This cross-sectional analysis utilized data from the first wave of ELSI-Brazil, conducted during 2015–2016. ELSI-Brazil implemented a sophisticated multistage stratified cluster sampling framework to ensure a comprehensive representation of urban and rural areas across small, medium, and large municipalities. The municipalities were categorized into four strata based on their population size. In the initial three strata (municipalities with up to 750,000 inhabitants), the sample was selected through three stages: municipality, census tract, and household. For the fourth stratum, encompassing the largest municipalities, the sample selection occurred in two stages: census tract and household. The selection of households followed a systematic approach, involving a four-house jump after an interview or after three unsuccessful contact attempts. This systematic jump was omitted in instances of refusal or ineligibility [(1) absence of residents aged 50 years and over; (2) vacant household; (3) collective living arrangements (pension, asylum, republic, shelter, or hostel); (4) interviewee with a disability preventing questionnaire response without a substitute informant (proxy)]. In such cases, the interviewer proceeded to the next household, adhering to the right-hand rule. All residents aged 50 years and older in the chosen households, inclusive of those with disabilities, bedridden individuals, and wheelchair users, were eligible for participation. ELSI-Brazil constitutes a nationally representative survey comprising individuals aged 50 years or older, residing in 70 municipalities across the five regions of Brazil. In our study, 438 participants were excluded from the total sample of 8,974 due to missing Body Mass Index (BMI) measurements for obesity calculation, and 1,222 were excluded due to missing ST values. Thus, the sample totaled 7,314 participants. Additional insights into ELSI Brazil's sample and its national representativeness have been previously documented [20]. For further details, the research homepage is accessible at http://elsi.cpqrr.fiocruz.br/en/home-english/.

The ethics board of FIOCRUZ, Minas Gerais, approved ELSI-Brazil (CAAE: 34649814.3.0000.5091). Participants provided separate informed consent for interviews and physical measurements, as well as access to administrative records.

Face-to-face interviews meticulously examined sociodemographic attributes, encompassing age (in years) and sex (categorized as male or female). Additionally, participants were queried about their smoking habits, distinguishing between daily and non-daily smokers. Response options included "yes, daily," "yes, less than daily," and "no." Accordingly, this variable was dichotomized, and categorized as "yes" (regardless of daily frequency) or "no" for the classification of smokers. Moreover, participants were queried about their medical history, including diagnoses for conditions such as hypertension, diabetes, hypercholesterolemia, a history of heart attack, angina, cardiac insufficiency, stroke, asthma, emphysema, bronchitis, lung disease, arthritis, rheumatism, osteoporosis, chronic back problems or back pain, depression, cancer, chronic renal failure, Parkinson's, and Alzheimer's. The total number of multimorbidity cases by each participant was aggregated, resulting in a new variable classified as follows: 0 = no multimorbidity, 1 = one multimorbidity, 2 = two multimorbidities, 3 = three multimorbidities, 4 = four multimorbidities, and 5 = five or more multimorbidities. The classification of multimorbidity in this study was based exclusively on medical diagnoses reported by the participants, provided by doctors or other qualified healthcare professionals. Although ELSI-Brasil also collects information on medication use, these were considered as complementary data and were not used directly to classify the presence of multimorbidity. Medication use provided additional insights into the management of participants' health, but the determination of multiple chronic conditions was made based on the medical diagnoses reported by the participants.

Height measurements, recorded in centimeters (cm), were obtained using a portable vertical stadiometer (NutriVida®, Brazil). Participants stood barefoot with legs and feet parallel, weight evenly distributed on both feet, arms relaxed at the sides, palms facing the body, and heads in the Frankfurt horizontal plane. Weight, measured in kilograms (kg), was assessed using a portable digital scale (SECA®, Germany) with participants in a barefoot stance. BMI was computed as the ratio of weight in kilograms (kg) to the square of height in meters (m2). BMI categories aligned with World Health Organization recommendations: underweight (< 18.5 kg/m2), eutrophic (18.5 to < 25.0 kg/m2), overweight (25.0 to < 30.0 kg/m2), and obese (≥ 30.0 kg/m2). Participants' BMIs were dichotomized into < 30.0 kg/m2 (normal) and (≥ 30.0 kg/m2) obese [21]. Obesity was also factored into the total count of diseases. All anthropometric variables underwent dual measurements during the home visit by trained interviewers, and the mean of these measurements was employed in subsequent analyses. Further information can be seen in the handbook on the survey homepage (http://elsi.cpqrr.fiocruz.br/en/home-english/questionnaires/).

