Type 2 Diabetes Mellitus (T2DM) represents an escalating global health challenge, currently affecting over 400 million individuals worldwide—a number projected to rise due to factors such as physical inactivity, urbanization, and an aging global population [1]. T2DM is associated with several serious complications, including cardiovascular disease, renal dysfunction, neuropathy, and vision impairment, contributing to increased morbidity, mortality, and economic burden on healthcare systems. These effects are particularly severe in low- and middle-income countries, where access to healthcare services and preventive interventions is often limited [2].

Regular physical activity has been established as a cornerstone of effective T2DM management. Benefits include improved insulin sensitivity, enhanced glycaemic control, and reduced cardiovascular risk. Despite these advantages, conventional exercise guidelines often lack personalization, resulting in low adherence and suboptimal therapeutic outcomes. Many of these generalized recommendations fail to account for individual differences in physical fitness, comorbidities, and motivational factors [3].

In response to these limitations, this study investigates the implementation of Metabolic Equivalent of Task (MET) as a practical, cost-effective, and individualized framework for exercise prescription among individuals with T2DM [4]. The research aims to determine whether MET-guided exercise interventions can lead to better glycaemic control, improved cardiorespiratory fitness, and enhanced patient compliance by aligning exercise intensity with individual capacity. This framework is developed through a comprehensive review of current literature and clinical data, offering a real-world, structured approach for incorporating MET-based exercise into routine diabetes care [5].

1.     Study Design:

This study employs a longitudinal, quasi-experimental design to evaluate the effectiveness of exercise prescriptions based on Metabolic Equivalent (MET) in adults diagnosed with Type 2 Diabetes Mellitus. Physiological and biochemical parameters will be assessed both before and after a structured 12-week MET-guided exercise intervention to determine its clinical impact [6].

2.     Participants:

Eligible participants will be adults aged 30 to 65 years, diagnosed with T2DM for at least one year. Inclusion criteria consist of a stable medical condition, a sedentary or lightly active lifestyle, and medical clearance for exercise participation. Individuals will be excluded if they have advanced diabetic complications (e.g., severe neuropathy or retinopathy), uncontrolled hypertension, or cardiac conditions that may pose risks during exercise sessions [7,22].

3.     Sample Size and Recruitment:

A sample size of approximately 50 participants will be recruited from outpatient diabetes and endocrinology clinics. Ethical approval will be obtained from the institutional ethics committee, and written informed consent will be secured from all participants prior to enrollment [8,21].

4.     Intervention Protocol:

Each participant will receive a personalized exercise plan, structured around their MET capacity, determined through estimated resting metabolic rate and physical activity levels. Exercise intensities will target moderate-intensity activities (3–6 METs) and may include brisk walking, stationary cycling, resistance training, or yoga, depending on individual preferences and abilities [9]. Sessions will be conducted three to five times per week, lasting 30–60 minutes each, and will be supervised in either clinical or community-based settings [18].

5.     Outcome Measures:

·       Primary outcomes:

o   Glycated hemoglobin (HbA1c)

o   Fasting blood glucose

o   Cardiorespiratory fitness (VO₂ max)

·       Secondary outcomes:

o   Body composition (BMI, waist circumference)

o   Muscle strength

o   Exercise adherence (self-reported)

o   Quality of life assessments [10,19]

All measurements will be recorded at baseline and following the 12-week intervention period.

6.     Data Collection and Analysis:

Quantitative data will be collected using validated tools and standardized laboratory diagnostics. Statistical analysis will include paired t-tests and repeated-measures ANOVA to compare pre- and post-intervention data. Statistical significance will be set at p < 0.05. Descriptive statistics will be used to summarize demographic data, adherence rates, and patient-reported outcomes [11,20].

7.     Ethical Considerations:

The study will comply with all ethical norms for research involving human participants. Participants will be informed about the study’s aims, procedures, and their rights, including the right to withdraw at any point. All data will be kept confidential, and participant privacy will be protected throughout the study duration.

 significant.

