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Research Article | | Peer-Reviewed

Diabetic Self-Management Practices and Glycaemic Control Among Type 2 Diabetes Patients Attending Diabetic Clinic at Nyeri County Referral Hospital

Rukwaro Grace Wanjiku*,  Regina Kamuhu,  Judith Munga
Published in International Journal of Nutrition and Food Sciences (Volume 15, Issue 2)
Received: 10 March 2026     Accepted: 3 April 2026     Published: 16 April 2026
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Abstract

Diabetic self-management practices and adherence are critical factors in the effective management of type 2 diabetes. Despite various policies and strategies, health status and outcomes in low- and middle-income countries like Kenya remain unsatisfactory. This study therefore sought to identify drivers of diabetes self-management practices among type 2 diabetics who attend the diabetic clinic in Nyeri County Referral Hospital, Kenya. This study employed a mixed methods cross-sectional analytical research design. Data for this study were collected using a semi-structured interviewer-administered questionnaire. Descriptive and chi-square and binary logistic regression statistics were used to analyse the data with the help of Statistical Package for the Social Sciences version 27 for Windows. Qualitative data were analysed using content analysis with the help of NVIVO. The study found that the prevalence of good glycaemic control and adherence to diabetes self-management were 13.4% and 58.6%, respectively. High adherence to DSM was observed among older patients, especially those aged 50–59 years (p=0.032) and 60–69 years (p=0.048), as well as patients who received cash transfer (p=0.008). A statistically significant association (p = 0.026) was found between diabetes self-management and glycaemic control. The study therefore concluded that diabetic self-management practices are associated with glycaemic control. However, structural barriers prevented adherence from translating into good glycaemic outcomes. The study recommends that improving diabetes outcomes requires not only strengthening patient adherence but also addressing systemic challenges that prevent adherence from translating into effective glycaemic control.

Published in International Journal of Nutrition and Food Sciences (Volume 15, Issue 2)
DOI 10.11648/j.ijnfs.20261502.16
Page(s) 70-83
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2026. Published by Science Publishing Group
Previous article
Keywords

