Physical activity and glycaemic control among adults with type 2 diabetes in Suva, Fiji: a cross sectional pilot study

Elizabeth Mundia; Ramneek Goundar; Kissinger Marfoh

doi:10.1071/HC25096

RESEARCH ARTICLE (Open Access)

Previous Next Online Early

Physical activity and glycaemic control among adults with type 2 diabetes in Suva, Fiji: a cross sectional pilot study

Elizabeth Mundia

¹ ^* , Ramneek Goundar ² , Kissinger Marfoh ²

+ Author Affiliations

- Author Affiliations

¹ Department of Basic Clinical Medicine, School of Medical Sciences, Fiji National University, Suva, Fiji.

² Department of Epidemiology and Environmental Health, School of Public Health and Primary Care, Fiji National University, Suva, Fiji.

^* Correspondence to: elidia360@gmail.com

Handling Editor: Felicity Goodyear-Smith

Journal of Primary Health Care https://doi.org/10.1071/HC25096

Submitted: 5 June 2025 Accepted: 5 August 2025 Published: 22 August 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of The Royal New Zealand College of General Practitioners. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Introduction

Type 2 diabetes mellitus is a major health burden in Fiji (19.3% prevalence). Evidence suggests increased physical activity improves glycaemic control and health outcomes; however, this remains unstudied in Fiji’s population.

Aim

This study aimed to assess physical activity levels and explore its relationship with glycaemic control among diabetic patients.

Methods

A quantitative, cross-sectional pilot study was conducted at Samabula Health Center, Fiji, from September to November 2022 using convenience sampling for 174 adults with diabetes. The International Physical Activity Questionnaire, short form, assessed physical activity, whereas capillary fasting and random blood sugar assessed glycaemic control targets. Logistic regression analysed associations.

Results

The study found 64% of participants were physically inactive, with females significantly less active than males (odds ratio (OR) = 0.49, 95% confidence interval (CI) = 0.25–0.98). Poor glycaemic control was common (75%), although adherence to lifestyle and pharmacological management plans were significantly associated with good control (OR = 2.37, 95% CI: 1.05–5.37). Increased physical activity levels were not significantly associated with meeting glycaemic control targets.

Discussion

Despite clinic attendance, patients with diabetes remained inactive, had poor glycaemic control and were non-adherent to lifestyle and drug treatment. Contradicting previous evidence, physical activity was not associated with meeting glycaemic control targets, possibly reflecting point-of-care glucose variability compared to the gold-standard glycated hemoglobin measure (HbA1c), and cross-sectional study design limiting causal interpretation. Future research should investigate glycaemic control and physical activity barriers, especially among women, physician practices and test culturally adapted interventions. Fiji’s National Wellness Policy and Non-Communicable Disease (NCD) Strategic Plan must consider strengthening diabetes management guidelines, clinician training and patient support to address systemic gaps

Keywords: blood glucose, diabetes, diabetes mellitus, diabetes mellitus type 2, exercise, glycaemic control, physical activity, sedentary behaviour.

WHAT GAP THIS FILLS

What is already known: Physical activity has a well-established role in diabetes primary care management. What this study adds: This research describes physical activity practices and glycaemic control in Fiji’s diabetic population, and identifies systemic gaps in guideline implementation and adherence while proposing future research and policy directions to improve diabetes care in Fiji.

Introduction

Type 2 diabetes mellitus (T2DM) represents a growing global health challenge, with estimated prevalence projections reaching 10.9% by 2045,¹ exceeding 40% in some Pacific nations,² and 19.3% in Fiji.³ T2DM and its complications contribute significantly to mortality,²^,⁴^,⁵ and consume up to 20% of government health expenditure in some Pacific countries.⁶

Although non-modifiable risk factors including genetic and environmental factors contribute to disease development,⁷^–⁹ modifiable risk factors such as diet and physical inactivity are hypothesised key contributors to rising T2DM rates in Fiji and the Pacific.¹⁰^–¹⁴ The World Health Organization (WHO) defines physical activity as energy expenditure by skeletal muscle movement, recommending ≥150 min of moderate, or ≥75 min of vigorous activity or an equivalent combination weekly.¹⁵ However, high inactivity is reported in females, the elderly, and Fijians of Indian descent from national surveys,¹⁴ and patients with diabetes in regional studies.¹⁶^,¹⁷

Global evidence including low-resource settings links physical activity with improved outcomes and glycaemic control.¹⁸^–²⁵ Pacific-based studies show mixed results,²⁶^–²⁸ possibly because of sample size limitations and cultural barriers affecting participation,²⁶^,²⁷^,²⁹ whereas no studies examine this relationship in Fiji’s diabetic population.

