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e777 (197)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e820 (188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e708 (158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e261 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e233 (61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e273 (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e247 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e222 (65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33 (21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVigorous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep count (/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,396 (737)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,158 (1,446)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,460 (1,710)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,697 (1,823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7,481 (1,895)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8,175 (2,494)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e The chi-square test was used for categorical variables, and one-way analysis of variance for continuous variables.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e The Jonckheere\u0026ndash;Terpstra test was used for trend analysis.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ec\u003c/sup\u003e Data were available for all days (n\u0026thinsp;=\u0026thinsp;72) and workdays (n\u0026thinsp;=\u0026thinsp;95).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding associations of AWT with physical activity and sedentary behavior (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), significant linear trends were observed with MPA and step counts in all-day averages (MPA: P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and step counts: P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.01). No significant linear associations of AWT with sedentary behavior, or light intensity or vigorous physical activity were observed (all P for trend\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We found significant positive trends for the associations of AWT with MPA and step counts on workdays (all P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.01). On non-workdays, no significant linear trends for the associations of AWT with MPA or step counts were observed (all P for trend\u0026thinsp;\u0026gt;\u0026thinsp;0.05; detailed in Supplementary Table S2).\u003c/p\u003e\u003cp\u003eAWT, MPA, and Step Counts\u003c/p\u003e\u003cp\u003eAfter adjusting for sex, age, company, and wear time, estimated MPA (min/d) and AWT showed significant linear associations in the all-day averages (linear contrast: β\u0026thinsp;=\u0026thinsp;18.6; 95% confidence interval [CI], 5.3 to 31.9; P\u0026thinsp;=\u0026thinsp;0.007). Further detailed analysis showed this relationship was particularly clear for workdays, with significant linear trends noted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Linear trend testing using polynomial contrasts confirmed that MPA increased by 16.2 min for each increase in the AWT category (linear contrast: β\u0026thinsp;=\u0026thinsp;16.2; 95% CI, 6.0 to 26.3; P\u0026thinsp;=\u0026thinsp;0.002). For non-workdays, linear trends were not significant (β\u0026thinsp;=\u0026thinsp;15.7; 95% CI, \u0026minus;\u0026thinsp;0.4 to 31.8; P\u0026thinsp;=\u0026thinsp;0.055). Because the CI included 0, no significant linear trend was observed for non-workdays, unlike the significant trend observed for workdays.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the association between AWT and step counts, significant linear trends were observed in the all-day averages (linear contrast: β\u0026thinsp;=\u0026thinsp;2,207; 95% CI, 532 to 3,882; P\u0026thinsp;=\u0026thinsp;0.011), with particularly clear linear trends observed for workdays (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Linear trend testing using polynomial contrasts confirmed that step counts increased by 1,748 steps for each increase in AWT category (linear contrast: β\u0026thinsp;=\u0026thinsp;1,748; 95% CI, 395 to 3,100; P\u0026thinsp;=\u0026thinsp;0.012). Linear trends were not significant for non-workdays (β\u0026thinsp;=\u0026thinsp;944; 95% CI, \u0026minus;\u0026thinsp;1,741 to 3,629; P\u0026thinsp;=\u0026thinsp;0.484).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSensitivity analysis was limited to participants with data available for both workdays and non-workdays (n\u0026thinsp;=\u0026thinsp;72); re-analyzing workday AWT\u0026ndash;physical activity relationships confirmed the main findings. The linear trends for MPA remained significant (linear contrast: β\u0026thinsp;=\u0026thinsp;19.8; 95% CI, 4.3 to 35.3; P\u0026thinsp;=\u0026thinsp;0.013), and similar results were confirmed for step counts (linear contrast: β\u0026thinsp;=\u0026thinsp;2,613; 95% CI, 655 to 4,571; P\u0026thinsp;=\u0026thinsp;0.010). The sensitivity analysis showed a tendency toward larger effect sizes, indicating that the associations observed in participants with data for both workdays and non-workdays were more consistent. This confirmed the association between AWT and physical activity under more stringent conditions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis validation study found that AWT showed criterion-related validity with accelerometer-measured MPA. Particularly for workdays, clear linear trends showed that MPA increased by 16.2 min and step counts increased by 1,748 steps for an increase of one level in AWT, for example, from \u0026le;\u0026thinsp;5 to 6\u0026ndash;10 min. The substantial difference in MPA (21.2 min/d) between the group with the lowest AWT (\u0026le;\u0026thinsp;2 min category; MPA: 13.5 min/d) and the group with the highest AWT (\u0026ge;\u0026thinsp;21 min category; MPA: 34.8 min/d) demonstrated that AWT is a useful indicator of individual differences in physical activity levels. Previous research on AWT relied on self-reported measurements, rendering its true predictive power uncertain due to problems related to overestimations of 150\u0026ndash;400% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The present study is the first to objectively verify the criterion-related validity of AWT using accelerometry. This study represents an important step toward organizing, in a uniform manner, AWT-related concepts that previously differed in terminology and measurement methods that differed between studies. By being able to validate objective measurements, the potential exists for AWT to function as a standard indicator in research involving walking as a mode of transport.