Using Daily Steps to Identify Older Adults with (Un)healthy Joint Profiles of Sedentary Time and Physical Activity: A Starting Point

Using Daily Steps to Identify Older Adults with (Un)healthy Joint Profiles of Sedentary Time and Physical Activity: A Starting Point | 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 Short Report Using Daily Steps to Identify Older Adults with (Un)healthy Joint Profiles of Sedentary Time and Physical Activity: A Starting Point Eduardo C. Costa, Yuri A. Freire, Charles P. de Lucena Alves, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3041511/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Herein, we investigated whether daily steps can discriminate between older adults with unhealthy and healthy joint profiles of sedentary time (ST) and moderate-to-vigorous physical activity (MVPA). Apparently healthy community-dwelling older adults aged 60–80 years were included in this cross-sectional analysis (n = 258). Daily steps, ST, and MVPA were assessed by accelerometry. Receiver Operating Characteristic (ROC) analysis was used to test the performance of daily steps in identifying older adults with unhealthy (high ST/low MVPA) and healthy (low ST/high MVPA) joint profiles of ST/MVPA. The cardiovascular disease risk of unhealthy/healthy profiles was compared using a continuous metabolic syndrome score (cMetS). Daily steps discriminated older adults with unhealthy (AUC 0.892, 0.850–0.934; cut-off: ≤5,263 steps/day; sensitivity/specificity: 82.5%/81%) and healthy (AUC 0.803, 0.738–0.868; cut-off: ≥7,134 steps/day; sensitivity/specificity: 79.5%/66.2%) joint profiles of ST/MVPA. The unhealthy profile showed a higher cMetS (β = 0.46; p = 0.008). Likewise, older adults who fell below the daily steps cut-off point to identify the unhealthy profile of ST/MVPA exhibited a higher cMetS (β = 0.34; p = 0.004). In summary, our results provide a starting point for considering daily steps as a single heuristic metric for identifying older adults with a joint profile of high ST/low MVPA, which makes them more susceptible to CVD. Aging Sedentary Behavior Exercise Cardiovascular Diseases Metabolic Syndrome Figures Figure 1 Introduction Cardiovascular diseases (CVDs) are the leading cause of death in older adults ( 1 ). The 2020 World Health Organization guidelines on physical activity and sedentary behavior state that individuals with joint high sedentary time (ST) and low moderate-to-vigorous physical activity (MVPA) are more vulnerable to CVD and all-cause mortality ( 2 ). Thus, it is crucial to identify older adults with this unhealthy joint movement behavior profile to provide appropriate care. Accelerometry enables the integrated assessment of ST/MVPA. However, the use of this technology is complex, high-cost, time-consuming, and currently limited to research purposes. Therefore, there is a clear need to identify simple, low-cost, easy-to-use, understandable, and less time-consuming tools and metrics for identifying older adults with unhealthy joint profiles of ST/MVPA. Sitting is the most common sedentary behavior ( 3 ), while walking is the most common physical activity ( 4 ). Therefore, it is reasonable to assume that when a human being is not engaging in sedentary behaviors, which refer to activities with low energy expenditure (< 1.5 metabolic equilalents; METs) while sitting, reclining, or lying down during waking hours ( 5 ), they are mostly walking, regardless of its intensity. Based on this premise, the metric, daily steps, may be helpful in identifying joint profiles of ST/physical activity. Herein, we investigated whether daily steps could discriminate between older adults with unhealthy and healthy joint profiles of ST/MVPA. Additionally, we analyzed the CVD risk of these groups. Methods Study design and participants This cross-sectional study was conducted at the Federal University of Rio Grande do Norte, Natal, Brazil. All procedures were approved by the Ethics Committee (CAAE 82609318.0.0000.5292) and were carried out according to the Declaration of Helsinki. Apparently healthy community-dwelling older adults aged 60–80 years were recruited through radio advertisements, healthcare units, senior centers, and e-flyers on social media. Inclusion criteria were: i) no major CVDs or events (i.e., coronary artery disease, peripheral vascular disease, arrhythmias, stroke, and acute myocardial infarction); ii) no uncontrolled hypertension (> 160/105 mmHg); iii) no decompensated diabetes (fasting glucose > 250 mg/dL); and iv) no mobility impairment. All participants provided written informed consent. More details can be found elsewhere ( 6 ). Daily steps, sedentary time, and moderate-to-vigorous physical activity Daily steps, ST, and MVPA were measured using the ActiGraph GT3X + accelerometer (Actigraph LLC, Pensacola, FL, USA), and ActiLife version 6.13.3.2 software. Participants wore the device on their hip for seven days, removing