Diabetes, Type 2 Diabetes Diabetic Self-Management, Glycaemic Control

1. Introduction
Diabetes is among the top four non-communicable diseases that cause premature death worldwide, and 90% of all diabetes cases are usually type 2 diabetes (T2DM)
[1]
. In 2019, the global prevalence of diabetes was 9.3%, 3.9% in Africa and 2.2% in Kenya
[2]
. The prevalence of diabetes mellitus in Africa has significantly increased, with Mauritius showing the highest incidence and prevalence rates, while Kenya and Niger exhibit the lowest rates for DM/T2DM and T1DM, respectively
[3]
. The stepwise survey conducted by Ministry of Health (MoH) in 2015 placed the diabetes prevalence in Kenya at 2% with a higher burden in urban areas (2.3%) compared to rural areas (1.9%) Another study conducted in 2016 in eight counties in Kenya to determine the prevalence of NCDs, placed the prevalence of diabetes in Kenya to be 2.3% and 7.7% in Nyeri County
[4]
.
A study done in Kenya among type 2 diabetes patients showed that 80% of type 2 diabetes patients had poor glycaemic control, contributing to increased diabetes complications
[5]
. Self-management is one of the key elements in the treatment of NCDs like type 2 diabetes; it helps improve care processes and diabetes related health outcomes
[6]
. Diabetes self-management/care activities for people with type 2 diabetes include taking a healthy diet, being physically active, self-monitoring of blood glucose, foot care and medication compliance
[7]
. A multi-national investigation of barriers and enablers to diabetes self-management showed that consistent engagement in diabetes self-management is correlated with the attainment of health outcomes in terms of good blood glucose control, fewer complications, reduction in diabetes-related death and improved quality of life. However, many patients in developing countries especially in Africa face a significant number still face barriers, especially those related to financial hardship and limited health education
[8, 9]
.
The level of adherence to diabetes self-management differs in people with type 2 diabetes, which implies that decision-making processes for self-management are influenced by various factors, which could serve as determinants to diabetes self-management
[10]
. Socioeconomic and medical characteristics, self-efficacy, patient social support, attitudes and knowledge have been shown to be some of the strongest predictors to diabetes self-management
[11]
. A study conducted in Kenyatta National Hospital to assess medication adherence among type 2 diabetes mellitus patients concluded that the majority of type 2 diabetes mellitus patients have suboptimal medication adherence. Family support, affordability of medications and good healthcare provider-patient communication are important in ensuring medication adherence
[12]
. Self-Management in Cardiovascular diseases like T2DM has been vastly studied in high-income countries; however, few studies have been performed in LMIC despite high prevalence as that observed in Nyeri county
[13]
. It was in light of this background that the study sought to determine the level of adherence to diabetic self-management practices and investigate its association with glycaemic control among people with type 2 diabetes attending the diabetic clinic at Nyeri County Referral Hospital.
2. Materials and Methods
2.1. Research Design
This study employed a mixed methods research design. This type of study collects data from individuals at one point in time and then uses that data to analyse the relationships between different factors.
2.2. Study Area
This study was carried out at Nyeri County Referral Hospital. This was the biggest and main referral hospital in Nyeri County. It offered a wide range of inpatient and outpatient services. Inpatient services included the medical and surgical wards for men, women and children. The study was conducted at the outpatient diabetes clinic in the facility. The clinic offered a variety of services to help those with diabetes, including blood sugar testing, insulin injections, and diet counselling. Given Nyeri County's notable prevalence of diabetes, coupled with Nyeri County Referral Hospital's status as the largest referral hospital in the area, receiving an average of 300 diabetic patients monthly as outpatients, as a county referral hospital, the facility provides services to a diverse and more representative population in the County.
2.3. Target Population
The researcher targeted Type II diabetes patients attending the outpatient diabetes clinic at Nyeri County Referral Hospital. The patients must have lived with T2DM for at least 1 year from the date of diagnosis, have had records of glycated haemoglobin (HbA1c) laboratory test not older than 2 months and be willing to take part in the study. Patients who did not meet the laid-down criteria, including one-time non-registered patients, were excluded from the study.
2.4. Sampling
The sample size was obtained using the Cochran (1977) sampling formula, which was as given below:
n0= z2pqe2
n0 = Sample size, Z = 95% confidence level = 1.96, p = proportions with poor glycaemic control = 63.4%
[14]
= 0.634, q = 1 – p = 1- 0.634 = 0.366 and e = Margin of error = 5% (0.05)
Thus,
n0= 1.962(0.634*0.366)0.052=357
Considering the number of patients visiting the diabetic clinic at Nyeri County Referral Hospital monthly (300 patients), the sample size was then adjusted using Cochran sample size formula for small sample size. The formula is as given below:
n= n01+ (n0-1)N
Where n0 = 385: Cochran’s sample size recommendation and N = number of monthly visits = 300
Therefore,
n= 3571+ (357-1)300=163
Thus, the sample size for this study is 163 diabetes Type II patients.10% was added to cater for non-responses to make a population of 179. Systematic random sampling was used to recruit respondents from a sample frame generated from patients in the medical records that met the inclusion criteria. Systematic sampling is a probability sampling method in which researchers select members of the population at a regular interval (or k) determined in advance
[12]
. Every 2nd Type II diabetes patient in the sample frame was recruited into the study. From the sample of 179, 36 respondents were randomly selected to take part in the study through focus group discussions. In addition, four healthcare workers - two clinicians and two nutritionists - were purposively recruited as key informants in the study.
2.5. Data Collection
Data for this study were collected using a researcher-administered questionnaire, focus group discussions and key informant interviews. The questionnaire was used to collect data from Type II diabetes patients. The questionnaire was divided into various sections to collect data on demographics, diabetic self-management (DSM) practices and glycaemic control. In this study, a focus group discussion guide was used to collect information on DSM practices and key challenges that prevent patients from adhering to the recommended practices. Three FGDs, comprised of 10-12 T2DM patients, were conducted, and the proceedings were transcribed and recorded upon consent by participants. A total of four key informant interviews with healthcare workers who work directly with people living with diabetes: two clinicians and two nutritionists, were conducted. The questionnaire and KII guide were pretested at Mt Kenya Sub-County Hospital in Nyeri. Pre-testing was conducted using 18 respondents representing 10% of the sample size respondents who were later not included in the actual study.
2.6. Data Analysis and Presentation
Collected data was cleaned, re-organized and coded before data analysis. Quantitative data were then analysed using SPSS version 27. Demographic characteristics were reported using frequencies and percentages. DSM predictors were established through binary logistic regression. The results were presented in the form of tables. Qualitative data was analysed using content analysis with the help of NVIVO. The results were presented using narration.
2.7. Logistical and Ethical Considerations
Clearance to conduct this study was sought and granted by Kenyatta University Graduate School and ethical approval from Kenyatta University Ethical Review Committee. A research permit was obtained from the National Commission for Science, Technology and Innovation (NACOSTI) before undertaking this study. All questionnaires were administered in a private and confidential manner throughout the study, and respondents were assured of their anonymity and assured that data will only be used for the stated research purposes. The research assistants sought consent from participants while encouraging voluntary participation before and during the interview. Infection prevention guidelines were adhered to, and the safety of both research assistants and respondents was ensured during the data collection exercise. The study participants also benefited from a nutrition and health education session that was conducted at the end of the interview to try to correct any misinformation that might have been identified during the interview.
3. Results
3.1. Social-Demographic Characteristics of T2DM Patients
A total of 169, which represents 94.4% of sampled T1DM patients, participated in the study. Table 1 presents findings on demographic characteristics. Higher proportion (68%) of the respondents were female, while the majority (93.5%) of the respondents were aged 40 years and above, with a mean age of 62±14.4 years. More than half (55%) of the respondents were married, while 24% were widowed. Results show that 37% and 35.2% of the respondents had completed primary and secondary education level respectively. Majority (85.6%) lived in a rural area. Main economic activity was farming (45.2%), while 25% were unemployed. Average monthly income was KES 6,032± 7,429, with majority (89.9%) of the respondents earning a monthly income of less than KES 10,000. Majority (75%) did not receive cash transfer or any form of social protection (Table 1).
Table 1. Respondents’ Social-Demographic Characteristics.