\u003c/p\u003e\u003cp\u003ePrevious studies have also reported associations between AWT and walking behavior. Hsia et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] reported positive associations between these variables in Taiwanese adults, with individuals who tolerated longer acceptable walking distance showing longer actual walking times. Watson et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] conducted a national survey of 3,653 adults in the United States and noted that, although \u0026gt;\u0026thinsp;90% considered walking for transportation reasonable, \u0026lt;\u0026thinsp;43% considered walking\u0026thinsp;\u0026ge;\u0026thinsp;1 mile (approximately 20 min) reasonable; the researchers reported that individuals who considered longer distances and or times reasonable had a higher frequency of actually walking for transportation. Furthermore, a recent survey of 881 Japanese adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] found that an AWT of 6\u0026ndash;10 min was most common (male, 29.0%; female, 26.3%). In that study, clear linear trends (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between AWT categories and weekly walking time were consistently confirmed across different types of transportation environments: public transportation-oriented metropolitan areas (Greater Tokyo) and car-dependent rural areas (Okinawa). The linear trends between AWT and MPA observed in the present study are consistent with previous findings and objectively demonstrate that AWT functions as a psychological factor that predicts the use of walking as a mode of transport [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Importantly, AWT was associated only with MPA and showed no associations with physical activity of other intensities or with sedentary behavior. This specificity indicates that AWT is a predictor specific to walking-equivalent physical activity.\u003c/p\u003e\u003cp\u003eThis specific predictive power for walking for transportation has important implications for practical health assessment. Individuals with an AWT\u0026thinsp;\u0026ge;\u0026thinsp;15 min exceeded an average of 7,000 steps on workdays, a value approaching 8,000 steps/d, which is recommended for maintaining health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Notably, when the weekly workday MPA was calculated, the group with an AWT\u0026thinsp;\u0026ge;\u0026thinsp;15 min reached an MPA of approximately 128 min (average, 25.6 min \u0026times; 5 d), achieving 85% of the 150 min/wk target recommended by the World Health Organization [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Reaching this level on weekdays alone suggests the possibility of achieving the recommended levels by engaging in additional physical activity on their days off. These findings suggest that AWT, despite being a simple single-question assessment, could serve as a practical tool for effectively screening individual physical activity levels. AWT may function as a tool for easily estimating daily levels of physical activity engaged in walking for transportation, similar to that way in which the Rating of Perceived Exertion predicts exercise intensity [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA notable finding of the current study is that, despite participants working in areas that were walkable, 66.3% reported relatively short AWT\u0026thinsp;\u0026le;\u0026thinsp;10 min. In Okinawa Prefecture, a region in which people are highly dependent on cars\u0026mdash;the modal share for cars is 71.3% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u0026mdash;few residents accepted walking for \u0026ge;\u0026thinsp;11 min. This may reflect a phenomenon whereby dependence on cars makes people \u0026ldquo;too lazy to walk\u0026rdquo; [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and raises psychological barriers to walking long distances. However, because the participants worked in walkable areas, appropriate health education approaches could potentially promote behavioral change toward walking for transportation. In the context of walkable community policies, such as that of 15-min cities [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], AWT may serve as a useful indicator for evaluating the potential that residents show for walking for transportation.\u003c/p\u003e\u003cp\u003eFrom an implementation perspective, the simplicity of AWT is a major advantage. Adding a single AWT question to large-scale travel surveys, such as person\u0026ndash;trip surveys [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], could enable the walking for transportation MPA levels of residents to be estimated. This approach could provide cost-effective and practical information for decision-making for urban planning and public health policy. It is also a much more feasible approach, compared with conventional detailed surveys of physical activity.