Evidence map›Paper›PMID 32656490›Full record

ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2019

Examining Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management.

Jessica Schroeder, Ravi Karkar, Natalia Murinova, James Fogarty, Sean A Munson

Abstract read
In one paragraph

Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Proceedings of the ACM on human-computer interaction · 2025
    Article
  8. Article
  9. MigraineTracker: Examining Patient Experiences with Goal-Directed Self-Tracking for a Chronic Health Condition.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    Article
  10. Approaches to Tailoring Between-Session Mental Health Therapy Activities.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    Article
  11. "Obviously, Nothing's Gonna Happen in Five Minutes": How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes Management.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    Article
  12. Article
  13. The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023
    Article
  14. Developing a Mobile App for Young Adults with Nonsuicidal Self-Injury: A Prototype Feedback Study.International journal of environmental research and public health · 2022
    Article
  15. Article
  16. Article
  17. Examining AI Methods for Micro-Coaching Dialogs.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2022
    Article
  18. Article
  19. "They don't always think about that": Translational Needs in the Design of Personal Health Informatics Applications.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2021
    Article
  20. 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
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

5 authors.

Jessica SchroederComputer Science & Engineering, University of Washington.
Ravi KarkarComputer Science & Engineering, University of Washington.
Natalia MurinovaHeadache Clinic, University of Washington.
James FogartyComputer Science & Engineering, University of Washington.
Sean A MunsonHuman Centered Design & Engineering, University of Washington.

Funding

Transform Dissemination and Implementation Science in CTSA ProgramsUL1TR002319 · NCATS · UNIVERSITY OF WASHINGTON · PI John K. Amory · 2017 to 2026
$100.0M
Institute of Translational Health SciencesUL1TR000423 · NCATS · UNIVERSITY OF WASHINGTON · PI DISIS, MARY L. · 2012 to 2016
$49.4M
Open Tools for Self-Tracking, Self Experimentation, and Patient-Provider Collaboration in Symptom Self-Management and Clinical CareR01LM012810 · NLM · UNIVERSITY OF WASHINGTON · PI FOGARTY, JAMES A · 2018 to 2021
$1.3M
NCATS NIH HHS UL1 TR000423NCATS NIH HHS UL1 TR002319NLM NIH HHS R01 LM012810
6 · The paper itself

Abstract

Although self-tracking offers potential for a more complete, accurate, and longer-term understanding of personal health, many people struggle with or fail to achieve their goals for health-related self-tracking. This paper investigates how to address challenges that result from current self-tracking tools leaving a person's goals for their data unstated and lacking explicit support. We examine supporting people and health providers in expressing and pursuing their tracking-related goals via goal-directed self-tracking, a novel method to represent relationships between tracking goals and underlying data. Informed by a reanalysis of data from a prior study of migraine tracking goals, we created a paper prototype to explore whether and how goal-directed self-tracking could address current disconnects between the goals people have for data in their chronic condition management and the tools they use to support such goals. We examined this prototype in interviews with 14 people with migraine and 5 health providers. Our findings indicate the potential for scaffolding goal-directed self-tracking to: 1) elicit different types and hierarchies of management and tracking goals; 2) help people prepare for all stages of self-tracking towards a specific goal; and 3) contribute additional expertise in patient-provider collaboration. Based on our findings, we present implications for the design of tools that explicitly represent and support an individual's specific self-tracking goals.

Indexed as

Chronic ConditionsHealthMigrainePatient-Provider CollaborationPersonal Informatics

Identifiers

PMID32656490
PMCPMC7351123

What OpenQuestion 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 OpenQuestion graph.