ABSTRACT
Background:
Literature suggests that middle age is a period with increasing sedentary behavior and health risks.
Objective:
We conducted the present study to assess physical activity levels of the adults aged 30–50 years and understand the motivators and barriers to regular physical activity.
Materials and Methods:
A cross-sectional study was conducted among 100 adults aged 30–50 years residing in Rourkela, Odisha. The physical activity levels of the adults were assessed using Bouchard’s Physical Activity Record. Height, weight, and waist circumference of the participants were measured using standard procedures. A self-administered questionnaire was prepared to identify the motivators and barriers to physical activity/exercise behavior.
Results:
Nearly half of the participants were obese, 23.3% were overweight and 28% had a normal body mass index. Based on WC and waist-to-height ratio (WHtR), 84% and 79.3% of the participants had metabolic risk, respectively. Over half of the participants were physically inactive. Predominantly, low-intensity activities (yoga, slow walking) were performed as it was assumed to be sufficient. The main motivators of physical activity/exercise behavior were health scare, health benefits, weight loss, availability of resources at convenient time, and better looks. The chief barriers for exercise behavior were lack of motivation, weather, safety concerns, and lack of time.
Conclusion:
Despite over two-third of participants being overweight/obese, 90% of the physically active participants failed to meet the World Health Organization recommendations. Government, community, and individual participation are imperative to formulate interventions strategies to reduce the barriers to physical activity.
KEYWORDS: Barriers, low-intensity activities, motivators, obesity, physical inactivity
INTRODUCTION
World Health Organization (WHO) recommended “at least 150–300 min of moderate-intensity aerobic physical activity per week or 75–150 min of vigorous-intensity aerobic physical activity per week”.[1] According to the WHO, insufficient physical activity is one of the leading risk factors for the development of noncommunicable diseases (NCDs) such as diabetes, hypertension, and cardiovascular diseases.[1] From 2001 to 2016, the prevalence of physical inactivity has doubled in high-income countries as compared to the low-income countries.[2] Even in India, a study conducted in Tamil Nadu, Jharkhand, Maharashtra, and Chandigarh indicated that only <10% engaged in recreational physical activity.[3]
Lately, there has been an increase in the awareness among people regarding the importance of physical activity. People have recognized the lack of sufficient activity as a potential risk factor for the development of several morbidity conditions. Studies have identified motivating factors such as health, body image, fitness, and social influence.[4-6] Lack of time, space, motivation, and environmental issues were common barriers.[4-7] Besides these, cultural factors, gender, and social factors may also influence the motivators and barriers. In India, very few studies have explored the barriers and facilitators of physical activity among adults.[4,8]
Middle age is the period where men and women shoulder immense responsibilities in their personal and professional lives. Their focus drifts from self to family, career, and other commitments. Several studies from India have reported that with an increase in age sedentary behavior became more prominent.[9-11] They are more likely to gain weight and are at a higher risk of developing chronic diseases than their younger counterparts. Keeping this in mind, the present study was conducted to assess the physical activity levels of the adults aged 30–50 years and identify factors influencing their exercise behavior. This study also attempts to uncover the sex-related differences in physical activity levels as well as the motivators and barriers for the same.
MATERIALS AND METHODS
Ethics
The study was approved by Institutional Human Ethics Committee, Mount Carmel College, Autonomous, Bengaluru (IHEC – MCC No. 008 Msc/2020-21).
Design and setting
A cross-sectional study was carried out among the adults of Rourkela city, Odisha. The participants included in the study were Steel Authority of India Limited (SAIL) employees who had shift jobs and weekly a day off on any day of the week. Other participants included in the study were their family members who worked in various sectors such as academics, banks, corporates, and homemakers. The study was conducted from December 2020 to March 2021. During this period, the lockdown restrictions of the first wave of the pandemic had been relaxed to some extent. The employees of SAIL were working physically at the steel plant while their family members who worked elsewhere were working from home.
Selection of the sample
The sample size for the study was computed using EZR (version 1.55)[12] using 54.4% prevalence of physical inactivity, 10% margin of error, and 95% confidence level.[3] The estimated sample size was 96 participants. One hundred adults aged between 30 and 50 years who were willing to observe their activity closely and record for a day were included in the study. Participants who were suffering from any physical disabilities or conditions wherein physical activity was limited, for example, cardiac, orthopedic conditions, and individuals involved in outdoor sports at professional level were excluded from the study.
Anthropometric measurements
Anthropometric measurements of the participants such as height, weight, and waist circumference (WC) were measured. The subjects were weighed using a weighing scale (Dr. Gene RTZ – 113) with an accuracy of 0.1 kg and without heavy clothing. The height and WC of the participants was measured with a flexible tape following standard procedures.[12] We classified the participants as per their body mass index (BMI), WC and WHtR.[13-15]
Physical activity
The physical activity level of the adults was assessed using the Bouchard activity record (BAR).[16] For the present study, BAR was used to record the activity of the participants only for a single working day. Maintaining a 3-day record would have been tedious for the participants. For each activity of the day, the metabolic equivalent of task (MET) values was gathered.[17] The activities for the day were broadly classified into three categories, i.e., low-intensity (MET - <3), moderate-intensity (MET - 3–6), and vigorous-intensity (MET - >6) activities.