it only during water-based activities. Sleep and non-wear periods were excluded from the analysis, with non-wear time defined as ≥ 90 minutes of consecutive zeros counts and a tolerance of up to 2 minutes of ≥ 100 counts/min ( 7 ). Participants needed at least three weekdays and one weekend day of accelerometer wear time, with each day requiring ≥ 10 hours of wear time to be valid ( 8 ). Data were collected at 60 Hz and integrated into 60-sec epochs. The accelerometer was programmed with a standard filter to avoid exaggerated estimates of steps ( 9 ). ST and MVPA were defined by cut-offs of 0–99 and ≥ 1952 cpm, respectively ( 10 , 11 ). Daily steps, ST, and MVPA were analyzed as a weighted average for weekdays and weekend days. Joint profiles of sedentary time and moderate-to-vigorous physical activity The unhealthy and healthy joint profiles of ST/MVPA were defined based on tertiles using the values from our sample, following the procedures of Ekelund et al. ( 12 ): i) unhealthy: highest tertile of ST and lowest tertile of MVPA; ii) healthy: lowest tertile of ST and highest tertile of MVPA. Cardiovascular disease risk The CVD risk was determined using a continuous metabolic syndrome score (cMetS). Fasting glucose, triglycerides, HDL-cholesterol, waist circumference, and blood pressure were considered to calculate cMetS following the procedures of Wijndaele et al. ( 13 ). The detailed procedures to assess the MetS components and the step-by-step approach to calculate the cMetS can be found elsewhere ( 6 ). Other variables Age, sex, post-secondary education, smoking history, and medication use for diabetes, hypertension, and dyslipidemia were collected from the participants through a face-to-face interview. Body mass index (BMI) was calculated by dividing body weight (kg) by the square of height (kg/m 2 ). Statistical analysis A generalized linear model and Chi-square test were used to compare the characteristics of older adults with unhealthy and healthy joint profiles of ST/MVPA. Receiver operating characteristic (ROC) curve analysis was utilized to assess the ability of daily steps to identify older adults with unhealthy and healthy joint profiles of ST/MVPA. The cut-off for daily steps to identify each joint profile of ST/MVPA was determined using the Union Index ( 14 ). A generalized linear model was used to compare the cMetS between older adults with unhealthy and healthy joint profiles of ST/MVPA, adjusting for age, sex, BMI, education, smoking, medication use for hypertension, diabetes, and dyslipidemia, and accelerometer wear time. The significance level was set at p < 0.05, and all statistical analyses were conducted using the IBM SPSS Statistics for Win/v.28.0 (IBM Corp., Armonk, NY). Results From 258 participants (66 ± 5 years, 28.8 ± 4.8 kg/m 2 ), 15.5% (n = 40) were identified with unhealthy (ST ≥ 11.4 h/day and MVPA ≤ 10 min/day) and 15.1% (n = 39) with healthy (ST ≤ 9.8 h/day and MVPA ≥ 24 min/day) joint profiles of ST/MVPA. Older adults with healthy profiles were younger (64 ± 3 vs. 68 ± 6 years) and had higher medication use for diabetes (p < 0.05; Table 1 ). On average, older adults with unhealthy profile performed 4,202 ± 1,227 steps/day, while those with healthy profile performed 9,415 ± 2,749 steps/day (p < 0.001). Table 1 Characteristics of older adults with unhealthy and healthy joint profiles of sedentary time (ST) and moderate-to-vigorous physical activity (MVPA) Joint profiles of ST/MVPA Unhealthy (n = 40) Healthy (n = 39) p Age, years 68 ± 6 64 ± 3 < 0.001 Females, n (%) 31 (77.5) 28 (71.8) 0.373 Post-secondary education, n (%) 11 (27.5) 3 (7.7) 0.021 Smokers/ex-smokers, n (%) 20 (50) 15 (38.5) 0.367 Body mass index, kg/m 2 28.7 ± 5.6 28.3 ± 5.1 0.717 Hypertension medication, n (%) 25 (62.5) 24 (61.5) 1.000 Diabetes medication, n (%) 4 (25) 12 (75) 0.021 Dyslipidemia medication, n (%) 13 (32.5) 11 (28.2) 0.808 Accelerometer wear time, hours 16.8 ± 1.2 16.0 ± 1.3 0.009 Waking hours in sedentary time, % 73.3 ± 5.4 53.7 ± 5.2 < 0.001 Waking hours in light activities, % 26.3 ± 5.3 41.9 ± 4.8 < 0.001 Waking hours in MVPA, % 0.4 ± 0.2 4.4 ± 2.5 < 0.001 Data are shown as mean ± standard deviation or absolute (n) and relative (%) frequencies. Unhealthy: ST ≥ 11.4 h/day and MVPA ≤ 10 min/day; Healthy: ST ≤ 9.8 h/day and MVPA ≥ 24 min/day. Bold values indicate statistical significance (p < 0.05). Insert Table 1 ROC analysis showed that daily steps discriminated older adults with unhealthy (AUC 0.892; cut-off: ≤5,263 steps/day; sensitivity/specificity: 83%/81%; Fig. 1 , Panel A) and healthy (AUC 0.803; cut-off: ≥7,134 steps/day; sensitivity/specificity: 80%/66%; Fig. 1 , Panel B) joint profiles of ST/MVPA. The unhealthy profile exhibited a higher cMetS (β = 0.46; p = 0.008; Fig. 1 , Panel C). Likewise, older adults who fell below the daily steps cut-off point to identify the unhealthy profile of ST/MVPA exhibited a higher cMetS (β = 0.34; p = 