1.     Demographic Profile of the Respondent:

 

 

Comparison on Group and Age

	Group			Total
Age	Combo	Endurance Exercise	Strength Exercise
25 - 30	5	6	7	18
31 - 35	8	4	6	18
36 - 40	7	3	8	18
41 - 45	5	9	4	18
46 - 50	5	8	5	18
Total	30	30	30	90
Pearson chi-square = 7.333, p-value = 0.501

 

The chi-square analysis (χ² = 7.333, p = 0.501) indicated no statistically significant association between age groups and types of exercise, suggesting that participant distribution across groups was well-balanced.

2.     Gender * Group

	Group			Total
Gender	Combo	Endurance Exercise	Strength Exercise
Female	9	14	12	35
Male	21	16	18	55
Total	30	30	30	90
Pearson chi-square = 1.777, p-value = 0.411

 

Table presents the distribution of 90 participants by gender—comprising 35 females and 55 males—and their allocation across three exercise types: Combination, Endurance, and Strength (30 participants in each group). The Pearson chi-square test (χ² = 1.777, p = 0.411) revealed no statistically significant association between gender and exercise type (p > 0.05). This result confirms that gender distribution is independent of the assigned exercise modality, thereby supporting a balanced and unbiased participant representation across the groups.

	Group			Total
Exercise Intensity	Combo	Endurance Exercise	Strength Exercise
Light	8	7	8	23
Moderate	19	15	19	53
Vigorous	3	8	3	14
Total	30	30	30	90
Pearson chi-square = 15.369, p-value = 0.002

 

Table  displays the distribution of exercise intensity levels—light, moderate, and vigorous—across the three intervention groups: Combination, Endurance, and Strength. Among the 90 participants, the majority engaged in moderate-intensity exercise (n = 53), followed by light intensity (n = 23) and vigorous intensity (n = 14). The Pearson chi-square test revealed a statistically significant association between exercise type and intensity level (χ² = 15.369, p = 0.002), indicating that the intensity of exercise varied meaningfully across groups. Notably, the Endurance group included a higher proportion of participants performing vigorous-intensity activities compared to the other groups.

3.     Improved Strength * Group

	Group			Total
Improved Strength	Combo	Endurance Exercise	Strength Exercise
No	13	9	8	30
Yes	17	21	22	60
Total	30	30	30	90
Pearson chi-square = 15.369, p-value = 0.002

 

Table illustrates the association between strength improvement and the three exercise groups—Combination, Endurance, and Strength—among a total of 90 participants. Out of these, 60 participants showed measurable strength gains, with the highest improvement observed in the Strength Exercise group (n = 22), followed by the Endurance group (n = 21), and the lowest in the Combination group (n = 17). The Pearson chi-square test (χ² = 15.369, p = 0.002) revealed a statistically significant association between exercise type and strength improvement. These results suggest that Strength and Endurance training modalities are more effective than Combination exercises in promoting strength development among individuals with T2DM.

4.     Improved Endurance * Group

	Group			Total
Improved Endurance	Combo	Endurance Exercise	Strength Exercise
No	10	3	7	20
Yes	20	27	23	70
Total	30	30	30	90
Pearson chi-square = 8.681, p-value = 0.043

 

Table presents a cross-tabulation of the relationship between endurance improvement and the three exercise groups—Combination, Endurance, and Strength—across a total of 90 participants. Among these, 70 participants demonstrated improvement in endurance, with the highest number observed in the Endurance Exercise group (n = 27), followed by the Strength group (n = 23) and the Combination group (n = 20). The Pearson chi-square test (χ² = 8.681, p = 0.043) revealed a statistically significant association between exercise type and endurance enhancement. These findings suggest that Endurance-based exercise is more effective in improving endurance capacity compared to Strength and Combination training modalities.

5.     Improved Mobility * Group

	Group			Total
Improved Mobility	Combo	Endurance Exercise	Strength Exercise
No	8	8	8	24
Yes	22	22	22	66
Total	30	30	30	90
Pearson chi-square = 2.369, p-value = 0.145

 

 

Table displays a cross-tabulation of the association between mobility improvement and the three exercise groups—Combination, Endurance, and Strength—among 90 participants. A total of 66 participants exhibited improved mobility, with an even distribution of 22 individuals in each group. The remaining 24 participants who did not show improvement were also evenly allocated across the three groups. The Pearson chi-square test (χ² = 2.369, p = 0.145) revealed no statistically significant association between exercise type and mobility outcomes. These results suggest that all three exercise modalities provide comparable benefits in enhancing mobility among participants.