Fiji’s Diabetes Guideline is similar to international standards,⁴ yet, most patients (66%) have poor glycaemic control.²^,⁴ Although recommending 30 min of moderate intensity physical activity, the guideline lacks specific instructions, potentially limiting patient understanding and implementation.

This pilot study addresses critical gaps, examining physical activity levels, sedentary behaviour and their association with glycaemic control among patients with diabetes in a Fijian clinic setting. Quantifying these relationships in Fiji’s high-burden, low-resource setting, will inform targeted interventions, manage guideline reforms and future research directions for Fiji, and in similar global or regional settings.

Methods

This single-centre, cross-sectional study conducted at Samabula Health Center, Suva, examined associations rather than causality in an urban setting (n = 18,054), comprising Fiji’s diverse socioeconomic and ethnic composition of predominantly iTaukei (Indigenous) and Fijians of Indian descent.

EpiInfo (CDC, Atlanta, GA, USA) determined a sample size of 200 (80% power, 5% error margin, 95% CI, 15.6% prevalence from Fiji’s 2011 STEPS Survey), with 220 structured questionnaires distributed by convenience sampling after considering 10% non-response (September to November 2022). Included were adults (≥18 years) with confirmed T2DM, attending two or more clinics or receiving pharmacological and/or lifestyle management. Unstable or newly diagnosed patients were excluded.

Sociodemographic characteristics

Participant sociodemographic (age, sex, marital status, ethnicity, education level, employment status, comorbidities) and clinical characteristics (body mass index (BMI), complications, adherence with management) were extracted from patient records.

Physical activity and sitting time measure

The International Physical Activity Questionnaire (IPAQ), short form, administered in English, Fijian and Hindi, measured physical activity as a metabolic equivalent task (MET-minutes/week) for each domain (walking, moderate and vigorous activity), and sitting time measured as a continuous variable (minutes/day). Incomplete data, extreme outliers (>960 min/week) and domain time variables >180 minutes/day were managed as per IPAQ scoring protocol data processing steps.³⁰ Physical activity levels were defined as low (<600 MET-minutes/week), moderate (600–3000 MET-minutes/week) or high (>3000 MET-minutes/week), with ≥600 MET-minutes/week defining physically ‘active’ status for analysis, as per the IPAQ protocol and WHO recommendations.¹⁵ Despite recall bias risk from self-reporting, the standardised protocol for quantitative data analysis enables cross-study comparability.

Glycaemia measure

Point-of-care (POC) capillary random blood sugar (RBS) or fasting blood sugar (FBS) readings were used for clinical practicality and availability during the study period, despite varying sensitivity (17–87%) compared to HbA1c.³¹^,³² Good specificity and clinical acceptability in stable patients³³ support their widespread use to report glycaemic control.³⁴^,³⁵ Following Fiji’s Diabetes Management Guideline (3rd edition) and international standards,⁴ glycaemic control was defined as ‘controlled’ if FBS is 4.0–7.0 mmol/L or RBS is 4.0–10.0 mmol/L, and ‘uncontrolled’ if FBS is >7.0 or RBS is >10.0 mmol/L.

Data analysis

Data were analysed using SPSS v28 (SPSS Inc, Chicago, IL, USA) with α = 0.05. Descriptive statistics included mean, median, standard deviation, frequencies and percentages. Bivariate and multivariable logistic regression assessed associations between sociodemographic parameters, glycaemic control and physical activity levels, with adjustment for covariates.

Purposeful selection of variables (Hosmer and Lemeshow method) was used for final multivariable logistical regression modelling,³⁶ including predetermined variables regardless of P-values, variables with P < 0.25 in bivariate analysis, and exclusion of non-significant variables (P > 0.05) unless they changed exposure variable regression coefficients by >10%.

Ethics

Ethics approval was obtained from the Fiji National University Human Health Research Ethics Committee (HHREC) on the 16 September 2022, following the Declaration of Helsinki principles.

Participant consent

All participants were given a patient information sheet detailing the nature of the study and signed a consent form prior to enrolment in the study.

Results

Patient characteristics

Of 220 distributed questionnaires, 208 were returned (94.5% response rate). Following IPAQ protocol exclusions (outliers, n = 8; incomplete data, n = 26), 174 were analysed (mean age 60.45 ± 11.5 years; 53% female). Participants comprised 72% Fijians of Indian descent, 25% Indigenous Fijians (Itaukei), and 3% Other ethnicities. Table 1 displays further characteristics.

