Artigos

INTRODUCTION
In addition to restrictions on activities of daily living, including work and active commuting 1–3, the COVID-19 pandemic had a direct impact on the leisure time activities routine 4, a period during which people often engage in physical activity. Consequently, the absence of specific drugs or vaccines for combating the virus and related health risks led to control and prevention measures by health authorities (e.g., restrictions on public parks, community programs, social isolation) 5,6. Thus, it is possible that the closure of public spaces or the imposition of restrictions on the use of widely recognized private places for physical activity, such as gyms and residential leisure areas, may have exacerbated levels of physical inactivity during the COVID-19 pandemic 7–9. However, when analyzing the consequences of these restrictive measures, it is necessary to consider regional disparities, as differences between the regions of Brazil may have influenced the population's response to the imposed restrictions 10,11. Considering the period before the pandemic, the absence of physical activity was already recognized as a problem directly associated with various health risks 12. However, as evidence confirmed the relationship between physical inactivity and non-communicable chronic diseases 12, the investigation of this interrelation becomes even more relevant in light of the worsening clinical conditions associated with COVID-19 13. Moreover, adverse outcomes related to physical inactivity and mental health during the pandemic, such as loneliness, sadness, and anxiety, deserve special attention 14. In this scenario, these interactions can represent an additional concern for health, intensified by an already challenging context related to physical inactivity.
Besides being recommended for individuals without a diagnosis of disease, during the COVID-19 pandemic, it was observed that among those with non-communicable chronic diseases, higher levels of physical activity were associated with less severe COVID-19 outcomes 13, emphasizing the need to encourage people to increase their physical activity levels. Additionally, it is known that excess body weight is a risk factor for non-communicable chronic diseases 15, and notably, during the pandemic, individuals with overweight/obesity may have faced additional challenges in preserving their health. This is because excess weight leads to substantial complications in COVID-19 patients, including changes in respiratory dynamics, such as increased ventilation demand and respiratory effort, which translates into an increased risk of more severe respiratory infections 15,16. In this context, strategies to promote physical activity, such as counseling, could have beneficial effects on the prevention or maintenance of the health of individuals with chronic conditions before the pandemic, as well as assist in mitigating the severity of these individuals in the pandemic context.
Conceptually, counseling can be defined as structured guidance aimed at promoting physical activity 17. Studies have reported that sex (females compared to males)18,19, higher education level 18,20, older age group 19,21, and participation in the community physical activity program 22 were directly associated with counseling on physical activity before the COVID-19 pandemic. However, it should be noted that information regarding specific groups of adults/older adults most likely to receive counseling on physical activity during the COVID-19 pandemic is not known 23.
In order to promote health promotion actions in the face of modifiable risk factors, such as physical inactivity, a community health promotion program, entitled "Academia da Saúde" (in English: Health Academy), was implemented in 2011 through the National Health Promotion Policy around Brazil 24.Due to the extensive reach of the Program 25 it was expected that, before the pandemic, participants could benefit significantly, including establishing a connection between professionals and users, enhancing individual knowledge about healthy lifestyles, and becoming familiar with the resources and services offered by the program, which could directly impact increased counseling. Additionally, it is possible that support for Program users continued, encouraging physical activity as a coping strategy for the challenges posed by the pandemic. However, considering that before the pandemic, Program users had regular access to activities at the center, and due to the restrictions of the pandemic context, the availability of these activities was compromised, leading to the suspension of actions 26, it is believed that the prolonged interruption of these may have contributed to changes in the prevalence of advice on physical activity during the COVID-19 pandemic.
For the present study, the following hypotheses were established: 1) the presence/absence of physical activity counseling differed depending on the period (before/during the COVID-19 pandemic); 2) the presence/absence of physical activity counseling before and during the COVID-19 pandemic varied according to weight status; 3) the duration of participation in the Program had a direct impact on the presence of physical activity counseling.
Thus, this study aimed to investigate the association between physical activity counseling before and during the COVID-19 pandemic and sociodemographic variables, according to body mass index and participation time in the program, among users of a national community health promotion program in Brazil.

