Impact of Mobile Phone Technologies on Diabetes Mellitus, Type 1 Management - A Systematic Review
Bibliographic record
Abstract
Background and aims: the implementation of medical technologies in health care can improve the quality of life for those living with acute and chronic health conditions. Since 2008, the usage of mobile phone technologies such as smart phones has rapidly developed, enabling wireless communication worldwide. As a result, the incorporation of mobile phones into large scale health care applications for the prevention and management of an acute or chronic health disease has led to the development of mobile health. Mobile health aims to provide an economic and effective health care delivery service as well as access to high quality health care. However, there are several factors that can influence its success rate in the clinical practice and management of individuals living with type 1 diabetes.\n\nThe highest incidence rates of diabetes mellitus, type 1 (DMT1) have been reported in countries such as New Zealand, Canada and Finland. In order to successfully maintain optimal glycaemic control in the long run, a type 1 diabetic individual must be self-motivated and determined to manage their diabetes. It is also crucial for these individuals to learn how to do so with the assistance of their health care team or families in order to reduce their risk of developing complications. The purpose of this thesis was to investigate and assess the evidence for the effectiveness of mobile phone technologies on the overall clinical management of children, adolescents and adults living with DMT1, as well as their health care and psychosocial outcomes.\n\nMethods: a comprehensive search strategy was formulated using appropriate keywords, truncation and Boolean operators such as AND/OR. This was conducted to systematically search and retrieve relevant publications. The six databases searched included AMED, CINAHL, Cochrane library, PsycINFO, PubMed and Web of Science (WOS). The bibliography of previous systematic reviews as well as their included studies were also screened to help identify further relevant studies.\n\nResults: a total of 536 research articles were retrieved and assessed for eligibility and two trials were selected to be included in this review. One of these trials was a randomised controlled trial (RCT) and the other a quasi-experiment. Both the studies’ validity and relevance of results were assessed using the CASP quality assessment tool. Descriptive and statistical data about these studies, such as the type of study design or patient age groups, were collected using a data extraction form and presented for further analysis. The outcomes of these studies were categorized into clinical, personal technology skill, communication, efficacy, adherence, education, perception, attendance and support outcomes. Each of the study’s findings were reported and themes and limitations for each study were derived. It was reported in both studies that the mobile phone intervention had a limited effect on a type 1 diabetic individual’s glycemic control exclusively. This effect was more likely to be positive and stronger depending on several factors such as an individual’s information technology (IT) skills, their motivation, measuring activity and type of insulin therapy intake. However, it was also reported that this type of intervention did lead to an improvement in some of the patients’ perceptions of using this type of technology, their self-efficacy and their self-adherence.\n\nDiscussion and conclusions: the evidence reported from both studies implied that mobile phone technologies do not improve the glycemic control of a type 1 diabetic individual directly and exclusively. However, it is likely that this type of technology could lead to such an improvement, but only if an individual is well motivated to do so and is willing to learn how to use such technology if they do not know how to. Therefore, future mobile technologies should be oriented to target individuals who lack motivation or awareness of managing diabetes. Further features should also be investigated in larger, more ethnically diverse populations with special needs in different countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".