N

%

Gender

Male

54

32.0%

Female

115

68.0%

Age

<40

11

6.5

>40

158

93.5

Marital Status

Single

25

14.8%

Married

93

55.0%

Separated/ divorced

10

5.9%

Widow/widower

41

24.3%

Religion

Christian

165

97.6%

Muslim

4

2.4%

Place of residence

Rural

143

85.6%

urban

24

14.4%

Ethnicity

Kikuyu

156

95.1%

Luo

2

1.2%

Kisii

1

0.6%

Kalenjin

3

1.8%

Kamba

1

0.6%

Meru

1

0.6%

Highest level of education

Primary

61

37.0%

Secondary

58

35.2%

Tertiary

22

13.3%

None

24

14.5%

Occupation

Student

2

1.2%

Farmer

76

45.2%

Casual laborer

11

6.5%

self-employed/ business

27

16.1%

Salaried

10

6.0%

unemployed

42

25.0%

Approximate monthly income

Below 10000ksh

152

89.9%

10000ksh and above

17

10.1%

Receives cash transfer or any form of social protection

Yes

42

25.0%
3.2. Medical Characteristics of T2DM Patients
As shown in Table 2, most (58.9%) of the respondents had a family history of diabetes. Over a third (37%) of the respondents had diabetes for over a decade, while 27.3% of the respondents had been diagnosed 1-3 years prior to the study. Majority of the T2DM patients - 86.8% and 78.9% neither smoked nor consumed alcohol respectively. The type of medication used was mainly oral hypoglycaemic drugs administered to 60.9% of the patients. A high proportion, 67.3% of the patients, had a confirmed diabetes complication. The most common complications reported were hypertension, retinopathy and neuropathy, which were mentioned by 84.7%, 46.8% and 41.4%, respectively.
Blood pressure of respondents in the study was assessed by computing systolic and diastolic blood pressure. Mean blood pressure was 134/82. Using the 140/90 recommendation, the results show that most (50.3%) of the respondents had elevated blood pressure. Mean waist circumference for males and females was similar at 95.89cm and 95.30 cm, respectively. Majority (81.7%) of the respondents were categorised as high risk from a larger than recommended waist circumference. Results show that more females (91.3%) are at high risk compared to males (61.1%). Mean weight was 70.4 kilograms, while the mean height was 1.62 feet. The mean BMI was 26.7 kg/m2. While 46% of the respondents were overweight and 20.2% obese. Majority (87.6%) of the respondents had poor glycaemic control (Table 2).
Table 2. Respondents’ Medical Characteristics.

Medical Characteristic

Category

N

%

Family history of diabetes

Yes

86

58.9%

Smoking

Currently yes

4

2.4%

Previously

18

10.8%

Never

145

86.8%

Alcohol intake

Currently yes

2

1.2%

Previously

33

19.9%

Never

131

78.9%

Period of disease

1-3 years

45

27.3%

4-6 years

28

17.0%

7-9 years

31

18.8%

>10 years

61

37.0%

Current treatment modality

Oral hypoglycemic

98

60.9%

Insulin

7

4.3%

Both

56

34.8%

Confirmed diabetes complication

Yes

111

67.3%

Retinopathy

Yes

52

46.8%

Neuropathy

Yes

46

41.4%

Nephropathy

Yes

8

7.2%

Cognitive impairment

Yes

4

3.6%

Heart disease

Yes

2

1.8%

Hypertension

Yes

94

84.7%

Hypoactive sexual arousal

Yes

3

2.7%

Diabetic foot

Yes

11

9.9%

Period of visiting diabetes clinic

<5

65

39.6%

6-10

62

37.8%

11-15

7

4.3%

16-20

15

9.1%

>20

15

9.1%

Frequency of visit

Monthly

20

12.5%

Every 2 months

3

1.9%

Every 3 months

127

79.4%

Every 6 months

6

3.8%

Yearly

4

2.5%

Blood pressure

Normal

84

49.7

Elevated

85

50.3

Waist Circumference

Normal

31

18.3

High

138

81.7

Body mass index

Underweight

4

2.5

Normal

51

31.3

Overweight

75

46.0

Obese

33

20.2

Glycaemia control (≤7%)

Poor

148

87.6

Good

21

12.4
3.3. Adherence to Diabetic Self-Management Practices
To assess the respondents’ adherence to diabetes medication, the Morisky medication adherence scale was administered. The data collected were subjected to descriptive analysis, and the results were summarised in Table 3. The responses to the Morisky Medication Adherence Scale provide insight into participants’ patterns of medication-taking behaviour. Majority of respondents (84.6%) reported that they do not forget to take their medicine, while 15.4% indicated that they do. Similarly, most participants (89.3%) denied feeling careless about taking their medication, though 10.7% admitted occasional carelessness. Regarding medication adherence during travel, 82.2% stated that they do not forget to bring their medicine when away from home, while 17.8% acknowledged that they do. When asked whether they stopped taking medicine due to feeling sick from side effects, 96.4% responded negatively, while only 3.6% reported doing so. Most respondents (95.9%) denied deciding to take less of their medication, with only 4.1% indicating they had made such a decision. Similarly, 95.3% said they do not stop taking their medicine when they feel better, compared to 4.7% who reported this behaviour. On emotional responses to medication routines, 88.8% disagreed with the statement that they sometimes get annoyed by having to take their medicine daily, while 11.2% admitted feeling this way. Finally, 82.2% of respondents reported that they do not miss taking their medicine because of running out at home, whereas 17.8% stated that they do.
Table 3. Morisky Medication Adherence Scale Responses.

Yes

No

N

%

N

%

Forget to take medicine

26

15.4%

143

84.6%

Feel careless at times about taking medicine

18

10.7%

151

89.3%

Forget to bring along medicine when they travel away from home

30

17.8%

139

82.2%

Stop taking your medicine because of feeling sick due to the side effects of the medicine

6

3.6%

163

96.4%

Decide to take less of medicine

7

4.1%

162

95.9%

Stop taking medicine because of feeling better

8

4.7%

161

95.3%

Get annoyed that they have to keep taking medicine every day

19

11.2%

150

88.8%

Miss taking medicine because they ran out of it at home

30

17.8%

139

82.2%
To analyse the prevalence of adherence to diabetes medication in the sample, the researcher summed up the responses in Table 3. According to Plakas et al. (2016), the scores of the MMAS-8 range from 0 to 8. A score below 6 indicates low adherence, a score between 6 < 8 medium adherence and a score of 8 high adherence. In the current study, the total scores ranged from 0 to 7 with a mean of 0.85+1.768. As shown in Table 4, the vast majority, 164 (97%) of the respondents had a low adherence to diabetes medication.
Table 4. Adherence to Diabetes Medication.

