Original research••

Civil servants’ physical activity status and its associated factors in Northeast Ethiopia: applying Health Belief Model

•,,,,,,.

...

Abstract

Background Identifying the associated factors of the physical activity of civil servants is necessary to increase the practice of physical activity. However, limited studies addressed associated factors of civil servants’ physical activity status in Ethiopia. Hence, this study assessed civil servants’ physical activity status and associated factors in Northeast Ethiopia.

Methods An institution-based cross-sectional study design was conducted from 28 February to 13 March 2020 and measured physical activity using WHO physical activity definition. Four hundred and eight civil servants were interviewed using a systematic random sampling technique. A descriptive statistics and multivariable logistic regression model were used.

Result The percentage of physical activity for the civil servants of the study was only 2.38, with the prevalence of physical activity status of 64.0% (95% CI 60.4% to 67.61%). Perceived self-efficacy (adjusted OR (AOR)=1.98, 95% CI 1.15 to 4.12), cues to action (AOR=2.12, 95% CI 1.04 to 3.22) and being a health professional (AOR=2.19, 95% CI 1.13, 4.23) were main associated factors of physical activity. However, physical activity was not affected by respondents’ sex (AOR=1.01, 95% CI 0.67 to 1.83), educational status (AOR=0.23, 95% CI 0.03 to 1.91), perceived barrier (AOR=0.97, 95% CI 0.91 to 1.04) or perceived benefit (AOR=1.02, 95% CI 0.95 to 1.09).

Conclusion The health profession, self-efficacy and cues to action were the main associated factors of physical activity. Hence, the Sports Bureau and Health Bureau should pay attention to providing information by focusing on improving self-confidence and creating a positive attitude toward physical activity.

What is already known on this topic

Physical activity has many health benefits. Sufficient activity positively affects body composition and musculoskeletal development, it reduces the risk of multiple non-communicable diseases heart disease and it improves an individual’s general well-being. Yet, most research on population activity levels stem from studies in western cultures.

What this study adds

Our study describes the physical activity and adds a detailed understanding of factors associated with the physical activity of Ethiopian civil servants.

How this study might affect research, practice or policy

Our results provide guidance for the development of policy and the design of training programmes and interventions to positively affect physical activity, specifically targeted at Ethiopian civil servants.

Background

Physical activity is any body movement produced by skeletal muscles that require energy expenditure. Popular ways of physical activity to be active include walking, cycling, wheeling, sports, active recreation and play. It can be done at any level of skill and for enjoyment by everybody. Globally, one in four adults does not currently meet the recommendations for physical activity stated by WHO.1 About 40% of adults in Africa were physically inactive.2 3 In Ethiopia, physical inactivity accounts for only 6% of the general population.4 5 However, less than 50% of Ethiopian civil servants fulfilled WHO recommendations for physical activity.6 7

Physical activity can be affected by demographic, cultural, psychological, behavioural, biomedical and environmental factors.8 Studies suggest that female respondents perform low physical activity compared to male respondents9–11; similarly, older aged and higher income was not met recommended physical activity.9 12–14 The lower level of perceived severity, perceived benefit, self-efficacy and cues to action were also factors for physical inactivity.13 15–19

Studies recommended regular physical activity to prevent and control non-communicable disease (NCD) related morbidity and mortality.1 20 21 However, population groups, like civil servants, tend to be less physically active.6

Physical inactivity was responsible for economic loss (accounts for 1%–3% of national healthcare costs),22 and it is also associated with NCDs, which caused 71% of global deaths, 22% of African premature adult deaths and 39% of all Ethiopian deaths in 2016.23 24

In Ethiopia, disease epidemiology is becoming a double burden (communicable and non-communicable).25 26

Sustainable development goal was set to reduce premature mortality from NCDs through prevention and treatment by one-third by 2030.27 Additionally, the Ethiopian health sector transformation plan and national strategic action plan for prevention and control of NCDs target to reduce NCD-associated morbidity and mortality.28 29

Practising moderate and/or vigorous physical activity plays a crucial role in achieving these targets, especially for civil servants, due to their routine work.