Table 1.Characteristics of patients with diabetes.

Variable	Number (%), n = 174
Age
Mean (s.d.)	60.45 (s.d. 11.48)
Sex
Female	93 (53)
Male	81 (47)
Ethnicity
Fijian of Indian descent	125 (72)
Itaukei	43 (25)
Others	6 (3)
Setting seen
Special outpatient clinic	127 (73)
General outpatient clinic	47 (27)
Marital status
Married	110 (63)
Widowed	43 (25)
Single	21 (12)
Education level
Uneducated/Primary	56 (32)
Secondary	78 (45)
Tertiary	40 (23)
Employment
Employed	47 (27)
Unemployed	127 (73)
Complications
Complications	94 (54)
No complications	80 (46)
Comorbidities
Comorbidities	154 (89)
No comorbidities	20 (11)
Adherence
Adherent	104 (60)
Non-adherent	70 (40)
BMI
Underweight	2 (1)
Normal	46 (27)
Overweight	60 (34)
Obese	67 (39)
Glycaemic control
Controlled	43 (25)
Uncontrolled	131 (75)

s.d., standard deviation.

Most attended the facility’s special outpatient department (79%) and had comorbidities (89%), primarily hypertension (n = 124, 71%), chronic kidney disease (n = 39, 22%), cardiac conditions (n = 31, 18%) and dyslipidemia (n = 33, 19%). Common complications included nephropathy (n = 39, 22%), ischaemic heart disease (n = 27, 16%), retinopathy (n = 25, 14%), neuropathy (n = 17, 10%) and cataracts (n = 7, 4%).

Prevalence and factors affecting physical activity levels

Only 63 (36%) participants met physical activity targets. Active and inactive participants showed no significant difference (P = 0.86) in age, analysed continuously (Table 2). Males were more likely to be physical active (48%), with multivariate regression modelling demonstrating significantly reduced odds of physical activity in females (adjusted OR = 0.49, 95% CI = 0.25–0.98, P = 0.04). More Fijians of Indian descent reported being physically active, which became non-significant after adjustment (adjusted OR = 0.46, 95% CI: 0.19–1.08, P = 0.07). No other significant covariates were identified (Table 3).

Table 2.Characteristics of patients with diabetes according to their physical activity levels.

Variable	Physical activity level		P-value
Variable	Active no. (%)	Inactive no. (%)	P-value
Age
Mean (s.d.)	60.3 (12.0)	60.6 (11.2)	0.86
Sex
Male	39 (48)	42 (52)	0.002
Female	24 (26)	69 (74)	0.002
Ethnicity
Fijian of Indian descent	51 (41)	74 (59)	0.04
Itaukei or other descent ^A	12 (24)	37 (76)	0.04
Marital status
Married	43 (39)	67 (61)	0.095
Widowed	10 (23)	33 (77)
Single	10 (48)	11 (52)
Education level
Primary/Uneducated	19 (34)	37 (66)	0.91
Secondary	29 (37)	49 (63)
Tertiary	15 (37.5)	25 (62.5)
Employment status
Employed	22 (47)	25 (53)	0.08
Unemployed	41 (32)	86 (68)	0.08
Complications
Complications	32 (34)	62 (66)	0.52
No complications	31 (39)	49 (61)	0.52
Adherence
Adherent	39 (37.5)	65 (62.5)	0.67
Non-adherent	24 (34)	46 (66)	0.67
Comorbidities
Comorbidities	54 (35)	100 (65)	0.38
No comorbidities	9 (45)	11 (55)	0.38
Glycaemic control
Controlled	15 (35)	28 (65)	0.84
Uncontrolled	48 (37)	83 (63)	0.84
BMI
Underweight/Normal ^B	21 (45)	26 (55)	0.19
Overweight	23 (38)	37 (62)
Obese	19 (28)	48 (72)

Bold data signifies statistical significance (p < 0.05).

^A Indigenous ‘Itaukei’ participants were merged with those of ‘Other’ ancestry.

^B Underweight and normal BMI categories were merged because only two participants were classified as being underweight.

Table 3.Evaluation of associations between characteristics of patients with diabetes and meeting physical activity recommendations.