Frequency

Percent

Low

164

97.0

Medium

5

3.0

Total

169

100.0
To establish respondents’ diabetes self-management practices, participants were asked questions regarding their diabetes self-care activities during the past 7 days. The self-management activity data provide an overview of participants’ diabetes care behaviours over the previous seven days. Most respondents (60.4%) reported adhering to a healthful eating plan on all seven days, while smaller proportions reported fewer days, with 11.2% indicating six days and 8.3% reporting five days. When asked about adherence to their eating plan on average over the past month, 58.0% indicated seven days per week, while 11.2% reported six days and 9.5% reported either four or five days. Regarding fruit and vegetable intake, 60.5% reported consuming five or more servings on all seven days, while 9.0% reported six days, and 8.4% reported three days. Smaller percentages reported fewer days, and none reported zero days. For high-fat food consumption, a large majority (82.6%) reported eating such foods on none of the past seven days. A significant proportion (81.9%) reported consuming a low-sugar diet on all seven days. In terms of physical activity, 75.6% engaged in at least 30 minutes of activity on all seven days. For specific exercise sessions, 68.9% reported participating on all seven days.
Blood sugar testing patterns showed that 42.0% tested their blood sugar on two days in the last week, while 32.5% did not test at all. When asked about testing blood sugar the number of times recommended by their healthcare provider, the majority (71.6%) reported doing so on two days. A smaller proportion (13.6%) did not meet the recommended frequency on any day, while only 1.8% did so on all seven days. Regarding foot care, 91.6% reported checking their feet on all seven days. Lower proportions reported fewer days, with only 2.4% checking their feet on six days and minimal percentages across other frequencies. Finally, 94.0% of participants indicated that they inspected the inside of their shoes daily during the past week. Very few reported doing so less frequently, with no participants indicating three days and less than 2% across all other day counts.
Table 5. Self-Management Activities.

0

1

2

3

4

5

6

7

Followed a healthful eating plan

0.0%

0.0%

4.7%

7.7%

7.7%

8.3%

11.2%

60.4%

Average days per week, over the past month followed eating plan

0.0%

0.6%

3.6%

7.7%

9.5%

9.5%

11.2%

58.0%

Ate five or more servings of fruits and vegetables

0.0%

3.0%

6.0%

8.4%

6.6%

6.6%

9.0%

60.5%

Ate high fat foods

82.6%

10.2%

2.4%

1.8%

2.4%

0.0%

0.6%

0.0%

Consumed a low-sugar diet

0.6%

3.0%

3.0%

2.4%

3.6%

3.0%

2.4%

81.9%

Participated in at least 30 minutes of physical activity

1.8%

1.8%

3.0%

2.4%

3.0%

6.0%

6.5%

75.6%

Participated in a specific exercise session

3.0%

2.4%

5.4%

2.4%

1.8%

8.4%

7.8%

68.9%

Tested blood sugar

32.5%

10.7%

42.0%

8.3%

2.4%

0.0%

0.0%

4.1%

Tested blood sugar the number of times recommended by your healthcare provider

13.6%

3.6%

71.6%

8.3%

0.6%

0.6%

0.0%

1.8%

Checked feet

0.6%

1.8%

0.6%

1.8%

0.6%

0.6%

2.4%

91.6%

Inspected the inside of shoes

0.6%

1.8%

0.6%

0.0%

1.2%

0.6%

1.2%

94.0%
A response of 5 days and above in Table 5 was deemed as good practice while the rest were categorised as having poor practices. As shown in Table 6, the majority (78.7%) of participants had good self-management practices.
Table 6. Self-Management Activities.