In Ethiopia, some studies assessed the prevalence and factors of physical activity among general populations.4 6 30 However, limited studies addressed the physical activity statuses of civil servants, especially in the study area. Moreover, none of those studies identified behavioural factors using the behavioural model as a conceptual framework. Therefore, this study assessed physical activity practice and identified behavioural factors using Health Belief Model (HBM) among civil servants.

Methods

Study design and area

An institutional-based cross-sectional study des conducted in Antsokia, North Shoa Zone, Amhara Region, from 28 February to 13 March 2020. Antsokia is 400 km north of Addis Ababa, the capital city of Ethiopia. One thousand five hundred and ten civil servants are employed among 25 governmental sector offices and provide different services for the district. The town of the district has one public sports place for residents.

Source and study population

Source population

The source population was all civil servants in Antsokia district.

Study population

Selected civil servants were the study population during the study period.

Inclusion and exclusion criteria

Inclusion

Any civil servant 18 years or older and those who lived more than 6 months in the district were included.

Exclusion criteria

Civil servants with a known disease or physical disability that limited physical activity were excluded from the study.

Operational definition

Physical activity:achieving at least 600 MET (maximum exercise per time) by practising at least 150 min of moderate physical activity and/or at least 75 min vigorous activity was considered as physically active, and practising moderate physical activity less than 150 min and/or 75 min vigorous activity throughout the week was considered as physically inactive. Following the International Physical Activity Questionnaire (IPAQ), practising at least 3000 MET was considered highly physically active; at least 600 MET was medium physically active while practising for less than 600 MET was considered inactive.

Perceived susceptibility: was measured by a summed score of items, approaching the maximum sum scores considered as high influence and to minimum sum scores as having a low influence on physical activity of civil servants with reference to 7 practices questionnaire regarding the prevention of NCD (hypertension, diabetes mellitus, cancer and others) by using a 5-point scale.

Perceived severity: was measured by a summed score of items, approaching the maximum sum scores considered as high influence and to minimum sum scores as having low influence on physical activity of civil servants with reference to five practices questionnaire regarding complications of facing NCD using a five-point scale.

Perceived benefit: was measured by a summed score of items focusing on the benefits of practising physical activity (PA) and approaching the maximum sum scores considered as high influence and minimum sum scores as having low influence on physical activity of civil servants with a reference of five questionnaires regarding on minimisation of different health problem by using a five-point scale.

Perceived barrier: was measured by a summed score of items focusing on barriers to practice PA and approaching the maximum sum scores considered as high influence and to minimum sum scores as having low influence on physical activity of civil servants with a reference of six questionnaires regarding on threat of physical activity by using a five-point scale.

Self-efficacy: was measured by a summed score of items focusing on self-confidence to practice PA and approaching the maximum sum scores considered as high influence and to minimum sum scores as having low influence on physical activity of civil servants with a reference of five practices questionnaire using a five-point scale.

Cues to action: were measured by a summed score of items focusing on motivators to practice PA and approaching the maximum sum scores considered as high influence and to minimum sum scores as having low influence on physical activity of civil servants with a reference of five practices questionnaire using a five-point scale.

Sample size determination and sampling technique

The calculated sample was 408 considering (p=40.9%) from a previous study,7 level of confidence = 95% and marginal error = 0.05, including a 10% non-response rate. Lists of total civil servants were obtained from Antsokia civil service office, and the sampling frame was prepared using the alphabetical order of their names. Then, we used a systematic random sampling technique to select study participants by calculating the selection interval\Kth interval\(total population/minimum sample size (1510/408=3.7)). The first participant was selected through the lottery method, and then we selected at every four intervals.