MATERIALS AND METHODS
Study Design, Ethical Considerations, and Participants
The present cross-sectional study utilized data from the population-based study entitled "MOTIVA-SUS: a cross-sectional epidemiological study on motivational determinants for physical activity among users of the ‘Academia da Saúde’ Program." The project was approved by the Human Research Ethics Committee of the Federal University of Santa Catarina, Brazil (protocol no. 5.040.451). Data collection was conducted through telephone interviews from February 2022 to June 2022. During this period, the country experienced a significant increase in COVID-19 cases (February 2022), attributed to the introduction of the Omicron variant at the end of 2021 and the resurgence of the Delta variant 27. By June 2022, this scenario had changed, with a decline in COVID-19 cases and, at the same time, regional challenges in achieving vaccination coverage 28.
The informed consent form was offered to all individuals prior to the interviews, and acceptance or refusal to participate in the research was given verbally over the phone. Telephone interviews, rather than in-person interviews, were conducted due to the COVID-19 pandemic.
The study's target population consisted of individuals aged 18 years or older of both sexes, who were users of the “Academia da Saúde” Program from the five geographic regions of Brazilian territory (Mid-West, Northeast, North, Southeast, and South).
The sampling process was conducted considering conglomerates. For sampling purposes, the municipal Human Development Index (HDI-m) of all municipalities was measured, classifying them into low HDI-m (<0.550), medium (0.550-0.699), and high (>0.699) HDI-m. From this classification, the social and economic development of each of the Brazilian municipalities with built poles of the “Academia da Saúde” Program was identified. With this information, it was possible to create 15 sample strata. However, one of the created strata (low HDI-m/South region of the country) did not present a municipality included with poles of the “Academia da Saúde” Program. Thus, 14 strata were established for final sampling. According to the HDI-m, three strata were created for the North, Northeast, Mid-West, and Southeast regions, and two strata in the South region of the country.
The cluster sampling process was developed in two stages. In the first stage, five municipalities were randomly selected within each stratum. The selected municipalities were contacted through the Brazilian Ministry of Health and municipal health secretariats, and invited to participate in the research. They were asked to provide the number of registered users in the Program. In the second stage of the cluster sampling process, each user was randomly selected within the municipality that agreed to participate in the research. The selected users were contacted by telephone and invited to participate in the study. If a contacted user declined to participate, another user was randomly selected from the same stratum to replace them.
Based on the research budget, 86 interviews per stratum were estimated to ensure that all strata had interviewed users. According to these estimates, the sample size was 1,204 users from the “Academia da Saúde” Program in Brazil. In fact, 1,212 users were interviewed, as eight participants who had initially declined the invitation returned the call to the research team expressing interest in participating and were included in the final sample.
Based on the number of 1,212 users who participated in the research and estimating the post hoc statistical power for association studies with alpha = 0.05, beta = 0.20, and a similar sample proportion between two groups (50%), it is possible to detect differences in bivariate analysis for Odds Ratio (OR) ?1.18 for risk factors and OR ?0.88 for protective factors. Among these users, 979 (80.7% of the original sample) provided complete information on the variables investigated in the present study. Since the present study used data from a smaller sample than initially estimated, statistical power calculations were conducted. The available statistical power for testing the associations of interest in this study can be observed in Supplementary Table 1, Supplementary Table 2, Supplementary Table 3, Supplementary Table 4, and Supplementary Table 5. In general, most of the tested associations showed insufficient statistical power (<80.0%) 29, indicating that some truly significant results may not have been identified due to the limited sample size.

Physical activity counseling
Users of the Program were asked whether "Before the COVID-19 pandemic, in a consultation at the Health Center, any professional (Doctor, Nurse, Nutritionist, Physical Education professional...) told you that you should do physical activity to improve your health?" and "During the last 12 months, i.e., during the pandemic, any health professional (doctor, nurse, nutritionist, physical education professional...) told you that you should do physical activity to improve your health?". In both questions, the response options were "no" (absence of counseling) and "yes" (presence of counseling). The questions used in this study were widely used in research on counseling 18,19,30.

Exposure variables
Information on sex (male/female), age range (adults - 20 to 59 years; older adults - 60 years or older), years of education (0 to 8 years, 9 to 11 years, 12 years of complete education), and marital status (single, married/common law, separated/divorced/widowed) were obtained. The exposure variables were selected and categorized according to the literature that indicates greater counseling among females 17,19, among those who are older19,21, with higher education18,20, and among those who are married or in a stable union21.

