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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A randomized wait-list control trial to evaluate the impact of a mobile application to improve self-management of individuals with type 2 diabetes: a study protocol
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, November 2016
|
| DOI | 10.1186/s12911-016-0381-5 |
| Pubmed ID | |
| Authors |
Laura Desveaux, Payal Agarwal, Jay Shaw, Jennifer M. Hensel, Geetha Mukerji, Nike Onabajo, Husayn Marani, Trevor Jamieson, Onil Bhattacharyya, Danielle Martin, Muhammad Mamdani, Lianne Jeffs, Walter P. Wodchis, Noah M. Ivers, R. Sacha Bhatia |
| Abstract |
Management of diabetes through improved glycemic control and risk factor modification can help prevent long-term complications. Much diabetes management is self-management, in which healthcare providers play a supporting role. Well-designed e-Health solutions targeting behavior change can improve a range of measures, including glycemic control, perceived health, and a reduction in hospitalizations. The primary objective of this study is to evaluate if a mobile application designed to improve self-management among patients with type 2 diabetes (T2DM) improves glycemic control compared to usual care. The secondary objectives are to determine the effects on patient experience and health system costs; evaluate how and why the intervention worked as observed; and gain insight into considerations for system-wide scale-up. This pragmatic, randomized, wait-list-control trial will recruit adult participants from three Diabetes Education Programs in Ontario, Canada. The primary outcome is glycemic control (measured by HbA1c). Secondary outcomes include patient-reported outcomes and patient-reported experience measures, health system utilization, and intervention usability. The primary outcome will be analyzed using an ANCOVA, with continuous secondary outcomes analyzed using Poisson regression. Direct observations will be conducted of the implementation and application-specific training sessions provided to each site. Semi-structured interviews will be conducted with participants, healthcare providers, organizational leaders, and system stakeholders as part of the embedded process evaluation. Thematic analysis will be applied to the qualitative data in order to describe the relationships between (a) key contextual factors, (b) the mechanisms by which they effect the implementation of the intervention, and (c) the impact on the outcomes of the intervention, according to the principles of Realist Evaluation. The use of mobile health and virtual tools is on the rise in health care, but the evidence of their effectiveness is mixed and their evaluation is often lacking key contextual data. Results from this study will provide much needed information about the clinical and cost-effectiveness of a mobile application to improve diabetes self-management. The process evaluation will provide valuable insight into the contextual factors that influence the application effectiveness, which will inform the potential for adoption and scale. Clinicaltrials.gov NCT02813343 . Registered on 24 June 2016 (retrospectively registered). Trial Sponsor: Ontario Telemedicine Network. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 360 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 358 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 78 | 22% |
| Student > Ph. D. Student | 36 | 10% |
| Researcher | 33 | 9% |
| Student > Bachelor | 31 | 9% |
| Student > Doctoral Student | 26 | 7% |
| Other | 62 | 17% |
| Unknown | 94 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 86 | 24% |
| Medicine and Dentistry | 68 | 19% |
| Social Sciences | 16 | 4% |
| Computer Science | 14 | 4% |
| Psychology | 11 | 3% |
| Other | 51 | 14% |
| Unknown | 114 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 May 2017.
All research outputs
#14,344,573
of 22,968,808 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,110
of 2,001 outputs
Outputs of similar age
#175,541
of 306,961 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#16
of 23 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,001 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 306,961 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.