Active Assistance Technology Reduces Glycosylated Hemoglobin and Weight in Individuals With Type 2 Diabetes: Results of a Theory-Based Randomized Trial

Orsama, Anna-Leena; Lähteenmäki, Jaakko; Harno, Kari; Kulju, Minna; Wintergerst, E. S.; Schachner, Holly C.; Stenger, Pat; Leppänen, Juha; Kaijanranta, Hannu; Salaspuro, Ville; Fisher, William A.

doi:10.1089/dia.2013.0056

Cited by 114 publications

(211 citation statements)

References 28 publications

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“…164,166,168,171,178 We also include in Table 5 a systematic review by Liang et al 173 and a Cochrane review. 174 The systematic review included 22 trials.…”

Section: Review Of Evidence For the Efficacy Of Mobile Technologybasementioning

confidence: 99%

Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention

Burke¹,

Ma²,

Azar³

et al. 2015

Circulation

477

389

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A lthough mortality for cardiovascular disease (CVD) has declined for several decades, heart disease and stroke continue to be the leading causes of death, disability, and high healthcare costs. Unhealthy behaviors related to CVD risk (eg, smoking, sedentary lifestyle, and unhealthful eating habits) remain highly prevalent. The high rates of overweight, obesity, and type 2 diabetes mellitus (T2DM); the persistent presence of uncontrolled hypertension; lipid levels not at target; and the ≈18% of adults who continue to smoke cigarettes pose formidable challenges for achieving improved cardiovascular health.1,2 It is apparent that the performance of healthful behaviors related to the management of CVD risk factors has become an increasingly important facet of the prevention and management of CVD. 3In 2010, the American Heart Association (AHA) made a transformative shift in its strategic plan and added the concept of cardiovascular health.2 To operationalize this concept, the AHA targeted 4 health behaviors in the 2020 Strategic Impact Goals: reduction in smoking and weight, healthful eating, and promotion of regular physical activity. Three health indicators also were included: glucose, blood pressure (BP), and cholesterol. On the basis of the AHA Life's Simple 7 metrics for improved cardiovascular health, <1% of adults in the United States follow a healthful eating plan, only 32% have a normal body mass index, and > 30% have not reached the target levels for lipids or BP. National Health and Nutrition Examination Survey (NHANES) data revealed that people who met ≥6 of the cardiovascular health metrics had a significantly better risk profile (hazard ratio for all-cause mortality, 0.49) compared with individuals who had achieved only 1 metric or none.2 The studies reviewed in this statement targeted these behaviors (ie, smoking, physical activity, healthful eating, and maintaining a healthful weight) and cardiovascular health indicators (ie, blood glucose, lipids, BP, body mass index) as the primary outcomes in the clinical trials testing mobile health (mHealth) interventions.eHealth, or digital health, is the use of emerging communication and information technologies, especially the Internet, to improve health and health care 4 (Table 1). mHealth, a subsegment of eHealth, is the use of mobile computing and communication technologies (eg, mobile phones, wearable sensors) for health services and information.4,5 mHealth technology uses techniques and advanced concepts from an array of disciplines, for example, computer science, electrical and

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“…164,166,168,171,178 We also include in Table 5 a systematic review by Liang et al 173 and a Cochrane review. 174 The systematic review included 22 trials.…”

Section: Review Of Evidence For the Efficacy Of Mobile Technologybasementioning

confidence: 99%

Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention

Burke¹,

Ma²,

Azar³

et al. 2015

Circulation

477

389

View full text Add to dashboard Cite

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“…Some studies only presented changes of HbA1c and in this case HbA1c values in different intervention periods and endpoint were calculated correspondingly based on baseline and changes of values [8,16,[18][19][20]. For studies that provided standard error (SE) rather than SD, SD value was deduced based on SE value [12,[21][22][23].…”

Section: Data Extraction and Quality Assessmentmentioning

confidence: 99%

“…For studies that provided standard error (SE) rather than SD, SD value was deduced based on SE value [12,[21][22][23]. Some studies gave 95% confidence interval (CI) of mean HbA1c and SD was derived according to 95% CI [14,18,24]. If neither endpoint SD nor other information that could be used to deduce SD was provided, baseline SD was adopted as endpoint SD [19,25].…”

Section: Data Extraction and Quality Assessmentmentioning

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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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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Active Assistance Technology Reduces Glycosylated Hemoglobin and Weight in Individuals With Type 2 Diabetes: Results of a Theory-Based Randomized Trial

Abstract: Sophisticated information technology platforms for remote patient reporting linked with theory-based health behavior change automated feedback have potential to improve patient outcomes in type 2 diabetes and merit scaled-up research efforts.

Cited by 114 publications

References 28 publications

Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention

Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention

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

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