Data collection and measurement

Using a pretested, structured, self-administered questionnaire, participants were interviewed in Amharic (local language). The questionnaire was adapted from the IPAQ31 and items for HBM constructs (perceived susceptibility, perceived severity, perceived benefit, perceived barriers, cues to action and self-efficacy) from previous literature.19 32 33 The questionnaire included sociodemographic variables, the physical activity status of civil servants and the Health Belief Model Contract of physical activity. The data were collected by six college-completing BSc nurses in the selected participant office, and the principal investigator supervised the data collection process. The training was given to data collectors about the data collection tool, how to collect data and taking consent to having a common understanding.

The physical activity status was assessed using six questions. The perceived susceptibility part consisted of 7 items in 5-point Likert scale and the score of individuals for each item was summed up ranging from 7 minimum to 35 maximum total scores, perceived severity consisted 5-point Likert scale items and the score of individuals for each item was summed up ranging from 5 minimum to 25 maximum total score, perceived benefit consisted 5-point Likert scale items and the score of individuals for each item was summed up ranging from 5 minimum to 25 maximum total score, perceived barriers consisted of 6 items with 5-point Likert scale and the score of individuals for each item were summed up ranging from 6 minimum to 30 maximum total score, cues to action consisted of 5 items in 5-point Likert scale and the score of individuals for each item were summed up ranging from 5 minimum to 25 maximum total score and self-efficacy consisted of 5 items in 5-point Likert scale and the score of individuals for each item were summed up ranging from 5 minimum to 25 maximum total score.

For all constructs, the sum of the score of individuals for each item was summed up after reverse coding for negatively worded items. All constructs were treated as continuous variables for analysis.

The questionnaire was translated from English to the local language (Amharic) and translated back to English by another individual to check the consistency. It was pretested on 5% (21) related participants in Kemisse town, which is 12 km far from the study area, before the actual data collection period. The reliability test was checked using Cronbach’s α of 0.7 as a cut-off point, and multicollinearity between independent variables was also checked.

Data management and analysis

The data were checked, coded and entered using Epi Data software V.3.1 (Epi Data Association, Odense, Denmark) and exported to SPSS software V.23 for further analysis. The descriptive result was presented using frequency and proportions for all variables, and a bivariable regression analysis was performed to determine the association between individual factors and physical activity status at a 95% CI and a p value of <0.2. To identify the independently associated factors of physical activity status, multivariable logistic regressions were performed, and variables with a p valueof <0.05 were considered statistically significant associated factors of outcome variables. The goodness of fit of the final model was checked using the Hosmer and Lemeshow test of goodness of fit, and the result was 0.69.

Results

Flow chart of the study

Here, the figure shows how the inclusion went from the ‘1510 civil servants employed among 25 governmental sector offices’ to the 408 participants finally interviewed (figure 1).

Request permission

Figure 1

The flow chart of the study.

Sociodemographic characteristics

Three hundred and eighty nine participants were interviewed from 28 February to 13 March 2020, with a response rate of 95.3%. Of the interviewed participants, 286 (73.5%) were males,184 (47.3%) participants were married, 61 (15.7%) were health professionals and 51 (13%) participants had a family history of NCD. The mean age of the respondents was 30 years old with SD ±6.5 years (table 1).

Table 1

Sociodemographic characteristics of study participants at Antsokia, North Shoa Ethiopia, 2020 (N=389)

Physical activity status and HBM constructs

Of the participants, only 64% (95% CI 60.4% to 67.61%) were physically active. Two hundred and five (82.3%) of the individuals who were physically active in the study did so at a moderate level, while 44 (17.7%) did it at a high level. For each construct of the Health Belief Model, which was framed around physical activity, respondents’ mean score was computed. Consequently, the mean scores were 19.4±4.2 (range of possible values: 7–35) for perceived susceptibility, 14.9±2.8 (range of possible values: 5–25) for perceived severity, 14.4±3.6 (range of possible values: 5–25) for perceived barriers, 20.4±3.5 (range of possible values: 6–30) for perceived benefit, 17.7±3.1 (range of possible values: 5–25) for the cue to action and 18.3±3.8 (range of possible values: 5–25) for self-efficacy. The Cronbach’s α of each construct was above 0.7.