Factors influencing physical activity
Based on the available literature, potential factors that were likely to motivate and deter their exercise behavior were identified. The identified factors were grouped under the following heads – health-related, availability of resources, perception about exercise, environmental, social influence, and individual factors.[4,7,18] The factors were assessed through a self-developed questionnaire on a five-point Likert scale (strongly agree to strongly disagree).
The questionnaire was divided into two sections, i.e., “Motivators for physical activity” and “Barriers for physical activity” [Table 1]. Participants who reported no physical activity were asked to fill the barriers and for the motivators, they were asked as to what could have motivated them to participate in regular physical activity. The scoring for the motivators and barriers was done on a scale of 1–5, wherein 1 = strongly agree, 2 = agree, 3 = neither agree nor disagree, 4 = disagree, and 5 = strongly disagree.
Table 1.
List of motivators and barriers for physical activity/exercise identified from literature
| Categories | Motivators for physical activity |
|---|---|
| Health related | I exercise because it is beneficial to health |
| I exercise because it reduces stress | |
| I exercise to lose weight | |
| I have been advised to exercise by a health professional | |
| A health scare prompted me to exercise | |
| Availability of resources | There are a wide range of exercise classes and facilities for me to attend |
| Exercise facilities are available at times that suit me | |
| An exercise trainer or a yoga instructor is always available at the time that suits me | |
| Social influence | My friends and family encourage me to exercise |
| I exercise because I like spending time with my friends while doing this activity | |
| I exercise because my friends and family want me to lose weight | |
| I exercise because I want to lose or maintain weight so that I look better | |
| Individual factors | Exercising makes me happy |
| Exercising keeps me active the whole day | |
| I like to set exercise goals for myself | |
|
| |
| Categories | Barriers for physical activity |
|
| |
| Perception about exercise | I lack the motivation and willpower required for exercising |
| I do not enjoy exercising | |
| Bad weather puts me off exercising | |
| I am too tired to exercise daily | |
| I worry that I might injure myself while exercising | |
| I am too old to exercise | |
| Environmental factors | Exercise facilities are not available at times that suit me |
| I don’t feel safe at the gym | |
| I don’t feel safe to walk on roads or parks alone | |
| I find it too expensive to join a gym for exercising daily | |
| I don’t have enough space around to exercise | |
| I don’t have proper access to gym facilities | |
| Social factors | I don’t feel like working out/exercising alone |
| I am more likely to perform physical activity daily when I have someone to do with me | |
| Individual factors | My friends and family believe I am too old to exercise |
| No time for exercise | |
| I don’t know which exercises to do and how | |
| I am often worried about the way I look during exercising | |
| I don’t exercise because I worry that everyone will be younger than I am | |
| I don’t have enough time to exercise daily | |
Socioeconomic status
The socioeconomic status (SES) of the participants was assessed using the Updated Modified Kuppuswamy scale.[19]
Data collection
Participants were explained about the purpose of the study in the local language and were enrolled after obtaining a written informed consent. Following this, the height, weight, and WC of the participants were measured. A physical activity record BAR was given to the participants wherein, the participants had to record their activities for a 24-h period on a weekday/working day. As this study was conducted post lockdown in the first wave of COVID-19, all appropriate safety precautions were followed while recording the anthropometric measurements and data collection by the researcher and the participant.
Statistical analysis
Statistical analysis was performed using IBM SPSS (version 20.0). The normality of the data was determined using the Shapiro–Wilk test. As the variables were not normally distributed nonparametric tests were carried out. Mean ± standard deviation (SD), median, and frequency distribution were done for quantitative and categorical variables, respectively. MannWhitney U test was done to find out the difference in the BMI, WC, WHtR, and physical activity levels between the sexes and the age groups.
RESULTS
Of 100 participants who took part in the study, 50 were male and 50 were female. The median age of the participants was 39.5 years. Among the participants, majority of the heads of the family were graduates (55%), 27% had completed honors or professional courses, and remaining 18% had completed their intermediate or diploma. About 53% of the family heads worked as technicians and associate professionals, 30% were professionals and 11% worked as legislators, senior officials, and managers. Majority of the participants belonged to the upper-middle (71%) class whereas 29% were from the upper class. Further, none of the participants were from the lower or lower middle class.
Nutritional status of the participants
The anthropometric measurements of the participants are summarized in Table 2. In the present study the overall median and mean BMI of the participants were 24.56 kg/m2 and 25.51 ± 4.30 kg/m,2 respectively. The median BMI of female participants was significantly higher compared to the male participants of 40–50 years (P = 0.047). In the 30–39 years of age group, the median WC of males was significantly greater than that of females (P = 0.003). Participants in 40–50 years (median = 103.75 cm) had greater WC than the participants in 30–39 years (median = 95.5 cm, U = 1050, P = 0.003). Similar findings were noted for WHtR as well (30–39 years = 0.58, 40–50 years = 0.61, U = 898.5, P = 0.015).
Table 2.
