DialBetics

Waki, Kayo; Fujita, Hideo; Uchimura, Yuji; Omae, Koji; Aramaki, Eiji; Kato, Shota; Lee, Hanae; Kobayashi, Haruka; Kadowaki, Takashi; Ohe, Kazuhiko

doi:10.1177/1932296814526495

Cited by 185 publications

(176 citation statements)

References 14 publications

(16 reference statements)

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“…In studies that provided automated feedbacks [18,27,30,37], heterogeneity was reduced to 0.0% and difference between groups was -0.50% (95% CI [-0.72, -0.28], P < .001). In unclear group [8,12,16,19,29,33,36], difference in HbA1c outcomes was not significant as P = .61 > .05. Overall test for heterogeneity between subgroups had a p value of 0.001, indicating statistically significant difference between groups ( figure 3C).…”

Section: Results Of Subgroup Analysis Based On Feedback Provisionmentioning

confidence: 84%

“…In the group of x ≤ 3 months [11,21,23,27,33,34,36], heterogeneity across studies was 28.5% and the pooled WMD was -0.45% (95% CI [-0.62, -0.28], P < .001).…”

Section: Results Of Subgroup Analysis Based On Intervention Periodsmentioning

confidence: 99%

“…However, moderate heterogeneity of 59.8% was also observed across studies in this subgroup. In the group of mobile only interventions [14,18,24,27,33,36], difference in HbA1c between intervention and control group decreased, with a pooled WMD of -0.23% (95% CI [-0.40, -0.06], P = .009) and heterogeneity of 27.0%. In combined mobile and website group [8,26,30,31,37], no heterogeneity was observed and the pooled effect was -0.46% (95% CI [-0.68, -0.24], P < .001).…”

Section: Results Of Subgroup Analysis Based On New Media Typementioning

confidence: 99%

“…Of the 28 studies included, 9 were conducted in the United States [11,12,19,20,22,25,28,30,31], 7 in the Republic of Korea [7,8,21,23,26,27,37], 2 in Poland [29,36] and Italy [16,35] respectively and 1 in China [34], Japan [33], Finland [18], Turkey [10], Canada [9], Spain [24], Norway [14] and England [32] each. Included studies were published between 2004 and 2016 and the intervention duration ranged from 6 weeks to 5 years.…”

Section: Characteristics Of Studiesmentioning

confidence: 99%

“…7 studies used web-based interventions solely [7,9,10,12,21,29,34], 6 used mobile technology [14,18,24,27,33,36] and 5 used the combination of both [8,26,30,31,37]. 10 studies utilized other forms of new media, including portable digital assistant, tablet computer and data transmission equipment only [11,16,19,20,22,23,25,28,32,35].…”

Section: Characteristics Of Interventionsmentioning

confidence: 99%

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Effectiveness of Internet-Based Interventions on Glycemic Control in Patients With Type 2 Diabetes: Meta-Analysis of Randomized Controlled Trials (Preprint)

Shen¹,

Wang²,

Zhang³

et al. 2017

Preprint

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BACKGROUND The popularity of internet as an area of research has grown manifold over the years. Given its rapid development and increasing coverage worldwide, internet-based interventions seem to offer a promising option to ameliorate huge burdens brought by type 2 diabetes mellitus. However, studies conducted by different researchers have provided contradictory results on the effect of internet-based interventions in glycemic control. OBJECTIVE This meta-analysis aims to summarize currently available evidence and evaluate the overall impact of internet-based interventions on glycemic management of type 2 diabetic patients. METHODS A systematic literature search was performed in PubMed, ScienceDirect, and Web of Science. Randomized controlled trials that used glycosylated hemoglobin values as the outcome measure of glycemic control were considered. Risk of bias and publication bias were evaluated. RESULTS Of the 492 studies, 35 were included in meta-analysis, and results indicated that the weighted mean difference (WMD) between usual care and internet-based interventions at endpoint was –0.426% (95% CI –0.540 to –0.312; P<.001). Subgroup analyses revealed that intervention duration ≤3 months yielded optimal performance (WMD –0.51%; 95% CI –0.71 to –0.31; P<.001). Combined mobile and website interventions were substantially superior to solely Web-based and mobile-based interventions in glycemic control (combined WMD –0.77%, 95% CI –1.07 to –0.47; P<.001; Web only: WMD –0.48%; 95% CI –0.71 to –0.24, P<.001; mobile only WMD –0.31%, 95% CI –0.49 to –0.14; P<.001). Furthermore, the effect of interventions with automated feedbacks was similar to those with manual feedbacks, and studies with internet-based educational contents were more effective in glycemic control. The assessment revealed a low risk of bias. CONCLUSIONS In conclusion, utilization of internet-based intervention is beneficial for patients with type 2 diabetes mellitus, and taking full advantage of this type of intervention may substantially reduce the incidence of complications and improve quality of life. CLINICALTRIAL International Prospective Register of Systematic Reviews (PROSPERO): CRD42017058032; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=58032 (Archived by WebCite at http://www.webcitation.org/6yY7eQNHr)