Variables	Odds of being physically active over inactive
	Unadjusted		Adjusted ^A
	OR (95% CI)	P-value	OR (95% CI) ^B	P-value
Age (years)	0.998 (0.97–1.03)	0.860	1.02 (0.98–1.05) ^B	0.36
Sex
Male	1.00 (Reference)		1.00 (Reference)
Female	0.38 (0.20–0.71)	0.003	0.49 (0.24–0.98)	0.04
Ethnicity
Fijian of Indian descent	1.00 (Reference)		1.00 (Reference)
Itaukei or other descent ^C	0.47 (0.22–0.99)	0.047	0.46 (0.19–1.08)	0.07
Marital status
Married	1.00 (Reference)	0.10	1.00 (Reference)	0.12
Widowed	0.47 (0.21–1.06)	0.07	0.47 (0.18–1.22)	0.12
Single	1.42 (0.55–3.62)	0.47	1.64 (0.60–4.48)	0.34
Education level
Primary/Uneducated	1.00 (Reference)	0.91	1.00 (Reference)	0.79
Secondary	1.15 (0.56–2.37)	0.70	1.29 (0.56–2.95)	0.55
Tertiary	1.17 (0.50–2.72)	0.72	1.40 (0.50–3.94)	0.53
Employment status
Unemployed	1.00 (Reference)
Employed	1.85 (0.93–3.66)	0.08
Complications
No complications	1.00 (Reference)
Complications	0.82 (0.44–1.52)	0.52
Comorbidities
No comorbidities	1.00 (Reference)
Comorbidities	0.66 (0.26–1.69)	0.39
Adherence
Non-adherent	1.00 (Reference)		1.00 (Reference)
Adherent	1.15 (0.61–2.17)	0.67	1.00 (0.50–2.00)	0.999
BMI
Underweight/Normal ^D	1.00 (Reference)		1.00 (Reference)
Overweight	0.77 (0.35–1.67)	0.51	0.81 (0.35–1.87)	0.63
Obese	0.49 (0.22–1.07)	0.07	0.61 (0.26–1.46)	0.27

Bold data signifies statistical significance (p < 0.05).

^A Adjusted OR with regards to age, sex, ethnicity, marital status, education level, adherence with treatment and BMI status being associated with the ‘Physically Active (PA)’ category or of meeting PA guidelines using purposeful selection of covariates.

^B Odds ratio reflects the odds of patients meeting PA guidelines.

^C Indigenous ‘Itaukei’ participants were merged with those of ‘Other’ ancestry.

^D Underweight and normal BMI categories were merged for multivariable logistical regression modelling.

Sitting behaviour

Mean daily sitting time was 315 min (s.d. = 214; range 30–1200 min; median 270 min). Inactive participants showed a non-significant 50-min increase (n = 332, s.d. = 227) compared to active individuals (n = 284, s.d. = 186, P = 0.15).

Factors affecting glycaemic control

Over 70% of participants had uncontrolled diabetes across all stratified demographic and clinical variables (Table 4). No significant association emerged between PA levels and glycaemic control (adjusted OR = 1.05, 95% CI: 0.47–2.34, P = 0.90), possibly because of POC measurements or sample size constraints. However, medication or lifestyle management adherence was significantly associated with glycaemic control (adjusted OR = 2.37, 95% CI: 1.05–5.37, P = 0.04) (Table 5). Overweight and obese participants showed non-significant trends towards control (overweight: OR = 2.36, P = 0.10; obese: OR = 1.87, P = 0.24), whereas no other variables reached significance (Table 5).

Table 4.Characteristics of patients with diabetes according to their glycaemic control status.

Variables	Controlled n (%)	Uncontrolled n (%)	Total (n)
Sex
Male	17 (21)	64 (79)	81
Female	26 (28)	67 (72)	93
Ethnicity
Fijian of Indian descent	32 (26)	93 (74)	125
Itaukei or Other descent ^A	11 (22)	38 (78)	49
Marital status
Married	30 (27)	80 (73)	110
Widowed	11 (26)	32 (74)	43
Single	2 (10)	19 (90)	21
Education level
Primary/No formal education	14 (25)	42 (75)	56
Secondary	22 (28)	56 (72)	78
Tertiary	7 (18)	33 (83)	40
Employment status
Employed	13 (28)	34 (72)	47
Unemployed	30 (24)	97 (76)	127
Complications
Complications	22 (23)	72 (77)	94
No complications	21 (26)	59 (74)	80
Adherence
Adherent	31 (30)	73 (70)	104
Non-adherent	12 (17)	58 (83)	70
Comorbidities
Comorbidities	40 (26)	114 (74)	154
No comorbidities	3 (15)	17 (85)	20
BMI
Underweight/Normal ^B	8 (17)	39 (83)	47
Overweight	18 (30)	42 (70)	60
Obese	17 (25)	50 (75)	67
Physical activity
Active	15 (24)	48 (76)	63
Inactive	28 (25)	83 (75)	111

^A Indigenous ‘Itaukei’ participants were merged with those of ‘Other’ ancestry.

