ArticleJournal of diabetes and metabolic disorders2022
A sports and health application for patients with type 1 diabetes mellitus -An end-user survey on expectations and requirements.
Article in Journal of diabetes and metabolic disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis.BMJ open · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The aim of this survey is to investigate T1DM patients' expectations for and requirements of an ideal mobile self-management app with a special focus on functions for sports and exercise. Methods: A total of 251 persons participated in the survey. After checking for completeness and plausibility, the answers of 167 patients diagnosed with T1DM (66% female, 34% male) were analyzed. Results: The key features/aspects that were identified as being "rather important"/"very important" by more than 75% of respondents are: data security (96.4%), integration of further health data (e.g., heart rate, step count, calories) from other apps already installed on their smartphone (92.2%), automatic import of glucose data from other apps (91.6%), individual target setting (87.4%), warnings about abnormal glucose levels (82.6%), warnings about other abnormal health data (81.4%), diary function (80.8%), information on the training session after the workout (80.8%) and displaying/processing of further fitness variables (such as heart rate, step count, etc.) from other health-related wearable systems (77.8%). Conclusions: This study identifies the most relevant features of an ideal self-management app with functions for sports and exercise targeted at patients with T1DM that should be considered in the development of such an app.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.