Median (mean±standard deviation) of anthropometric measurements and measures of central adiposity of the male and female participants
| Variables | Male | Female | Mann–Whitney U (Z) | P |
|---|---|---|---|---|
| 30–39 (years) | ||||
| Weight (kg) | 72 (71.44±6.37) | 58 (61.16±8.04) | 110.00 | 0.0001* |
| Height (cm) | 172 (172.10±9.80) | 156 (156.24±6.03) | 45.50 | 0.0001* |
| BMI (kg/m2) | 23.78 (24.26±2.75) | 24.45 (25.13±3.74) | 281.00 | 0.541 (NS) |
| WC (cm) | 100 (99.08±8.75) | 91 (89.96±11.14) | 160.50 | 0.003* |
| WHtR | 0.57 (0.57±0.52) | 0.57 (0.57±0.74) | 299.00 | 0.793 (NS) |
| 40–50 (years) | ||||
| Weight (kg) | 72 (74.64±12.09) | 65 (66.04±8.79) | 196.00 | 0.024* |
| Height (cm) | 172 (173.94±12.32) | 155 (155.06±6.82) | 43.50 | 0.0001* |
| BMI (kg/m2) | 23.79 (25.07±5.61) | 28.57 (27.57±4.10) | 210.00 | 0.047* |
| WC (cm) | 102 (107.70±17.24) | 106 (100.22±14.81) | 257.50 | 0.286 (NS) |
| WHtR | 0.58 (0.62±0.12) | 0.69 (0.64±0.10) | 270.00 | 0.410 (NS) |
*P<0.05 (significant). NS: Nonsignificant, BMI: Body mass index, WC: Waist circumference, WHtR: Waist-to-height ratio
The nutritional status of the participants according to their BMI is given in Figure 1. Nearly half of the participants (48.7%) were obese and 23.3% of participants were overweight. Twenty-eight per cent of the participants had a normal BMI (18.5–22.9 kg/m2). Overall, greater proportion of female participants was found to be overweight (Female = 24% vs. male = 22%) and obese (Female = 54% vs. Male = 38%) compared to the male participants (χ2 = 0.132, P = 0.037). In addition, among the participants in 30–39 years, 34% had normal BMI, 26% were overweight and 40% were obese. Among the participants in 40–50 years, 28% had normal BMI, 20% were overweight and about 52% were obese. Further, about 84% of the participants were at metabolic risk according to their WC and about 79.3% according to their WHtR.
Figure 1.
Distribution of participants across the body mass index categories
Physical activity of the men and women
The physical activity levels of the study participants were quite poor. Half of the participants (52%) were physically inactive and 18% were found to be involved in regular physical activity. Majority of the participants belonging to the upper class (41.4%) engaged in regular physical activity compared to the participants belonging to the upper-middle class (8.7%). Across the ages, it was observed that a greater proportion of male participants performed exercise daily (28%) compared to the female (4%) participants. Further, the participants belonging to 40–50 years (28%) were involved in regular physical activity compared to the participants from 30 to 39 years (8%).
Intensity of the physical activity of the participants
Table 3 represents the median and mean ± SD of the duration of time (minutes) spent by the participants and MET values of different intensity activities. The participants spent the majority of their time in low-intensity (959.85 ± 56.30 min) activities compared to moderate (19.80 ± 34.89 min) or vigorous-intensity (0.30 ± 2.11 min) activities. The predominant low-intensity activities performed by male and female participants were slightly different. Majority of the female participants were homemakers due to which they were mostly involved in low-intensity household chores such as cooking, serving, and cleaning. Males were mostly involved in desk work or supervision which involved long sitting hours. Other low-intensity activities performed by the participants included yoga, sitting and talking, watching television, lying down, eating, and sleeping. The moderate-intensity activities performed by the participants were walking, household chores such as cleaning that required moderate effort, and grocery purchasing. The vigorous-intensity activities that were carried out by the participants were jogging and aerobic training in the gym. From the results of the present study, it was further noted only 6% of participants fulfilled the WHO recommendations on physical activity. Others were found to engage only in low-intensity activities such as yoga and slow walking.
Table 3.