^B Underweight and BMI categories were merged for multivariable logistical regression modelling.

Table 5.Evaluation of the associations between characteristics of patients with diabetes and meeting glycaemic targets.

Variables	Odds of meeting glycaemic targets over not meeting them
	Unadjusted		Adjusted ^A
	OR (95% CI)	P-value	OR (95% CI) ^B	P-value
Age (years)	1.01 (0.98–1.04)	0.40	1.026 (0.98–1.08)	0.27
Sex
Male	1.00 (Reference)
Female	1.46 (0.73–2.94)	0.29	1.76 (0.70–4.39)	0.23
Ethnicity
Fijians of Indian descent	1.00 (Reference)
Itaukei or other descent ^C	0.84 (0.39–1.84)	0.67	1.03 (0.40–2.65)	0.95
Marital status
Married	1.00 (Reference)	0.26		0.34
Widowed	0.92 (0.41–2.05)	0.83	0.77 (0.26–2.26)	0.64
Single	0.28 (0.06–1.28)	0.47	0.32 (0.07–1.53)	0.15
Education level
Primary/Uneducated	1.00 (Reference)	0.45		0.54
Secondary	1.18 (0.55–2.57)	0.68	1.33 (0.52–3.39)	0.55
Tertiary	0.64 (0.23––1.76)	0.38	0.76 (0.23–2.48)	0.65
Employment status
Unemployed	1.00 (Reference)
Employed	1.24 (0.58–2.64)	0.58	1.88 (0.67–5.26)	0.23
Complications
No complications	1.00 (Reference)
Complications	0.86 (0.43–1.71)	0.67	0.83 (0.36–1.90)	0.66
Comorbidities
No comorbidities	1.00 (Reference)
Comorbidities	1.99 (0.55–7.15)	0.29	1.59 (0.38–6.75)	0.53
Adherence
Non-adherent	1.00 (Reference)
Adherent	2.05 (0.97–4.35)	0.06	2.37 (1.05–5.37)	0.04
BMI
Underweight/Normal ^D	1.00 (Reference)			0.25
Overweight	2.09 (0.82–5.35)	0.12	2.36 (0.86–6.46)	0.10
Obese	1.66 (0.65–4.24)	0.29	1.87 (0.66–5.30)	0.24
Physical activity
Inactive	1.00 (Reference)
Active	0.93 (0.45–1.91)	0.84	1.05 (0.47–2.34)	0.90

Bold data signifies statistical significance (p < 0.05).

^A Adjusted odds ratio of the factors affecting meeting glycaemic targets, using purposeful selection of covariates.

^B Odds ratio refers to the odds of patients meeting glycaemic targets or of having ‘Controlled Diabetes’.

^C Indigenous ‘Itaukei’ participants were merged with those of ‘Other’ ancestry.

^D Underweight and BMI categories were merged for multivariable logistical regression modelling.

Discussion

This study assessed physical activity and glycaemic control associations among patients with T2DM attending an urban health centre in Suva, Fiji. Despite clinic attendance, high inactivity and uncontrolled diabetes were observed, with no significant association between physical activity and glycaemic control.

High inactivity rates (64%) are comparable to Pacific Islander population studies,¹⁶^,¹⁷ and low resource settings utilising IPAQ, short form,³⁷^,³⁸ whereas average sitting time (>5 h/day) aligns with global findings (4.8–5.8 h/day).³⁹^,⁴⁰ Females had significantly lower odds of being active, which is consistent with regional²⁷^,²⁹ and global findings.⁴¹^,⁴² Previous studies suggest cultural barriers, poor family support and familial responsibilities²⁷^,²⁹ influence physical activity in Pacific populations. Further exploration of local physical activity barriers is needed, particularly for females with diabetes. No other factors significantly affected physical activity levels (BMI, age), contrasting Pacific population-based studies.¹⁶^,²⁷^,²⁹

