Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus - PubMed (original) (raw)

Review

Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus

Kingshuk Pal et al. Cochrane Database Syst Rev. 2013.

Abstract

Background: Diabetes is one of the commonest chronic medical conditions, affecting around 347 million adults worldwide. Structured patient education programmes reduce the risk of diabetes-related complications four-fold. Internet-based self-management programmes have been shown to be effective for a number of long-term conditions, but it is unclear what are the essential or effective components of such programmes. If computer-based self-management interventions improve outcomes in type 2 diabetes, they could potentially provide a cost-effective option for reducing the burdens placed on patients and healthcare systems by this long-term condition.

Objectives: To assess the effects on health status and health-related quality of life of computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus.

Search methods: We searched six electronic bibliographic databases for published articles and conference proceedings and three online databases for theses (all up to November 2011). Reference lists of relevant reports and reviews were also screened.

Selection criteria: Randomised controlled trials of computer-based self-management interventions for adults with type 2 diabetes, i.e. computer-based software applications that respond to user input and aim to generate tailored content to improve one or more self-management domains through feedback, tailored advice, reinforcement and rewards, patient decision support, goal setting or reminders.

Data collection and analysis: Two review authors independently screened the abstracts and extracted data. A taxonomy for behaviour change techniques was used to describe the active ingredients of the intervention.

Main results: We identified 16 randomised controlled trials with 3578 participants that fitted our inclusion criteria. These studies included a wide spectrum of interventions covering clinic-based brief interventions, Internet-based interventions that could be used from home and mobile phone-based interventions. The mean age of participants was between 46 to 67 years old and mean time since diagnosis was 6 to 13 years. The duration of the interventions varied between 1 to 12 months. There were three reported deaths out of 3578 participants.Computer-based diabetes self-management interventions currently have limited effectiveness. They appear to have small benefits on glycaemic control (pooled effect on glycosylated haemoglobin A1c (HbA1c): -2.3 mmol/mol or -0.2% (95% confidence interval (CI) -0.4 to -0.1; P = 0.009; 2637 participants; 11 trials). The effect size on HbA1c was larger in the mobile phone subgroup (subgroup analysis: mean difference in HbA1c -5.5 mmol/mol or -0.5% (95% CI -0.7 to -0.3); P < 0.00001; 280 participants; three trials). Current interventions do not show adequate evidence for improving depression, health-related quality of life or weight. Four (out of 10) interventions showed beneficial effects on lipid profile.One participant withdrew because of anxiety but there were no other documented adverse effects. Two studies provided limited cost-effectiveness data - with one study suggesting costs per patient of less than $140 (in 1997) or 105 EURO and another study showed no change in health behaviour and resource utilisation.

Authors' conclusions: Computer-based diabetes self-management interventions to manage type 2 diabetes appear to have a small beneficial effect on blood glucose control and the effect was larger in the mobile phone subgroup. There is no evidence to show benefits in other biological outcomes or any cognitive, behavioural or emotional outcomes.

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Conflict of interest statement

None known.

Figures

1

1

A model to demonstrate how self‐management interventions might affect outcomes in type 2 diabetes

2

2

Study flow diagram.

3

3

'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

4

4

'Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.

5

5

Forest plot of comparison: 1 HbA1c, outcome: 1.1 HbA1c [%].

6

6

Forest plot of comparison: 4 Weight, outcome: 4.1 Weight.

1.1

1.1. Analysis

Comparison 1 HbA1c, Outcome 1 HbA1c.

1.2

1.2. Analysis

Comparison 1 HbA1c, Outcome 2 Sensitivity analysis ‐ removing Christian 2008.

1.3

1.3. Analysis

Comparison 1 HbA1c, Outcome 3 Sensitivity analysis ‐ removing Leu 2005.

1.4

1.4. Analysis

Comparison 1 HbA1c, Outcome 4 Sensitivity analysis ‐ removing cluster randomised trials.

1.5

1.5. Analysis

Comparison 1 HbA1c, Outcome 5 Sensitivity analysis ‐ remove Glasgow 2003.

1.6

1.6. Analysis

Comparison 1 HbA1c, Outcome 6 Subgroup analysis ‐ outcomes at less than 6 months.

1.7

1.7. Analysis

Comparison 1 HbA1c, Outcome 7 Subgroup analysis ‐ outcomes at later than 6months.

1.8

1.8. Analysis

Comparison 1 HbA1c, Outcome 8 Subgroup analysis ‐ mobile phone based interventions.

1.9

1.9. Analysis

Comparison 1 HbA1c, Outcome 9 Subgroup analysis ‐ interventions based at home.

2.1

2.1. Analysis

Comparison 2 Dietary change, Outcome 1 Fruit and vegetable screener score.

2.2

2.2. Analysis

Comparison 2 Dietary change, Outcome 2 Estimated daily fat intake.

2.3

2.3. Analysis

Comparison 2 Dietary change, Outcome 3 Change in calorific intake.