Category

Frequency

Percent

Poor

36

21.3%

Good

133

78.7%

Total

169

100.0%
Patients consistently linked their dietary choices to changes in blood glucose levels and overall well-being. For example, one participant stated, “Blood sugar changes when you don’t take vegetables—you find it spikes. But when you eat them, you feel better. The doctor tells us that most of our plate should be vegetables.” Another added, “You have to take lots of fruits and vegetables.” These views reflected a clear understanding that diet played a central role in controlling diabetes. However, while participants acknowledged the advice given by nutritionists, adherence was not always possible. Respondents admitted that economic and environmental constraints limited their ability to consistently follow dietary guidance. As one put it, “It’s been a long time since I saw a nutritionist. Sometimes I don’t follow their advice because what they recommend, like vegetables, may not always be available.” Another echoed this challenge by saying, “The nutritionist tells us to take proteins, carbohydrates, and vitamins—carbohydrates like ugali or maize, proteins like beans, vegetables like sukuma wiki. But sometimes it’s hard to follow because either you can’t afford it or it’s not available.” Social settings also presented barriers, with one respondent noting, “Sometimes you may go somewhere where the food you need isn’t there. For example, at a function, they may only have rice, so you eat it—not because you want to, but because of the circumstances.” These responses showed that while patients strongly recognized the role of diet in self-management, barriers such as food cost, availability, and social environment limited adherence.
Respondents described varying strategies, with some checking their blood sugar only when they felt physically unwell. For instance, one participant stated, “For me, I test when I feel my body isn’t right. Sometimes I feel the sugar is high, then I check.” Another added, “Keep measuring with a machine and record the results. The doctor doesn’t say after how long, so you just check depending on how you feel.” These responses suggested that monitoring was often guided by symptoms rather than routine medical advice. However, cost and access emerged as critical barriers. One respondent explained, “The main reason we don’t monitor often is cost. You may have the machine, but the strips are so expensive that you can’t always buy them.” Another added, “If you don’t have the equipment, you’re forced to go to a nearby clinic and pay. That’s still a challenge for us.” Even those who managed to test regularly struggled with logistical difficulties, as one participant highlighted, “I measure about once a week. I have to go to the clinic, which is far, and sometimes the strips aren’t even available there.” These responses showed that patients were aware of the value of blood glucose monitoring but faced structural and financial constraints that shaped their practice.
Participants expressed frustration that medicines were not consistently stocked in health facilities, which undermined their ability to follow medical advice. One respondent explained, “Medicine is a great challenge. It’s been years since diabetic medicine was fully available. Sometimes you get one type, but another isn’t there.” Another participant highlighted the gap between health worker advice and systemic limitations by stating, “Doctors do their part, but medicine availability is our biggest problem.” Financial constraints linked to health insurance also emerged, with one respondent noting, “The current health insurance (SHA) doesn’t cover diabetes. The government should provide medicine—or else tell us not to contribute to SHA.” These responses suggested that patients were motivated to adhere to prescribed treatment but were hindered by systemic weaknesses, particularly the chronic shortage of essential diabetes medicines and inadequate health insurance coverage.
Both the clinician and the nutritionist highlighted nutrition as the cornerstone of diabetes self-management, though the nutritionist emphasised it most strongly by stating, “In my view, 65% of diabetes management is diet. Honestly, diet is the single biggest factor in getting positive outcomes for type 2 patients.” Similarly, the clinician noted that nutrition and exercise were “the least expensive, the most effective and the more practical practices” compared to medication, which was costly and often more difficult for patients to sustain. These responses reinforced the idea that dietary change, when combined with physical activity, offered patients the most sustainable and affordable path to controlling blood glucose levels. The nutritionist also drew from experience by stating, “My role mainly involves nutrition counselling, giving education on diet and exercise, and even helping establish support groups where patients learn about foot care, medication use, and how to live better with the condition.” This signifies that nutrition was not only about food choices but also about education, social support, and adherence strategies.
Participants suggested that there were significant challenges that patients faced in adhering to recommended self-management practices. Both the clinician and the nutritionist acknowledged a wide range of barriers, including social, cultural, economic, and personal factors. The clinician highlighted issues such as stigma and peer pressure, stating, “There are foods that are said to be safe, but they are not safe. Another is victimisation of the patient, where the patient may look like he is being victimised in front of his friends, and there may also be pressure to consume some of these commodities that are not really healthy, like fast food. So they end up not adhering to the nutrition guidelines provided.” The clinician further noted that some patients deliberately avoided taking medication in public because “some of the drugs are huge, they look like the patient may not be very confident to take them in public. So sometimes… they may start weaning themselves off the drugs without going for medical opinion.” The nutritionist echoed these concerns but framed socioeconomic constraints as the most important barrier, stating, “The biggest challenge is socioeconomic status. Some patients simply don’t have the money to buy healthy foods or even medication. Alcohol use is also a big problem, and many people work long hours with no time for exercise. Religion can come into play too; for example, during fasting periods, some Muslims end up with poorly controlled sugars.” These responses showed that while knowledge and motivation were important, patients’ adherence was profoundly shaped by financial constraints, social-cultural practices, and the daily demands of life.
The results show that respondents' age (p=0.029), marital status (p=0.033), occupation (p=0.007) and receipt of cash transfer or any form of social protection (p=0.019) were significantly associated with adherence to diabetes self-management. These results therefore show that social-demographic characteristics were associated with adherence to diabetes self-management. As such, the first hypothesis is rejected, and the study concludes that there is a significant association between social-demographic characteristics and adherence to diabetes self-management.
Table 7. Association Between Social-Demographic Characteristics and Adherence to Diabetes Self-Management.

Social-Demographic Characteristic

Categories

N

Adherence

Chi-square / Fischer’s exact test.

Low

High

Gender

Male

54

24

30

χ2 =2.99, df=1, p=0.584

Female

115

46

69

Age (years)

<40

11

8

3

0.02

>40

158

62

96

Marital Status

Single

25

8

17

0.033

Married

93

33

60

Separated/ divorced

10

4

6

Widow/widower

41

25

16

Religion

Christian

165

68

97

0.724

Muslim

4

2

2

Place of residence

Rural

143

58

85

χ2 =2.36, df=1, p=0.627

Urban

24

11

13

Level of education

Primary

61

24

37

0.173

Secondary

58

22

36

Tertiary

22

8

14

None

24

15

9

Occupation

Student

1

1

0

p=0.007

Farmer

2

0

2

Casual labourer

76

22

54

self-employed/ business

11

4

7

Salaried

27

12

15

Unemployed

10

5

5

Income

0-5000ksh

138

55

83

P=0.728

5000-10000ksh

14

7

7

10000-30000ksh

9

5

4

Above 30000ksh

7

3

4

Cash transfer/social protection

Yes

126

46

80

χ2 =5.519, df=1, p=0.019

No

42

24

18
To find out the association between demographic characteristics and DSM, binary logistic regression was conducted. Significant variables in Table 7 were used as predictors. Age of the patients and receipt of cash transfer remained significant in the binary regression analysis. Patients aged 50–59 had 3.84 times higher odds of good DSM adherence compared to those aged 20–29, 95% CI [1.12, 13.21], p =.032, while those aged 60–69 had 3.12 times higher odds, 95% CI [1.01, 9.61], p =.048. Additionally, patients receiving cash transfers had 2.67 times higher odds of good DSM adherence compared to those not receiving support, 95% CI [1.29, 5.54], p =.008 (Table 8).
Table 8. Regression Between Demographic Characteristics and DSM.

