Relationship Between Physical Inactivity and Obesity in the Urban Slums of Lahore

Relationship Between Physical Inactivity and Obesity in the Urban Slums of Lahore

Introduction

There are several factors such as physical inactivity, sedentary lifestyle, and diet that can be responsible for weight gain or obesity. Regular physical activity is important for better physical and emotional well-being. The objective of the study is to observe the prevalence of obesity or over-weight and how lack of physical activity contributes to weight gain and other health issues.

Methods

This cross-sectional study was conducted in Shalamar Town, Lahore on 646 participants. Data was collected using the WHO STEPS instrument. The inclusion criteria were a minimum age of 30 years and residents of Shalimar Town, Lahore for more than five years. The exclusion criteria were participants with comorbid conditions like HIV, TB, and terminal stage of cancer. Test of association and binary logistic regression analysis was performed to observe a significant association between demographic variables and non-communicable diseases among the participants involved in performing physical exercise.

Results

About 22.1{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the participants had normal BMI, 5.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} were underweight whereas 34.2{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the participants were overweight and 32.4{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} obese. Male participants were found to be more physically active compared to females. Hypertension and diabetes were statistically significantly associated with physical activity. BMI and waist-hip ratio were found to be associated with moderate physical exercise.

Conclusion

Most of the participants were not involved in moderate physical activity. Overall, an alarming 66.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the participants were either overweight or obese. In general, males were found to participate more in intense physical activity.

Introduction

Physical activity refers to the action that consumes energy and produces skeletal muscle. It is the prime element for the improvement of health [1]. It has a considerable role in fat loss and healthy weight gain. Regular physical activity is important for better physical and mental well-being, and for the prevention of various health issues [2].

Promoting physical activity in the early stages of life is imperative for the healthy growth of children and adults [2,3]. Children and adolescents of age 5-17 years must do moderate to intense physical activity for at least 60 minutes as given in the World Health Organization (WHO) recommendations [3]. Moderate physical activity requires moderate physical effort and causes a small increase in breathing or heart rate or carrying light loads for at least 10 minutes continuously whereas intense physical activity requires hard physical effort and causes a large increase in breathing or heart rate or carrying or lifting heavy loads for at least 10 minutes continuously. Mild physical activity is riding a bicycle or walking for 10 minutes [4].

Increased physical activity has numerous social benefits like community engagement, better social interaction as well as reduced anxiety and depression, increased muscular strength, reduced odds for the development of non-communicable diseases (NCDs), improved respiratory system, strong immune system, improved stamina, and endurance [2,3].

Obesity is related to eating disorders such as binge and night eating disorders [5]. Balanced nutrition together with physical activity leads to a healthy lifestyle that enhances lifelong health [6]. Another global recommendation from the WHO is to do moderate to intense physical activity for 150 min/week to attain and maintain good health [7]. Due to the rapid growth in technology and more scope of social media, physical inactivity has turned into a universal pandemic. Adults mostly prefer to remain sedentary, which makes them more vulnerable to disease or ill health [8].

Chronic health problems and various NCDs are mainly emanated from physical inactivity [9]. The most common and significant health issue associated with physical inactivity is obesity or increased BMI [9]. Physical inactivity and obesity are among the leading risk factors for morbidity and mortality [10]. Obesity is the root cause of many NCDs, like diabetes, hypertension, stroke, and osteoporosis [11].

Weight is maintained by the physical mechanism of a balance between energy expenditure and consumption. When the human body burns fewer calories either because of decreased physical exercise or increased eating, the result is obesity. The final image is of excessive and abnormal fat accumulated in the body [12].

The extent of weight gain varies from factors such as age, gender, ethnicity, etc. [12]. Obesity is said to occur when the body weight exceeds 20{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the ideal weight in respect to a person’s age, weight, and height [12]. Obesity has become the fifth leading risk factor for global deaths. Overweight is the sixth principal risk factor contributing to the overall burden of NCDs [13]. BMI is a measure used to assess obesity. WHO has developed criteria for the global and South Asian populations for obesity [14].

