Evidence map›Paper›PMID 26071681›Full record

ArticleJournal of biomedical informatics2015

Adopting the sensemaking perspective for chronic disease self-management.

Lena Mamykina, Arlene M Smaldone, Suzanne R Bakken

Registry-linked trialAbstract read
In one paragraph

Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05058547 (An Evidence-based Digital Support for One Year to Facilitate a Sustainable Return to Work for Persons With Chronic Musculoskeletal Pain and Their Employers), which is not on this map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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.

NCT05058547 naunknown statusnot on this mapstarted 2022, after this paper: background citation

An Evidence-based Digital Support for One Year to Facilitate a Sustainable Return to Work for Persons With Chronic Musculoskeletal Pain and Their Employers: Study Protocol for a Registry-based Multicentre Randomized Controlled Trial.

TypeinterventionalSponsorLinkoeping UniversityRan2022 to 2024Enrolled360ConditionsChronic PainArmsSWEPPE
3 · Its place in the literature

Who cites it

50 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Article
  5. Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2025
    Article
  6. The Voice of Endo: Leveraging Speech for an Intelligent System That Can Forecast Illness Flare-ups.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2025
    Article
  7. Article
  8. A Multi-Agent LLM Network for Suggesting and Correcting Human Activity and Posture Annotations.Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. "You Can See the Connections": Facilitating Visualization of Care Priorities in People Living with Multiple Chronic Health Conditions.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

3 authors.

Lena MamykinaDepartment of Biomedical Informatics, Columbia University, United States. Electronic address: olm2196@cumc.columbia.edu.
Arlene M SmaldoneSchool of Nursing, Columbia University, United States.
Suzanne R BakkenDepartment of Biomedical Informatics, Columbia University, United States; School of Nursing, Columbia University, United States.

Funding

HIT for Facilitating Problem Solving in Diabetes ManagementR01DK090372 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M, MAMYKINA, OLENA · 2011 to 2015
$3.3M
New York City Hispanic Dementia Caregiver Research Program (NHiRP)R01NR014430 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAKKEN, SUZANNE, LUCHSINGER, JOSE ALEJANDRO · 2013 to 2017
$2.9M
NIDDK NIH HHS 5R01DK090372NIDDK NIH HHS R01 DK090372NINR NIH HHS R01 NR014430
6 · The paper itself

Abstract

backgroundSelf-monitoring is an integral component of many chronic diseases; however few theoretical frameworks address how individuals understand self-monitoring data and use it to guide self-management. PURPOSE: To articulate a theoretical framework of sensemaking in diabetes self-management that integrates existing scholarship with empirical data.

methodsThe proposed framework is grounded in theories of sensemaking adopted from organizational behavior, education, and human-computer interaction. To empirically validate the framework the researchers reviewed and analyzed reports on qualitative studies of diabetes self-management practices published in peer-reviewed journals from 2000 to 2015.

resultsThe proposed framework distinguishes between sensemaking and habitual modes of self-management and identifies three essential sensemaking activities: perception of new information related to health and wellness, development of inferences that inform selection of actions, and carrying out daily activities in response to new information. The analysis of qualitative findings from 50 published reports provided ample empirical evidence for the proposed framework; however, it also identified a number of barriers to engaging in sensemaking in diabetes self-management.

conclusionsThe proposed framework suggests new directions for research in diabetes self-management and for design of new informatics interventions for data-driven self-management.

Indexed as

Self CareChronic DiseaseCognitionDatabases, FactualData CollectionDiabetes MellitusHealth BehaviorHumansMedical InformaticsModels, TheoreticalPatient ParticipationProblem SolvingUser-Computer InterfaceChronic diseaseDiabetesFrameworkSelf-care

Identifiers

PMID26071681
PMCPMC4626451

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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 OpenQuestion graph.