Original Data

Rev Diabet Stud, 2012, 9(1):36-45 DOI 10.1900/RDS.2012.9.36

Telephone Counseling Intervention Improves Dietary Habits and Metabolic Parameters of Patients with the Metabolic Syndrome: A Randomized Controlled Trial

Evaggelia Fappa1, Mary Yannakoulia1, Maria Ioannidou1, Yannis Skoumas2, Christos Pitsavos2, Christodoulos Stefanadis2

1Department of Nutrition and Dietetics, Harokopio University, Athens, Greece
2First Cardiology Clinic, School of Medicine, University of Athens, Athens, Greece
Address correspondence to: Mary Yannakoulia, Department of Nutrition and Dietetics, Harokopio University, El. Venizelou 70, Athens, 17671, Greece, e-mail: myianna@hua.gr


BACKGROUND: Patients with the metabolic syndrome (MetS) can suffer from poor metabolic parameters through lack of adherence to requisite lifestyle changes in dietary and physical activity. Usually, interventions in MetS patients are infrequent face-to-face consultations. The low frequency or absence of counseling interviews leads to a shortage of information and motivation to adhere to the recommended lifestyle changes. Telephone interventions could be an additional low-cost tool for effective interventions. AIM: To evaluate the effectiveness of telephone intervention in improving lifestyle habits and metabolic parameters in MetS patients compared with similar face-to-face or a usual care interventions. METHODS: Eighty-seven MetS patients recruited from the outpatient clinic of a major public hospital were randomly assigned to one of the three intervention groups: “usual care”, “telephone” or “face-to-face”. At the beginning of the study, all patients were provided with a hypocaloric Mediterranean-type diet. Afterwards, patients in the telephone group received 7 dietary counseling calls, patients in the face-to-face group participated in 7 one-to-one dietary counseling sessions, while patients in the usual care group received no other contact until the end of the study, 6 months later. All patients underwent full medical and nutritional evaluation at the beginning and at the end of the intervention. RESULTS: At the end of the intervention, 42% of the participants no longer showed symptoms of MetS; the reduction rates differed significantly between the groups (p = 0.024), with those in the face-to-face and telephone group exhibiting similar rates (52% and 54%, respectively, vs. 21% in the usual care group). Between-group analysis revealed that the face-to-face group achieved the greatest improvement in metabolic parameters, while the telephone group had the greatest improvement in dietary adherence compared with the usual care group. CONCLUSIONS: Telephone counseling is an effective way to implement behavioral counseling to improve lifestyle habits in MetS patients.

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Rev Diabet Stud, 2012, 9(1):46-54 DOI 10.1900/RDS.2012.9.46

Quality of Life and Patient-Perceived Difficulties in the Treatment of Type 2 Diabetes

Orly Tamir1, Julio Wainstein2,3, Itamar Raz4, Joshua Shemer1,3,5, Anthony Heymann3,6

1Israel Center for Technology Assessment in Health Care, Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel
2Diabetes Unit, Edith Wolfson Medical Center, Holon, Israel
3Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
4Diabetes Unit, Hadassah Medical Center, Jerusalem, Israel
5Assuta Medical Centers, Israel
6Maccabi Healthcare Services, Israel
Address correspondence to: Orly Tamir, Israel Center for Technology Assessment in Health Care, The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel-Hashomer, Israel, 52621, e-mail: orlyt@gertner.health.gov.il


BACKGROUND: Clinical evidence points to patient-perceived difficulties and compliance problems in implementing early insulin therapy. Therefore, individual treatment aims are necessary to optimize diabetes therapy, as currently acknowledged by the new ADA/EASD guidelines. Better characterization of patient-perceived difficulties in the implementation of early insulin treatment may contribute to improved compliance and optimal tailoring of treatment regimens for the individual patient. OBJECTIVES: To assess differences in quality of life (QoL) and patient-perceived difficulties in health care with every addition of oral hypoglycemic agents (OHAs) and insulin therapy. METHODS: The analysis was conducted on a cross-sectional sample of 714 diabetic patients treated with OHAs or with insulin once or twice daily. Differences in diabetes-specific QoL, overall QoL, and perception of difficulties associated with specific diabetes treatment attributes were evaluated using trend analysis and comparisons between groups. The contribution of each diabetes treatment attribute to QoL measures and glycemic control was also assessed. RESULTS: No significant differences were found in QoL measures among patients treated exclusively with OHAs when these patients were assessed by the number of oral agents, irrespective of the degree of glycemic control. Better controlled patients treated with 2 OHAs, compared with poorly controlled patients treated with a single OHA, had a lower perception of difficulties associated with diabetes treatment attributes. Poorly controlled patients treated with 2 OHAs and better controlled patients treated with 3 OHAs had similar QoL and perceived difficulties with care. However, the insulin-based alternative was consistently associated with a significantly higher perception of pain and lower overall QoL when compared with the oral regimens. Multivariate models accounted for 52% and 32% of the variance in QoL measures. CONCLUSIONS: From the patients’ perspective, oral therapy is the preferred strategy for attaining the treatment goals since the addition of OHAs was not associated with lower QoL or patient-perceived difficulties with care. If early insulin treatment is considered, physicians should address specific diabetes treatment characteristics, mainly the issue of pain, to promote improved QoL and disease control.

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Rev Diabet Stud, 2012, 9(1):55-62 DOI 10.1900/RDS.2012.9.55

Computational Intelligence-Based Diagnosis Tool for the Detection of Prediabetes and Type 2 Diabetes in India

Shankaracharya1, Devang Odedra1, Subir Samanta2, Ambarish S. Vidyarthi1

1Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi 835215, India
2Department of Pharmaceutical Sciences, Birla Institute of Technology, Mesra, Ranchi 835215, India
Address correspondence to: Shankaracharya, e-mail: shankaracharya@bitmesra.ac.in


BACKGROUND: The incidence of diabetes is increasing rapidly across the globe. India has the highest proportion of diabetic patients, earning it the doubtful distinction of the 'diabetes capital of the world'. Early detection of diabetes could help to prevent or postpone its onset by taking appropriate preventive measures, including the initiation of lifestyle changes. To date, early identification of prediabetes or type 2 diabetes has proven problematic, such that there is an urgent requirement for tools enabling easy, quick, and accurate diagnosis. AIM: To develop an easy, quick, and precise tool for diagnosing early diabetes based on machine learning algorithms. METHODS: The dataset used in this study was based on the health profiles of diabetic and non-diabetic patients from hospitals in India. A novel machine learning algorithm, termed "mixture of expert", was used for the determination of a patient's diabetic state. Out of a total of 1415 subjects, 1104 were used to train the mixture of expert system. The remaining 311 data sets were reserved for validation of the algorithm. Mixture of expert was implemented in matlab to train the data for the development of the model. The model with the minimum mean square error was selected and used for the validation of the results. RESULTS: Different combinations and numbers of hidden nodes and expectation maximization (EM) iterations were used to optimize the accuracy of the algorithm. The overall best accuracy of 99.36% was achieved with an iteration of 150 and 20 hidden nodes. Sensitivity, specificity, and total classification accuracy were calculated as 99.5%, 99.07%, and 99.36%, respectively. Furthermore, a graphical user interface was developed in java script such that the user can readily enter the variables and easily use the algorithm as a tool. CONCLUSIONS: This study describes a highly precise machine learning prediction tool for identifying prediabetic, diabetic, and non-diabetic individuals with high accuracy. The tool could be used for large scale screening in hopsitals or diabetes prevention programs.

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