Variable

Category

AOR (95% CI)

p-value

Age (years)

30–39

1.42 (0.28–7.21)

0.671

Age (years)

40–49

1.56 (0.35–6.89)

0.558

Age (years)

50–59

3.84 (1.12–13.21)

0.032

Age (years)

60–69

3.12 (1.01–9.61)

0.048

Age (years)

70–79

2.41 (0.79–7.31)

0.118

Age (years)

80–89

1.09 (0.31–3.88)

0.893

Marital Status

Married

1.36 (0.42–4.38)

0.602

Marital Status

Separated/Divorced

0.74 (0.18–3.01)

0.671

Marital Status

Widow/Widower

1.91 (0.56–6.48)

0.301

Occupation

Student

0.62 (0.05–7.91)

0.712

Occupation

Farmer

1.88 (0.14–25.3)

0.645

Occupation

Casual laborer

1.27 (0.39–4.12)

0.694

Occupation

Self-employed/business

1.44 (0.36–5.74)

0.603

Occupation

Salaried

1.73 (0.51–5.88)

0.379

Cash transfer

Yes

2.67 (1.29–5.54)

0.008

Marital Status

Married

1.36 (0.42–4.38)

0.602

Marital Status

Separated/Divorced

0.74 (0.18–3.01)

0.671

Marital Status

Widow/Widower

1.91 (0.56–6.48)

0.301

Occupation

Student

0.62 (0.05–7.91)

0.712

Occupation

Farmer

1.88 (0.14–25.3)

0.645

Occupation

Casual laborer

1.27 (0.39–4.12)

0.694

Occupation

Self-employed/business

1.44 (0.36–5.74)

0.603

Occupation

Salaried

1.73 (0.51–5.88)

0.379

Cash transfer

Yes

2.67 (1.29–5.54)

0.008
3.4. Diabetes Self-Management and Glycaemic Control
The Fisher’s exact test results in Table 9 show a statistically significant association (p = 0.026) between diabetes self-management and glycaemic control. Among patients with high self-management, the likelihood of good glycaemic control was 54.7% compared with 45.3% for poor control. As such null hypothesis is rejected and the study concludes that there is a significant relationship between adherence of diabetes self-management on glycaemic control among Type 2 diabetes patients attending the diabetic clinic at Nyeri County Referral Hospital.
Table 9. Diabetes Self-Management and Glycaemic Control.

Glycemic control

Chi-square/ Fischer’s exact test

Good

Poor

Diabetes Self-Management

High

99

82

17

0.026

Low

70

66

4
4. Discussion
The results showed that 58.6% of the respondents had a high adherence to diabetes self-management. Results showed that 76.3% adhered to dietary practices, 73.4% engaged in regular physical activity, and 72.2% consistently followed foot care routines. The qualitative findings on the other hand revealed that many respondents struggled to maintain consistent adherence due to financial constraints, limited access to diverse food options and competing household priorities. However, respondents had a low adherence to diabetes medication, with 97% scoring below the recommended threshold on the Morisky Medication Adherence Scale, and on blood glucose monitoring, where most failed to meet provider-recommended testing frequencies across the week. This was consistent with the quantitative findings, where patients expressed a strong desire to follow prescribed regimens in the focus group discussions, but systemic barriers such as inconsistent drug availability and inadequate health insurance coverage undermined their efforts. Many reported frustrations with the healthcare system’s inability to provide essential medications, despite receiving clear instructions from clinicians. Results of a study in India found that 78% of subjects followed a healthy eating plan, a figure consistent with the high dietary adherence reported in the current study
[15]
. The high adherence to foot care, specifically avoiding barefoot walking, echoes the finding that this was the most followed practice (90%)
[16]
. Furthermore, the systematic review in sub-Saharan Africa described how patients "rarely self-monitored their glucose levels," which directly concurs with the low adherence to blood glucose monitoring observed in the current study
[17]
. However, the exceptionally low medication adherence contrasts with studies which reported a very high medication adherence rate of 95.7%, and in Iraq, where most participants properly adhered to the anti-diabetic medications
[18, 19]
.
A statistically significant association (p = 0.026) between diabetes self-management and glycaemic control was obtained, whereby the likelihood of good glycaemic control was 54.7% for those with high diabetes self-management. This result is consistent with results of a multicentre randomised controlled trial whereby a patient-centred self-management intervention (PACE-SMI) led to modest but significant improvement in HbA1c and substantial enhancements in self-efficacy and self-care behaviours in adults with T2DM
[20]
. Greater adherence to diabetes self-care behaviours was also associated with better glycaemic control in a cross-sectional study conducted in medical and endocrinology outpatient clinics of three hospitals in northern Jordan
[21]
. Similarly, in a study in Ethiopia, self-care activities were independent predictors of glycaemic control among patients with T2DM
[22]
. However, no diabetes self-management (DSM) variable emerged as an independent predictor of glycaemic control in these adjusted models
[23]
. The result suggests that diabetes self-management influences glycaemic control because it directly shapes daily behaviours that determine blood glucose levels. When a patient adheres to medication schedules, follows recommended diets, monitors blood glucose, and engages in regular physical activity, they reduce glucose fluctuations and prevent sustained hyperglycaemia. Conversely, poor self-management leads to missed doses, unhealthy diets, and delayed response to abnormal glucose levels, which increases the risk of persistent poor control.
5. Conclusion
The diabetic self-management practices and adherence among Type 2 diabetes patients attending the diabetic clinic at Nyeri County Referral Hospital were moderate. Results showed that overly, more than half of respondents exhibited good self-management practices. Low adherence to specific practices like medication based on the Morisky scale and self-monitoring of blood glucose. Some of the factors identified to affect adherence included systemic barriers, including inconsistent medication availability, lack of insurance coverage, and logistical challenges in accessing monitoring tools. Diabetes self-management influenced glycaemic control among Type 2 diabetes patients attending the diabetic clinic at Nyeri County Referral Hospital. Majority of the respondents in the study had a poor glycaemic control. The results demonstrated that patients with high diabetes self-management had a higher likelihood and higher odds of achieving good glycaemic control than poor control, compared with those with low self-management.
There is therefore need to address these systemic barriers to increase the adherence of T2DM patients to the recommended self-management practices as this would in turn improve their glycaemic control, which will reduce and delay complications associated with T2DM. The government should, therefore, strengthen community-based support systems; this can be done by enhancing community-level diabetes support. The county government of Nyeri needs to build the capacity of community health promoters and lower-cadre health workers to provide diabetes education, adherence follow-up and basic foot-care screening under the supervision of clinical staff. This will extend diabetes care closer to the community. Type 2 DM patients should take active ownership to diabetes self-management by adhering to dietary, exercise and foot care recommendations and report early any symptoms of complications to healthcare providers. They should also seek and maintain social support by participating in peer-support groups and involving family members in their self-care routine to stay motivated and accountable. Future research should consider use of longitudinal study design to better capture patterns of diabetes self-management over time and provide a more accurate assessment of adherence. For dietary and physical activity measurements, more specific methods of measuring these practices are recommended like the 24-hour recall or food diaries as they would offer more precise data with limited respondent biases.
Abbreviations