Duration (min) and metabolic equivalent of task values for different activities across age groups and sex
| Intensity of activity | 30–39 (years) | 40–50 (years) | Overall | Mann-Whitney U (Z) | P | ||
|---|---|---|---|---|---|---|---|
|
|
|
|
|||||
| Median | Mean±SD | Median | Mean±SD | Mean±SD (median) | |||
| Time (min) | |||||||
| Low intensity | 960 | 956.10±57.48 | 982.50 | 963.60±55.43 | 959.85±56.30 (960) | 1125.50 | 0.384 (NS) |
| Moderate intensity | 0 | 15.30±30.37 | 0 | 24.30±38.67 | 19.80±34.89 (0) | 1075 | 0.156 (NS) |
| Vigorous intensity | 0 | 0.30±2.12 | 0 | 0.30±2.12 | 0.30±2.11 (0) | 1250 | 1.000 (NS) |
| MET | |||||||
| Low intensity | 1540.50 | 1535.48±180.25 | 1585.50 | 1609.31±182.50 | 1572.39±184.23 (1553.75) | 964 | 0.049* |
| Moderate intensity | 0 | 59.37±121.38 | 0 | 95.66±157.33 | 77.51±140.98 (0) | 1076 | 0.159 (NS) |
| Vigorous intensity | 0 | 2.35±15.90 | 0 | 2.64±18.66 | 2.44±17.25 (0) | 1249.50 | 0.989 (NS) |
|
| |||||||
| Intensity of activity | Male | Female | Overall | Mann-Whitney U | P | ||
|
|
|
|
|||||
| Median | Mean±SD | Median | Mean±SD | Mean±SD (median) | |||
|
| |||||||
| Time | |||||||
| Low intensity | 960 | 953.70±55.39 | 990 | 966±57.09 | 959.85±56.30 (960) | 1036.5 | 0.136 (NS) |
| Moderate intensity | 0 | 30±42.10 | 0 | 9.60±21.80 | 19.80±34.89 (0) | 887.5 | 0.003* |
| Vigorous intensity | 0 | 0.30±2.12 | 0 | 0.30±2.12 | 0.30±2.11 (0) | 1250 | 1.000 (NS) |
| MET | |||||||
| Low intensity | 1475.25 | 1480.04±126.85 | 1648 | 1664.75±187.29 | 1572.39±184.23 (1553.75) | 537 | 0.0001* |
| Moderate intensity | 0 | 118.94±172.92 | 0 | 36.09±82.19 | 77.51±140.98 (0) | 892 | 0.004* |
| Vigorous intensity | 0 | 2.64±18.66 | 0 | 2.25±15.90 | 2.44±17.25 (0) | 1249.50 | 0.989 (NS) |
*P<0.05 (significant). NS: Nonsignificant, MET: Metabolic equivalent of task, SD: Standard deviation
Table 3 indicates the time spent and the corresponding MET values for different intensities of activity of the participants according to their age group and gender. No significant difference was observed between time spent in low (P = 0.384), moderate (P = 0.156), and vigorous (P = 1.000) intensity activities between the age groups. A significant difference was observed in the MET values for low-intensity activities for both the age groups, indicating that the majority of the participants belonging 40–50 years performed low-intensity activities of high MET values compared to the participants belonging to 30–39 years. Men reportedly spent more time in moderate-intensity activities. Further, men performed low-intensity (P = 0.0001) and moderate-intensity activities (P = 0.004) with higher MET values as compared to women.
Table 4 represents the time spent in different intensities of activities performed by the participants across their SESs. Upper SES spent higher amount of time in moderate-intensity activities than upper-middle SES (P = 0.001). Further, it was also found that upper SES participants were involved in moderate-intensity activities with higher MET values compared to the upper middle class (P = 0.001).
Table 4.
Time spent and metabolic equivalent of task values in different activity intensities by the participants belonging to different socioeconomic status
| Levels of activity intensities | Socioeconomic status | n | Mean rank | Median | Mann–Whitney U (Z) | P |
|---|---|---|---|---|---|---|
| Time | ||||||
| Low intensity | Upper | 29 | 46.52 | 960 | 914 | 0.375 (NS) |
| Upper middle | 71 | 52.13 | 975 | |||
| Moderate intensity | Upper | 29 | 63.47 | 30 | 653.50 | 0.001* |
| Upper middle | 71 | 45.20 | 0 | |||
| Vigorous-intensity | Upper | 29 | 49.50 | 0 | 1000.50 | 0.364 (NS) |
| Upper middle | 71 | 50.91 | 0 | |||
| MET | ||||||
| Low intensity | Upper | 29 | 53.72 | 1563 | 936 | 0.478 (NS) |
| Upper middle | 71 | 49.18 | 1548.50 | |||
| Moderate intensity | Upper | 29 | 63.57 | 105 | 650.5 | 0.001* |
| Upper middle | 71 | 45.16 | 0 | |||
| Vigorous-intensity | Upper | 29 | 49.50 | 0 | 1000.50 | 0.364 (NS) |
| Upper middle | 71 | 50.91 | 0 |
*P<0.05 (significant). NS: Nonsignificant, MET: Metabolic equivalent of task
Factors motivating exercise behavior
The factors that motivated the male and female participants were different [Table 5]. Men were motivated to exercise owing to the perceived health benefits of exercise (54%) followed by health scare (46%), advice by health professional (40%), and availability of resources at a convenient time (40%). On the other hand, female participants were drawn toward exercise due to health scare (76%), weight loss (68%), availability of resources at a convenient time (66%), availability of resources (56%), better looks (56%), and health benefits (52%).
Table 5.
Factors motivating male and female participants to exercise/physical activity
| Motivators | Male, n (%) | Female, n (%) |
|---|---|---|
| Health | ||
| Health benefits | 27 (54) | 26 (52) |
| Reduces stress | 15 (30) | 20 (40) |
| Weight loss | 19 (38) | 34 (68) |
| Advised by health professional | 20 (40) | 24 (48) |
| Health scare | 23 (46) | 38 (76) |
| Availability of resources | ||
| Availability of facility/resources | 15 (30) | 28 (56) |
| Availability of facilities at convenient time | 20 (40) | 33 (66) |
| Availability of trainer at suitable time | 6 (12) | 16 (32) |
| Social influences | ||
| Encouraged by family/friends | 17 (34) | 18 (36) |
| Others want you to lose weight | 4 (8) | 20 (40) |
| Individual factors | ||
| Better looks | 10 (20) | 28 (56) |
| Makes you happy | 3 (6) | 7 (14) |
| Keeps you active whole day | 8 (16) | 10 (20) |
| Exercise goals | 2 (4) | 6 (12) |
Barriers to exercise
Among the barriers to exercise, majority of the men considered bad weather (92%) as the main deterrent followed by lack of motivation to exercise (70%) and lack of time (38%). For women, unsafe roads/parks (76%) followed by lack of motivation to exercise (70%), bad weather (68%), lack of time (66%), and unavailability of resources at suitable time (52%) were the main barriers to exercise [Table 6].