Physical activity showed no significant association with glycaemic control, contrasting global evidence,¹⁸^,¹⁹^,²⁷^,²⁸^,⁴³ potentially because of small sample size, POC measurement, recall bias and the IPAQ, short form used, which variably correlates with objective measures of activity.⁴⁴ Despite POC glucose variability, poor glycaemic control rates (75%) were similar to local and regional studies using HbA1c (66%).²^,⁴ Consistent with existing literature, adherence to medication and lifestyle management improved glycaemic control (adjusted OR = 2.37, 95% CI: 1.05–5.37, P = 0.04).³⁵^,⁴⁵^–⁴⁷ Although a potential confounding factor subject to social desirability bias, self-reported adherence was retained to assess self-management behavioural impacts. Despite this risk, physical activity remained unassociated with glycaemic control regardless of adherence status. Contrasting global literature, no other variables including sociodemographic, clinical and patient factors significantly influenced glycaemic control in this study.⁴⁶

Study strengths include providing standardised measures (IPAQ) with confounder adjustment, identifying disparities and systemic gaps in diabetes guideline implementation. However, small sample size, potential selection and recall bias, cross-sectional design and single-site convenience sampling limit causal inference and generalisability.

Future research in Fiji should use mixed-methods design evaluating adherence, physical activity (particularly in women), glycaemic control and physician guideline adherence. Culturally adapted physical activity questionnaires and self-management interventions, proven to improve glycaemic control among Pacific populations,²⁸ should be tested in Fiji. Where feasible, engaging multiple sites, random sampling, HbA1c and other confounders should be considered (smoking status, diabetes duration, medication type, family history, lipid profiles) to strengthen findings.

Findings demonstrate critical baseline gaps and opportunities for diabetes management and policy reforms in Fiji’s primary care setting. Persistently poor glycaemic control trends in Fiji suggest unaddressed systemic management barriers, whereas significant adherence-related improvements highlight promising intervention priority. Fiji’s National Wellness Policy and Non-Communicable Disease (NCD) Strategic Plan should strengthen guidelines, enhance primary care physician training and guideline adherence, and prioritise patient self-management strategies to improve outcomes and address these barriers.

Data availability

The datasets used and analysed during this study are available from the corresponding author on reasonable request.

Conflicts of interest

The authors declare that they have no competing interests.

Declaration of funding

A research grant was provided by the Fiji National University to support this study.

Acknowledgements

The authors wish to acknowledge the assistance of Dr Eunice Okyere, who provided considerable advice regarding the design of the study.

References

1 Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edn. Diabetes Res Clin Pract 2019; 157: 107843.
| Crossref | Google Scholar | PubMed |

2 Win Tin ST, Lee CMY, Colagiuri R. A profile of diabetes in Pacific Island countries and territories. Diabetes Res Clin Pract 2015; 107(2): 233-46.
| Google Scholar |

3 Morrell S, Lin S, Tukana I, et al. Diabetes incidence and projections from prevalence surveys in Fiji. Popul Health Metr 2016; 14(1): 45.
| Crossref | Google Scholar | PubMed |

4 Ibrahim AM, Lawrence S. Improving diabetes care: a Fijian diabetes service improvement study. Int J Chronic Dis 2022; 2022: 9486679.
| Crossref | Google Scholar | PubMed |

5 Kumar K, Snowdon W, Ram S, et al. Descriptive analysis of diabetes-related amputations at the Colonial War Memorial Hospital, Fiji, 2010-2012. Public Health Action 2014; 4(3): 155-8.
| Crossref | Google Scholar | PubMed |

6 Win Tin ST, Iro G, Gadabu E, et al. Counting the cost of diabetes in the Solomon Islands and Nauru. PLoS One 2015; 10(12): e0145603.
| Google Scholar |

7 Meigs JB, Cupples LA, Wilson PW. Parental transmission of type 2 diabetes: the Framingham Offspring Study. Diabetes 2000; 49(12): 2201-7.
| Crossref | Google Scholar | PubMed |

8 Bellou V, Belbasis L, Tzoulaki I, et al. Risk factors for type 2 diabetes mellitus: an exposure-wide umbrella review of meta-analyses. PLoS One 2018; 13(3): 0194127.
| Crossref | Google Scholar | PubMed |

9 Weires MB, Tausch B, Haug PJ, et al. Familiarity of diabetes mellitus. Exp Clin Endocrinol Diabetes 2007; 115(10): 634-40.
| Google Scholar |

10 Taylor R, Badcock J, King H, et al. Dietary intake, exercise, obesity and noncommunicable disease in rural and urban populations of three Pacific Island countries. J Am Coll Nutr 1992; 11(3): 283-93.
| Crossref | Google Scholar | PubMed |

