Correlates associated with the compliance for moderate, vigorous, and overall physical activity recommendations of public park users in Oeiras – Portugal

ABSTRACT

A cross-sectional study with data from 403 users from public parks in Oeiras – Portugal was conducted to measure their amount of physical activity and sedentary behavior and investigate the distinct correlates associated with the chance of complying with the overall, moderate, and vigorous physical activity recommendations by the World Health Organization. We collected sociodemographic data, physical activity, sedentary behavior, the prevalence of disease, amount and quality of sleep, subjective well-being, where and with whom they live, and the usual way of commuting. Descriptive analysis and binary logistic regression models were used, and the results indicated that 75.2% of the Oeiras’ public park users met the overall physical activity guidelines from the World Health Organization and accumulated 315 min/day (±165) of sedentary behavior. Besides that, the main correlates associated with a lower compliance to physical activity recommendations were a higher sedentary behavior (for total, moderate, and vigorous physical activity) and age (for total and vigorous physical activity), a lower education level (for total and moderate physical activity), lower sleep quality (only for moderate physical activity), and ‘seeming sad’ at the moment of the interview (for total, moderate, and vigorous physical activity).

KEYWORDS:

• Sedentary behavior
• guidelines
• green spaces
• environmental, physical inactivity

Introduction

The World Health Organization (WHO) recommends the practice of 150–300 min of moderate intensity physical activity or 75–150 min of vigorous intensity per week, or an equivalent combination (Bull et al. 2020) for benefits in health. The benefits include a lower risk of cardiovascular disease, hypertension, obesity, diabetes, and breast and colon cancer (Pedersen and Saltin 2015) and improved quality of life and well-being (Marquez et al. 2020). Moreover, physical activity in green spaces or outdoor spaces has been described to promote health and well-being through exposure to pleasant environments and encouraging health promotion behaviors (Lovell et al. 2014).

The WHO also recommends the reduction of sedentary behavior, which is defined as any activity while awake, with ≤1.5 metabolic equivalents, in a seated or reclining posture (Tremblay et al. 2017). Excessive sedentary behavior accumulation is associated with all-cause mortality, cardiovascular disease, type II diabetes, abdominal obesity, some types of cancer, and depression (Júdice et al. 2015, Patterson et al. 2018, Wang et al. 2019). In addition, sedentary behavior is negatively associated with physical activity, mainly in low intensities, and although it has a weak association with moderate physical activity (Mansoubi et al. 2014) it can reduce the chance of meeting the recommendations.

Despite these recommendations, data from the Eurobarometer (Desporto e atividade física - Setembro 2022 - Inquérito Eurobarómetro 2022) with 28 countries of the European Union indicate that Portugal has one of the highest prevalence of physical inactivity, defined as noncompliance with the recommended amount of physical activity (Thivel et al. 2018) with just 4% of the population performing regularly and 72% never performing physical activity. In addition, in the same report, 20% of the interviewed reported spending between 5 h 31 min/day and 8 h 30 min/day in sedentary behavior, and 9% reported spending more than 8 h 31 min/day in sedentary behavior. These values are considered troubling, given that the data of the prospective studies indicate that for each sitting hour, the risk of all-cause mortality increases by 2%, and this risk can rise up to 5% in the case of more than 7 hours of sitting per day, independent of physical activity levels (Chau et al. 2013).

Previous systematic reviews indicate that the built environment, the quality of urban infrastructures, and the installation of fitness equipment or spaces for walking or bicycle use favor the practice of physical activity (Smith et al. 2017). For example, an investigation using the System for Observing Play and Recreation in Communities (SOPARC) assessed park users and their physical activity levels before and after two parks were renovated and compared them to parks that have not been renovated (Cohen et al. 2015). The new parks doubled the number of visitors and a substantial increase in energy expended in the parks (Cohen et al. 2015). However, many of these investigations do not use valid instruments to capture the intensity or the frequency of physical activity, and do not consider other variables that can be positively associated (or negatively) with the compliance of physical activity recommendations, i.e. their correlates. Prior investigations have explored the correlates associated with physical activity with the aim of understanding how these factors associate with behavior over the lifespan (Bauman et al. 2012). For example, a study with Swedish adults found that those aged <35 years, living in small towns and villages, having a body mass index <29.9 kg/m², or having a very good or excellent self-perceived health were more likely to reach the physical activity recommendations (Bergman et al. 2008). In the same study, the less likely to reach the recommendations were women and those with a university degree. Furthermore, most studies analyze the fulfillment of overall physical activity recommendations, and do not go deeper on the correlates for the moderate intensity recommendation and the correlates for the vigorous one, which we believe can be very different. In fact, some intensities can be more appropriate and impactful to specific groups. For example, a systematic-review on 1039 randomized control trials indicated that moderate-intensity exercise had a more significant effect on anxiety reduction (compared to mild and vigorous), while vigorous-intensity exercise was better for depression (Singh et al. 2023). In diabetic patients, vigorous-intensity exercise (aerobic and strength) had better efficacy for glycemic control, as well as increased insulin sensitivity and pancreatic β cell function in adults (Kanaley et al. 2022). Thus, recommending physical activity of a specific intensity may also entail specific correlates, which deserved our attention.

