Scale Space Methods for Analysis of Type 2 Diabetes Patients′ Blood Glucose Values
Open accessAbstract
We describe how scale space methods can be used for quantitative analysis of blood glucose concentrations from type 2 diabetes patients. Blood glucose values were recorded voluntarily by the patients over one full year as part of a self‐management process, where the time and frequency of the recordings are decided by the patients. This makes a unique dataset in its extent, though with a large variation in reliability of the recordings. Scale space and frequency space techniques are suited to reveal important features of unevenly sampled data, and useful for identifying medically relevant features for use both by patients as part of their self‐management process, and provide useful information for physicians.
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The generally decreasing trend is in line with the result for blood glucose measurements found previously.
“The generally decreasing trend is in line with the result for blood glucose measurements found previously [43].”
From Long-Term Engagement With a Mobile Self-Management System for People With Type 2 Diabetes (2013) · cites this paper as [43]
A decreasing tendency in measurement frequency was generally observed in statistical analysis of aggregated blood glucose readings by all the participants for 1 year.
“However, a decreasing tendency in measurement frequency was generally observed in statistical analysis of aggregated blood glucose readings by all the participants for 1 year [43].”
From Long-Term Engagement With a Mobile Self-Management System for People With Type 2 Diabetes (2013) · cites this paper as [43]
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Research works printed but not linked: 2
- [2]Årsand E The few touch digital diabetes diary—user involved design of mobile self-help tools for people with diabetes 2009 Tromsø, Norway University of Tromsø Ph.D. thesis
- [3]Årsand E Andersson N Hartvigsen G No-touch wireless transfer of blood glucose sensor data In: Proceedings of the Cognitive systems with Interactivie Sensors (COGIS ’07) 2007 Palo Alto, Calif, USA Societe de l’Electricite, del’Electronique et des Technologies de l’Information et de la Communication, Stanford University
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Mobile health applications to assist patients with diabetes: lessons learned and design implications
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Self-citationLong-Term Engagement With a Mobile Self-Management System for People With Type 2 Diabetes
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Self-citation
- Authors
- Skrøvseth, S. O., & Godtliebsen, F.
- Affiliations
- University Hospital of North Norway (NO)
- UiT The Arctic University of Norway (NO)
- Corresponding author
- Skrøvseth, Stein Olav
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2011-01
- Volume
- 2011
- Issue
- 1
- Pages
- 672039-672039
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- PMC
- PMC3049346
- OpenAlex
- W2028564628
- Cited by
- 11
- References
- 20
- Citations by year
- 2016: 2 · 2015: 2 · 2014: 1 · 2013: 2 · 2012: 4
- FWCI
- 1.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- Northern Norway Regional Health Authority (3919/HST952-10)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- type 2 diabetes, blood glucose, self-monitoring of blood glucose, irregularly sampled data, diabetes self-management, patient-generated health data
- MeSH terms
- Blood Glucose, Data Interpretation, Statistical, Diabetes Mellitus, Type 2, Humans, Models, Biological, Blood Glucose Self-Monitoring, Models, Statistical, Cohort Studies
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