For the global population, BMI ranging from 18.5 to 24.9 is considered normal whereas BMI ranging from 25-29.9 means over-weight, 30-34.9 is obesity class I, 35-39.9 is obesity class II and > 40 is obesity class III. Normal BMI ranges from 18.5 to less than 23 in South Asian countries, 23-27.5 is labeled over-weight and 27.5-32.5, 32.5-37.5, and above 37.5 are obesity class I, II, and III [1].

The objective of the study is to find out the prevalence of obesity and overweight as a result of physical inactivity in the urban slums of Lahore.

Materials & Methods

The study included 646 participants, living in Union Council 120 and 122 of District Lahore, Pakistan. The minimum sample size calculated was 317 using the WHO sample size calculator taking the prevalence of obesity as 29{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} [1], 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} confidence and 80{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} power of the study [15]. The data was collected from September 2018 to September 2019. Two-stage sampling was used to collect data. Union Council (UC) was the first stage unit and within each UC, blocking was done as the secondary unit. Samples were selected from a selected block using systematic sampling.

The inclusion criteria were residents living in UC 120 and 122 for more than five years of either sex, with a minimum age of 30 years; exclusion criteria were participants with comorbid conditions like HIV, TB, and terminal stage of cancer. Also excluded were those participants who refused to participate.

The Institutional Review Board of Shalamar Medical College permitted the study. The data was collected from personal interviews using a section of physical activity, physical measurements, and biochemical measurements of the WHO STEPS questionnaire.

The descriptive statistics for the continuous variables were given. Test of association was applied to observe the associated factors with physical activity. Binary logistic regression was performed in light of significant Hosmer-Lemeshow statistics. One-way analysis of variance was performed to observe whether significant differences existed in BMI, blood sugar ratio, waist-hip ratio, heart rate, and blood pressure of the participants who were involved in intense physical exercise. Data analysis was done using SPSS v.26 (IBM Corp., Armonk, NY) [16].

Results

The average age of the respondents was 44.5 + 13.3 SD (in years). Most of the respondents were female. The proportion of male respondents was 36.8{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}. Female participants were more obese and overweight (Table 1). Nearly 40{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} and 22.5{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the male participants were overweight and obese out of the total proportion of overweight and obese participants.

BMI Total Male Female
Under-weight 52 (8{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) 26 (10.9{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) 26 (6.4{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf})
Normal 164 (25.4{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) 77 (32.4{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) 87 (21.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf})
Over-weight 221 (34.2) 88 (37{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) 133 (32.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf})
Obese 209 (32.4{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) 47 (19.7{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) 162 (39.7{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf})

Out of the total sample, 76.5{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the participants performed mild physical activity on a regular basis whereas 22.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} had a routine of doing vigorous to intense physical activity. About 44{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} were attending moderate physical activities. Male participants were found to be more physically active compared to females. Intense physical activity and gender showed a statistically significant association (Table 2).

Variables Categories Mild Physical Activity Moderate Physical Activity Intense Physical Activity
Yes No p-value Yes No p-value Yes No p-value
Gender Male 191 47  0.08 105 133  1.00 90 148 0.00*
Female 303 105 180 228 93 315
Education-al Level Illiterate 164 62 0.27 97 129  0.31 58 168 0.22
Less than Primary 81 31 44 68 29 83
Primary 23 06 14 15 12 17
Secondary 88 15 41 62 39 64
High school 90 25 61 54 28 87
College or university 37 09 19 27 13 33
Postgraduate 10 04 08 06 04 10
Non-response 01 0 01 0 0 01
Ethnicity Punjabi 204 67 0.15 109 162 0.23 87 184 0.05*
Urdu 197 59 122 134 57 199
Pushtoon 74 15 43 46 29 60
Others 19 11 11 19 10 20
Marital Status Never Married 24 10 0.01* 21 13 0.00* 11 23 0.62
Currently Married 452 128 258 322 166 414
Separated 02 0 01 01 0 02
Widowed 16 14 05 25 06 24
Occupation Government Job 49 09 0.02* 23 35 0.00* 26 32 0.01*
Private Job 93 16 63 46 37 72
Self-employed 35 11 19 27 18 28
Student 02 01 02 01 01 02
Retired 19 06 06 19 03 22
Unemployed (can work) 13 10 03 20 03 20
Unemployed (disabled) 08 07 06 09 05 10
Household Chores 273 92 162 203 89 276
Non-response 02 0 01 01 01 01

Statistically, significant association was found between mild physical activity and diabetes (p-value=0.000). Hypertension and diabetes were statistically linked with physical activity of moderate nature (p-value=0.008, 0.018). BMI was significantly related to moderate physical exercise (p-value=0.028). Figure 1 illustrates the proportion of participants who fall in various categories of BMI who were involved in moderate physical activity across age and waist-hip ratio.