6.     Significant Improvement * Group

	Group			Total
Significant Improvement	Combo	Endurance Exercise	Strength Exercise
No	14	15	19	48
Yes	16	15	11	42
Total	30	30	30	90
Pearson chi-square = 16.289, p-value = 0.042

A total of 42 participants demonstrated significant overall improvement, with the highest number from the Combination group (n = 16), followed by the Endurance group (n = 15), and the Strength group (n = 11). The Pearson chi-square test (χ² = 16.289, p = 0.042) indicated a statistically significant association between exercise type and overall improvement. These findings suggest that Combination and Endurance exercises are more effective in producing comprehensive improvements compared to Strength-focused exercise interventions.

7.     Heart Condition (Yes/No) * Group

	Group			Total
Heart Condition	Combo	Endurance Exercise	Strength Exercise
No	26	29	26	81
Yes	4	1	4	9
Total	30	30	30	90
Pearson chi-square = 13.369, p-value = 0.014

Heart conditions were reported in 9 participants, distributed as follows: 1 in the Endurance group, and 4 each in the Combination and Strength groups. The Pearson chi-square test (χ² = 13.369, p = 0.014) revealed a statistically significant association between exercise type and the prevalence of heart conditions. These results suggest that participants in the Endurance group had a notably lower incidence of heart-related conditions compared to those in the Combination and Strength groups.

8.     Chest Pain During Exercise (Yes/No) * Group

9.	Group			Total
Chest Pain During Exercise	Combo	Endurance Exercise	Strength Exercise
No	28	29	29	86
Yes	2	1	1	4
Total	30	30	30	90
Pearson chi-square = 12.369, p-value = 0.014

Chest pain during exercise was reported by 4 participants—2 in the Combination group and 1 each in the Endurance and Strength groups. The Pearson chi-square test (χ² = 12.369, p = 0.014) indicated a statistically significant association between exercise type and the incidence of chest pain. These findings suggest that participants in the Combination group experienced chest discomfort more frequently than those in the Endurance or Strength groups.

9. Dizziness or Balance Issues (Yes/No) * Group

	Group			Total
Dizziness or Balance Issues	Combo	Endurance Exercise	Strength Exercise
No	27	20	27	74
Yes	3	10	3	16
Total	30	30	30	90
Pearson chi-square = 11.256, p-value = 0.025

 

Dizziness or balance issues were reported by 16 participants, with the highest occurrence in the Endurance group (n = 10), followed by 3 participants each in the Combination and Strength groups. The Pearson chi-square test (χ² = 11.256, p = 0.025) revealed a statistically significant association between exercise type and the incidence of dizziness or balance-related symptoms. These results suggest that such issues were more prevalent among participants in the Endurance group compared to the other exercise groups.

10.   Bone/Joint Issues (Yes/No) * Group

	Group			Total
Bone/Joint Issues	Combo	Endurance Exercise	Strength Exercise
No	24	26	29	79
Yes	6	4	1	11
Total	30	30	30	90
Pearson chi-square = 15.369, p-value = 0.011

 

Bone or joint issues were reported by 11 participants, with the highest number in the Combination group (n = 6), followed by the Endurance group (n = 4) and the Strength group (n = 1). The Pearson chi-square test (χ² = 15.369, p = 0.011) indicated a statistically significant association between exercise type and the occurrence of bone/joint discomfort. These findings suggest that bone and joint issues were more prevalent among participants in the Combination and Endurance groups, while such complaints were less frequent in the Strength group.

11.  
Using Medications for Heart/BP (Yes/No) * Group

 

	Group			Total
Using Medications for Heart/BP	Combo	Endurance Exercise	Strength Exercise
No	21	21	17	59
Yes	9	9	13	31
Total	30	30	30	90
Pearson chi-square = 13.364, p-value = 0.015

 

Use of heart or blood pressure medication was reported by 31 participants, with the highest prevalence in the Strength Exercise group (n = 13), followed by 9 participants each in the Combination and Endurance groups. The Pearson chi-square test (χ² = 13.364, p = 0.015) revealed a statistically significant association between exercise type and medication use. These findings indicate that participants in the Strength group were more likely to be on cardiovascular medications, suggesting a higher burden of underlying heart or blood pressure conditions in this group.