ADA

American Diabetes Association

BMI

Body Mass Index

DSME

Diabetes Self-Management Education

DSMS

Diabetes Self-Management support

DSM

Diabetes Self-Management

FGD

Focus Group Discussion

HBA1C

Glycated Haemoglobin

IDF

International Diabetes Federation

IRDSS

Disease Related Social Support

KAP

Knowledge Attitudes and Practices

KII

Key Informant Interview

LMIC

Low, Middle-income Country

MOH

Ministry of Health

NCD

Non- Communicable Diseases

RBS

Random Blood Sugar

SES

Social Economic Status

SMBG

Self-monitoring of Blood Glucose

T2DM

Type 2 Diabetes Mellitus

WHO

World Health Organization

WHR

Waist Hip Ratio
Acknowledgments
I wish to express my deepest gratitude to God for the gift of life, health and strength throughout my academic journey.
I am sincerely grateful to my supervisors, Dr. Regina Kamuhu and Dr. Judith Munga, for their invaluable insights, guidance and constructive feedback that shaped this study. Your encouragement and mentorship have been a great source of inspiration. My appreciation also goes to the management and staff of Nyeri County Referral Hospital for granting me access to conduct this research and to the study participants who willingly shared their time and experience. I am also grateful to my colleagues and classmates for their encouragement that kept me motivated. Finally, I extend heartfelt thanks to my parents for their unwavering support, prayer and patience during this journey.
Author Contributions
Rukwaro Grace Wanjiku: Conceptualization, Data curation, Formal Analysis, Methodology, Writing – original draft, Writing – review & editing
Regina Kamuhu: Conceptualization, Supervision, Writing – review & editing
Judith Munga: Conceptualization, Supervision, Writing – review & editing
Data Availability Statement
The data is available from the corresponding author upon reasonable request.
Conflicts of Interest
The authors declare no conflicts of interest.
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    Wanjiku, R. G., Kamuhu, R., Munga, J. (2026). Diabetic Self-Management Practices and Glycaemic Control Among Type 2 Diabetes Patients Attending Diabetic Clinic at Nyeri County Referral Hospital. International Journal of Nutrition and Food Sciences, 15(2), 70-83. https://doi.org/10.11648/j.ijnfs.20261502.16

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    Wanjiku, R. G.; Kamuhu, R.; Munga, J. Diabetic Self-Management Practices and Glycaemic Control Among Type 2 Diabetes Patients Attending Diabetic Clinic at Nyeri County Referral Hospital. Int. J. Nutr. Food Sci. 2026, 15(2), 70-83. doi: 10.11648/j.ijnfs.20261502.16

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    AMA Style

    Wanjiku RG, Kamuhu R, Munga J. Diabetic Self-Management Practices and Glycaemic Control Among Type 2 Diabetes Patients Attending Diabetic Clinic at Nyeri County Referral Hospital. Int J Nutr Food Sci. 2026;15(2):70-83. doi: 10.11648/j.ijnfs.20261502.16

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  • @article{10.11648/j.ijnfs.20261502.16,
  author = {Rukwaro Grace Wanjiku and Regina Kamuhu and Judith Munga},
  title = {Diabetic Self-Management Practices and Glycaemic Control Among Type 2 Diabetes Patients Attending Diabetic Clinic at Nyeri County Referral Hospital},
  journal = {International Journal of Nutrition and Food Sciences},
  volume = {15},
  number = {2},
  pages = {70-83},
  doi = {10.11648/j.ijnfs.20261502.16},
  url = {https://doi.org/10.11648/j.ijnfs.20261502.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijnfs.20261502.16},
  abstract = {Diabetic self-management practices and adherence are critical factors in the effective management of type 2 diabetes. Despite various policies and strategies, health status and outcomes in low- and middle-income countries like Kenya remain unsatisfactory. This study therefore sought to identify drivers of diabetes self-management practices among type 2 diabetics who attend the diabetic clinic in Nyeri County Referral Hospital, Kenya. This study employed a mixed methods cross-sectional analytical research design. Data for this study were collected using a semi-structured interviewer-administered questionnaire. Descriptive and chi-square and binary logistic regression statistics were used to analyse the data with the help of Statistical Package for the Social Sciences version 27 for Windows. Qualitative data were analysed using content analysis with the help of NVIVO. The study found that the prevalence of good glycaemic control and adherence to diabetes self-management were 13.4% and 58.6%, respectively. High adherence to DSM was observed among older patients, especially those aged 50–59 years (p=0.032) and 60–69 years (p=0.048), as well as patients who received cash transfer (p=0.008). A statistically significant association (p = 0.026) was found between diabetes self-management and glycaemic control. The study therefore concluded that diabetic self-management practices are associated with glycaemic control. However, structural barriers prevented adherence from translating into good glycaemic outcomes. The study recommends that improving diabetes outcomes requires not only strengthening patient adherence but also addressing systemic challenges that prevent adherence from translating into effective glycaemic control.},
 year = {2026}
}