Thus, acknowledging the importance of the built environment and public infrastructures to increase physical activity levels, and taking into a count the above described, this investigation aimed to measure the amount of physical activity and sedentary behavior of the public park users in a Portuguese city and to identify the correlates associated with the chance of compliance with the recommendations by WHO, while differentiating the two main guidelines (i.e. moderate, or vigorous) that can potentially imply distinct correlates, thus, facilitating the development of effective interventions to promote active people.

Methods

This investigation was conducted according to the STROBE guidelines (von Elm et al. 2007).

Study design and settings

This is cross-sectional data from an observational investigation carried out through a questionnaire applied by face-to-face interview in public areas of the municipality of Oeiras/Portugal. This investigation was carried out with data from a larger study that aimed to describe the profile of public park users in the city of Oeiras, regardless of where they lived. The main goal of the current investigation was to measure the amount of physical activity and sedentary behavior of the public park users and to identify the correlates associated with the chance of complying with current WHO guidelines.

Oeiras is a Portuguese city, in the metropolitan area of Lisbon, with an area of 45.88 km² and with 171,767 inhabitants (Census, 2021). Oeiras is one of the cities with the highest gross income per person in the country (18,456 €), more than the national median (12,568 €). Oeiras has 26 gardens (Verdes 2023) and green spaces dedicated to meetings, sports, and recreation, which we chose 9 outdoor infrastructures for the practice of physical activity (or exercise), such as green parks, outdoor fitness equipment, and walking/running paths with distinct contexts (e.g. close to the ocean, in an urban setting). The data collection visits took place between March and April 2022, as presented in Figure 1, the chosen infrastructures were:

Figure 1. Map of the city of Oeiras indicating data collection points.

• Seaside promenade – Paço de Arcos’ beach – area dedicated to running, walking, cycling, rollerblading, etc.

• Seaside promenade – Oeiras’ Marina – area dedicated to running, walking, cycling, rollerblading, etc.

• Seaside promenade – Santo Amaro’s beach – area dedicated to running, walking, cycling, rollerblading, etc.

• Seaside promenade – Caxias’ beach – area dedicated to running, walking, cycling, rollerblading, etc.

• Jamor’s Urban park – Green area inside of National Sports Center of Jamor, with equipment fitness outdoor and several options to distinct physical activity practice.

• Miraflores’ Urban park – Green area, with equipment fitness outdoor and several options for physical activity practice.

• Equipment fitness outdoor – Tower’s beach

• Equipment fitness outdoor – Oeiras’ Forum

• Equipment fitness outdoor – Paço de Arcos

The interviews were performed by well-trained interviewers recruited and instructed for the purpose of this study. First, they explained the goal of the investigation to potential participants and that their participation would be voluntary, meaning that they could give up at any time and that no personal data would be necessary, only their answers. The study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of Universidade Lusófona under the number J1822, on 18 January 2022, and the participants gave their informed consent prior to participation in the study.

Participants

A convenience sample of 403 participants was recruited, and the approach was performed over people who were using outdoor public infrastructures and invited to participate in the survey, regardless of where they lived. The main inclusion criteria were being adult (>18 years old) and being available to answer the questions for a few minutes. The description of how many participants were evaluated at each point is described in Table 1.

Table 1. Descriptive analysis of the sample (N = 403).