Table 6.
Perceived barriers to exercise/physical activity by male and female participants
| Barriers | Male, n (%) | Female, n (%) |
|---|---|---|
| Perception about exercise | ||
| Lack of motivation | 35 (70) | 35 (70) |
| Lack of enjoyment | 16 (32) | 20 (40) |
| Too tired to exercise | 17 (34) | 18 (36) |
| Worried of injury | 6 (12) | 9 (18) |
| Too old to exercise | 5 (10) | 1 (2) |
| Environmental factors | ||
| Bad weather | 46 (92) | 32 (68) |
| Unavailability of facilities at suitable time | 10 (20) | 26 (52) |
| Unsafe at gym | 0 | 12 (24) |
| Unsafe on roads/parks | 0 | 38 (76) |
| Too expensive to join gym | 7 (14) | 13 (26) |
| Unavailability of space to exercise | 8 (16) | 13 (26) |
| No access to gym | 70 (14) | 17 (34) |
| Social influence | ||
| Do not feel like exercising alone | 7 (14) | 13 (26) |
| Likely to exercise with someone | 10 (20) | 24 (48) |
| Individual factors | ||
| Lack of time | 19 (38) | 33 (66) |
| Worried everyone would be younger | 1 (2) | 2 (4) |
| Worried about looks | 3 (6) | 16 (32) |
| Lack of knowledge about exercise | 4 (8) | 11 (22) |
DISCUSSION
Our study highlights a high prevalence (72%) of overweight/obesity and abdominal obesity (80%) among the participants. Despite this, a miniscule of 6% of the participants met the WHO’s recommendation for physical activity. Majority of them engaged in low-intensity activities which they considered would suffice to maintain their health. We found sex-related differences in the type of activities performed as also the motivators and barriers for the same.
We observed a high prevalence of obesity (48.7%) and overweight (23.3%) among our participants. Cross-sectional studies using nationally representative data support the rise in the prevalence of obesity to 34–36% in the past decade.[20,21] Further, during the pandemic, the prevalence of obesity has been noted to have escalated.[22] Furthermore, it is important to note that several studies have used Caucasian BMI cut-off (overweight >24.9 kg/m2; obesity >29.9 kg/m2) for classifying Indians. On the other hand, we have used the Asian cut off (overweight >22.9 kg/m2; obesity >27.5 kg/m2).[23] These points can explain the high prevalence reported by our study.
We found high levels (52%) of physical inactivity despite high levels of obesity. Similar results were also reported several other studies.[3,10,24-26] Similar to the present study Aslesh et al.[27] in their study revealed that there was no significant difference between the level of physical activity and age groups (P = 0.900). However, our findings highlighted those participants aged 40–50 years performed low-intensity activities of higher MET than those aged 30–39 years. This difference was because many participants aged over 40 years were suffering from NCDs. As a result of this, they participated in yoga and other low-intensity exercises to manage their health.
We found gender differences in the duration and MET of the activities. Females spent more time in low-intensity activities (particularly household chores) than males. In Indian households, women are so actively involved in household chores that they barely find time to actively engage in structured physical activity. Evidence shows that the majority of adult women consider doing their household chores as a form of exercise, thereby failing to meet the physical activity recommendations by the WHO.[4,9] However, in our study, females were involved in low-intensity activities with higher MET values (household work such as cooking, cleaning, and washing) compared to the male participants who were involved in activities with lower MET values (such as deskwork). Similar findings were reported by Tripathy et al.[28] where females showed substantially higher levels of light physical activity than males.
We found that participants belonging to the upper class were found to be more engaged in moderate-intensity activities with higher MET values compared to the others belonging to the upper-middle class. This was due to easy access and availability of the resources to the upper-class participants. Eime et al.[29] reported that there was an increase in the regular physical activity participation with an increase in the SES of the participants. High SES has been associated with greater choices, availability, accessibility, and affordability of resources. Further, the built environments such as parks, gyms, well-maintained broad roads, sidewalks had a reasonable effect in motivating participants to engage in physical activity more frequently.