0.004; Fig. 1 , Panel D). Insert Fig. 1 Discussion Our main finding was that daily steps discriminated between older adults with unhealthy (≤ 5,263 steps/day) and healthy (≥ 7,134 steps/day) joint profiles of ST/MVPA. Furthermore, older adults with an unhealthy profile exhibited a higher CVD risk, as indicated by the cMetS. Our data support the idea that ~ 5,000 or fewer daily steps are helpful in identifying older adults with high ST/low MVPA, while ~ 7,000 or more can identify those with low ST/high MVPA. It is common for older adults to spend ~ 60–65% of their waking hours in ST, 30–35% in light activities, and < 3% in MVPA ( 15 , 16 ). In our study, the unhealthy group spent 73.3% of their time in ST, 26.3% in light activities, and 0.4% in MVPA, while the healthy group spent 53.7% of their time in ST, 41.9% in light activities, and 4.4% in MVPA (p < 0.001; Table 1 ). Additionally, there was an almost perfect negative correlation between ST and time spent in light activities (r = -0.974). This suggests that when older adults were not engaged in sedentary behaviors, they mostly engaged in light activities, such as ligh-intensity walking. Moreover, our results showed that daily steps could discriminate between older adults with unhealthy and healthy joint profiles of ST/MVPA, even though the time spent in moderate-to-vigorous activities is a very small portion of their waking hours. In addition, we demonstrated that those with both an unhealthy joint profile of ST/MVPA and daily steps had a higher CVD risk. The cMetS is positively associated with adverse CVD events in adults and older adults ( 13 ). We previously demonstrated that older adults with ≥ 7,500 daily steps had lower pulse wave velocity ( 17 ) compared to their inactive peers (≤ 5,000 daily steps). Based on the cut-offs found in this study, it is probable that older adults with ≥ 7,500 daily steps had both lower ST and higher MVPA compared to those with ≤ 5,000 daily steps, which could partially explain the lower CVD risk of those who are more active. Two previous harmonized meta-analyses, including self-reported ( 18 ) and accelerometer-based ( 12 ) measures of ST and MVPA, demonstrated that the unhealthy and healthy joint profile of ST/MVPA is associated with the highest and lowest rates of CVD and all-cause mortality, respectively. Based on the tertiles of accelerometer-measured ST/MVPA, the unhealthy group had a median time of 10.7 h/day and 2.3 min/day of ST and MVPA, respectively, while the healthy group had 8.5 h/day of ST and 34 min/day of MVPA ( 12 ). Of note, this study included individuals aged ≥ 40 years from Sweden, Norway, the USA, and the UK, which could partially explain the differences in the tertiles of ST and MVPA. From a clinical perspective, our results provide a starting point for considering daily steps as a single metric for identifying older adults with a joint profile of high ST/low MVPA, which makes them more susceptible to CVD. It is noteworthy that daily steps can easily be obtained from common wearable technologies and smartphones ( 19 ), providing a user-friendly method to identify an unhealthy joint profile of ST/MVPA. Therefore, the daily steps metric could be valuable for clinicians and healthcare providers in identifying older adults with this unhealthy movement behavior profile, enabling them to deliver appropriate care. Studies with larger sample sizes and different age groups are welcome to confirm (or not) our initial perspective on using daily steps to identify an unhealthy joint profile of ST/MVPA. Conclusions Daily steps may serve as a useful single heuristic metric for identifying older adults with joint profiles of ST/MVPA that are more or less susceptible to CVD. Declarations Funding This work was carried out with the support of the Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES) – Financing Code 001. ECC is supported by the The National Council for Scientific and Technological Development (CNPq; 306537/2022-2) and the Coordination for the Improvement of Higher Education Personnel (CAPES/PRINT; 88887.717099/2022-00), Brazil. References Roth GA, Mensah GA, Fuster V (2020) The global burden of cardiovascular diseases and risks: A compass for global action. J Am Coll Cardiol 76(25):2980–2981. 10.1016/J.JACC.2020.11.021 Bull FC, Al-Ansari SS, Biddle S et al (2020) World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med 54(24):1451–1462. 10.1136/bjsports-2020-102955 O’Brien MW, Daley WS, Schwartz BD et al (2023) Characterization of detailed sedentary postures using a tri-monitor ActivPAL configuration in free-living conditions. Sensors 23(2):587. 10.3390/s23020587 Sartini C, Wannamethee SG, Iliffe S et al (2015) Diurnal patterns of objectively measured physical activity and sedentary behaviour in older men. BMC Public Health 15(1). 10.1186/S12889-015-1976-Y Tremblay MS, Aubert S, Barnes JD et al (2017) Sedentary Behavior Research Network (SBRN) – Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act 14(1):1–17. 