11 Russell-Jones DL, Hoskins P, Kearney E, et al. Rural/urban differences of diabetes--impaired glucose tolerance, hypertension, obesity, glycosolated haemoglobin, nutritional proteins, fasting cholesterol and apolipoproteins in Fijian Melanesians over 40. Q J Med 1990; 74(273): 75-81.
| Google Scholar | PubMed |

12 Zimmet P, Taylor R, Ram P, et al. Prevalence of diabetes and impaired glucose tolerance in the biracial (Melanesian and Indian) population of Fiji: a rural-urban comparison. Am J Epidemiol 1983; 118(5): 673-88.
| Crossref | Google Scholar | PubMed |

13 Taylor R, Ram P, Zimmet P, et al. Physical activity and prevalence of diabetes in Melanesian and Indian men in Fiji. Diabetologia 1984; 27(6): 578-82.
| Crossref | Google Scholar | PubMed |

14 Taylor R, Lin S, Linhart C, et al. Overview of trends in cardiovascular and diabetes risk factors in Fiji. Ann Hum Biol 2018; 45(3): 188-201.
| Crossref | Google Scholar | PubMed |

15 World Health Organization. WHO guidelines on physical activity and sedentary behaviour: web annex: evidence profiles. Geneva: World Health Organization; 2020.

16 Felix H, Li X, Rowland B, et al. Physical activity and diabetes-related health beliefs of Marshallese adults. Am J Health Behav 2017; 41(5): 553-60.
| Crossref | Google Scholar | PubMed |

17 Wang ML, McElfish PA, Long CR, et al. BMI and related risk factors among U.S. Marshallese with diabetes and their families. Ethn Health 2021; 26(8): 1196-208.
| Crossref | Google Scholar | PubMed |

18 Grace A, Chan E, Giallauria F, et al. Clinical outcomes and glycaemic responses to different aerobic exercise training intensities in type II diabetes: a systematic review and meta-analysis. Cardiovasc Diabetol 2017; 16(1): 37.
| Crossref | Google Scholar | PubMed |

19 Chen L, Pei JH, Kuang J, et al. Effect of lifestyle intervention in patients with type 2 diabetes: a meta-analysis. Metabolism 2015; 64(2): 338-47.
| Crossref | Google Scholar | PubMed |

20 Ren C, Liu W, Li J, et al. Physical activity and risk of diabetic retinopathy: a systematic review and meta-analysis. Acta Diabetol 2019; 56(8): 823-37.
| Crossref | Google Scholar | PubMed |

21 Matos M, Mendes R, Silva AB, et al. Physical activity and exercise on diabetic foot related outcomes: a systematic review. Diabetes Res Clin Pract 2018; 139: 81-90.
| Crossref | Google Scholar | PubMed |

22 Cai Z, Yang Y, Zhang J. Effects of physical activity on the progression of diabetic nephropathy: a meta-analysis. Biosci Rep 2021; 41(1): BSR20203624.
| Crossref | Google Scholar | PubMed |

23 Rao CR, Chandrasekaran B, Ravishankar N, et al. Physical activity interventions for glycaemic control in African adults – A systematic review and meta-analysis. Diabetes Metab Syndr: Clin Res Rev 2022; 16(12): 102663.
| Crossref | Google Scholar | PubMed |

24 Al-Ma'aitah OH, Demant D, Jakimowicz S, et al. Glycaemic control and its associated factors in patients with type 2 diabetes in the Middle East and North Africa: an updated systematic review and meta-analysis. J Adv Nurs 2022; 78(8): 2257-76.
| Crossref | Google Scholar | PubMed |

25 Boniol M, Dragomir M, Autier P, et al. Physical activity and change in fasting glucose and HbA1c: a quantitative meta-analysis of randomized trials. Acta Diabetol 2017; 54(11): 983-91.
| Crossref | Google Scholar | PubMed |

26 Sukala WR, Page R, Lonsdale C, et al. Exercise improves quality of life in indigenous Polynesian peoples with type 2 diabetes and visceral obesity. J Phys Act Health 2013; 10(5): 699-707.
| Crossref | Google Scholar | PubMed |

27 McElfish PA, Bridges MD, Hudson JS, et al. Family model of diabetes education with a Pacific Islander community. Diabetes Educ 2015; 41(6): 706-15.
| Crossref | Google Scholar | PubMed |

28 Sinclair KA, Makahi EK, Shea-solatorio C, et al. Outcomes from a diabetes self-management intervention for Native Hawaiians and Pacific People: partners in care. Ann Behav Med 2013; 45(1): 24-32.
| Crossref | Google Scholar | PubMed |