\u003c/p\u003e\u003cp\u003eThis study has some limitations. First, due to constraints in wearing the accelerometer, swimming and high-intensity sports may not have been evaluated. However, because the present study targeted walking-equivalent MPA as the outcome, with exercise or sports activities of \u0026ge;\u0026thinsp;6 METs being outside the scope of the analysis, this impact was limited. Second, seasonal differences in physical activity levels may have influenced the results. Although this impact was minimized by statistically adjusting for company variables, residual seasonal effects cannot be completely ruled out. Third, participants were workers in Naha City, Okinawa Prefecture, a region with high car dependency (private vehicle modal share: 71.3%). Caution is required in generalizing findings to different transportation environments. Fourth, the extreme categories of AWT (\u0026le;\u0026thinsp;2 and \u0026ge;\u0026thinsp;21 min) included small numbers of participants (7 and 8, respectively), resulting in wide CIs for estimates in these groups. Despite the relatively small sample, the detection of statistically significant and consistent linear associations between AWT and physical activity suggests that the association is strong, and supports the criterion-related validity of AWT. Larger studies that more precisely examine the relationships in the extreme categories of AWT could further clarify its application range. Finally, the detailed psychological mechanisms through which AWT predicts walking as a mode of transport remain insufficiently elucidated. Future research should construct comprehensive models of behavioral science [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] clarifying how AWT, as an individual psychological factor, interacts with environmental factors and individual capabilities. Elucidating such mechanisms could enable tailored interventions to be developed according to individual levels of AWT [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAWT showed clear associations with MPA, which was measured using accelerometers. Thus, AWT was confirmed as a useful tool for evaluating physical activity in terms of walking for transportation, particularly on workdays. This simple assessment method shows potential as an effective marker for evaluating individual physical activity levels and may find applications in promoting policies for walking for transportation among working populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participants for their cooperation in the study. We would like to thank Editage (www.editage.com) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.K., A.K. and N.K. conceived the idea and designed the study. Y.K. analyzed the data and drafted the paper. Y.K., A.K., K.I., and M.T. contributed to the writing and assisted with the interpretation. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.K. is supported by a grant from the President\u0026apos;s Discretionary Fund of Meio University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnonymized data are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. \u003cem\u003eGlobal Action Plan on Physical Activity 2018\u0026ndash;2030: More Active People for a Healthier World\u003c/em\u003e https://iris.who.int/bitstream/handle/10665/272722/9789241514187-eng.pdf (2018).\u003c/li\u003e\n\u003cli\u003eGuthold, R., Stevens, G. 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The behaviour change wheel: a new method for characterising and designing behaviour change interventions. \u003cem\u003eImplement. Sci.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 42 (2011).\u003c/li\u003e\n\u003cli\u003eGhanvatkar, S., Kankanhalli, A. \u0026amp; Rajan, V. User models for personalized physical activity interventions: scoping review. \u003cem\u003eJMIR Mhealth Uhealth\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, e11098 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Accelerometer, ActiGraph, Criterion Validity, Walking for Transportation, Workday Activity","lastPublishedDoi":"10.21203/rs.3.rs-7737707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7737707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhysical inactivity is a global challenge and can be combated by integrating walking for transportation into the lifestyle of working-age populations. Acceptable walking time (AWT) is a psychological predictor of walking for transportation but requires objective validation. We used accelerometry to compare the criterion-related validity of AWT against objectively measured moderate-intensity physical activity (MPA) and step counts in Japanese office workers (n\u0026thinsp;=\u0026thinsp;95; 65.3% female; mean age, 45.5 years) in Okinawa Prefecture. AWT was assessed by asking \u0026ldquo;What distance (minutes) would you walk to a destination?\u0026rdquo; Physical activity was objectively measured using ActiGraph GT3X accelerometers for 7\u0026ndash;8 d and analyzed for all-day averages and for workdays and non-workdays. Linear regression was used, adjusting for covariates. Significant linear trends between AWT and physical activity were observed for all-day averages, with MPA increasing by 18.6 min and step counts by 2,207 steps per AWT category. On workdays, MPA increased by 16.2 min and step counts by 1,748 steps per category. No consistent trends were observed on non-workdays. The criterion-related validity of AWT was confirmed, showing linear trends with objectively measured MPA and step counts on workdays. AWT is useful for evaluating physical activity levels through walking for transportation among working-age populations.\u003c/p\u003e","manuscriptTitle":"Association Between Acceptable Walking Time and Moderate-intensity Physical Activity Among Japanese Office Workers: A Cross-sectional Validation Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 15:27:17","doi":"10.21203/rs.3.rs-7737707/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-27T03:14:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294030545041404879181759759779227149242","date":"2025-11-12T05:53:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T03:59:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-08T06:11:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T05:31:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-04T03:05:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-29T04:06:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d3e9608-e3a2-4665-b7df-769b0106795f","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58089090,"name":"Health sciences/Health care"},{"id":58089091,"name":"Health sciences/Health occupations"},{"id":58089092,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-21T15:27:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 15:27:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7737707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7737707","identity":"rs-7737707","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}