Hosmer-Lemeshow statistic with a p-value of less than 5{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} showed that the Binary logistic regression model is a good fit (Table 3). Age was observed as an insignificant factor for intense physical activity. The reference category was male for gender. The reference category for BMI was underweight. Gender, hypertension, stroke or heart attack, and BMI were found as significant factors for intense physical activity. The odds for doing physical activity was 2.008 times higher in males as compared to females. In general, males were found to participate more in intense physical activity. Similarly, participants with a heart attack or stroke were 2.020 times more involved in intense physical activity. However, the negative coefficient of regression for hypertension indicated that odds were 44{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} lower for hypertensive people.

Factors B S.E. Sig. OR 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} C.I. Lower-Upper
Age 0.015 0.011 0.150 1.016 0.994-1.037
Gender (Male) 0.697 0.281 0.013 2.008 1.157-3.484
Raised Cholesterol (Yes) 0.197 0.405 0.627 1.217 0.550-2.693
Hypertension (Yes) -0.579 0.281 0.039 0.560 0.323-0.972
Diabetes (Yes) -0.522 0.354 0.141 0.593 0.296-1.188
Stroke (Yes) 0.703 0.330 0.033 2.020 1.058-3.856
BMI (Under-weight)     0.039    
BMI (Normal) -0.278 0.586 0.634 0.757 0.240-2.385
BMI (Over-weight) -1.006 0.561 0.073 0.366 0.122-1.099
BMI (Obese) -1.120 0.566 0.048 0.326 0.108-0.988

Discussion

Current research projects a clear image of insufficient physical activity in the urban slums of Lahore. The study revealed that 44{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the participants were engaged in moderate physical activity and 22.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} were engaged in intense physical activity. A study reported that 42.8{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of participants had a routine of doing a moderate physical activity which is quite close to our finding [17]. Another study in contrast reported 72.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} as the prevalence of physical inactivity [18]. We observed a significant association between physical activity and gender. Comparatively, female participants were more inactive. In a study conducted in Peshawar, Pakistan among undergraduate students, male students were found to be more physically active. The possible reason could be that most females spend their time working at home [19].

The proportion of participants with normal BMI was 25.4{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} which is quite low in comparison to a study where a proportion of normal BMI was 65.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} [19]. Another research based on the age group 18-65 years also reported the percentage of respondents with a normal BMI of 54.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} [19]. A study conducted in Pakistan in 2020 reported the proportion of participants with a normal BMI as 21{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} which is very close to our findings [20].

Our study revealed a statistically significant association of BMI with moderate physical activity. Gender and BMI were observed as independently and significantly related to intense physical activity by applying the modal of binary logistic regression. Multivariate logistic regression showed that BMI greater than 33 and age greater than 33 years were significantly independently associated factors for physical activity [19].

A major reason for this public health issue in Pakistan is the lack of awareness [21]. People living in big cities seem to be more exposed to the risk of obesity due to their busy and sedentary lifestyles [21]. Obesity has multiple effects on other NCDs. Pakistan, among the South Asian countries, has the highest percentage of diabetic patients [21]. In the present era, our country Pakistan is in the phase where obesity is directly related to diabetes which is very common irrespective of age, gender, and other socio-demographic characteristics [22-24].

Limitations of the study are the small sample size and the relationship of obesity in the urban slums of Lahore with only one factor, i.e., physical activity. Several other factors such as eating habits, dietary patterns, and other genetic and behavioral factors can also contribute to weight gain and obesity. The association between obesity and these factors must be assessed to observe the contribution of each factor besides physical inactivity.

