Comparison of In-Person and MMS -Based Education in Telegram on Self-care and Fasting Blood Sugar of Patients with Diabetes Mellitus: A Randomized Clinical Trials.
Mahtab AligholipourHossein FeizollahzadehMozaffar GhaffariFaranak JabbarzadehPublished in: Journal of caring sciences (2019)
Introduction: Diabetes is a disease whose control requires effective self-care and patient education. Multimedia Messaging Service-based (MMS) education is one of the new methods for education. The purpose of this study was to investigate the effect of two types of in-person and MMS-based education in the Telegram application on self-care and weekly fasting blood sugar levels in patients with insulin-dependent diabetes. Methods: In this clinical trial, a sample of 66 patients with diabetes who referred to the Sina hospital in Tabriz, were randomly assigned into two groups: in person and MMSM-based education. Data gathering tools included a demographic form, Toobert's self-care activities questionnaire (as primary outcome), and a checklist to record fasting blood sugar weekly measured by a glucometer. Data were analyzed using independent and paired sample t-tests, chi-square, and repeated measures ANOVA. Results: After the education the mean scores of self-care in terms of diet, exercise, foot care, and blood sugar testing activity significantly increased in both groups and results of ANCOVA of the scores for all dimensions revealed no significant difference between two groups. Reduction in the fasting weekly blood sugar levels over a 12-week period were statistically significant in both groups. But there was no significant difference between the two groups. Conclusion: MMS-based education same as in-person, improves self-care in patients with diabetes. Given the disadvantages of in-person education, this new educational strategy can be used to facilitate the patient education process and improve its quality.
Keyphrases
- healthcare
- quality improvement
- clinical trial
- type diabetes
- randomized controlled trial
- palliative care
- emergency department
- machine learning
- mental health
- adipose tissue
- big data
- blood pressure
- high intensity
- open label
- pain management
- deep learning
- health insurance
- resistance training
- psychometric properties
- drug induced