Places	N	mean	± SD	%
Age (years)	403	48.5	17.1
Age group
18-24	37			9.2
25-39	96			23.8
39-64	187			79.4
>65 years old	83			20.6
Seaside promenade – Paço de Arcos’ beach	93			23.0
Seaside promenade – Marina de Oeiras	65			16.1
Seaside promenade – Santo Amaro’s beach	28			6.9
Seaside promenade – Caxias’ beach	36			8.9
Jamor’s Urban park	72			17.8
Miraflores’ Urban park	75			18.6
Equipment fitness outdoor Tower’s beach	8			1.9
Equipment fitness outdoor Oeiras’ forum	7			1.7
Equipment fitness outdoor Paço de Arcos	19			4.7
Body mass index (kg/m^2)	403	24.5	3.69
Moderate physical activity (min/week)	403	417	657
Vigorous physical activity (min/week)	403	153	291
Sedentary behavior (min/day)	403	315	165
Sleep (hours/day)	403	7.96	1.18
Subjective well-being (scale of 0 to 10)	403	7.62	1.69
Sedentary behavior (categories)
High (≥420 min/day)	116			28.8
Medium (181-419 min/day)	168			41.7
Low (≤180 min/day)	119			29.5
Sex
Female	231			57.3
Male	172			42.7
Do you live in Oeiras?
Yes	238			59.1
No	165			40.9
Do you live alone or accompanied?
Alone	70			17.4
Accompanied	333			82.6
Education level completed
Less than high school	46			11.4
High school	123			30.5
More than Bachelor	234			58.1
Financial situation perceived
Very difficult	6			1.5
Difficult	19			4.7
Enough to pay the bills	175			43.4
Comfortable	178			44.2
Very comfortable	25			6.2
Have a diagnosed disease?
Yes	122			30.3
No	281			69.7
Do you sleep continuously?
Yes	233			57.8
No	170			42.2
What is the most common way of commuting in your daily life?
By car/motorcycle	254			63.0
By public transportation	52			12.9
By walking	92			22.8
Bicycle	5			1.2
Did the interviewees look sad or happy to the interviewers
Happy	328			81.4
Sad	75			18.6
Met the recommendation for physical activity
Yes	303			75.2
No	100			24.8
Met the recommendation for moderate physical activity
Yes	227			56.3
No	176			43.7
Met the recommendation for vigorous physical activity
Yes	175			43.4
No	228			56.6

Main variables

Physical activity

Physical activity was assessed with the short version of the International Physical Activity Questionnaire (IPAQ). The short version has been shown to have acceptable test–retest reliability (rho = 0.8) and criterion-related validity compared with accelerometers (rho = 0.3) in a 12-country evaluation study that included Portugal (Craig et al. 2003). The IPAQ assesses total physical activity by asking the frequency (the number of days per week) and duration (the average time in minutes per day) that a person performed vigorous and moderate intensity physical activity, and walking. The responses were then categorized into ‘meeting overall recommendations’ (≥150 min/week of moderate or ≥75 min/week of vigorous physical activity) or ‘not meeting the overall recommendations’ (<150 min/week of moderate or <75 min/week of vigorous physical activity), ‘meeting the recommendations for moderate intensity’ (≥150 min/week of moderate-intensity PA) or ‘not meeting the recommendations for moderate intensity’, and finally ‘meeting the recommendations for vigorous intensity’ (≥75 min/week of vigorous PA) or ‘not meeting the recommendations for vigorous intensity’, according to the WHO guidelines.

Sedentary behavior

Sedentary behavior was also assessed using the IPAQ by asking the duration (the average time in hours per day) that the person spends sitting during the weekdays and weekends. The answers were weighted by day [min/day]) through the weighted arithmetic media of the responses of the time in sedentary behavior on weekdays and weekend days and then also transformed into a categorical variable: low (≤180 min/day), medium (181–419 min/day), and high sedentary behavior (≥420 min/day), according to previous cut points (Chau et al. 2013, Rezende et al. 2016).

Covariates

Based on previous evidence (Bergman et al. 2008, Bauman et al. 2012, Dolezal et al. 2017, Marquez et al. 2020, Bakker et al. 2020) about the associations of physiological and psychosocial variables with physical activity and sedentary behavior, the following covariates were included in the statistical models.