The present study was conducted post lockdown during the first wave of COVID-19 in India. About 23% of our participants revealed that their physical activity levels had increased and 14% reported that their activity had decreased during the pandemic. The reasons that were cited for the decrease in their physical activity levels were reduced accessibility to exercise facilities (parks and gyms) due to the lockdown. Thus, they were involved in low-intensity activities such as yoga and slow-paced walking. A similar pattern in physical activity changes was also seen in the study done by Pérez-Rodrigo et al.[30] where about 29.5% of the adults were less active, 31.4% were more active, and 39.1% were active as usual during the pandemic. Chopra et al.[31] reported that there was a decline in the moderate-intensity exercise participation (P < 0.05) among their participants and a significant increase in the household tasks (P < 0.001) during lockdown restrictions due to COVID-19. It was also observed in another study among young adults (20–21 years) that there was a decline in the physical activity levels of active individuals during the lockdown whereas an opposite pattern was observed in nonactive participants.[32]
Motivators for physical activity and exercise behavior
Health-related issues were considered to be one of the major motivating factors. Abraham[33] reported that family history of diseases and health scare in the family proved to be a positive influencing factor for physical activity among the participants. In our study, health scare was a more common motivator in the younger women (30–39 years) as a majority of the participants reported that they were aware of the risk of developing chronic disorders later in life due to their BMI, central adiposity, family history, and lifestyle. Further, majority of the women participants aged 30–39 years considered engaging in physical activity when advised by a health professional (64%) compared to other participants.
Weight loss was considered as a main motivator by women aged 30–39 years (80%) as compared to men and older women. Participants of the present study indicated that their attempts to lose weight were motivated by dissatisfaction with their body size and determination to achieve their desired weight. Clothing was also a concern among the female participants. When compared to plus-size clothes, smaller-size clothes were more readily available in the markets with greater varieties and at a cheaper price. Similar findings were also reported by other studies wherein dissatisfaction with their physical appearance was the major driving factor for women to participate in physical activity.[8,34-36] Despite the fact that they were aware of the health benefits of exercise, body image was a powerful motivator to exercise.
The availability of resources at a convenient time was seen to as a motivator among women more than men. van Uffelen et al.[37] also observed in their study that women preferred to exercise at a fixed time according to their convenience compared to men. This is owing to the responsibilities at home and the professional front that women shoulder.
Majority of the female participants of the age group 30–39 years (80%) reported better looks as another motivator for being physically active compared to the older female participants (32%). van Uffelen et al.[37] observed that women were twice as likely as men to be motivated by reducing or maintaining weight and better looks for engaging in regular physical activity. Women are known to be more conscious about their looks. They are often judged at their workplace and home for it. Moreover, good looks boost their confidence and self-esteem.[8]
Barriers to physical activity/exercise behavior
Bad weather such as rain and winter was the most commonly reported barrier among the environmental factors by men (92%) than women (66%). Winter cold, incessant rains, cyclones, and harsh summers hinders their regular walks and going to the gyms/parks. Further, women felt unsafe at the gym (24%) and on roads/parks alone (76%). Isolated, poorly lit streets, distance to the gym/parks, presence of greater proportion of males at gyms/parks, fear of dogs, eve-teasing, and instances of sexual harassment rise security concerns for women and their family. Studies have reported that the physical environment, including the availability and accessibility of services and facilities, travel time/distance, walkability, built environment, maintenance, safety, costs, and weather influence adult physical activity levels.[5,7,8] Owing to such unconducive environmental factors, majority of participants favored walking as a preferred exercise because it was inexpensive and could be performed anywhere.
Several studies have reported a lack of motivation as a key barrier.[4,38] Self-determination theory (SDT) explains motivation as a continuum from amotivation-intrinsic motivation– extrinsic motivation. Antony and Azeem[39] found that obese university students had higher amotivation and poor intrinsic motivation. However, we did not study the motivation levels as per SDT.
Lack of time was a common barrier reported especially by females (66%) as compared to males (38%). This is because women were mostly involved in household activities and had several family responsibilities and professional commitments, leaving little time for being physically active. Participants also acknowledged that time management was an issue even when they had ample time. Several studies reported similar findings.[33,40-42]
CONCLUSION
Despite the high levels of overweight/obesity among the adults, the level of physical activity was dismal. This is a huge cause of concern keeping in mind the ongoing pandemic. Government, communities, and individuals need to work together to reinforce the motivating factors and reduce the barriers. Micro-communities such as residential complexes, housing societies, and offices need to take cognizance of the state of the roads, pavements, street lights, parks/gyms and strive to improve the infrastructure with the support of the local government. At the individual level, people need to enhance time-management skills to include exercise as an integral part of their routine. Schools/colleges, health professionals, and government should focus on educating the individuals/communities to involve in regular physical activity since early years and not only when there is a metabolic risk or only for disease prevention. Awareness and education about the types of physical activities to be performed are likely to play a major role in increasing the activity levels of the participants.