Conclusions

The most common physical activity was mild physical activity which was a 10-minute walk among the urban slum dwellers of Lahore. Nearly two-thirds of the participants walked on a regular basis. Less than half of the respondents said that they do moderate physical exercise. Female participants were least involved in moderate and intense physical activity. Overall, 66.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the participants were either overweight or obese. BMI and hypertension were significant risk factors for physical inactivity. BMI was significantly associated with moderate physical exercise. In general, males were found to participate more in intense physical activity.

What does our study add?

Obesity is more prevalent among adult females as compared to males in the slum areas of Lahore, Pakistan.

The population-based study showed an alarming rate of participants were either overweight or obese in the urban slums of Lahore, Pakistan.

A significant association was found between mild physical activity and diabetes.

The odds for doing physical activity were two times higher in males compared to females in the slum areas of Lahore.

The conceptual framework for a combined food literacy and physical activity intervention to optimize metabolic health among women of reproductive age in urban Uganda | BMC Public Health

The conceptual framework for a combined food literacy and physical activity intervention to optimize metabolic health among women of reproductive age in urban Uganda | BMC Public Health

Step I: Needs assessment

Findings from our systematic review [16] were used to design a theoretical framework for the qualitative study [17]. Notable determinants identified in the systematic review were financial and time limitations, health/beauty paradox (= overweight/obesity as a sign of beauty and wealth), and lack of knowledge, self-efficacy, and skills. Qualitative study findings re-affirmed the systematic review findings concerning health/beauty paradox, knowledge, self-efficacy, and skills gaps. In addition, the qualitative study showed socio-cultural misconceptions around lifestyle PA, fruits, vegetables, and habitual orientation towards carbohydrate foods. We also found that there is a high trust in nutrition information shared on social and mass media, yet skills to evaluate this nutrition information are limited. Figure 1 below shows the logical model of needs assessment, summarises the determinants of dietary and PA in urban Uganda [16, 17].

Fig. 1
figure 1

Logical model of needs assessment, summarizing the personal and environmental determinants of dietary and PA behavior in urban Uganda. Adapted from Yiga et al., [16] and Yiga et al., [17]

Step II: Formulation of behavioral intervention, performance, and change objectives

We hypothesised that changing the overall existing behaviours towards WHO healthy lifestyle guidelines in one intervention may meet strong resistance and thus may not be effective. For example, the planning group hypothesised that due to the existing health/beauty paradox and habitual orientation towards carbohydrate rich foods, interventions focusing directly on weight loss and reduction of portion sizes of foods rich in carbohydrates may meet strong resistance. Therefore, we decided to go for more feasible gradual changes able to enact clinically relevant metabolic improvements. We hypothesised that increased consumption of vegetables and fruits will indirectly translate into reduction of portion sizes of carbohydrate rich foods. In line with WHO health recommendations, the intervention aims to stimulate WRA to consume at least 400 g fruits and vegetables [13]. Moderate intensity PA that can be incorporated in daily life activities may be the achievable type of PA among WRA compared to structural high intensity PA [26]. Non-factual nutrition information influences dietary and PA behaviors in urban Uganda [17]. Thus, we decided to supplement the intervention with a component on information evaluation; to enact ability to distinguish evidence-based information from nonfactual information.

Accordingly, three behavioural intervention objectives were formulated.

  1. 1.

    Women evaluate the accuracy of food, nutrition, and PA information.

  2. 2.

    Women engage in moderate intensity PA for at least 150 min a week.

  3. 3.

    Women consume at least one portion of vegetables and one portion of fruit every day.

Table 1 shows the behavioral intervention objectives, subdivided into POs providing the answer to the question; “what do the participants of the intervention need to do to achieve the behavioural objectives”. The model of food literacy [27] guided the formulation of POs. Food literacy is the interrelated combination of knowledge, skills and self-efficacy to (i) plan, (ii) select, (iii) prepare, (iv) eat food with the ultimate goal of developing a lifelong healthy, sustainable and gastronomic relationship with food within the prevailing environment [27, 28]. The POs were based on the above mentioned four components of food literacy (plan, select, prepare, and eat). For PA, a similar model was adopted, where “eat” was replaced with “do”, that is; plan, select, prepare, and do. The model of food literacy was chosen as it is a holistic behavior change model focusing on a “how to do approach” to initiate and sustain healthy eating habits [27, 28]. Evidence shows a positive association between food literacy and healthy dietary behaviors, particularly increased intake of vegetables and fruits [29, 30]. Table 2 shows the determinants considered to have a strong influence on accomplishing the created POs. Matrices of change objectives are presented in Additional file 3.