Continuous covariables: age and body weight, which were self-referred to in the questionnaire, and then the BMI calculated as weight/height² [kg/m²]. The hours of sleep were based on the questions ‘what time do you go to bed’ and ‘what time do you wake up in the morning in most of your days’?. We also assessed their subjective well-being through one item from ‘the scale of life satisfaction’ (Diener et al. 1985). The question was ‘Overall, how satisfied are you with life these days’? Nothing was ‘0’ satisfied and ‘10’ extremely satisfied.

Categorical covariables: sex (male/female), prevalence of disease (‘Do you have a disease diagnosis made by a doctor? yes/no’), the level of education completed (What is your highest completed educational level? less than high school, high school, and graduated [classified as bachelor or more]), if lived alone or accompanied (alone/accompanied), housing area (living in the Oeiras municipality – yes/no), the most common way to commute (i.e. by car/motorcycle, by public transportation, by walking or by bicycle), perception of financial status (i.e. very difficult, difficult, enough to pay the bills, comfortable, very comfortable), and sleep quality in a binary manner (i.e. sleeping continuously? yes/no). Finally, at the end of the questionnaire, the interviewers subjectively assessed if the responders looked ‘happy or sad’ at the moment of the interview, according to his/her perception during the interview (for example: if they were smiling, in a good mood, with a ‘fresh air’ and comfortable with the interview, etc.).

Statistical analysis

Binary logistic regression models were used to identify the correlates associated with 1) meeting overall physical activity recommendations, 2) meeting moderate intensity recommendation, and 3) meeting vigorous intensity physical activity recommendation, using a manual backward elimination approach (p value for removal set at 0.05). The model started with all the variables (complete model), and then being removed one by one in the order of least significance, thus mathematically finding the model with the best fit for the adherence to guidelines (overall, moderate, and vigorous) (final model). Collinearity diagnostic tests were performed for all independent variables to comply with the assumptions of the logistic regression analysis. Odd ratios and 95% confidence intervals were calculated, and a p value < 0.05 was considered statistically significant. The chance estimates were presented in the final model. The software used was The Jamovi Project, version 2.2. The odd ratios were calculated against the reference categories (i.e. men, living in Oeiras, getting around by bike, not having chronic disease, living in a very comfortable way financially, having continuous sleep, having a university degree or postgraduation, living alone, and seemed happy at the time of the face-to-face interview).

Results

Participants and descriptive data

Descriptive analysis was performed to describe the characteristics of the participants, and the data are presented as the mean and standard deviation for the continuous variables and frequencies for the non-continuous variables (Table 1). The sample consisted of 403 participants (N = 231 women [57.3%]), with an average age of 48.5 years (±17.1 years), ranging from 18 to 87 years, and 20.6% were older adults (≥65 years) (N = 83). The BMI of the participants was on average 24.5 kg/m² (±3.69 kg/m²), and 58.1% had graduated (bachelor’s, master’s, or Ph.D. degrees), 30.5% had at least a high school education, and 11.4% had less than a high school education. The participants presented on average, 7.6 (±1.69) subjective well-being score. Based on the IPAQ, users of public places reported performing 153 (±291) min/week of vigorous-intensity physical activity and 417 (±657) min/week of moderate-intensity physical activity. Regarding sedentary behavior, participants accumulated on average 315 (±165) min/day (5.23 hours/day).

According to the WHO recommendations, 75.2% (N = 303) of respondents complied with the minimum recommended weekly amount of total physical activity, while 24.8% (N = 100) did not achieve these values. Compliance with moderate guideline was 56.3% (N = 227), and 43.4% (N = 175) attained the guideline for vigorous physical activity (more information on Table 1).

Mains results

The test of collinearity (values of variance inflation factor [VIF]) allowed the inclusion of all variables on the model, including sedentary behavior, and all were below 1.5 in a scale of 1–5 (Akinwande et al. 2015).

Table 2 presents the models from the logistic regression to identify the correlates associated with the chance of adherence to physical activity recommendations (i.e. overall, moderate, and vigorous). For overall, after withdrawing the nonsignificant variables, those who had fewer chances to comply with the recommendations of physical activity were the oldest (β −0.034 [OR: 0.96; 95% CI = 0.95-0.98]), with just high school (β −0.677 [OR: 0.50; 95% CI = 0.29-0.88]), with higher sedentary behavior (β −1.798 [OR: 0.16; 95% CI = 0.07-0.34]), and ‘seemed sad’ in the interview (β −0.840 [OR: 0.43; 95% CI = 0.24-0.77]).