Availability of data and materials
Data and materials can be assessed upon request to the corresponding authors
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
REFERENCES
- 1.World Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva, Switzerland: World Health Organization; 2020. [Google Scholar]
- 2.Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6:e1077–86. doi: 10.1016/S2214-109X(18)30357-7. [DOI] [PubMed] [Google Scholar]
- 3.Anjana RM, Pradeepa R, Das AK, Deepa M, Bhansali A, Joshi SR, et al. Physical activity and inactivity patterns in India –Results from the ICMR-INDIAB study (Phase-1) [ICMR-INDIAB-5. Int J Behav Nutr Phys Act. 2014;11:26. doi: 10.1186/1479-5868-11-26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Mathews E, Lakshmi JK, Ravindran TK, Pratt M, Thankappan KR. Perceptions of barriers and facilitators in physical activity participation among women in Thiruvananthapuram City, India. Glob Health Promot. 2016;23:27–36. doi: 10.1177/1757975915573878. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lo BK, Morgan EH, Folta SC, Graham ML, Paul LC, Nelson ME, et al. Environmental influences on physical activity among rural adults in Montana, United States: Views from built environment audits, resident focus groups, and key informant interviews. Int J Environ Res Public Health. 2017;14:1173. doi: 10.3390/ijerph14101173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Ashton LM, Hutchesson MJ, Rollo ME, Morgan PJ, Collins CE. Motivators and barriers to engaging in healthy eating and physical activity. Am J Mens Health. 2017;11:330–43. doi: 10.1177/1557988316680936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Anjali, Sabharwal M. Perceived barriers of young adults for participation in physical activity. Curr Res Nutr Food Sci J. 2018;6:437–49. [Google Scholar]
- 8.Garg S, Kutty VR. “Do I need exercise?”A qualitative study on factors affecting leisure-time physical activity in India. Qual Rep Ft Lauderdale. 2019;24:1065–82. [Google Scholar]
- 9.Devamani C, Oommen A, Mini G, Abraham V, George K. Levels of physical inactivity in rural and Urban Tamil Nadu, India: A cross-sectional study. J Clin Prev Cardiol. 2019;8:13. [Google Scholar]
- 10.Newtonraj A, Murugan N, Singh Z, Chauhan RC, Velavan A, Mani M. Factors associated with physical inactivity among adult Urban population of Puducherry, India: A population based cross-sectional study. J Clin Diagn Res. 2017;11:C15–7. doi: 10.7860/JCDR/2017/24028.9853. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Patil C, Dhoble M, Kaware A. A study of physical activity levels and its correlates among adults: A cross-sectional study. Int J Community Med Public Health. 2017;4:1154–8. [Google Scholar]
- 12.Kanda Y. Investigation of the freely available easy-to-use software 'EZR'for medical statistics. Bone Marrow Transplantation. 2013;48:452–458. doi: 10.1038/bmt.2012.244. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.World Health Organization. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation, Geneva, 8-11 December 2008. Geneva: World Health Organization; 2011. [Google Scholar]
- 14.World Health Organization, Regional Office for the Western Pacific, International Association for the Study of Obesity, International Obesity Task Force. The Asia-Pacific Perspective: Redefining Obesity and its Treatment. World Health Organization Location; Geneva, Switzerland: 2000. [Google Scholar]
- 15.Prasad DS, Kabir Z, Suganthy JP, Dash AK, Das BC. Appropriate anthropometric indices to identify cardiometabolic risk in South Asians. WHO South East Asia J Public Health. 2013;2:142–8. doi: 10.4103/2224-3151.206760. [DOI] [PubMed] [Google Scholar]
- 16.Bouchard C, Tremblay A, Leblanc C, Lortie G, Savard R, Thériault G. A method to assess energy expenditure in children and adults. Am J Clin Nutr. 1983;37:461–7. doi: 10.1093/ajcn/37.3.461. [DOI] [PubMed] [Google Scholar]
- 17.Robert-McComb JJ, Carnero EÁ, Iglesias-Gutiérrez E. Estimating Energy Requirements. In: Robert-McComb JJ, Norman RL, Zumwalt M, editors. The Active Female. New York, NY: Springer New York; 2014. [Last accessed on 2021 Feb 07]. pp. 411–49. Available from:http://link.springer.com/10.1007/978-1-4614-8884-2_27 . [Google Scholar]
- 18.Arzu D, Tuzun EH, Eker L. Perceived barriers to physical activity in University students. J Sports Sci Med. 2006;5:615–20. [PMC free article] [PubMed] [Google Scholar]
- 19.Saleem S. Modified Kuppuswamy socioeconomic scale updated for the year 2020. [Last accessed on July 22, 2021];Indian J Forensic Community Med. 2020 7:1–3. Available from:https://www.researchgate.net/publication/340828985_Modified_Kuppuswamy_socioeconomic_scale_updated_for_the_year_2020 . [Google Scholar]
- 20.Pengpid S, Peltzer K. Prevalence and correlates of underweight and overweight/obesity among women in India: Results from the national family health survey 2015-2016. Diabetes Metab Syndr Obes. 2019;12:647–53. doi: 10.2147/DMSO.S206855. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Luhar S, Mallinson PA, Clarke L, Kinra S. Trends in the socioeconomic patterning of overweight/obesity in India: A repeated cross-sectional study using nationally representative data. BMJ Open. 2018;8:e023935. doi: 10.1136/bmjopen-2018-023935. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Bakaloudi DR, Barazzoni R, Bischoff SC, Breda J, Wickramasinghe K, Chourdakis M. Impact of the first COVID-19 lockdown on body weight: A combined systematic review and a meta-analysis. Clin Nutr. 2021. [Last accessed on 2022 Sep 27]. S0261-5614(21)00207-7. doi:10.1016/j.clnu.2021.04.015. Epub ahead of print. PMID:34049749;PMCID: PMC8056819. Available from:https://www.sciencedirect.com/science/article/pii/S0261561421002077 . [DOI] [PMC free article] [PubMed]
