Evidence map›Paper›PMID 37011275›Full record

ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2021

"They don't always think about that": Translational Needs in the Design of Personal Health Informatics Applications.

Susanne Kirchner, Jessica Schroeder, James Fogarty, Sean A Munson

Abstract read
In one paragraph

Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Susanne KirchnerHuman Centered Design & Engineering DUB Group, University of Washington.
Jessica SchroederComputer Science & Engineering DUB Group, University of Washington.
James FogartyComputer Science & Engineering DUB Group, University of Washington.
Sean A MunsonHuman Centered Design & Engineering DUB Group, University of Washington.

Funding

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
NLM NIH HHS R01 LM012810
6 · The paper itself

Abstract

Personal health informatics continues to grow in both research and practice, revealing many challenges of designing applications that address people's needs in their health, everyday lives, and collaborations with clinicians. Research suggests strategies to address such challenges, but has struggled to translate these strategies into design practice. This study examines translation of insights from personal health informatics research into resources to support designers. Informed by a review of relevant literature, we present our development of a prototype set of design cards intended to support designers in re-thinking potential assumptions about personal health informatics. We examined our design cards in semi-structured interviews, first with 12 student designers and then with 12 health-focused professional designers and researchers. Our results and discussion reveal tensions and barriers designers encounter, the potential for translational resources to inform the design of health-related technologies, and a need to support designers in addressing challenges of knowledge, advocacy, and evidence in designing for health.

Indexed as

Health Design• Human-Centered Computing → Human Computer Interaction (HCI)Personal Health InformaticsTranslational Research

Identifiers

PMID37011275
PMCPMC10065672

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.