Stratification variables/Adjustment variables
Body mass index (BMI) and time of participation in the Program were used as adjustment variables and, in another stage of statistical modeling, these variables (BMI and time of participation in the Program) were used as stratification due to their possible relationship with the receipt of counseling on physical activity 17,18,20,21.
Anthropometric indicators (body mass and height) were measured through self-reported questions "Do you know your weight (even if it is an approximate value)?" and "What is your height?". Body mass was then self-reported in kilograms, and height in centimeters. The participants' BMI was then determined using the following formula: BMI = body mass/height2 (kg/m2), and the value generated by BMI (continuous variable) was categorized as "normal weight" (<25.0 kg/m2) or "overweight/obese" (? 25.0 kg/m2)31. According to literature recommendations, self-reported measures can be used in identifying overweight and obesity in epidemiological studies 32.
Information regarding the duration of participation in the program was assessed through the following question: "In total, adding the period before the pandemic and the current one, for how long have you been participating in the Program?" In this question, there were no predefined response options, and participants verbally provided their participation duration, which was recorded in years and months (continuous variable) and later dichotomized into participation duration <1 year or ? 1 year.

Data Analysis
Descriptive and inferential statistics were adopted to describe the analyzed information. To estimate possible differences between groups according to physical activity counseling, the Chi-square test of heterogeneity and Fisher's exact test were used. Binary logistic regression with Odds ratio (OR) estimation and 95% confidence interval (CI95%) was used to analyze the association between the dependent variables (physical activity counseling before and during the COVID-19 pandemic) and independent variables (sex, age, education level, and marital status). Considering the possible relationship between BMI and participation time in the Program with receiving physical activity counseling 17,18,20–22, the analyses were conducted and stratified according to these variables before and during the pandemic. In the adjusted analysis, the model used to test the associations included the dependent variable and the sociodemographic variables under investigation. In these analyses, the other sociodemographic variables, BMI, and program participation time were included as controls. Additionally, the association between physical activity counseling before and during the pandemic and sociodemographic variables was investigated, with results stratified by BMI and program time. When stratified by BMI or program time, the adjusted models considered sociodemographic variables and BMI (analysis stratified by program time) or program time (analysis stratified by BMI) as controls. The sociodemographic and control variables were introduced into the adjusted analyses, regardless of the statistical significance level in the association with the outcomes.
The analyses were performed using Stata software (Stata Corp LP, College Station, TX, USA), version 16.0, considering sample weights and survey design, with a significance level of 5%.

RESULTS

A total of 979 individuals (906 females) with complete information for all investigated outcomes participated in the study (80.7% of the original sample). General information about the participants and the investigated variables, according to physical activity counseling before and during the COVID-19 pandemic, is described in Table 1. Physical activity counseling was identified in 86.60% (n=848) of participants before the COVID-19 pandemic and in 47.70% (n=467) of participants during the COVID-19 pandemic. Among participants who received counseling before the COVID-19 pandemic, 93.15% were female (n=790), aged 20 to 59 years (n=612, 72.45%), with nine to 11 years of education (n=396, 46.78%), and married or in a stable union (n=537, 63.84%). Among participants who received counseling during the COVID-19 pandemic, 94.03% were female (n=440), aged 20 to 59 years (n=336, 72.02%), had nine to 11 years of education (n=219, 47.71%), and were married or in a stable union (n=306, 65.75%).
General information about the participants and the investigated variables in relation to physical activity counseling according to BMI (Supplementary Table 6), or information about the participants according to program duration (Supplementary Table 7) considering physical activity counseling before (Supplementary Table 8 and Supplementary Table 9 and Supplementary Table 10) and during (Supplementary Table 8 and Supplementary Table 11 and Supplementary Table 12) the COVID-19 pandemic can be accessed in supplementary tables.
There was no association between physical activity counseling and sociodemographic variables before and during the COVID-19 pandemic (Table 2). Therefore, associations were tested by stratifying the sample according to BMI (normal weight and overweight/obesity). For the analysis before the pandemic, no association was observed between the tested variables (Table 3). For the "during the pandemic" moment (Table 4), it was observed that, for the "normal weight" group, greater physical activity counseling was associated with adults aged 60 or older (OR: 1.68; 95% CI: 1.05-2.69) and with more years of education (12 years or more) (OR: 1.26; 95% CI: 1.09-1.47); for the "overweight/obesity" group, greater physical activity counseling was associated with being separated/widowed (OR: 1.23; 95% CI: 1.09-1.38).