Table 2. Correlates associated with compliance to physical activity recommendations of public park users (overall, moderate, or vigorous PA).

	Factors associated with compliance to overall PA guidelines					Factors associated with compliance to moderate PA guideline					Factors associated with compliance to vigorous PA guideline
	Complete model		Final model			Complete model		Final Model			Complete model		Final Model
Variables	OR (95% CI)	P value	OR (95% CI)	P value	Estimate	OR (95% CI)	P value	OR (95%CI)	P value	Estimate	OR (95% CI)	P value	OR (95%CI)	P value	Estimate
Age (years)	.97 (.95-.98)	.001	.96 (.95-.98)	<.001	−.034	1.00 (.98-1.01)	.697				.96 (.94-.97)	<.001	.96 (.94-.97)	<.001	−.040
Body mass index (kg/m^2)	.94 (.88-1.02)	.150				.94 (.88-1.01)	.105				.96 (.90-1.03)	.283
Sleep (hours/day)	.95 (.75-1.20)	.702				1.01 (.83-1.23)	.869				.88 (.72-1.07)	.217
Life satisfaction (scale of 0 to 10)	1.07 (.91-1.25)	.391				1.03 (.89-1.19)	.616				1.07 (.92-1.25)	.356
SB (category)
Low	Reference		Reference			Reference		Reference			Reference		Reference
Medium	.53 (.26-1.08)	.082				.40 (.22-.70)	.002	.39 (.22-.67)	<.001	−.939	.99 (.57-1.72)	.980
High	.15 (.06-.33)	<.001	.16 (.07-.34)	<.001	−1.798	.15 (.08-.30)	<.001	.17 (.09-.32)	<.001	−1.751	.47 (.24-.89)	.021	.51 (.28-.92)	.027	−.063
Live in Oeiras
Yes	Reference					Reference					Reference
No	1.06 (.62-1.82)	.814				1.18 (.75-1.86)	.456				.90 (.57-1.41)	.652
Way of transport
By bike	Reference					Reference					Reference
Transport by car/motorcycle	1.12 (.09-12.9)	.925				.29 (.02-3.28)	.319				.89 (.12-6.60)	.917
Walk	.95 (.07-11.5)	.969				.21 (.01-2.47)	.216				.88 (.11-6.71)	.902
Public transport	1.04 (.08-12.9)	.974				.21 (.01-2.46)	.214				.72 (.09-5.62)	.756
Diagnosed disease
No	Reference					Reference					Reference
Yes	.83 (.46-1.49)	.536				.79 (.47-1.33)	.387				.79 (.46-1.35)	.391
Perceived financial situation
Very comfortable	Reference					Reference					Reference
Comfortable	.92 (.31-2.70)	.883				1.20 (.47-3.03)	.698				.40 (.15-1.06)	.067
Enough to pay the bills	.87 (.30-2.55)	.810				1.04 (.40-2.63)	.934				.57 (.21-1.49)	.254
Difficult	.98 (.20-4.82)	.988				1.51 (.37-6.08)	.560				1.28 (.30-5.31)	.731
Very difficult	4.02 (.26-62.1)	.313				9.22 (.72-117)	.087				.33 (.02-3.85)	.381
Sleep quality
Yes	Reference					Reference		Reference			Reference
No	.67 (.39-1.14)	.146				.60 (.38-.96)	.034	.60 (.39-.93)	.023	−.504	.64 (.40-1.02)	.064
Sex
Male	Reference					Reference					Reference
Female	.87 (.51-1.48)	.611				1.41 (.90-2.22)	.130				.72 (.45-1.13)	.158
Live accompanied
Alone	Reference					Reference					Reference
Accompanied	.81 (.40-1.64)	.563				.78 (.43-1.44)	.443				.57 (.31-1.06)	.078
Education
Bachelor or more	Reference					Reference					Reference
High school	.52 (.29-.94)	.032	.50 (.29-.88)	.017	−.677	.54 (.32-.91)	.020	.50 (.31-.82)	.006	−.683	.83 (.50-1.39)	.491
Less than high school	.61 (.25-1.48)	.278				.88 (.39-1.97)	.764				.65 (.27-1.54)	.329
Perceived mood
Happy	Reference		Reference			Reference		Reference			Reference		Reference
Sad	.43 (.22-.85)	.016	.43 (.24-.77)	.005	−.840	.49 (.26-.92)	.028	.50 (.29-.86)	.013	−.690	.44 (.22-.87)	.019	.50 (.27-.91)	.025	−.702

Complete model: all the variables. Final model: just the significant variables. Bold: p < 0.05.