Associated factors of physical activity status

The relationship between physical activity status and predictor variables was first assessed through bivariable analysis. In this analysis, the sex of respondents, profession, educational status, perceived barriers, perceived benefit, self-efficacy and cues to action were significantly associated with physical activity status at a 20% significance level (table 2). However, in a multivariable logistic regression, only profession, self-efficacy and cues to action were significantly associated with physical activity at α = 0.05.

Table 2

The bivariable binary logistic regression analysis of factors associated with physical activity status in Antsokia, Northeast Ethiopia, 2020

Accordingly, health professionals were 2.19 times (AOR=2.19; 95% CI 1.13 to 4.23) more likely to be physically active than non-health professionals. For every one-point increase in the self-efficacy sum score, the odds of physical activity were increased by 1.98 (AOR=1.98; 95% CI 1.15 to 4.12). Likewise, a unit increase in the cues-to-action sum of the score also increases the odds of physical activity by 2.12 (AOR=2.12; 95% CI 1.04 to 3.22) (table 3).

Table 3

Factors associated with physical activity status in Antsokia, Northeast Ethiopia, 2020

Discussion

This study provides insight into the habit of physical activity of civil servants in Northern Ethiopia. WHO recommended that a physically active individual achieve at least 600 MET.34 According to the WHO recommendations, 64% of the civil servants were physically active in this study. The current study finding is higher than studies conducted in Ethiopia, Addis Ababa (51.8%)17 and Ambo (40.9%).18 The difference could be the inclusion of rural area civil servants in this study. Rural civil servants might not have transport access, and due to this reason, they used food and bicycles.

However, this finding is lower than studies conducted in Tanzania (96%),14 Nigeria (68.6%),15 Canada (75%)12 and China (93%).35 The possible explanation for this difference could be the inaccessibility of sports fields, the lack of an extensive awareness campaign about the benefit of physical exercise and poor attention by concerned government bodies in this study.

In this study, physical activity was more likely among health professionals than non-health professionals. This finding aligned with a study conducted in Turkey, where walking activities were significantly higher among health professionals than among teachers.36 Moreover, a study conducted in Malaysia supported this study finding that medical staff were more likely to be physically active than non-medical staff.37 The consistency of the current and previous studies might be due to the presence of knowledge and positive attitudes about the effect of physical activity on health promotion, disease prevention and treatment among health professionals.

Self-efficacy was positively and significantly associated with physical activity in the current study; participants with a higher score in self-efficacy were more likely to practice physical activity. This finding aligns with studies conducted in Iran, China and Poland.13 16–18 This implies that, as HBM stated, whenever an individual has higher confidence to perform the behaviour, ending with the practice is more likely.33

The other important construct of this study was respondents who had higher scores of cues to action were more likely to practice physical activity. This finding was consistent with studies conducted in Indonesia15 and the USA,19 in which direct associations were observed between cues to action and physical activity. This implies that, as HBM stated, getting the external motivation to practice behaviour could improve the practice.33

Health Belief Model explained 13.8% of the variance for physical activity. It was lower than studies conducted in Iran at 33%38 and in Poland, where HBM explained 24% of the variance for vigorous physical activity but higher than the variance for moderate physical activity, which was 4% in this similar study.17

Based on this and previous studies, self-efficacy and cues to action seem to be important factors of physical activity and focusing on improving self-confidence and motivation will have a higher effect on promoting physical activity.

As a limitation, even if the Health Belief Model is used, only distal factors (perception) of physical activity were identified. However, proximal factors like environmental factors were not addressed, and another limitation was the study being conducted in a single district due to budget and time constraints.

Conclusions

The percentage of civil servants engaged in physical activity was low. As a health professional, self-efficacy and cues to action were identified as the main contributing factors to increased physical activity. This study recommends researchers apply other behavioural models and identify immediately associated factors like attitude, intention and environmental factors.

Contributors: AMD, CYD and MS contributed to the proposal development, data collection, analysis and result interpretation. EG, YW, HG and EK contributed to the conceptualisation and writing of the paper and edited the overall improvement of the manuscript. All authors read and approved the final submitted paper.
Funding: This research was funded by Bahir Dar University.
Competing interests: None declared.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Data are available upon reasonable request.