10.1186/s12966-017-0525-8 Freire YA, Cabral LLP, Browne RAV et al (2022) Daily step volume and intensity moderate the association of sedentary time and cardiometabolic disease risk in community-dwelling older adults: A cross-sectional study. Exp Gerontol 170:111989. 10.1016/J.EXGER.2022.111989 Choi L, Liu Z, Matthews CE, Buchowski MS (2011) Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sport Exerc 43(2):357–364. 10.1249/MSS.0b013e3181ed61a3 Trost SG, Mciver KL, Pate RR (2005) Conducting accelerometer-based activity assessments in field-based research. Med Sci Sport Exerc 37(11):S531–S543. 10.1249/01.mss.0000185657.86065.98 Wanner M, Martin BW, Meier F, Probst-Hensch N, Kriemler S (2013) Effects of filter choice in GT3X accelerometer assessments of free-living activity. Med Sci Sport Exerc 45(1):170–177. 10.1249/MSS.0b013e31826c2cf1 Matthews CE, Chen KY, Freedson PS et al (2008) Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol 167(7):875–881. 10.1093/aje/kwm390 Freedson PS, Melanson E, Sirard J (1998) Calibration of the computer science and applications, Inc. accelerometer. Med Sci Sports Exerc 30(5):777–781. 10.1097/00005768-199805000-00021 Ekelund U, Tarp J, Fagerland MW et al (2020) Joint associations of accelero-meter measured physical activity and sedentary time with all-cause mortality: A harmonised meta-analysis in more than 44 000 middle-aged and older individuals. Br J Sports Med 54(24):1499–1506. 10.1136/bjsports-2020-103270 Wijndaele K, Beunen G, Duvigneaud N et al (2006) A continuous metabolic syndrome risk score: Utility for epidemiological analyses. Diabetes Care 29(10):2329. 10.2337/dc06-1341 Unal I (2017) Defining an optimal cut-point value in ROC analysis: An alternative approach. Comput Math Methods Med 2017. 10.1155/2017/3762651 Manns P, Ezeugwu V, Armijo-Olivo S, Vallance J, Healy GN (2015) Accelerometer-derived pattern of sedentary and physical activity time in persons with mobility disability: National Health and Nutrition Examination Survey 2003 to 2006. J Am Geriatr Soc 63(7):1314–1323. 10.1111/jgs.13490 Dohrn IM, Dohrn IM, Gardiner PA et al (2020) Device-measured sedentary behavior and physical activity in older adults differ by demographic and health-related factors. Eur Rev Aging Phys Act 17(1). 10.1186/S11556-020-00241-X Cabral LLP, Freire YA, Browne RAV et al (2022) Associations of steps per day and peak cadence with arterial stiffness in older adults. Exp Gerontol 157:111628. 10.1016/J.EXGER.2021.111628 Ekelund U, Steene-Johannessen J, Brown WJ et al (2016) Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet 388(10051):1302–1310. 10.1016/S0140-6736(16)30370-1 Laranjo L, Ding D, Heleno B et al (2021) Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression. Br J Sports Med 55(8):422–432. 10.1136/bjsports-2020-102892 Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3041511","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":208536111,"identity":"655866b6-effc-427c-88e0-81b2a5054c79","order_by":0,"name":"Eduardo C. Costa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYNACGwYGfhCdUEC0ljQGBskGkBYDUrQYHAAxiNHCz374ATNPwr084/OrEz88MGCQ5xc7gF+LZE+aAVBLcbHZjbebJYAOM5w5OwG/FoMbPAzMvD8SErfdOLsBpCXB4DYBLfYgLTwJCYmbZ5zd/IMoLQYSUC0b+Hu3EWeLxJk0g4NzgFpm3ODdZpFgIEHYL/zthx8+eAPU0t9/dvPNHxU28vzSBLSAwAGIfWCVEoSVI9l3gBTVo2AUjIJRMJIAAHQaQQn8F5e2AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2807-7109","institution":"Federal University of Rio Grande do Norte: Universidade Federal do Rio Grande do Norte","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"C.","lastName":"Costa","suffix":""},{"id":208536112,"identity":"28c8d1b7-ded5-4626-bc04-7e97e9ac3811","order_by":1,"name":"Yuri A. Freire","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte: Universidade Federal do Rio Grande do Norte","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuri","middleName":"A.","lastName":"Freire","suffix":""},{"id":208536113,"identity":"4afc5831-cb71-4312-bbf8-0c958f94c850","order_by":2,"name":"Charles P. de Lucena Alves","email":"","orcid":"","institution":"Federal University of Pelotas: Universidade Federal de Pelotas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Charles","middleName":"P. de Lucena","lastName":"Alves","suffix":""},{"id":208536114,"identity":"22bbd169-458f-45cf-b65c-aa3c62cc68d6","order_by":3,"name":"Ludmila L.P. Cabral","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte: Universidade Federal do Rio Grande do Norte","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ludmila","middleName":"L.P.","lastName":"Cabral","suffix":""},{"id":208536115,"identity":"aa3c6eb2-0557-4b4b-ab4a-a520598dcb14","order_by":4,"name":"Tiago V. Barreira","email":"","orcid":"","institution":"Syracuse University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tiago","middleName":"V.","lastName":"Barreira","suffix":""},{"id":208536116,"identity":"37e6eb52-efe4-423e-9c71-3159de11c9b6","order_by":5,"name":"Debra