**TABLE 1**
**TABLE 2**
**TABLE 3**
**TABLE 4**


Regarding the analysis stratified by the program time participation (<1 year; >1 year), it was observed that among individuals with up to one year of participation, being 60 years old or older (OR: 2.01; 95% CI: 1.12-3.61) was an aspect associated with physical activity counseling before the COVID-19 pandemic (Table 5). In the analysis related to counseling during the pandemic, adjusted results showed that females (OR: 1.74; 95% CI: 1.43-2.11) and individuals with a higher number of years of education (OR: 1.63; 95% CI: 1.09-2.44) were more counseled about physical activity than their peers (Table 6).

**TABLE 5**
**TABLE 6**
DISCUSSION
The present study had three main findings: 1) there was an association between physical activity counseling and sociodemographic variables before and during the COVID-19 pandemic, only after stratification by BMI and time participation in the program; 2) before the pandemic, being 60 years of age or older was associated with physical activity counseling in users with up to one year in the Program; 3) during the pandemic, being 60 years of age or older and having a higher number of years of education were aspects associated with physical activity counseling among participants with normal weight. Furthermore, being separated/widowed among those classified as overweight/obesity; and being female; and having a higher number of years of education among those with up to one year in the Program were aspects associated with physical activity counseling during the pandemic.
It is speculated that the individual relationship that BMI has with both physical activity counseling (e.g. compared to those with normal weight status, individuals with overweight/obesity were more likely to receive physical activity counseling) 18,30,33, as well as with sociodemographic factors (e.g. higher purchasing power and higher age regardless of sex were described in the literature as aspects directly associated with higher weight status)34, could justify the relevant role played by BMI in the interrelation between physical activity counseling and sociodemographic factors.
Regarding the association between physical activity counseling and advanced age (?60 years), since changes caused by biological aging are part of an inherent human process 35–37, it is possible that healthcare professionals perceive physical activity as a means of healthy aging 38. This perception may be because Primary Health Care, specifically in the context of the Health Academy Program, aims to promote health and prevent many conditions associated with aging 24,39. Additionally, during the pandemic, there was speculation about the great concern regarding the negative impact on the physical and mental health of the older adults, since there was a limitation on social participation and family activities40, justifying the use of counseling as a strategy to increase or maintain levels of physical activity and act as a protective factor for various health conditions.
It is hypothesized that interrelation entre between higher education and counseling may be related to the greater understanding individuals with higher educational levels have in adhering to the recommendations used in physical activity counseling 18. Additionally, it is speculated that being separated/widowed may have been associated with physical activity counseling because this group is assumed to engage in collective social programs with the aim of socializing and forming new emotional bonds 41,42. Furthermore, among Program users, there may be a focus on counseling for physical activity for individuals who are overweight, considering the associated health risks and the need for increased care 43, which could justify the identified association.
Although the time of participation in the program has not been explored as a variable possibly related to the interrelation of physical activity counseling and sociodemographic variables by studies previously published 44,45, the time of participation in a program that aims to modify lifestyle habits makes it possible to identify human behaviors related to health, such as whether individuals who receive information related to lifestyle changes are more willing to make such changes or not, that is, if they believe that the pros are more important than the cons for adopting these new habits46. In this sense, it is hypothesized that the older age among program users may have been associated with physical activity counseling because in addition to individuals of older age having a better perception regarding the adversities that the addition of years of life has on their own health 47, it is speculated that participants with less than one year of the program are more motivated - and thus more receptive to performing the activities developed in the program, such as physical activity counseling. In addition, it is possible that individuals who started the program less than a year ago feel the need for support to perform physical activity safely and adequately, being more prone to receiving counseling.
Regarding females being more likely to receive physical activity counseling during the pandemic, it is speculated that the higher proportion of morbidities associated with the development of severe forms of COVID-19 in females (non-communicable chronic diseases)48 may have contributed to the identified association. Furthermore, due to the prioritization of care for individuals with COVID-19 infection and the consequent delay or suspension of elective procedures and medical appointments, individuals with other adverse clinical conditions, such as chronic illnesses, may have been neglected, contributing to the establishment of an even sicker population 49. Additionally, the emergence of the pandemic, coupled with technical and managerial challenges 26,50 (i.e., resource shortages, difficulties in conducting tests, challenges in communicating with the population), may have impacted the quality of healthcare services and, consequently, resulted in a reduction in physical activity counseling for the population. Also, in relation to the period during COVID-19, higher education may be related to physical activity counseling due to the fact that individuals with higher education supposedly had to perform work activities at home 51, presenting higher levels of stress and compromising mental health 51, being engaged in physical activity given the relationship of this variable as a protective factor for these morbidities 52. Additionally, it is speculated these individuals possess greater financial resources to participate in physical activity during this period (e.g., purchasing adequate equipment and the possibility of joining paid physical activity programs)53.
This study has several noteworthy strengths, including the presentation of data from all five geographical regions of Brazil, stratified information by subgroups according to BMI and time of participation in the program, and comparability across different periods of physical activity counseling (pre-pandemic and during the pandemic). Another positive aspect is the sample design (complex sampling), which assigns different weights to the probabilistic selection of sample elements, resulting in more precise and representative population estimates54. However, some limitations must be noted, including that the total sample of participants was smaller than initially estimated (80.7% of the total sample). The use of questionnaires to obtain information about past events can introduce recall bias and, in turn, reduce the accuracy of the information. Furthermore, in the present study, only the duration of participation and frequency in the Program or Center were investigated, which prevents the distinction between participants in physical activity groups and participants in other activities, which can be considered a limitation. Another limitation of this study was its cross-sectional design, which does not allow for the establishment of causality or temporality for the tested associations. The insufficient number of male individuals in some association analyses of this study is another limitation. Nevertheless, this subgroup was included given the relevance of descriptive data about this population, as despite being a minority, they also represent the population in community health promotion services. Additionally, only the presence/absence of physical activity counseling was evaluated in this study. Therefore, future studies reporting on other conditions such as frequency, duration of counseling, and the method used are necessary to verify whether physical activity counseling can be a predictor of increased levels of physical activity and to confirm results on the direct relationship between physical activity counseling and sociodemographic variables. Finally, the insufficient available statistical power for most of the tested associations may have contributed to the non-identification of associations between exposures and outcomes.