Table 1 Behavioural intervention objectives subdivided into performance objectives
Table 2 Determinants of performance objectives for behavior intervention objectives

Step III: Selection of theory-based methods and practical strategies

We aimed to create an intervention capable of initiating and sustaining behaviour change. Eleven BCTs scientifically shown to enact changes in knowledge, skills, self-efficacy, subjective norms, and social support were selected, Additional file 4. The selected BCTs are supported by the self-regulation theory and self-determination theory which specifies the need for autonomy, competence, and relatedness to attain a positive behaviour change [33, 34]. Accordingly, our intervention aims to create behavioural change through enacting autonomy, competence, and relatedness. Providing information coupled with motivation interviewing creates a positive intention [35]. Implementation intentions can be achieved through goal setting [24, 34, 35]. Goal setting necessitates competence, which we hypothesised to be attained through a combination of (i) action planning; (ii) guided practice; ii) self-monitoring; iv) feedback on performance and v) planning of coping plans [24, 26, 34,35,36]. To sustain the behavioural goals requires relatedness, which can be achieved using a combination of social support, role modelling, feedback, planning coping responses and motivation interviewing [20, 24, 34].

The selected BCTs were then operationalised into practical strategies. BCTs; motivational interviewing, role modelling, feedback, guided practice, social support through exchanging ideas and planning coping responses were translated into interactive group-based sessions. Brainstorming workshops with planning group II and FGDs with target group revealed that group sessions may be the best strategy to deliver the intervention in this setting.

“Through education sessions, like you come in this group and give us a health talk, like the way you have come, you teach us and then us we can go and teach our other friends out there. Like for us every Tuesday we be meeting here, very many of us, so if you say you will give us one Tuesday in a week or month, or the last Tuesday of a month and you come and teach us”. “It would be very nice, because literally I share the information with others, so it will move, it moves much faster, because these groups are not only here, but also have these groups in other dioceses, so we can go visit them, and the teach them, but in health centers you only visit when you’re sick”. “Yes it helps, what I know is good, I wish it for my friends and we act as a support for each, and we as well spread it to other groups, example of myself, I used to never eat pumpkin, but I got it from these ladies, that this pumpkin is good and with time I gradually started to eat it until it become part of my diet”, participants in FGD 4 and 6.

Additionally, a recent systematic review shows that diet and PA interventions delivered through group sessions are effective in promoting clinically relevant weight loss [34]. These groups provide opportunities for social support, experience sharing, and may create a motivating atmosphere [22, 34]. Our needs assessment as well revealed that the community and church small groups are an opportunity to share dietary and PA counselling [16, 17]. Our environmental asset assessment revealed existence of women groups within religious structures. Existing groups boosts social cohesion, a facilitator for behavioural change [22].

The reading culture of Ugandans is low.

“We need more of practical, and also the pamphlet, some of us don’t really understand so much, but if it brings out the picture very well, even I can pick interest in it”. “Pamphlets, some people are lazy to read”, participants in FGD 5.

So, the BCT of “providing information through imagery” was translated into infographics with less text and more locally recognisable visuals. Evidence as well shows that visuals increase attention, interest, and credibility of the messages [20].

During FGDs with the target group, participants emphasised the need for practical vegetable preparation skills.

“like we are trying to reduce cooking oil and other stuff from our daily life, so maybe we meet in a group, there is a demonstration whereby some food stuffs are prepared in the best possible way which is to the taste, and people learn how to prepare them, because most of us, do not know how to cook, that is the truth, but somebody may not even fry food, but it tastes so good, if you know how to mix the ingredients and so on. Yes, include cooking demonstrations”, participants in FGD 2.