Abbreviations: CI: confidence interval; OR: odds ratio; PA: physical activity; SB: sedentary behavior.

Those who had fewer chances of complying with the recommendation of moderate physical activity were the ones belonging to the categories of medium (β −0.939 [OR: 0.39: 95% CI = 0.22-0.679]), and high sedentary behavior (β − 1.751 [OR: 0.17; 95% CI = 0.09-0.32]), lower sleep quality (β −0.504 [OR: 0.60; 95% CI = 0.39-0.93]), with just high school (β −0.683 [OR: 0.50; 95% CI: 0.31-0.82]), and ‘seemed sad’ in the interview (β − 0.690 [OR: 0.50; 95% CI = 0.29-0.86]).

Finally, those presenting fewer chances of complying with vigorous physical activity were older (β −0.040 [OR: 0.96; 95% CI = 0.94-0.97]), with high sedentary behavior (β −0.063 [OR: 0.51; 95% CI = 0.28-0.92]), and that ‘seemed sad’ in the interview (ββ-0.702 [OR: 0.50; 95% CI = 0.27-0.91]).

Discussion

Complying with current physical activity recommendations can reduce the risk of mortality from cardiovascular diseases by 47% (Coelho-Ravagnani et al. 2021) as well as other numerous benefits to society. However, given the high values of inactivity in Portugal (Desporto e atividade física - setembro 2022 - Inquérito Eurobarómetro 2022) it is important to further understand, among those who comply with recommendations, the characteristics or correlates most associated with this behavior, given that the recommendations can be indicated as interchangeable. The present study found that 75.2% of the public park users in Oeiras – Portugal met the overall physical activity guidelines from the WHO. These values are above those found in other recent international surveys and in a vast national investigation, the National Food, Nutrition, and Physical Activity Survey (IAN-AF, 2015–2016) (Teixeira et al. 2019) which found that only 27.1% of the Portuguese population complied with the recommendations.

One of the factors that may explain this difference is the fact that our sample consisted of potential regular users of the infrastructures for practicing physical activity, as they were approached in these places. In addition, the current public policies of the Municipality of Oeiras incentivize the practice of physical activity through the Municipal Plan for Development and Innovation in Sports and Physical Activity (2021–2030). This plan aims to promote physical activity for all, from conceptualization as a social activity in school and leisure contexts to the practice of competition and high performance. In addition, the various attributes of the local environment can also contribute to higher levels of physical activity because the municipal plan also contemplated the reformulation of green spaces and hiking trails and bike paths, as well as the installation of quality outdoor fitness equipment. In fact, walkability components, the provision of quality parks and playgrounds, and the installation of or improvements in active transport infrastructures have been shown to be positively associated with physical activity levels in previous studies (Smith et al. 2017) which may partly explain why users of these sites were more active than the rest of the Portuguese population. By itself, this result is of interest, as it shows that random people evaluated in these public park sites seem to be more active in comparison to the general population, thus representing a relevant finding that shows the impact that investing in the cities’ environment can have on people’s health habits. However, our sample was evaluated in a place where there is a tendency to find more active volunteers, since it considered public places for the practice of physical activity, thus preventing the generalization to other populations, that may not benefit from these types of infrastructures. Furthermore, the aim of this study was not to assess the impact of these installations on the physical behavior of users, but instead to identify the correlates associated with the chance of compliance with physical activity recommendations by WHO, while differentiating the two main guidelines (i.e. moderate, or vigorous).

Regarding the main goal of the present investigation, our findings indicate that depending on the physical activity intensity, the correlates for the noncompliance to these guidelines can be different, with age and sedentary behavior being the most significant (emerging in all), but other common correlates found in previous studies (e.g. BMI, duration and quality of sleep, perceived well-being, sex, living in the municipality, financial status, living alone or accompanied, having or not having a chronic disease, or the usual way to commute), being non-significant.