L. Waters","email":"","orcid":"","institution":"University of Otago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Debra","middleName":"L.","lastName":"Waters","suffix":""}],"badges":[],"createdAt":"2023-06-09 05:45:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3041511/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3041511/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38492470,"identity":"666aa38c-ef94-4635-9975-bb8160e6ae24","added_by":"auto","created_at":"2023-06-13 19:29:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1211315,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis using daily steps for discriminating older adults with unhealthy (Panel A) and healthy (Panel B) joint profiles of sedentary time and moderate-to-vigorous physical activity (ST/MVPA). Comparison of estimated marginal means (EMM) of continuous metabolic syndrome score (cMetS) between older adults with unhealthy and healthy joint profile of ST/MVPA (Panel C). Comparison of EMM of cMetS between older adults with unhealthy and healthy daily steps (Panel D).\u003c/p\u003e\n\u003cp\u003eUnhealthy joint profile of ST/MVPA: ST ≥11.4 h/day and MVPA ≤10 min/day; Healthy joint profile of ST/MVPA: ST ≤9.8 h/day and MVPA ≥24 min/day.\u003c/p\u003e\n\u003cp\u003eUnhealthy daily steps: ≤5,263 steps/day; Healthy daily steps: ≥7,134 steps/day.\u003c/p\u003e\n\u003cp\u003eAnalyses adjusted for age, sex, BMI, education, smoking, medication use for hypertension, diabetes, and dyslipidemia, as well as accelerometer wear time.\u003c/p\u003e","description":"","filename":"Figure.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3041511/v1/c64017fd929fe14a5ec95421.jpg"},{"id":39890196,"identity":"4502c150-d54e-4dc2-a312-9c0c1e3a04b4","added_by":"auto","created_at":"2023-07-12 04:48:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":340068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3041511/v1/7b67120b-08bd-401e-8a9b-8359afbd10c9.pdf"}],"financialInterests":"","formattedTitle":"Using Daily Steps to Identify Older Adults with (Un)healthy Joint Profiles of Sedentary Time and Physical Activity: A Starting Point","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) are the leading cause of death in older adults (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The 2020 World Health Organization guidelines on physical activity and sedentary behavior state that individuals with joint high sedentary time (ST) and low moderate-to-vigorous physical activity (MVPA) are more vulnerable to CVD and all-cause mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Thus, it is crucial to identify older adults with this unhealthy joint movement behavior profile to provide appropriate care. Accelerometry enables the integrated assessment of ST/MVPA. However, the use of this technology is complex, high-cost, time-consuming, and currently limited to research purposes. Therefore, there is a clear need to identify simple, low-cost, easy-to-use, understandable, and less time-consuming tools and metrics for identifying older adults with unhealthy joint profiles of ST/MVPA.\u003c/p\u003e \u003cp\u003eSitting is the most common sedentary behavior (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), while walking is the most common physical activity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, it is reasonable to assume that when a human being is not engaging in sedentary behaviors, which refer to activities with low energy expenditure (\u0026lt;\u0026thinsp;1.5 metabolic equilalents; METs) while sitting, reclining, or lying down during waking hours (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), they are mostly walking, regardless of its intensity. Based on this premise, the metric, daily steps, may be helpful in identifying joint profiles of ST/physical activity. Herein, we investigated whether daily steps could discriminate between older adults with unhealthy and healthy joint profiles of ST/MVPA. Additionally, we analyzed the CVD risk of these groups.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted at the Federal University of Rio Grande do Norte, Natal, Brazil. All procedures were approved by the Ethics Committee (CAAE 82609318.0.0000.5292) and were carried out according to the Declaration of Helsinki. Apparently healthy community-dwelling older adults aged 60\u0026ndash;80 years were recruited through radio advertisements, healthcare units, senior centers, and e-flyers on social media. Inclusion criteria were: i) no major CVDs or events (i.e., coronary artery disease, peripheral vascular disease, arrhythmias, stroke, and acute myocardial infarction); ii) no uncontrolled hypertension (\u0026gt;\u0026thinsp;160/105 mmHg); iii) no decompensated diabetes (fasting glucose\u0026thinsp;\u0026gt;\u0026thinsp;250 mg/dL); and iv) no mobility impairment. All participants provided written informed