Ethics statements

Patient consent for publication:

Ethics approval:

This study involves human participants. Ethical clearance was obtained from the institutional review board of the College of Medicine and Health Sciences, Bahir Dar University. An official support letter was obtained from Amhara Public Health Institute. Written consent was obtained from the participants after informing them all purpose, benefits and risks of the study and that the procedures complied with the Declaration of Helsinki. Participants gave informed consent to participate in the study before taking part.

Acknowledgements

We would like to acknowledge the assistance of Bahir Dar University, the College of Medicine and Health Science and the School of Public Health in undertaking this research. We would also like to express our gratitude to the study participants and officials working at Amhara Regional Health Institute and Antsokia Civil Service Office, that deserve acknowledgement for their cooperation. Last, our thanks also go to data collectors, supervisors and advisors.

WHO. Global action plan on physical activity 2018–2030: more active people for a healthier world. 2018;
Google Scholar
John B, Todd J, Mboya I, et al. Physical activity and associated factors from a cross-sectional survey among adults in Northern Tanzania. BMC Public Health 2017; 17.
doi:10.1186/s12889-017-4512-4•Google Scholar
Oyeyemi AL, Oyeyemi AY, Jidda ZA, et al. Prevalence of physical activity among adults in a metropolitan Nigerian city. J Epidemiol 2013; 23:169–77.
doi:10.2188/jea.je20120116•Google Scholar
EPHI. Ethiopia STEPS report on risk factors for non communicable disease and prevalence of selected communicable disease. Addis Ababa 2016;
Google Scholar
Tedila K. Magnitude and determinants of physical inactivity in Ethiopia: evidence from 2015 Ethiopia national NCD survey. 2015;
Google Scholar
Bogale KA, Zewale TA. Determinant factors of overweight/obesity among federal ministry civil servants in Addis Ababa, Ethiopia: a call for sector-wise occupational health program. BMC Res Notes 2019; 12.
doi:10.1186/s13104-019-4489-4•Google Scholar
Abebe D. Leisure time physical activity and associated factors among civil servants working in Ambo town. Acta Scientific Medical Sciences 2018; 4:76–81.
Google Scholar
Ana O. Effects of personal, social and environmental factors on physical activity behavior among adults, 28. Portugal 2010;
Google Scholar
Asiamah N, Khurana V. Socio-demographic determinants of physical activity (PA): a working class perspective. Cogent Medicine 2016; 3:1276037.
doi:10.1080/2331205X.2016.1276037•Google Scholar
Endozo AN. Current exercise habits and factors affecting physical activity participation among university students. Glob J Health Sci 2019; 11:117.
doi:10.5539/gjhs.v11n4p117•Google Scholar
Melkamu MM, Roba HS, Ayele BH, et al. Level of physical activity among urban adults and the socio-demographic correlates. BMC 2019; 19.
doi:10.1186/s12889-019-7465-y•Google Scholar
Rhodes RE, Janssen I, Bredin SSD, et al. Physical activity: health impact, prevalence, correlates and interventions. Psychol Health 2017; 32:942–75.
doi:10.1080/08870446.2017.1325486•Google Scholar
Habibollah H. Determinants of physical activity in middle-aged woman in Isfahan using the health belief model. J Educ Health Promot 2017; 6.
doi:10.4103/jehp.jehp_68_15•Google Scholar
Abraham T. Prediction of physical activity among type-2 diabetes patients attending Jimma University specialized hospital, Southwest Ethiopia: application of health belief model. Science Journal of Public Health 2014; 2:524–31.
Google Scholar
Puspita RC, Tamtomo D, Indarto D, et al. Dono Indarto 3, health belief model for the analysis of factors affecting hypertension preventive behavior among adolescents in Surakarta. J Health Promot Behav 2017; 02:183–96.