To attain the overall physical activity guideline, age, having at least the high school level of education, and ‘looking sad’ were the significant correlates. When looking specifically to the 150 min/week moderate guideline, age did not remain a significant factor, and sleep quality, educational level, sedentary behavior, and ‘looked sad’ emerged as important correlates. Thus, compared to those that reported better sleep quality, the poor sleepers were less prone (β −0.504 [p = 0.023]) to meet the moderate physical activity guideline, as well, compared with the graduated, the ones who only had high school level were less prone to be active (β −0.683 [p = 0.006]), and compared with ‘looking happy’, the ones ‘looking sad’ in the moment of the interview, were less prone to meet the moderate intensity-based guideline (β −0.690 [p = 0.025]). Even more significantly, the participants who had high (≥420 min/day) and medium sedentary behavior (181–419 min/day), had less 1.75 and 0.939 times per minute of probability to meet the moderate physical activity recommendation, respectively (both, p < .001).

The chance of complying with the 75 min/week vigorous guideline was lower for older individuals (β −0.040 for life year [p < .001]), for those with high sedentary behavior (β −0.063 [p = 0.027]), and the ones that ‘looked sad’ in the interview (β −0.702 [p = 0.025]), thus suggesting some differences in terms of correlates that are associated with attaining a specific physical activity guideline.

In our analyses, the older adults were more likely to comply with the moderate physical activity recommendation (51.9% complied) than the vigorous recommendation (26.5% complied). Previous European studies also indicate that age is a major factor in physical activity adherence (Nikitara et al. 2021) which can explain why it was negatively associated with attaining the vigorous guideline but not the moderate-related guideline. Older adults report functional limitations, lack of motivation, and not having a practice partner as important barriers to involvement in physical activity (Stathi et al. 2012). In addition, they usually report that factors such as the difficulty of access to group-oriented activities, the perception of discomfort and insecurity toward physical activity in the absence of professional instructions, the lack of adequate leisure activities, and the lack of transport to existing points as consistent barriers to the practice (Simmonds et al. 2016). Thus, it is possible that older adults achieve the amount of physical activity mainly through walking; therefore, we consider that it would be important for this age group to be contemplated within the current and future public projects to reduce their feelings of insecurity about vigorous physical activity, with specialized instructors and appropriated activities to their age group.

Being in the high sedentary behavior category was the most negative factor for compliance with overall, moderate, and vigorous physical activity guidelines, which means that even though sedentary behavior and physical activity are fairly independent behaviors, they can impact each other. However, evidence indicates that sedentary behavior truly competes with low intensity physical activity and not with moderate and vigorous intensity (Mansoubi et al. 2014). A good example of this is the results of a large database study with NHANES data from 2003 to 2004 (von Rosen 2023) where it was found that the percentage of the day required to comply with the WHO guidelines was 2.4% to 4.7% of the day. This means that if we exclude about 33.3% (8 hours/day) for sleep, we remain with 35.7% to 38% of the day, that can be spent in either low intensity physical activity or sedentary behavior. These are the two behaviors that truly compete with each other.

Since the prevalence of sedentary behavior has increased in recent years (López-Valenciano et al. 2020) and the harmful health impact associated with excessive accumulation, it is necessary to create specific interventions to reduce this behavior in its most diverse domains, that is, at work, in transportation or in leisure time (Harvey et al. 2013, Mielke et al., 2014). Although the quality of evidence is low, previous reviews indicate that multicomponent interventions in work (i.e. warnings on posters to sit less, incentives for time breaks from sitting to standing, time control sitting by electronic devices, and standing workstations) are strategies that have been presented as more effective (Prince et al. 2014, Martin et al. 2015) and should be adopted and encouraged by health professionals and entities for all sedentary workers. In addition, when cities manage to become more attractive to physical activity, with more available leisure active spaces, such as the ones targeted in the current study, a shift from sedentary behavior to more physically active behavior is expected to happen.

Our data indicated that the amount of sleep (i.e. duration) was not associated with compliance with physical activity recommendations, but a dimension of sleep quality and continuous sleep (Buysse 2014) was a relevant factor that was positively associated with complying to the moderate physical activity recommendation. Most likely, this is due to sleep being a set of many physiologic processes under primarily neurobiological regulation that impact many physiologic systems. Disturbances in sleep have been associated with several adverse health outcomes, such as mortality, obesity, diabetes, inflammation, cardiovascular disease, neurocognitive functioning, and mental health (Grandner 2017). In addition, sleep must be restorative, allowing physical and mental well-being, attending to social demands, and sustained alertness during waking hours (Buysse 2014); thus, discontinuing sleep can affect vitality or disposition to the practice of physical activity. Previous reviews reported the positive association of physical exercise with sleep quality in adults (Kredlow et al. 2015) and older adults (Vanderlinden et al. 2020); however, the majority of these studies were observational, not allowing to establish causality.