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  • TY  - JOUR
T1  - Diabetic Self-Management Practices and Glycaemic Control Among Type 2 Diabetes Patients Attending Diabetic Clinic at Nyeri County Referral Hospital
AU  - Rukwaro Grace Wanjiku
AU  - Regina Kamuhu
AU  - Judith Munga
Y1  - 2026/04/16
PY  - 2026
N1  - https://doi.org/10.11648/j.ijnfs.20261502.16
DO  - 10.11648/j.ijnfs.20261502.16
T2  - International Journal of Nutrition and Food Sciences
JF  - International Journal of Nutrition and Food Sciences
JO  - International Journal of Nutrition and Food Sciences
SP  - 70
EP  - 83
PB  - Science Publishing Group
SN  - 2327-2716
UR  - https://doi.org/10.11648/j.ijnfs.20261502.16
AB  - Diabetic self-management practices and adherence are critical factors in the effective management of type 2 diabetes. Despite various policies and strategies, health status and outcomes in low- and middle-income countries like Kenya remain unsatisfactory. This study therefore sought to identify drivers of diabetes self-management practices among type 2 diabetics who attend the diabetic clinic in Nyeri County Referral Hospital, Kenya. This study employed a mixed methods cross-sectional analytical research design. Data for this study were collected using a semi-structured interviewer-administered questionnaire. Descriptive and chi-square and binary logistic regression statistics were used to analyse the data with the help of Statistical Package for the Social Sciences version 27 for Windows. Qualitative data were analysed using content analysis with the help of NVIVO. The study found that the prevalence of good glycaemic control and adherence to diabetes self-management were 13.4% and 58.6%, respectively. High adherence to DSM was observed among older patients, especially those aged 50–59 years (p=0.032) and 60–69 years (p=0.048), as well as patients who received cash transfer (p=0.008). A statistically significant association (p = 0.026) was found between diabetes self-management and glycaemic control. The study therefore concluded that diabetic self-management practices are associated with glycaemic control. However, structural barriers prevented adherence from translating into good glycaemic outcomes. The study recommends that improving diabetes outcomes requires not only strengthening patient adherence but also addressing systemic challenges that prevent adherence from translating into effective glycaemic control.
VL  - 15
IS  - 2
ER  -

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Author Information

  • Rukwaro Grace Wanjiku

    Department of Food Nutrition and Dietetics, Kenyatta University, Nairobi, Kenya

    Biography: Rukwaro Grace Wanjiku: A Master’s student at Kenyatta University in the Department of Foods, Nutrition and Dietetics, she holds a Bachelor of Science in Foods, Nutrition and Dietetics from Egerton University. Her focus areas include Type 2 diabetes, non-communicable diseases (NCDs), and health data utilisation. She is interested in understanding the links between nutrition and the growing burden of NCDs, particularly diabetes. Her work centres on the use of health data to support better decision-making and improve outcomes.

    Research Fields: Diabetes type 2, NCDs, Health data utilisation

    Contact Email

    http://orcid.org/0009-0001-1894-7273

  • Regina Kamuhu

    Department of Food Nutrition and Dietetics, Kenyatta University, Nairobi, Kenya

    Biography: Regina Kamuhu (PhD): is a lecturer at Kenyatta University in the Department of Foods, Nutrition and Dietetics. She holds a PhD in Foods, Nutrition and Dietetics from Kenyatta University (2016) and a Master’s degree from the University of Punjab, India. Her areas of focus include HIV-related dyslipidaemia and the utilisation of groundnuts (peanuts) in the treatment of lipid disorders in individuals with HIV and diabetes. Her work centres on nutrition-based approaches to managing lipid abnormalities, with particular interest in the role of locally available foods in supporting health outcomes among affected populations.

    Research Fields: HIV dyslipidaemia, Utilisation of groundnuts/peanuts in the treatment of lipid disorders in HIV and diabetes.

    Contact Email

    http://orcid.org/0000-0002-4437-4536

  • Judith Munga

    Department of Food Nutrition and Dietetics, Kenyatta University, Nairobi, Kenya

    Biography: Judith Munga (PhD) is a Nutrition Consultant. She holds a PhD in Foods, Nutrition and Dietetics from Kenyatta University (2015) and a Master’s degree from Kenyatta University. Her areas of focus include diabetes, chromium supplementation, nutrition education, and the glycaemic index of foods. Her work centres on the role of nutrition in the prevention and management of diabetes, with particular interest in micronutrient supplementation and dietary approaches that support glycaemic control. She is also engaged in advancing nutrition education to promote informed dietary choices and improve health outcomes among diverse populations.

    Research Fields: Diabetes, chromium supplementation, Nutrition education, Glycaemic index of foods

    Contact Email

    http://orcid.org/0009-0004-3716-089X

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  • Abstract
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  • Document Sections

    1. 1. Introduction
    2. 2. Materials and Methods
    3. 3. Results
    4. 4. Discussion
    5. 5. Conclusion
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  • Author Contributions
  • Data Availability Statement
  • Conflicts of Interest
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