Hence, BCT of “guided practice” was specifically translated into a practical vegetable group cooking session. We also included vegetable recipes based on locally available vegetables in the intervention infographics. Intervention strategies linked to personal metabolic health and lifestyle needs, and environmental opportunities may help drive behaviour change and positively influence health outcomes [37]. Thus, BCT of; implementation intentions, goal setting and action planning were translated in to; (i) creating “if then plans” in line with metabolic health, (ii) SMART fruit/vegetable/PA goals, detailed action plans to achieve set SMART goals drawn considering environmental opportunities. Figure 2 below shows the hypothesised intervention logical model (conceptual framework) of behavioural change. Practical strategies built from BCT are hypothesized to effect changes in the change objectives, which in turn translate in changes in the determinants. Changes in the determinants in turn result in attainment of POs and corresponding behavioural intervention objectives.

Fig. 2
figure 2

hypothesised intervention logical model for behavioural change (conceptual framework for the intervention)

Step IV: Development of the intervention programme

The practical strategies were built into the intervention scope and sequence, Additional file 5. The intervention consists of five interactive group sessions, 150 min each, Fig. 3. A booklet (infographics); on benefits/recommendations, local vegetable recipes, and practical tips to eat more fruits, vegetables and do more PA is included as a guide, Additional file 6. Tools to assess PA and food environment for opportunities were included, Additional file 7. As well a self-monitoring tool for PA, fruit and vegetable intake was included for participants to track their behaviour daily goals for use in the feedback sessions, Additional file 8. The infographics were designed with locally recognisable images as cultural relevance of health promotion materials is vital for the success of an intervention [20]. Messages on the infographics were framed in a positive and active tone as evidence shows that positively framed messages are more acceptable [20].

Fig. 3
figure 3

Showing delivery timeline of the intervention sessions, intervention content (organised practical strategies from step III), role of participants, and anticipated outcome per session

Brain storming workshop with planning group I and FGDs with the target group identified religious institution women group structures as an appropriate potential delivery channel. The women group structures boosts established social networks, community reach (85{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} Ugandans are Christians) and trust. The channel offers an opportunity for assessing the intervention effectiveness in an unrestricted real-life community setting.

“Religious institutions because they are transparent, religious organizations because they reach out to a bigger community and then they are transparent. The health centers, there is that rudeness, and still for health centers will only meet those people who come to them, but the church, you get a bigger audience”, “Come to churches like this, people really belong to this communities, then you say every third Saturday or Sunday of the month, from 4 to 5 pm, there will always be a nutritional class, for the first-time people may not come, but eventually they come, if it is a free class”, participants in FGD 4.

STEP V: Adoption and implementation plan

The intervention will be delivered through institutional religious women groups (results of environmental asset assessment framework – see step IV). Through meetings with the strategic community leaders, a collaboration was established with Our Lady of Africa Catholic Parish, Mbuya. Mbuya Catholic Parish has six sub parishes. Within these sub parishes they are existing women groups, and these groups will be utilized for face-to-face intervention group sessions. FGDs with target group and meetings with planning group II pointed at the importance of opinion peer leaders being part of the implementation team.

“Our women group leader has helped us a lot, she taught us the dangers of cooking in polyethene bags and taught us the use of banana leaves, us we had got so much used to using the polyethene bags, she can’t eat the food you have prepared in polyethene bags, even if she visits you and if you have cooked like that, she can’t eat that food. “We have musawo (village health team) in our group, she usually brings for us education sessions on how to eat, she goes a lot for these education sessions and what she learns she brings them back to us”, participants in FGD 6.

Scientific evidence shows that the efficacy and acceptability of health promotion interventions increases if peer opinion leaders within the target group are part of the implementation team [38]. Peer opinion leaders provide entry and legitimacy to the external change agents and may help drive changes in social norms. Selection of peer opinion leaders: the intervention will be delivered within existing women groups. Leaders of these existing groups will be selected to work as peer opinion leaders on the implementation team. The main role and responsibilities peer opinion leaders will be to (i) mobilize fellow women to participate in the intervention, (ii) follow up and (iii) give social support to participating women to attain set intervention goals. Women leaders will be given a two – day refresher training on mobilization and leadership skills, as mobilization is the routine responsibility for women leaders in their usual group meetings. The planning group I designed the sessions to be moderated by health behavior coach (PhD researcher) following the techniques of motivational interviewing [39]. A general guide (scope & sequence) will ensure consistency during the group sessions.