The Brazilian version of the International Physical Activity Questionnaire—Short Version (IPAQ-SV) was used to assess the level of PA. ST was expressed as total sitting time. The question about sedentary time in the IPAQ-SV is formulated as follows: “During the last 7 days, how much time did you spend sitting, whether at work, at home, during leisure activities, or while using transportation?” Data from the responses were used to calculate the total sitting time, considering this time on weekdays and weekends. A weighted average calculation was performed as follows: the weekday time was multiplied by 5, added to the weekend time multiplied by 2, and divided by 7 to obtain the average number of hours per day spent in the sitting position. For analyses and graphics, ST was divided into groups by hours per day (0 > ST ≤ 1; 1 > ST ≤ 2; 2 > ST ≤ 3; 3 > ST ≤ 4; 4 > ST ≤ 5; 5 > ST ≤ 6; 6 > ST ≤ 7; 7 > ST ≤ 8; and ST > 8).

Regarding MVPA, the IPAQ-SV was also used, this instrument assesses the domains and intensity of PA, including walking and sitting time, that people perform as part of their everyday lives. The IPAQ-SV conceptualizes the categories as follows: (a) sedentary: does not perform any PA for a minimum of 10 continuous minutes during the week; (b) insufficiently active: practices PA for a minimum of 10 continuous minutes per week, but not enough to be classified as active. (c) Active: meets the following recommendations: (i) VPA: ≥ 3 days/week and ≥ 20 min/session; (ii) MPA or walking: ≥ 5 days/week and ≥ 30 min/session; (iii) any added activity: ≥ 5 days/week and ≥ 150 min/week. (d) Very active: meets the following recommendations: (i) vigorous activity: ≥ 5 days/week and ≥ 30 min/session; (ii) vigorous activity: ≥ 3 days/week and ≥ 20 min/session + moderate activity and/or walking ≥ 5 days/week and ≥ 30 min/session. Classification of daily MVPA complied with the American College of Sports Medicine recommendations (American College of Sports Medicine, 2021), as sedentary (< 30 min/day); moderately active (30–60 min/day); active (460 min/day). When combined, the duration of vigorous activities is doubled and then added to the time spent in moderate activities. PA categories were defined according to the duration of time spent in MVPA, distinguishing between those not meeting MVPA recommendations (< 150 min/week) and those meeting MVPA recommendations (≥ 150 min/week).

After downloading ELSI’s Brazil data we uploaded the dataset in the STATA software, version 16.0 (Stata Corporation, College Station, Texas, USA), then downloaded it in a Microsoft Excel® spreadsheet format. Two researchers independently coded the data, and the validation was performed by double checking in Microsoft Excel® to minimize the risk of bias in data tabulation. The variables, including age group (50 to 54; 55 to 59; 60 to 64; 65 to 69; 70 to 74; 75 to 79; 80 to 84; and ≥ 85 years), sex (male [code = 0]; female [code = 1]), MVPA (≥ 150 min/week) [code = 0] or (< 150 min/week) [code = 1], smokers (no) [code = 1]; (yes) [code = 0], and diagnostic for each diseases were presented as absolute (n) and relative (%) frequency. To address the study's objectives, the dependent variable was defined as the "total number of multimorbidity cases," categorized into 0 = no multimorbidity, 1 = one multimorbidity, 2 = two illnesses, 3 = three multimorbidities, 4 = four multimorbidities, and 5 = five or more multimorbidities. It was classified this way because studies have shown differences in outcomes for those with five or more multimorbidities [22, 23]. Ordinary regression analysis was employed to determine the odds ratio (OR) for individuals in different groups of hours per day of ST in relation to the proportional escalation in the number of illnesses, taking into account the presence of covariates (MVPA achievement, age group, sex, and smoking). Assumptions for conducting ordinal regression were confirmed (VIF < 10) [24] to avoid multicollinearity between the factor and covariates, and the proportional odds assumption was satisfied (p > 0.05) [25]. For better comprehension, the OR was transformed in percentage according to the equation: [% = (OR − 1) × 100%]. Statistical analysis was performed using the SPSS® version 20.0 program with a significance level of α = 5%.