CONCLUSIONS
In conclusion, nine out of 10 users of the “Academia da Saúde” Program reported receiving some type of physical activity counseling before the COVID-19 pandemic, but this number reduced to five out of 10 users during the pandemic. Furthermore, the association between physical activity counseling and sociodemographic variables differs according to BMI and participation time in the program. Before the COVID-19 pandemic, being 60 years of age or older was directly associated with physical activity counseling among individuals with up to one year of program participation. During the COVID-19 pandemic, being 60 years of age or older and having a higher number of years of education were directly associated with physical activity counseling among those classified as having normal weight, and being separated/widowed was directly associated with physical activity counseling among those classified as overweight/obesity. Additional results from this study indicated that during the pandemic, being female and having a higher number of years of education were directly associated with physical activity counseling. These results could help healthcare professionals and public health managers define better strategies, taking into account the groups susceptible to lower likelihood of receiving physical activity counseling. Furthermore, they could assist in the continuity of this strategy even in challenging circumstances, such as the COVID-19 pandemic, by considering a personalized and effective approach within the context of the Brazilian community health promotion program.

Ethical approval
The study was approved by the Ethics and Research Committee with Human Beings of the Federal University of Santa Catarina (protocol no. 5.040.451).

Funding
The research project was funded by the National Council for Scientific and Technological Development – CNPq - Brazil - Call No. 27/2020 (Process number: 441310/2020-6).