Step VI: Development of an evaluation plan

Study design, setting and timing

The effectiveness of the intervention will be evaluated through a cluster-randomized controlled trial. The intervention will be evaluated in Kampala, the capital city of Uganda. The six sub parishes of Mbuya catholic parish will be randomized to treatment and control arms, Fig. 4. The treatment arm will be exposed to both the developed intervention infographics and face to face group sessions while the control arm will only receive the developed intervention infographics. An awareness session will be organized to distribute the infographics to the control arm. Within the sub parishes, there are existing women groups. These existing groups will be utilized for face-to-face intervention group sessions. For the intervention purposes, each group will be limited to a maximum of 14 members. The study period is divided into two phases: a three-month intervention and a three-month post-intervention follow-up phase.

Fig. 4
figure 4

Recruitment

The PhD researcher and women leaders of existing groups will make presentations about the intervention during one of the routine meetings. Flyers with details of the intervention will be distributed for sharing with members who are absent during the briefing. At the end of the presentations, interested participants will be invited for the first session to test their eligibility to participate in the study. Eligible participants will be provided with an informed consent form to endorse.

Inclusion criteria

  1. i)

    Sex (women),

  2. ii)

    Age (18 to 45 years),

  3. iii)

    Central obesity [waist circumference ≥ 80 cm]),

  4. iv)

    Fluent in either Luganda or English (sessions will be conducted in Luganda/English).

  5. v)

    Willingness to follow the three-months intervention and three months follow-up,

  6. vi)

    Willingness to sign the informed consent.

Exclusion criteria

  1. i.

    Being treated for diabetes Mellitus Type 1 or Type 2, hypertension, high cholesterol, or any other cardio-metabolic related disease.

  2. ii.

    Pregnancy.

Outcomes

Primary outcome is reduction in waist circumference. Decreases in waist circumference are recommended as critically important treatment target for reducing adverse cardiometabolic health risks [15]. Secondary outcomes include optimisation of, fasting blood glucose, total cholesterol, HDL, LDL, triglycerides, body composition, food literacy, PA, and fruit and vegetable intake.

Sample size calculation

Sample size calculation is based on waist circumference.

To calculate the sample size, we used the formula described by Rutterford, Copas [40], Table 3.

Table 3 Description of sample size calculation

Randomization

The six sub parishes (clusters) will be listed alphabetically. A cluster randomization with a 1:1 allocation will then be applied to randomize the sub parishes to either the treatment or control arm. In the sub parishes, women group leaders and participants will be blinded about the study arms.

Data collection

Table 4 gives an overview of the different measurements and time points during the study.

Table 4 Measurements and time points

Data analysis

Data will be analysed using R software. To evaluate the effects of the intervention, multilevel analysis will be used. Using this technique, regression coefficients will be adjusted for the clustering of observations within sub parishes. We will define two levels in our multi-level analysis: (1) participant and (2) sub parishes. Linear mixed effect models will be used to examine the effect of the intervention on each of the outcome values. All analyses will be performed according to the intention-to treat-principle [42]. To assess changes in metabolic health between the intervention and control groups, a linear mixed effect model will be built where “time” (end line measurement (M2) will be compared with base-line measurement (M1) and post-follow up measurement (M3)), treatment (and interaction of time and treatment) as well as age will be specified as fixed effects, and sub parishes and participants as random factors. For all linear mixed models, compatibility with mixed-model assumptions will be checked by inspection of residual plots and Q-Q plots. In the case of heteroscedastic residuals, data will be log transformed. Tukey or Benjamini–Hochberg procedures will be applied when performing post hoc analyses to further identify differences within treatments as well as between time points. Statistical outliers will be defined as any observation which has an absolute residual exceeding 3 times the residual standard deviation. p < 0.05 will be considered significant in all analyses.