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Section: Results Of Subgroup Analysis Based On Feedback Provisionmentioning

confidence: 84%

“…In the group of x ≤ 3 months [11,21,23,27,33,34,36], heterogeneity across studies was 28.5% and the pooled WMD was -0.45% (95% CI [-0.62, -0.28], P < .001).…”

Section: Results Of Subgroup Analysis Based On Intervention Periodsmentioning

confidence: 99%

Section: Results Of Subgroup Analysis Based On New Media Typementioning

confidence: 99%

Section: Characteristics Of Studiesmentioning

confidence: 99%

Section: Characteristics Of Interventionsmentioning

confidence: 99%

See 3 more Smart Citations

Effectiveness of Internet-Based Interventions on Glycemic Control in Patients With Type 2 Diabetes: Meta-Analysis of Randomized Controlled Trials (Preprint)

Shen¹,

Wang²,

Zhang³

et al. 2017

Preprint

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Evaluation of Patient Willingness to Adopt Remote Digital Monitoring for Diabetes Management

et al. 2021

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IMPORTANCE Patients will decide whether to adopt remote digital monitoring (RDM) for diabetes by weighing its health benefits against the inconvenience it may cause. OBJECTIVE To identify the minimum effectiveness patients report they require to adopt 36 different RDM scenarios. DESIGN, SETTING, AND PARTICIPANTSThis survey study was conducted among adults with type 1 or type 2 diabetes living in 30 countries from February to July 2019. EXPOSURES Survey participants assessed 3 randomly selected scenarios from a total of 36.Scenarios described different combinations of digital monitoring tools (glucose, physical activity, food monitoring), duration and feedback loops (feedback in consultation vs real-time telefeedback by a health care professional or by artificial intelligence), and data handling modalities (by a public vs private company), reflecting different degrees of RDM intrusiveness in patients' personal lives. MAIN OUTCOMES AND MEASURESParticipants assessed the minimum effectiveness for 2 diabetes-related outcomes (reducing hypoglycemic episodes and preventing ophthalmologic complications) for which they would adopt each RDM (from much less effective to much more effective than their current monitoring). RESULTSOf 1577 individuals who consented to participate, 1010 (64%; 572 [57%] women, median [interquartile range] age, 51 [37-63] years, 524 [52%] with type 1 diabetes) assessed at least 1 vignette. Overall, 2860 vignette assessments were collected. In 1025 vignette assessments (36%), participants would adopt RDM only if it was much more effective at reducing hypoglycemic episodes compared with their current monitoring; in 1835 assessments (65%), participants would adopt RDM if was just as or somewhat more effective. The main factors associated with required effectiveness were food monitoring (β = 0.32; SE, 0.12; P = .009), real-time telefeedback by a health care professional (β = 0.49; SE, 0.15; P = .001), and perceived intrusiveness (β = 0.36; SE, 0.06; P < .001).Minimum required effectiveness varied among participants; 34 of 36 RDM scenarios (94%) were simultaneously required to be just as or less effective by at least 25% of participants and much more effective by at least 25% of participants. Results were similar for participant assessments of scenarios regarding the prevention of ophthalmologic complications. CONCLUSIONS AND RELEVANCEThe findings of this study suggest that patients require greater health benefits to adopt more intrusive RDM modalities, food monitoring, and real-time feedback by a health care professional. Patient monitoring devices should be designed to be minimally intrusive.The variability in patients' requirements points to a need for shared decision-making.

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Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes

Yogasingam

Konnyu

Lépine

et al. 2023

Cochrane Database of Systematic Reviews

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Background There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision‐makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations. Objectives To update existing systematic reviews of diabetes QI programmes and apply novel meta‐analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care. Search methods We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top‐up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section. Selection criteria We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system‐ or provider‐targeted QI strategy alone or in combination with a patient‐targeted strategy. ‐ System‐targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). ‐ Provider‐targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). ‐ Patient‐targeted: patient education (PE); promotion of self‐management (PSM); patient reminders (PR). Patient‐targeted QI strategies needed to occur with a minimum of one provider or system‐targeted strategy. Data collection and analysis We dual‐screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low‐density lipoprotein cholesterol (LDL‐C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta‐regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outco...

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DialBetics

Cited by 185 publications

References 14 publications

Effectiveness of Internet-Based Interventions on Glycemic Control in Patients With Type 2 Diabetes: Meta-Analysis of Randomized Controlled Trials (Preprint)

Effectiveness of Internet-Based Interventions on Glycemic Control in Patients With Type 2 Diabetes: Meta-Analysis of Randomized Controlled Trials (Preprint)

Evaluation of Patient Willingness to Adopt Remote Digital Monitoring for Diabetes Management

Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes

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