REFERENCES

1. de Oliveira da Silva Scaranni P, Griep RH, Pitanga FJG, Barreto SM, Matos SMA, de Jesus Mendes da Fonseca M. Work from home and the association with sedentary behaviors, leisure-time and domestic physical activity in the ELSA-Brasil study. BMC Public Health. 2023;23(1). doi:10.1186/s12889-023-15167-z
2. de Palma A, Vosough S, Liao F. An overview of effects of COVID-19 on mobility and lifestyle: 18 months since the outbreak. Transp Res Part A Policy Pract. 2022;159:372-397. doi:10.1016/j.tra.2022.03.024
3. Tison GH, Avram R, Kuhar P, et al. Worldwide Effect of COVID-19 on Physical Activity: A Descriptive Study. Ann Intern Med. Published online June 29, 2020:M20-2665. doi:10.7326/M20-2665
4. Huls SPI, Sajjad A, Kanters TA, Hakkaart-van Roijen L, Brouwer WBF, van Exel J. Productivity of Working at Home and Time Allocation Between Paid Work, Unpaid Work and Leisure Activities During a Pandemic. Pharmacoeconomics. 2022;40(1):77-90. doi:10.1007/s40273-021-01078-7
5. Anselmo César Vasconcelos Bezerra. Factors associated with the population's behavior during social isolation in the COVID-19 pandemic. Ciênc saúde coletiva. 2020;25:2411-2421. doi:10.1590/1413-81232020256.1.10792020
6. Mattos SM, Pereira DS, Moreira TMM, Cestari VRF, Gonzalez RH. Recommendations for physical activity and exercise during the Covid-19 pandemic: scoping review of publications in Brazil. Brazilian Journal of Physical Activity & Health. 2020;25:1-12. doi:10.12820/rbafs.25e0176
7. Caputo EL, Feter N, Leite JS, et al. Physical activity trajectory in the first 10 months of the COVID-19 pandemic in Southern Brazil: a follow-up study. BMC Sports Sci Med Rehabil. 2022;14:58. doi:10.1186/s13102-022-00450-0
8. Caputo EL, Reichert FF. Studies of Physical Activity and COVID-19 During the Pandemic: A Scoping Review. J Phys Act Health. 2020;17(12):1275-1284. doi:10.1123/jpah.2020-0406
9. Crochemore-Silva I, Knuth AG, Wendt A, et al. Physical activity during the COVID-19 pandemic: a population-based cross-sectional study in a city of South Brazil. Cien Saude Colet. 2020;25(11):4249-4258. doi:10.1590/1413-812320202511.29072020
10. Hallal PC, Hartwig FP, Horta BL, et al. SARS-CoV-2 antibody prevalence in Brazil: results from two successive nationwide serological household surveys. Lancet Glob Health. 2020;8(11):e1390-e1398. doi:10.1016/S2214-109X(20)30387-9
11. Szwarcwald CL, Souza Júnior PRB de, Malta DC, et al. Adherence to measures to restrict physical contact and spread of COVID-19 in Brazil. Epidemiol Serv Saúde.2020;29:e2020432. doi:10.1590/S1679-49742020000500018
12. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45-52. doi:10.1016/S0140-6736(14)60648-6
13. Reyes-Sánchez F, Basto-Abreu A, Torres-Alvarez R, et al. Fraction of COVID-19 hospitalizations and deaths attributable to chronic diseases. Prev Med. 2022;155:106917. doi:10.1016/j.ypmed.2021.106917
14. Werneck AO, Silva DR, Malta DC, et al. Physical inactivity and elevated TV-viewing reported changes during the COVID-19 pandemic are associated with mental health: A survey with 43,995 Brazilian adults. Journal of Psychosomatic Research. 2021;140:110292. doi:10.1016/j.jpsychores.2020.110292
15. dos Reis EC, de Freitas Monteiro EL, Meneguci J, et al. Body mass index and sex differences for mortality in hospitalized COVID-19 patients: a path analysis using a brazilian national database. BMC Public Health. 2023;23:1655. doi:10.1186/s12889-023-16218-1
16. Zhou Y, Yang Q, Chi J, et al. Comorbidities and the risk of severe or fatal outcomes associated with coronavirus disease 2019: A systematic review and meta-analysis. Int J Infect Dis. 2020;99:47-56. doi:10.1016/j.ijid.2020.07.029
17. Souza Neto JM de, Brito GEG de, Loch MR, Silva SS da, Costa FF da. Advice for physical activity in primary health care: an integrative review. Movement. 2021;26. doi:10.22456/1982-8918.104360