Although it is a subjective and an interpretative measure from the interviewers, and therefore, under the bias of the evaluator, we collected information about how the participants looked at the time of the interview, with only two options: ‘happy or sad’ response. Interestingly, the participants who seemed happy at the time of the interview were the ones who were most likely to comply with all physical activity recommendations, irrespective of the specific recommendation, and this variable remained in all models as a significant factor. A systematic review of 28 studies comparing physical exercise between green spaces and indoor settings found that outdoor exercise was associated with affective valence and pleasure (Lahart et al. 2019) which may explain our perception of happiness in the participants. In another systematic review that included 11 studies with 833 adults, mostly young students, the results indicated that compared with exercising indoors, exercising in natural environments was associated with greater feelings of revitalization and positive engagement, decreased tension, confusion, anger and depression symptoms, and increased energy (Thompson Coon et al. 2011). However, both investigations reported low-quality studies; in addition, they evaluated physical exercise (not physical activity), which may overestimate the effects on mood in comparison with physical activity (e.g. exercise in gym versus walking) (Meyer et al. 2016, Schuch et al. 2018).

Limitations

Although our research provides new insights into the correlates that may hinder compliance with physical activity guidelines, the main limitations of our study are the cross-sectional nature of the data, which does not allow inferring causality, in addition to using questionnaire prone to recall, social desirability, and bias in the perception of physical activity intensity by the interviewed subjects can result in an overestimation of physical activity or an underestimation of sedentary behavior. Another compromising point is the fact that our sample was evaluated in a place where there is a tendency to find more active volunteers, since it considered public places for the practice of physical activity, thus preventing the generalization to other populations. However, we must mention as a strength the fact that we considered a substantial number of older adults (20.6%), which reflects the reality of the country (23.4% in Census 2021), in addition to the use of one of the main measurement instruments validated in the literature, such as the IPAQ.

Conclusions

Our investigation indicates that the correlates for the fulfillment of physical activity recommendations may differ according to the targeted intensity of the recommendation itself. Our findings suggest that in addition to investing in the environmental context, in order to facilitate the practice of physical activity, targeting reductions on sedentary behavior, improving sleep quality, and promoting a higher educational level, may further reduce physical inactivity of the population, more specifically for a fraction of the population that is already more active than most of their peers.

Ethical statement

The study was approved by the ethics committee of the Universidade Lusófona (ULHT) under nº J1822, on 18 January 2022.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author (SCT) disclosed receipt of the following financial support for the research: This work was supported by Oeiras City Council, through the Technical and Scientific Cooperation Program (2020/22).

Notes on contributors

Sabrina C. Teno

Sabrina C. Teno is a Ph.D. candidate in Physical Activity and Health at Lusófona University. Her background is in Physical Education, besides that, she is a specialist in exercise prescription, with a Master's in Exercise and Wellbeing. She won the Program of Technical and Scientific Cooperation of the city council of Oeiras - Portugal. Her main interests are in physical activity, sedentary behavior, and their relations with health, besides that, she is interested in how the context relates to physical activity.

Hélio Silva

Hélio Silva is a Ph.D. candidate in Physical Activity and Health at Lusófona University. His background is in Sport Sciences with a Master's in Exercise and Wellbeing. His main interests are in physical activity and sedentary behavior, and their relations with health.

Pedro B. Júdice

Pedro B. Júdice is an assistant professor at Faculty of Sport in Lusófona University. His background is in Sport Sciences, with a Master in Exercise and Health and a PhD in Physical Activity and Health (2016) at Faculty of Human Kinetics – University of Lisbon, followed by 3 years of a post-doc, both supported by Foundation for science and technology (FCT). His main interest is on sedentary behavior and all the implications that different patterns of accumulation can have on body composition, energy expenditure, and metabolic outcomes. He is also interested in investigating the impact of the environment on physical activity levels.