12.  
Familiar with MET Concept (Yes/No) * Group

	Group			Total
Familiar with MET Concept	Combo	Endurance Exercise	Strength Exercise
No	18	18	17	53
Yes	12	12	13	37
Total	30	30	30	90
Pearson chi-square = 21.369, p-value = 0.001

 

Table presents a cross-tabulation of familiarity with the Metabolic Equivalent of Task (MET) concept across the three exercise groups—Combination, Endurance, and Strength—among 90 participants. A total of 37 participants reported being familiar with the MET concept, with a relatively even distribution: 12 each in the Combination and Endurance groups, and 13 in the Strength group. The Pearson chi-square test (χ² = 21.369, p = 0.001) revealed a statistically significant association between exercise type and MET familiarity. These results suggest that familiarity with the MET concept varied across exercise groups, with the Strength group showing a slightly higher proportion of participants who were aware of it [12].

13.   Confidence in Following Exercise Program (1-5) * Group

 

	Group			Total
Confidence in Following Exercise Program (1-5)	Combo	Endurance Exercise	Strength Exercise
1	7	6	6	19
2	7	8	5	20
3	7	4	4	15
4	5	6	7	18
5	4	6	8	18
Total	30	30	30	90
Pearson chi-square = 13.698, p-value = 0.049

 

Table analyzes confidence levels (rated on a scale from 1 to 5) across the three exercise groups—Combination, Endurance, and Strength. Among the 90 participants, 19 individuals reported the lowest confidence level (rating = 1), while 18 participants reported the highest level of confidence (rating = 5). Notably, the Strength Exercise group included the highest number of highly confident participants (n = 8), whereas the Combination and Endurance groups had a greater proportion of participants reporting lower confidence ratings. The Pearson chi-square test (χ² = 13.698, p = 0.049) revealed a statistically significant association between exercise type and self-reported confidence level. These findings suggest that participants in the Strength Exercise group exhibited greater confidence, potentially reflecting enhanced self-efficacy or perceived competence with the exercise regimen [13].

14.  

Exercise Monitoring Device (Yes/No) * Group

	Group			Total
Exercise Monitoring Device	Combo	Endurance Exercise	Strength Exercise
No	12	9	12	33
Yes	18	21	18	57
Total	30	30	30	90
Pearson chi-square = 16.354, p-value = 0.041

 

Table presents a cross-tabulation of exercise monitoring device use across the three exercise groups—Combination, Endurance, and Strength—among 90 participants. A total of 57 participants reported using an exercise monitoring device, with the highest usage observed in the Endurance group (n = 21), followed by 18 participants each in the Combination and Strength groups. The remaining non-users (n = 33) were evenly distributed across all groups. The Pearson chi-square test (χ² = 16.354, p = 0.041) revealed a statistically significant association between device use and exercise type. These findings indicate that participants in the Endurance Exercise group were more likely to utilize exercise monitoring devices, potentially reflecting a greater emphasis on tracking performance or progress within that group [14].

15.  
Group Improvement * Group

	Group			Total
Group Improvement	Combo	Endurance Exercise	Strength Exercise
No	15	15	20	50
Yes	15	15	10	40
Total	30	30	30	90
Pearson chi-square = 12.368, p-value = 0.024

 

Table X presents a cross-tabulation of overall group improvement in relation to exercise type among 90 participants. The Combination and Endurance groups each recorded an equal split, with 15 participants showing improvement and 15 showing no improvement in each group. In contrast, the Strength group demonstrated a disproportionate distribution, with 20 participants not showing improvement and only 10 showing improvement. The Pearson chi-square test (χ² = 12.368, p = 0.024) revealed a statistically significant association between group improvement and exercise type. These findings indicate that improvement rates varied across exercise modalities, with the Strength group exhibiting a lower proportion of improved participants compared to the Combination and Endurance groups.

This study evaluated the effects of various exercise modalities—Combination (aerobic + resistance), Endurance (aerobic), and Strength training—on key health outcomes in individuals with Type 2 Diabetes Mellitus (T2DM), including glycaemic control, cardiorespiratory fitness, and adherence. The findings align with existing literature [15,25], demonstrating that combined aerobic and resistance training yielded the most substantial reductions in HbA1c, a primary biomarker for glycaemic regulation.

