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The Effect of Massage Therapy on Glycemic Control (FBS, HBA1C) in Women with Diabetes Type 2
Mahboobeh Ghasemi Pour, Dr Samereh Abdoli, Mahboobeh Valiani, Dr Avat Feizi,
Volume 10, Issue 0 (Supplementary 2013)
Abstract

  Background and Objective: Type 2 diabetes is a major public health problem with increasing morbidity and mortality. Complementary therapies are widely used by the people with diabetes. Massage therapy (MT) is an increasingly popular complementary therapy. Since few studies have been conducted on the effect of MT on people with diabetes, we aimed to investigate the effect of massage therapy on glycemic control (FBS, HBA1C) in women with diabetes.

  

  Material and Methods: this clinical trial was conducted on fifty women with type 2 diabetes selected by convenience sampling and placed randomly in treatment and control groups. The test group received 30 mins-long massage therapies in a week (10 sessions) and the control group received just standard care. Glycemic control indices including HbA1C, FBS were measured at baseline, after 10 weeks of therapy and then analyzed by paired-t test and MANOVA (p≤ 0.05) .

  

  Results: the mean and standard deviation of HBA1C of massage group (7.18±0.14) was significantly (P < 0.01) different from control (8.06±0.22) and the difference of FBS in massage group (1.46±5.28) from control (1.6±4.87) was meaningful statistically (P < 0.05).

  

  Conclusion: accordingly, massage therapy can be useful for glycemic control (FBS, HBA1C) in women with type 2 diabetes.

 


Editorial Note: Addressing diabetes in rural communities through integrated social care and innovation
Samereh Abdoli ,
Volume 22, Issue 3 (9-2025)
Abstract
This editorial note highlights the persistent barriers to effective diabetes management in rural communities globally. It emphasizes the significant impact of social determinants of health (SDoH) such as food insecurity, transportation, and social isolation on diabetes outcomes, noting that emotional and social needs remain largely unaddressed by current healthcare systems. The Social Care Logic Model is presented as a comprehensive framework to bridge this gap, offering a multi-pathway approach encompassing social risk screening, emotional support, community service connections, and tailored clinical care. The editorial advocates for integrating Artificial Intelligence (AI) with Community Health Workers (CHWs) to deliver scalable, culturally tailored interventions that improve access to services, reduce social risk, enhance emotional well-being, and support chronic disease self-management in these vulnerable populations. It concludes by urging a continued focus on innovative, compassionate models that integrate clinical and social care to better serve those most impacted by healthcare disparities.

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