Evidence map›Paper›PMID 26547253›Full record

ArticleInternational journal of medical informatics2016

Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management.

Heather J Cole-Lewis, Arlene M Smaldone, Patricia R Davidson, Rita Kukafka, Jonathan N Tobin, Andrea Cassells, Elizabeth D Mynatt, George Hripcsak, Lena Mamykina

Abstract read
In one paragraph

Article in International journal of medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Examining AI Methods for Micro-Coaching Dialogs.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2022
    Article
  5. From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2021
    Article
  6. Automated vs. Human Health Coaching: Exploring Participant and Practitioner Experiences.Proceedings of the ACM on human-computer interaction · 2021
    Article
  7. Article
  8. Observational
  9. Towards Supporting Patient Decision-making In Online Diabetes Communities.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017
    Article
  10. Article
  11. Article
  12. T2 Coach: A Qualitative Study of an Automated Health Coach for Diabetes Self-Management.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Heather J Cole-LewisDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; ICF International, Rockville, MD, USA.
Arlene M SmaldoneSchool of Nursing, Columbia University, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA.
Patricia R DavidsonCollege of Health Sciences, Nutrition Department, West Chester University, West Chester, PA, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA.
Rita KukafkaDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA.
Jonathan N TobinClinical Directors Network, NY, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA.
Andrea CassellsClinical Directors Network, NY, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA.
Elizabeth D MynattDepartment of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA; Georgia Institute of Technology, Atlanta, GA, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA.
Lena MamykinaDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA. Electronic address: lena.mamykina@dbmi.columbia.edu.

Funding

Training in Biomedical Informatics at Columbia UniversityT15LM007079 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI NOEMIE ELHADAD, GEORGE M HRIPCSAK · 1992 to 2026
$28.9M
HIT for Facilitating Problem Solving in Diabetes ManagementR01DK090372 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M, MAMYKINA, OLENA · 2011 to 2015
$3.3M
NIDDK NIH HHS R01 DK090372NIDDK NIH HHS R01DK090372NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

objectiveTo develop an expandable knowledge base of reusable knowledge related to self-management of diabetes that can be used as a foundation for patient-centric decision support tools. MATERIALS AND

methodsThe structure and components of the knowledge base were created in participatory design with academic diabetes educators using knowledge acquisition methods. The knowledge base was validated using scenario-based approach with practicing diabetes educators and individuals with diabetes recruited from Community Health Centers (CHCs) serving economically disadvantaged communities and ethnic minorities in New York.

resultsThe knowledge base includes eight glycemic control problems, over 150 behaviors known to contribute to these problems coupled with contextual explanations, and over 200 specific action-oriented self-management goals for correcting problematic behaviors, with corresponding motivational messages. The validation of the knowledge base suggested high level of completeness and accuracy, and identified improvements in cultural appropriateness. These were addressed in new iterations of the knowledge base. DISCUSSION: The resulting knowledge base is theoretically grounded, incorporates practical and evidence-based knowledge used by diabetes educators in practice settings, and allows for personally meaningful choices by individuals with diabetes. Participatory design approach helped researchers to capture implicit knowledge of practicing diabetes educators and make it explicit and reusable.

conclusionThe knowledge base proposed here is an important step towards development of new generation patient-centric decision support tools for facilitating chronic disease self-management. While this knowledge base specifically targets diabetes, its overall structure and composition can be generalized to other chronic conditions.

Indexed as

Knowledge BasesProblem SolvingSelf CareDiabetes MellitusHumansCommunity-based participatory research (H01.770.644.193)Decision-support systems (L01.700.508.300.190)Diabetes mellitus (C18.452.394.750)Knowledge bases (L01.224.065.480)

Identifiers

PMID26547253
PMCPMC4699307

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.