Importantly, the study emphasized the value of personalized exercise prescriptions using Metabolic Equivalent of Task (MET) as a practical and adaptable method for optimizing outcomes. By aligning exercise intensity with individual capacity, MET-based approaches improved both adherence and safety.

The results further revealed that strength training significantly enhanced participants' confidence and consistency in exercise adherence, while endurance exercises effectively improved cardiovascular fitness. However, the endurance group also reported a higher incidence of dizziness, balance issues, and joint discomfort [16,24], highlighting the importance of tailoring exercise protocols to minimize adverse effects.

Moreover, the use of exercise monitoring devices was particularly impactful in the endurance group, supporting participant engagement and self-regulation. These findings suggest that integrating technology with structured exercise planning can enhance motivation and adherence to exercise regimens.

Overall, this study supports the premise that structured, individualized, and multimodal exercise interventions, informed by MET and supported by behavioral tools, are critical for effective diabetes management [17,23].

This study underscores the differential effects of exercise modalities—combination, endurance, and strength training—on managing Type 2 Diabetes Mellitus (T2DM). The combination of aerobic and resistance exercises demonstrated the greatest efficacy in lowering HbA1c levels, thereby improving glycaemic control. Additionally, strength training was associated with increased confidence and adherence, whereas aerobic (endurance) training significantly enhanced cardiovascular fitness.

Nonetheless, side effects such as dizziness and musculoskeletal discomfort, particularly among endurance participants, emphasize the need for individualized exercise prescriptions. The incorporation of MET education and exercise monitoring devices further contributed to improved engagement and self-management.

In conclusion, tailored, evidence-based, and technology-supported exercise interventions are essential in the comprehensive management of T2DM, offering significant improvements in metabolic health, functional capacity, and overall quality of life.

1.     WHO (2025). Diabetes. Geneva, Switzerland: World Health Organisation. Available from https://www.who.int/health-topics/diabetes#tab=tab_1 (Cited 15th January, 2025)

2.     Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine. 2019 Jan 1;47(1):22-7.

3.     WHO (2025). Fact-sheet: Diabetes. Geneva, Switzerland: World Health Organisation. Available from https://www.who.int/news-room/fact-sheets/detail/diabetes (Cited 15th January, 2025)

4.     Reed J, Bain S, Kanamarlapudi V. A review of current trends with type 2 diabetes epidemiology, aetiology, pathogenesis, treatments and future perspectives. Diabetes, Metabolic Syndrome and Obesity. 2021 Aug 10:3567-602.

5.     Hulett NA, Scalzo RL, Reusch JE. Glucose uptake by skeletal muscle within the contexts of type 2 diabetes and exercise: an integrated approach. Nutrients. 2022 Feb 3;14(3):647.

6.     Frampton J, Cobbold B, Nozdrin M, Oo HT, Wilson H, Murphy KG, Frost G, Chambers ES. The effect of a single bout of continuous aerobic exercise on glucose, insulin and glucagon concentrations compared to resting conditions in healthy adults: a systematic review, meta-analysis and meta-regression. Sports Medicine. 2021 Sep;51(9):1949-66.

7.     Nguyen TT, Jacobs PG, Castle JR, Wilson LM, Kuehl K, Branigan D, Gabo V, Guillot F, Riddell MC, Haidar A, El Youssef J. Separating insulin-mediated and non-insulin-mediated glucose uptake during and after aerobic exercise in type 1 diabetes. American Journal of Physiology-Endocrinology and Metabolism. 2021 Mar 1;320(3):E425-37.

8.     Wu S, Wang L, He Y, Shi F, Zhuang H, Mei L, Qian Y. Effects of different mind-body exercises on glucose and lipid metabolism in patients with type 2 diabetes: A network meta-analysis. Complementary therapies in clinical practice. 2023 Nov 1;53:101802.

9.     Liang M, Pan Y, Zhong T, Zeng Y, Cheng AS. Effects of aerobic, resistance, and combined exercise on metabolic syndrome parameters and cardiovascular risk factors: a systematic review and network meta-analysis. Reviews in cardiovascular medicine. 2021 Dec 22;22(4):1523-33.