doi:10.26911/thejhpb.2017.02.02.08•Google Scholar
Rahmati-Najarkolaei F, Tavafian SS, Gholami Fesharaki M, et al. Factors predicting nutrition and physical activity behaviors due to cardiovascular disease in Tehran university students: application of health belief model. Iran Red Crescent Med J 2015; 17.
doi:10.5812/ircmj.18879•Google Scholar
Krzysztof S. Health belief model variables as associated factors of light, moderate and vigorous physical activity among young adults. New Educational Review J 2013;
Google Scholar
Lu C, Stolk RP, Sauer PJJ, et al. Factors of physical activity among Chinese children and adolescents. Int J Behav Nutr Phys Act 2017; 14.
doi:10.1186/s12966-017-0486-y•Google Scholar
King KA, Vidourek RA, English L, et al. Vigorous physical activity among college students: using the health belief model to assess involvement and social support. Arch Exerc Health Dis 2014; 4:267–79.
doi:10.5628/aehd.v4i2.153•Google Scholar
Ahmed SH, Meyer HE, Kjøllesdal MK, et al. The prevalence of selected risk factors for non-communicable diseases in Hargeisa, Somaliland: a cross-sectional study. BMC Public Health 2019; 19.
doi:10.1186/s12889-019-7101-x•Google Scholar
Olawuyi AT, Adeoye IA. The prevalence and associated factors of noncommunicable disease risk factors among civil servants in Ibadan, Nigeria. PLOS ONE 2018; 13.
doi:10.1371/journal.pone.0203587•Google Scholar
SharmaI B. Prevalence and correlates of insufficient physical activity in school adolescents in Peru. RSP 2015;
Google Scholar
WHO. Non communicable disease country profile. Geneva 2018;
Google Scholar
DemilewI YM, Firew BS. Factors associated with noncommunicable disease among adults in Mecha district, Ethiopia: a case control study. PLOS ONE 2017;
Google Scholar
Endriyas M, Mekonnen E, Dana T, et al. Burden of NCDs in SNNP region, Ethiopia: a retrospective study. BMC Health Serv Res 2018; 18.
doi:10.1186/s12913-018-3298-0•Google Scholar
Fassil S. Non-communicable diseases in Ethiopia: disease burden, gaps in health care delivery and strategic directions. Ethiop J Health Dev 2018;
Google Scholar
UNDP. Sustainable development goal. 2015;
Google Scholar
Kebede W. National strategic action plan for prevention and control of non communicable disease in Ethiopia. 2014-2016. Addis Ababa, Ethiopia
Google Scholar
EMOH. Ethiopia health system transformation plan. Addis Ababa 2015;
Google Scholar
EMOH. Non communicable disease and injuries in report. Addis Ababa 2018;
Google Scholar
Burns N, Grove SK. Understanding nursing research building an evidence-based practice. U.S.A, Elsevier Saunders 2011;
Google Scholar
Gristwood J. Applying the health belief model to physical activity engagement among older adults. Illuminare 2011; 9:59–71.
Google Scholar
Charles A. The health belief model. 2015;
Google Scholar
WHO. Global recommendations on physical activity for health. 2010;
Google Scholar
Lijun S. Physical activity level and associated factors among civil servants in Xi’An, China. Science and Medicine in Sport 2015;
Google Scholar
Atan T, Tural E, Imamoglu O, et al. Physical activity levels of teachers and health professionals in Turkey. HealthMED 2012; 6.
Google Scholar
Jamil AT. Levels of physical activity and its associated factors among health care workers. Malaysian J Public Health Med 2016; 16:127–33.
Google Scholar
Hosseini H, Moradi R, Kazemi A, et al. Determinants of physical activity in middle-aged woman in Isfahan using the health belief model. J Educ Health Promot 2017; 6.
doi:10.4103/jehp.jehp_68_15•Google Scholar

Overview

Abstract
Background
Methods
Results
Discussion
Conclusions
References

Supplementary files
Footnotes
Publication history
Rapid Responses

Article metrics

Altmetric

Dimensions