18. Ahmed NU, Delgado M, Saxena A. Trends and disparities in the prevalence of physicians’ counseling on exercise among the U.S. adult population, 2000–2010. Preventive Medicine. 2017;99:1-6. doi:10.1016/j.ypmed.2017.01.015
19. Moraes S de Q, Souza JH de, Araújo PAB de, Rech CR. Prevalence of counseling for physical activity in Primary Health Care: a systematic review. Brazilian Journal of Physical Activity & Health.2019;24:1-12. doi:10.12820/rbafs.24e0073
20. Barbosa JMV, Souza WV de, Ferreira RWM, Carvalho EMF de, Cesse EAP, Fontbonne A. Correlates of physical activity counseling by health providers to patients with diabetes and hypertension attended by the Family Health Strategy in the state of Pernambuco, Brazil. Prim Care Diabetes. 2017;11(4):327-336. doi:10.1016/j.pcd.2017.04.001
21. Duro SM, Tomasi E, Siqueira FV, Silveira DS, Thumé E, Facchini LA. Adult Physical Activity Counseling by Health Professionals in Brazil: A National Urban Population Survey. J Phys Act Health. 2015;12(8):1177-1183. doi:10.1123/jpah.2013-0213
22. Lopes ACS, Toledo MTT de, Câmara AMCS, Menzel HJK, Santos LC dos. Health conditions and advice on nutrition and physical activity in Primary Health Care in Belo Horizonte-MG. Epidemiol Serv Saúde. 2014;23:475-486. doi:10.5123/S1679-49742014000300010
23. Gonçalves L, Moraes MS, Silva DAS. Counseling for Physical Activity in Adults during the COVID-19 Pandemic: A Scope Review. Int J Environ Res Public Health. 2022;19(14):8687. doi:10.3390/ijerph19148687
24. Brasil. Ordinance No. 719, of April 7, 2011. Establishes the Health Academy Program within the scope of the Unified Health System. Official Gazette of the Union. Published online 2011.
25. Brasil. Ordinance No. 2,681, of November 7, 2013. Redefines the Academia da Saúde Program within the scope of the Unified Health System (SUS). Official Diary of the Union. Published online 2013:37-38.
26. Tusset D, Lacerda DA de M, Magalhães LL, Cavalcante FVSA, Silva JRM da, Santana D da S. Health Academy Program: adaptations at centers during the Covid-19 pandemic. Health debate.2023;46:62-74. doi:10.1590/0103-11042022E805
27. Lamarca AP, de Souza UJB, Moreira FRR, et al. The Omicron Lineages BA.1 and BA.2 (Betacoronavirus SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub. Viruses. 2023;15(4):888. doi:10.3390/v15040888
28. Fiocruz. MonitoraCovid-19. Published 2020. Accessed November 17, 2023. https://bigdata-covid19.icict.fiocruz.br/
29. Cohen J. Statistical Power Analysis. Curr Dir Psychol Sci. 1992;1(3):98-101. doi:10.1111/1467-8721.ep10768783
30. Santos LPD, Silva AT da, Rech CR, Fermino RC. Physical Activity Counseling among Adults in Primary Health Care Centers in Brazil. Int J Environ Res Public Health. 2021;18(10):5079. doi:10.3390/ijerph18105079
31. Nuttall FQ. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutrition Today. 2015;50(3):117-128. doi:10.1097/NT.0000000000000092
32. Ferriani LO, Coutinho E da SF, Silva DA, Bivanco-Lima D, Benseñor IJM, Viana MC. Validity of self-reported measurements of body weight and height in participants of the São Paulo Megacity study. Cad health collect. 2019;27:225-233. doi:10.1590/1414-462X201900020242
33. Ko JY, Brown DR, Galuska DA, Zhang J, Blanck HM, Ainsworth BE. Weight Loss Advice U.S. Obese Adults Receive from Health Care Professionals. Prev Med. 2008;47(6):587-592. doi:10.1016/j.ypmed.2008.09.007
34. Machado ÍE, Pereira SCL, Dias Júnior CS, Abreu MNS, Borges AM, Filgueiras JH. Factors associated with excess weight in adults who use popular restaurants in Belo Horizonte, Brazil. Collective health science. 2014;19:1367-1377. doi:10.1590/1413-81232014195.21772013
35. Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002;50(5):889-896. doi:10.1046/j.1532-5415.2002.50216.x
36. Manini TM, Clark BC. Dynapenia and aging: an update. J Gerontol A Biol Sci Med Sci. 2012;67(1):28-40. doi:10.1093/gerona/glr010
37. Oliveira DV de, Oliveira VB de, Caruzo GA, et al. The level of physical activity as an intervening factor in the cognitive status of elderly people in primary health care. Collective health science. 2019;24:4163-4170. doi:10.1590/1413-812320182411.29762017
38. Eckstrom E, Neukam S, Kalin L, Wright J. Physical Activity and Healthy Aging. Clinics in Geriatric Medicine. 2020;36(4):671-683. doi:10.1016/j.cger.2020.06.009
39. Sá GBAR de, Dornelles GC, Cruz KG, et al. The Academia da Saúde Program as a strategy for promoting health and healthy lifestyles: national implementation scenario. Collective health science. 2016;21:1849-1860. doi:10.1590/1413-81232015216.09562016
40. Brooks SK, Webster RK, Smith LE, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet. 2020;395(10227):912-920. doi:10.1016/S0140-6736(20)30460-8
41. Evedove AUD, Loch MR. Health-risk behaviors among widowed Brazilian men and women. Collective Health. 2020;16:e2255. doi:10.18294/sc.2020.2255
42. Williams K, Umberson D. Marital Status, Marital Transitions, and Health: A Gendered Life Course Perspective. J Health Soc Behav. 2004;45(1):81-98.
43. dos Santos LP, da Silva AT, Rech CR, Fermino RC. Physical Activity Counseling among Adults in Primary Health Care Centers in Brazil. Int J Environ Res Public Health. 2021;18(10):5079. doi:10.3390/ijerph18105079
44. Häfele V, Siqueira F. Counseling for physical activity and behavior change in Basic Health Units. Brazilian Journal of Physical Activity & Health. 2016;21(6):581-592. doi:10.12820/rbafs.v.21n6p581-592
45. Toledo MTT de, Abreu MN, Lopes ACS. Adherence to healthy ways of life through advice from health professionals. Rev Saúde Pública. 2013;47:540-548. doi:10.1590/S0034-8910.2013047003936
46. Tessaro VCZ, Silva AMR, Loch MR. Stages of behavior change for leisure-time physical activity in Brazilian adults: longitudinal study. Collective health science.2021;26:2969-2980. doi:10.1590/1413-81232021268.18022020
47. Farías-Antúnez S, Fassa AG. Prevalence and factors associated with frailty in the elderly population in southern Brazil, 2014. Epidemiol Serv Saúde. 2019;28. doi:10.5123/S1679-49742019000100008
48. Mesenburg MA, Hallal PC, Menezes AMB, et al. Chronic non-communicable diseases and COVID-19: EPICOVID-19 Brazil results. Rev Saude Publica. 2021;55:38. doi:10.11606/s1518-8787.2021055003673
49. da Silva BRG, Corrêa AP de V, Uehara SC da SA. Primary health care organization in the Covid-19 pandemic: scoping review. Rev Saude Publica. 2022;56:94. doi:10.11606/s1518-8787.2022056004374
50. Giovanella L, Martufi V, Mendoza DCR, et al. The contribution of Primary Health Care to the SUS network to combat Covid-19. Health debate. 2021;44:161-176. doi:10.1590/0103-11042020E410
51. Xiao Y, Becerik-Gerber B, Lucas G, Roll SC. Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. J Occup Environ Med. 2021;63(3):181-190. doi:10.1097/JOM.0000000000002097
52. Violant-Holz V, Gallego-Jiménez MG, González-González CS, et al. Psychological Health and Physical Activity Levels during the COVID-19 Pandemic: A Systematic Review. Int J Environ Res Public Health. 2020;17(24):9419. doi:10.3390/ijerph17249419
53. Teran-Escobar C, Forestier C, Ginoux C, et al. Individual, Sociodemographic, and Environmental Factors Related to Physical Activity During the Spring 2020 COVID-19 Lockdown. Front Psychol. 2021;12:643109. doi:10.3389/fpsyg.2021.643109
54. Szwarcwald CL, Damacena GN. Complex samples in population surveys: planning and implications for statistical data analysis. Rev bras epidemiol. 2008;11:38-45. doi:10.1590/S1415-790X2008000500004