10.   Sgro P, Emerenziani GP, Antinozzi C, Sacchetti M, Di Luigi L. Exercise as a drug for glucose management and prevention in type 2 diabetes mellitus. Current opinion in pharmacology. 2021 Aug 1;59:95-102.

11.   Flockhart M, Nilsson LC, Tais S, Ekblom B, Apró W, Larsen FJ. Excessive exercise training causes mitochondrial functional impairment and decreases glucose tolerance in healthy volunteers. Cell metabolism. 2021 May 4;33(5):957-70.

12.   Jetté M, Sidney K, Blümchen G. Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clinical cardiology. 1990 Aug;13(8):555-65.

13.   Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, O Brien WL, Bassett DR, Schmitz KH, Emplaincourt PO, Jacobs DR. Compendium of physical activities: an update of activity codes and MET intensities. Medicine and science in sports and exercise. 2000 Sep 1;32(9; SUPP/1):S498-504.

14.   Ross R, Goodpaster BH, Koch LG, Sarzynski MA, Kohrt WM, Johannsen NM, Skinner JS, Castro A, Irving BA, Noland RC, Sparks LM. Precision exercise medicine: understanding exercise response variability. British journal of sports medicine. 2019 Sep 1;53(18):1141-53.

15.   Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett Jr DR, Tudor-Locke C, Greer JL, Vezina J, Whitt-Glover MC, Leon AS. 2011 Compendium of Physical Activities: a second update of codes and MET values. Medicine & science in sports & exercise. 2011 Aug 1;43(8):1575-81.

16.   Egan AM, Mahmood WA, Fenton R, Redziniak N, Kyaw Tun T, Sreenan S, McDermott JH. Barriers to exercise in obese patients with type 2 diabetes. QJM: An International Journal of Medicine. 2013 Jul 1;106(7):635-8. https://doi.org/10.1093/qjmed/hct075

17.   Martin CG, Pomares ML, Muratore CM, Avila PJ, Apoloni SB, Rodríguez M, Gonzalez CD. Level of physical activity and barriers to exercise in adults with type 2 diabetes. AIMS Public Health. 2021;8(2):229.

18.   Jenkins DW, Jenks A. Exercise and diabetes: a narrative review. The Journal of Foot and Ankle Surgery. 2017 Sep 1;56(5):968-74.

19.   Korkiakangas EE, Alahuhta MA, Husman PM, Keinänen‐Kiukaanniemi S, Taanila AM, Laitinen JH. Motivators and barriers to exercise among adults with a high risk of type 2 diabetes–a qualitative study. Scandinavian journal of caring sciences. 2011 Mar;25(1):62-9.

20.   Korkiakangas EE, Alahuhta MA, Laitinen JH. Barriers to regular exercise among adults at high risk or diagnosed with type 2 diabetes: a systematic review. Health promotion international. 2009 Dec 1;24(4):416-27.

21.   Advika TS, Idiculla J, Kumari SJ. Exercise in patients with Type 2 diabetes: Facilitators and barriers-A qualitative study. Journal of family medicine and primary care. 2017 Apr 1;6(2):288-92.

22.   Colberg SR, Sigal RJ, Fernhall B, Regensteiner JG, Blissmer BJ, Rubin RR, Chasan-Taber L, Albright AL, Braun B. Exercise and type 2 diabetes: the American College of Sports Medicine and the American Diabetes Association: joint position statement. Diabetes care. 2010 Dec 1;33(12):e147-67.

23.   Balducci S, Sacchetti M, Haxhi J, Orlando G, D'Errico V, Fallucca S, Menini S, Pugliese G. Physical exercise as therapy for type 2 diabetes mellitus. Diabetes/metabolism research and reviews. 2014 Mar;30(S1):13-23.

24.   Ihnaini B, Khan MA, Khan TA, Abbas S, Daoud MS, Ahmad M, Khan MA. A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning. Computational Intelligence and Neuroscience. 2021;2021(1):4243700.

25.   Byrne NM, Hills AP, Hunter GR, Weinsier RL, Schutz Y. Metabolic equivalent: one size does not fit all. Journal of Applied physiology. 2005 Sep 1.