29 Aitaoto N, Campo SL, Snetselaar LG, et al. Factors inhibiting physical activity as treatment for Diabetic Chuukese in Chuuk and Hawai'i. Hawaii J Med Public Health 2017; 76(9): 247-52.
| Google Scholar | PubMed |

30 Forde C. Scoring the international physical activity questionnaire (IPAQ). University of Dublin; 2018. p. 3.

31 Khambule L, Chikomba C, Adam Y, et al. Performance of point-of-care glucose testing for the diagnosis of gestational diabetes in South Africa. Int J Gynaecol Obstet 2025; 168(2): 812-21.
| Crossref | Google Scholar | PubMed |

32 Maciel EDS, Quaresma FRP, Figueiredo F, et al. The sensitivity, specificity, and agreement of a point of care method: an assessment of the diagnostic accuracy. Cien Saude Colet 2019; 24(11): 4297-305.
| Crossref | Google Scholar | PubMed |

33 Rebel A, Rice MA, Fahy BG. Accuracy of point-of-care glucose measurements. J Diabetes Sci Technol 2012; 6(2): 396-411.
| Crossref | Google Scholar | PubMed |

34 Asgharzadeh M, Pourasghary S, Pourasghary B, et al. Effective factors in controlling diabetes progression among patients in the northwest of Iran. J Nat Sci Biol Med 2016; 7(1): 68-71.
| Crossref | Google Scholar | PubMed |

35 Alramadan MJ, Afroz A, Hussain SM, et al. Patient-related determinants of glycaemic control in people with type 2 diabetes in the Gulf Cooperation Council countries: a systematic review. J Diabetes Res 2018; 2018: 9389265.
| Crossref | Google Scholar | PubMed |

36 Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied logistic regression. John Wiley & Sons; 2013.

37 Duarte CK, Almeida JC, Merker AJ, et al. Physical activity level and exercise in patients with diabetes mellitus. Rev Assoc Med Bras (1992) 2012; 58(2): 215-21.
| Google Scholar | PubMed |

38 Oguntibeju OO, Odunaiya N, Oladipo B, et al. Health behaviour and quality of life of patients with type 2 diabetes attending selected hospitals in south western Nigeria. West Indian Med J 2012; 61(6): 619-26.
| Google Scholar | PubMed |

39 Oyewole OO, Odusan O, Oritogun KS, et al. Physical activity among type-2 diabetic adult Nigerians. Ann Afr Med 2014; 13(4): 189-94.
| Crossref | Google Scholar | PubMed |

40 Kennerly AM, Kirk A. Physical activity and sedentary behaviour of adults with type 2 diabetes: a systematic review. Practical Diabetes 2018; 35(3): 86-9g.
| Crossref | Google Scholar |

41 Kadariya S, Aro AR. Barriers and facilitators to physical activity among urban residents with diabetes in Nepal. PLoS One 2018; 13(6): 0199329.
| Crossref | Google Scholar | PubMed |

42 Abbasi IN. Socio-cultural barriers to attaining recommended levels of physical activity among females: a review of literature. Quest 2014; 66(4): 448-67.
| Crossref | Google Scholar |

43 Adeniyi AF, Uloko AE, Ogwumike OO, et al. Time course of improvement of metabolic parameters after a 12 week physical exercise programme in patients with type 2 diabetes: the influence of gender in a Nigerian population. BioMed Res Int 2013; 2013: 310574.
| Crossref | Google Scholar | PubMed |

44 Lee PH, Macfarlane DJ, Lam TH, et al. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act 2011; 8: 115.
| Crossref | Google Scholar | PubMed |

45 Pihau-Tulo ST, Parsons RW, Hughes JD. An evaluation of patients’ adherence with hypoglycemic medications among Papua New Guineans with type 2 diabetes: influencing factors. Patient Prefer Adherence 2014; 8: 1229-37.
| Crossref | Google Scholar | PubMed |

46 Cheng LJ, Wang W, Lim ST, et al. Factors associated with glycaemic control in patients with diabetes mellitus: a systematic literature review. J Clin Nurs 2019; 28(9–10): 1433-50.
| Crossref | Google Scholar | PubMed |

47 Yigazu DM, Desse TA. Glycemic control and associated factors among type 2 diabetic patients at Shanan Gibe Hospital, Southwest Ethiopia. BMC Res Notes 2017; 10(1): 597.
| Crossref | Google Scholar | PubMed |