- 23.WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63. doi: 10.1016/S0140-6736(03)15268-3. [DOI] [PubMed] [Google Scholar]
- 24.Dhivyadharshini J, Mohanraj KG. Knowledge, awareness, prevalence, and frequency of daily physical activity in young adults of the present generation. Drug Invent Today. 2019;12:3. [Google Scholar]
- 25.Ravikiran M, Bhansali A, Ravikumar P, Bhansali S, Dutta P, Thakur JS, et al. Prevalence and risk factors of metabolic syndrome among Asian Indians: A community survey. Diabetes Res Clin Pract. 2010;89:181–8. doi: 10.1016/j.diabres.2010.03.010. [DOI] [PubMed] [Google Scholar]
- 26.Singh H, Singh S, Singh A, Baker JS. Physical activity levels among the adults of Majha region of Punjab, India: A cross-sectional study. Am J Hum Biol. 2021;33:e23533. doi: 10.1002/ajhb.23533. [DOI] [PubMed] [Google Scholar]
- 27.Aslesh OP, Mayamol P, Suma RK, Usha K, Sheeba G, Jayasree AK. Level of Physical activity in population aged 16 to 65 years in rural Kerala, India. Asia Pac J Public Health. 2016;28:53S–61S. doi: 10.1177/1010539515598835. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Tripathy JP, Thakur JS, Jeet G, Chawla S, Jain S, Prasad R. Urban rural differences in diet, physical activity and obesity in India: Are we witnessing the great Indian equalisation?Results from a cross-sectional STEPS survey. BMC Public Health. 2016;16:816. doi: 10.1186/s12889-016-3489-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Eime RM, Charity MJ, Harvey JT, Payne WR. Participation in sport and physical activity: Associations with socio-economic status and geographical remoteness. BMC Public Health. 2015;15:434. doi: 10.1186/s12889-015-1796-0. https://doi.org/10.1186/s12889-015-1796-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Perez-Rodrigo C, Gianzo Citores M, Hervás Bárbara G, Ruiz-Litago F, Casis Sáenz L, Arija V, et al. Patterns of change in dietary habits and physical activity during lockdown in Spain due to the COVID-19 pandemic. Nutrients. 2021;13:300. doi: 10.3390/nu13020300. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Chopra S, Ranjan P, Singh V, Kumar S, Arora M, Hasan MS, et al. Impact of COVID-19 on lifestyle-related behaviours-a cross-sectional audit of responses from nine hundred and ninety-five participants from India. Diabetes Metab Syndr. 2020;14:2021–30. doi: 10.1016/j.dsx.2020.09.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Karuc J, Sorić M, Radman I, Mišigoj-Duraković M. Moderators of change in physical activity levels during restrictions due to COVID-19 pandemic in young urban adults. Sustainability. 2020;12:6392. [Google Scholar]
- 33.Abraham J. Factors Influencing Physical Activity among Immigrants from South India: A Qualitative Descriptive Study. [Dissertation on Internet]. University of Texas. [Last cited on 2021 Jan 20]. Available from:https://digitalcommons.library.tmc.edu/uthson_etd/26/
- 34.Laus MF, Costa TM, Almeida SS. Body image dissatisfaction and its relationship with physical activity and body mass index in Brazilian adolescents. J Bras Psiquiatr. 2011;60:315–20. [Google Scholar]
- 35.Pruis TA, Janowsky JS. Assessment of body image in younger and older women. J Gen Psychol. 2010;137:225–38. doi: 10.1080/00221309.2010.484446. [DOI] [PubMed] [Google Scholar]
- 36.Craft BB, Carroll HA, Lustyk MK. Gender differences in exercise habits and quality of life reports: Assessing the moderating effects of reasons for exercise. Int J Lib Arts Soc Sci. 2014;2:65–76. [PMC free article] [PubMed] [Google Scholar]
- 37.van Uffelen JG, Khan A, Burton NW. Gender differences in physical activity motivators and context preferences: A population-based study in people in their sixties. BMC Public Health. 2017;17:624. doi: 10.1186/s12889-017-4540-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Hardcastle SJ, Maxwell-Smith C, Kamarova S, Lamb S, Millar L, Cohen PA. Factors influencing non-participation in an exercise program and attitudes towards physical activity amongst cancer survivors. Support Care Cancer. 2018;26:1289–95. doi: 10.1007/s00520-017-3952-9. [DOI] [PubMed] [Google Scholar]
- 39.Antony VC, Azeem K. Association of physical on exercise motivation and body mass index among university students. Phys Educ Stud. 2021;25:129–35. [Google Scholar]
- 40.Deliens T, Deforche B, De Bourdeaudhuij I, Clarys P. Determinants of physical activity and sedentary behaviour in University students: A qualitative study using focus group discussions. BMC Public Health. 2015;15:201. doi: 10.1186/s12889-015-1553-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Oluyinka S, Endozo A. Factors affecting physical activity participation among University students. J Soc Sci Res. 2019;14:3161–70. [Google Scholar]
- 42.Withall J, Jago R, Fox KR. Why some do but most don't. Barriers and enablers to engaging low-income groups in physical activity programmes: A mixed methods study. BMC Public Health. 2011;11:507. doi: 10.1186/1471-2458-11-507. [DOI] [PMC free article] [PubMed] [Google Scholar]
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Data Availability Statement
Data and materials can be assessed upon request to the corresponding authors