2.4

2.4. Analysis

Comparison 2 Dietary change, Outcome 4 Pooled effect on diet.

3.1

3.1. Analysis

Comparison 3 Impact on weight or BMI, Outcome 1 Pooled effect on weight or BMI.

4.1

4.1. Analysis

Comparison 4 Lipids, Outcome 1 Total cholesterol.

4.2

4.2. Analysis

Comparison 4 Lipids, Outcome 2 Change in total cholesterol.

4.3

4.3. Analysis

Comparison 4 Lipids, Outcome 3 High density lipoprotein (HDL).

4.4

4.4. Analysis

Comparison 4 Lipids, Outcome 4 Change in HDL.

4.5

4.5. Analysis

Comparison 4 Lipids, Outcome 5 Low density lipoprotein (LDL).

4.6

4.6. Analysis

Comparison 4 Lipids, Outcome 6 Change in LDL.

4.7

4.7. Analysis

Comparison 4 Lipids, Outcome 7 TC:HDL ratio.

4.8

4.8. Analysis

Comparison 4 Lipids, Outcome 8 Change in triglycerides.

4.9

4.9. Analysis

Comparison 4 Lipids, Outcome 9 Pooled effect on cholesterol.

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Trief 2007 {published data only}
    1. Trief PM, Teresi JA, Izquierdo R, Morin PC, Goland R, Field L, et al. Psychosocial outcomes of telemedicine case management for elderly patients with diabetes: the randomized IDEATel trial. Diabetes Care 2007;30(5):1266‐8. - PubMed
Tsang 2001 {published data only}
    1. Tsang MW, Mok M, Kam G, Jung M, Tang A, Chan U, et al. Improvement in diabetes control with a monitoring system based on a hand‐held, touch‐screen electronic diary. Journal of Telemedicine & Telecare 2001;7(1):47‐50. - PubMed
Turnin 1992 {published data only}
    1. Turnin MC, Beddok RH, Clottes JP, Martini P, Abadie R, Buisson JC, et al. Telematic expert system Diabeto. New tool for diet self‐monitoring for diabetic patients. Diabetes Care 1992;15(2):204‐12. - PubMed
van Bastelaar 2011 {published data only}
    1. Bastelaar K, Cuijpers P, Pouwer F, Riper H, Snoek FJ, Bastelaar Kim, et al. Development and reach of a web‐based cognitive behavioural therapy programme to reduce symptoms of depression and diabetes‐specific distress. Patient Education & Counseling 2011;84(1):49‐55. - PubMed
van Bastelaar 2011a {published data only}
    1. Bastelaar KMP. Web‐based depression treatment for type 1 and type 2 diabetic patients: A randomized, controlled trial. Diabetes Care 2011;34(2):320‐5. - PMC - PubMed
Vespasiani 2008 {published data only}
    1. Vespasiani G, Rossi MC, Nicolucci A, Bartolo P, Sardu C, Bruttomesso D, et al. Comparison between the "Diabetes Interactive Diary" system and standard carbohydrate counting education: an open label, multicentre, randomised study. Diabetologia 2008;51:230.
Wakefield 2011 {published data only}
    1. Wakefield BJH. Effectiveness of home telehealth in comorbid diabetes and hypertension: a randomized, controlled trial. Telemedicine journal and e‐health : the official journal of the American Telemedicine Association 2011;17(4):254‐61. - PubMed
Yeh 2006 {published data only}
    1. Yeh YT, Chiu YT, Liu CT, Wu SJ, Lee TI, Yeh YT, et al. Development and evaluation of an integrated patient‐oriented education management system for diabetes. Studies in Health Technology & Informatics 2006;122:172‐5. - PubMed
Yoo 2008 {published data only}
    1. Yoo HJ, An HG, Park SY, Ryu OH, Kim HY, Seo JA, et al. Use of a real time continuous glucose monitoring system as a motivational device for poorly controlled type 2 diabetes. Diabetes Research & Clinical Practice 2008;82(1):73‐80. - PubMed
Yoon 2008 {published data only}
    1. Yoon KH, Kim HS. A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Research & Clinical Practice 2008;79(2):256‐61. - PubMed

References to studies awaiting assessment

Faridi 2008 {published data only}
    1. Faridi Z, Liberti L, Shuval K, Northrup V, Ali A, Katz D L. Evaluating the impact of mobile telephone technology on type 2 diabetic patients' self‐management: the NICHE pilot study. Journal of Evaluation in Clinical Practice 2008;14(3):465‐9. - PubMed
Lorig 2006 {published data only}
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Noh 2010 {published data only}
    1. Noh JH, Cho YJ, Nam HW, Kim JH, Kim DJ, Yoo HS, et al. Web‐Based comprehensive information system for self‐management of diabetes mellitus. Diabetes Technology & Therapeutics 2010;12(5):333‐7. - PubMed

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