consent. More details can be found elsewhere (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eDaily steps, sedentary time, and moderate-to-vigorous physical activity\u003c/h2\u003e \u003cp\u003eDaily steps, ST, and MVPA were measured using the ActiGraph GT3X\u0026thinsp;+\u0026thinsp;accelerometer (Actigraph LLC, Pensacola, FL, USA), and ActiLife version 6.13.3.2 software. Participants wore the device on their hip for seven days, removing it only during water-based activities. Sleep and non-wear periods were excluded from the analysis, with non-wear time defined as \u0026ge;\u0026thinsp;90 minutes of consecutive zeros counts and a tolerance of up to 2 minutes of \u0026ge;\u0026thinsp;100 counts/min (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Participants needed at least three weekdays and one weekend day of accelerometer wear time, with each day requiring\u0026thinsp;\u0026ge;\u0026thinsp;10 hours of wear time to be valid (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Data were collected at 60 Hz and integrated into 60-sec epochs. The accelerometer was programmed with a standard filter to avoid exaggerated estimates of steps (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). ST and MVPA were defined by cut-offs of 0\u0026ndash;99 and \u0026ge;\u0026thinsp;1952 cpm, respectively (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Daily steps, ST, and MVPA were analyzed as a weighted average for weekdays and weekend days.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eJoint profiles of sedentary time and moderate-to-vigorous physical activity\u003c/h2\u003e \u003cp\u003eThe unhealthy and healthy joint profiles of ST/MVPA were defined based on tertiles using the values from our sample, following the procedures of Ekelund et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e): i) unhealthy: highest tertile of ST and lowest tertile of MVPA; ii) healthy: lowest tertile of ST and highest tertile of MVPA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCardiovascular disease risk\u003c/h2\u003e \u003cp\u003eThe CVD risk was determined using a continuous metabolic syndrome score (cMetS). Fasting glucose, triglycerides, HDL-cholesterol, waist circumference, and blood pressure were considered to calculate cMetS following the procedures of Wijndaele et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The detailed procedures to assess the MetS components and the step-by-step approach to calculate the cMetS can be found elsewhere (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eOther variables\u003c/h2\u003e \u003cp\u003e Age, sex, post-secondary education, smoking history, and medication use for diabetes, hypertension, and dyslipidemia were collected from the participants through a face-to-face interview. Body mass index (BMI) was calculated by dividing body weight (kg) by the square of height (kg/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA generalized linear model and Chi-square test were used to compare the characteristics of older adults with unhealthy and healthy joint profiles of ST/MVPA. Receiver operating characteristic (ROC) curve analysis was utilized to assess the ability of daily steps to identify older adults with unhealthy and healthy joint profiles of ST/MVPA. The cut-off for daily steps to identify each joint profile of ST/MVPA was determined using the Union Index (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A generalized linear model was used to compare the cMetS between older adults with unhealthy and healthy joint profiles of ST/MVPA, adjusting for age, sex, BMI, education, smoking, medication use for hypertension, diabetes, and dyslipidemia, and accelerometer wear time. The significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and all statistical analyses were conducted using the IBM SPSS Statistics for Win/v.28.0 (IBM Corp., Armonk, NY).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFrom 258 participants (66\u0026thinsp;\u0026plusmn;\u0026thinsp;5 years, 28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 kg/m\u003csup\u003e2\u003c/sup\u003e), 15.5% (n\u0026thinsp;=\u0026thinsp;40) were identified with unhealthy (ST\u0026thinsp;\u0026ge;\u0026thinsp;11.4 h/day and MVPA\u0026thinsp;\u0026le;\u0026thinsp;10 min/day) and 15.1% (n\u0026thinsp;=\u0026thinsp;39) with healthy (ST\u0026thinsp;\u0026le;\u0026thinsp;9.8 h/day and MVPA\u0026thinsp;\u0026ge;\u0026thinsp;24 min/day) joint profiles of ST/MVPA. Older adults with healthy profiles were younger (64\u0026thinsp;\u0026plusmn;\u0026thinsp;3 vs. 68\u0026thinsp;\u0026plusmn;\u0026thinsp;6 years) and had higher medication use for diabetes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On average, older adults with unhealthy profile performed 4,202\u0026thinsp;\u0026plusmn;\u0026thinsp;1,227 steps/day, while those with healthy profile performed 9,415\u0026thinsp;\u0026plusmn;\u0026thinsp;2,749 steps/day (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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 older adults with unhealthy and healthy joint profiles of sedentary time (ST) and moderate-to-vigorous physical activity (MVPA)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eJoint profiles of ST/MVPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnhealthy (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-secondary education, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers/ex-smokers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension medication, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes medication, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia medication, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccelerometer wear time, hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaking hours in sedentary time, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaking hours in light activities, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaking hours in MVPA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or absolute (n) and relative (%) frequencies.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eUnhealthy: ST\u0026thinsp;\u0026ge;\u0026thinsp;11.4 h/day and MVPA\u0026thinsp;\u0026le;\u0026thinsp;10 min/day; Healthy: ST\u0026thinsp;\u0026le;\u0026thinsp;9.8 h/day and MVPA\u0026thinsp;\u0026ge;\u0026thinsp;24 min/day.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBold values indicate statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003eROC analysis showed that daily steps discriminated older adults with unhealthy (AUC 0.892; cut-off: \u0026le;5,263 steps/day; sensitivity/specificity: 83%/81%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Panel A) and healthy (AUC 0.803; cut-off: \u0026ge;7,134 steps/day; sensitivity/specificity: 80%/66%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Panel B) joint profiles of ST/MVPA. The unhealthy profile exhibited a higher cMetS (β\u0026thinsp;=\u0026thinsp;0.46; p\u0026thinsp;=\u0026thinsp;0.008; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Panel C). Likewise, older adults who fell below the daily steps cut-off point to identify the unhealthy profile of ST/MVPA exhibited a higher cMetS (β\u0026thinsp;=\u0026thinsp;0.34; p\u0026thinsp;=\u0026thinsp;0.004; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Panel D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur main finding was that daily steps discriminated between older adults with unhealthy (\u0026le;\u0026thinsp;5,263 steps/day) and healthy (\u0026ge;\u0026thinsp;7,134 steps/day) joint profiles of ST/MVPA. Furthermore, older adults with an unhealthy profile exhibited a higher CVD risk, as indicated by the cMetS.\u003c/p\u003e \u003cp\u003eOur data support the idea that ~\u0026thinsp;5,000 or fewer daily steps are helpful in identifying older adults with high ST/low MVPA, while\u0026thinsp;~\u0026thinsp;7,000 or more can identify those with low ST/high MVPA. It is common for older adults to spend\u0026thinsp;~\u0026thinsp;60\u0026ndash;65% of their waking hours in ST, 30\u0026ndash;35% in light activities, and \u0026lt;\u0026thinsp;3% in MVPA (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In our study, the unhealthy group spent 73.3% of their time in ST, 26.3% in light activities, and 0.4% in MVPA, while the healthy group spent 53.7% of their time in ST, 41.9% in light activities, and 4.4% in MVPA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, there was an almost perfect negative correlation between ST and time spent in light activities (r = -0.974). This suggests that when older adults were not engaged in sedentary behaviors, they mostly engaged in light activities, such as ligh-intensity walking. Moreover, our results showed that daily steps could discriminate between older adults with unhealthy and healthy joint profiles of ST/MVPA, even though the time spent in moderate-to-vigorous activities is a very small portion of their waking hours.\u003c/p\u003e \u003cp\u003eIn addition, we demonstrated that those with both an unhealthy joint profile of ST/MVPA and daily steps had a higher CVD risk. The cMetS is positively associated with adverse CVD events in adults and older adults (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). We previously demonstrated that older adults with \u0026ge;\u0026thinsp;7,500 daily steps had lower pulse wave velocity (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) compared to their inactive peers (\u0026le;\u0026thinsp;5,000 daily steps). Based on the cut-offs found in this study, it is probable that older adults with \u0026ge;\u0026thinsp;7,500 daily steps had both lower ST and higher MVPA compared to those with \u0026le;\u0026thinsp;5,000 daily steps, which could partially explain the lower CVD risk of those who are more active. Two previous harmonized meta-analyses, including self-reported (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and accelerometer-based (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) measures of ST and MVPA, demonstrated that the unhealthy and healthy joint profile of ST/MVPA is associated with the highest and lowest rates of CVD and all-cause mortality, respectively. Based on the tertiles of accelerometer-measured ST/MVPA, the unhealthy group had a median time of 10.7 h/day and 2.3 min/day of ST and MVPA, respectively, while the healthy group had 8.5 h/day of ST and 34 min/day of MVPA (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Of note, this study included individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years from Sweden, Norway, the USA, and the UK, which could partially explain the differences in the tertiles of ST and MVPA.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, our results provide a starting point for considering daily steps as a single metric for identifying older adults with a joint profile of high ST/low MVPA, which makes them more susceptible to CVD. It is noteworthy that daily steps can easily be obtained from common wearable technologies and smartphones (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), providing a user-friendly method to identify an unhealthy joint profile of ST/MVPA. Therefore, the daily steps metric could be valuable for clinicians and healthcare providers in identifying older adults with this unhealthy movement behavior profile, enabling them to deliver appropriate care. Studies with larger sample sizes and different age groups are welcome to confirm (or not) our initial perspective on using daily steps to identify an unhealthy joint profile of ST/MVPA.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDaily steps may serve as a useful single heuristic metric for identifying older adults with joint profiles of ST/MVPA that are more or less susceptible to CVD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was carried out with the support of the Coordination for the Improvement of Higher Education Personnel \u0026ndash; Brazil (CAPES) \u0026ndash; Financing Code 001. 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Br J Sports Med 55(8):422\u0026ndash;432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bjsports-2020-102892\u003c/span\u003e\u003cspan address=\"10.1136/bjsports-2020-102892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aging, Sedentary Behavior, Exercise, Cardiovascular Diseases, Metabolic Syndrome","lastPublishedDoi":"10.21203/rs.3.rs-3041511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3041511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHerein, we investigated whether daily steps can discriminate between older adults with unhealthy and healthy joint profiles of sedentary time (ST) and moderate-to-vigorous physical activity (MVPA). Apparently healthy community-dwelling older adults aged 60\u0026ndash;80 years were included in this cross-sectional analysis (n\u0026thinsp;=\u0026thinsp;258). Daily steps, ST, and MVPA were assessed by accelerometry. Receiver Operating Characteristic (ROC) analysis was used to test the performance of daily steps in identifying older adults with unhealthy (high ST/low MVPA) and healthy (low ST/high MVPA) joint profiles of ST/MVPA. The cardiovascular disease risk of unhealthy/healthy profiles was compared using a continuous metabolic syndrome score (cMetS). Daily steps discriminated older adults with unhealthy (AUC 0.892, 0.850\u0026ndash;0.934; cut-off: \u0026le;5,263 steps/day; sensitivity/specificity: 82.5%/81%) and healthy (AUC 0.803, 0.738\u0026ndash;0.868; cut-off: \u0026ge;7,134 steps/day; sensitivity/specificity: 79.5%/66.2%) joint profiles of ST/MVPA. The unhealthy profile showed a higher cMetS (β\u0026thinsp;=\u0026thinsp;0.46; p\u0026thinsp;=\u0026thinsp;0.008). Likewise, older adults who fell below the daily steps cut-off point to identify the unhealthy profile of ST/MVPA exhibited a higher cMetS (β\u0026thinsp;=\u0026thinsp;0.34; p\u0026thinsp;=\u0026thinsp;0.004). In summary, our results provide a starting point for considering daily steps as a single heuristic metric for identifying older adults with a joint profile of high ST/low MVPA, which makes them more susceptible to CVD.\u003c/p\u003e","manuscriptTitle":"Using Daily Steps to Identify Older Adults with (Un)healthy Joint Profiles of Sedentary Time and Physical Activity: A Starting Point","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-13 19:29:41","doi":"10.21203/rs.3.rs-3041511/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba163653-c3c4-4c0e-bd09-5e4e23946eef","owner":[],"postedDate":"June 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-12T04:48:14+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-13 19:29:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3041511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3041511","identity":"rs-3041511","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}