Evidence map›Paper›PMID 34941548›Full record

ArticleJournal of medical Internet research2021

Lessons Learned: Beta-Testing the Digital Health Checklist for Researchers Prompts a Call to Action by Behavioral Scientists.

Rebecca Bartlett Ellis, Julie Wright, Lisa Soederberg Miller, Danielle Jake-Schoffman, Eric B Hekler, Carly M Goldstein, Danielle Arigo, Camille Nebeker

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. 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. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

8 authors.

Rebecca Bartlett EllisSchool of Nursing, Indiana University, Indianapolis, IN, United States.ORCID 0000-0001-5990-3595
Julie WrightExercise and Health Sciences, University of Massachusetts, Boston, Boston, MA, United States.ORCID 0000-0002-5115-7011
Lisa Soederberg MillerDepartment of Human Ecology, University of California, Davis, Davis, CA, United States.ORCID 0000-0001-8261-1814
Danielle Jake-SchoffmanDepartment of Health Education and Behavior, University of Florida, Gainesville, FL, United States.ORCID 0000-0001-6381-7323
Eric B HeklerHerbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, United States.ORCID 0000-0002-7434-0775
Carly M GoldsteinDepartment of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, United States.ORCID 0000-0002-6150-5649
Danielle ArigoDepartment of Psychology, Rowan University, Glassboro, NJ, United States.ORCID 0000-0002-7807-5913
Camille NebekerHerbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, United States.ORCID 0000-0001-6819-1796

Funding

Identifying and Targeting Unique Physical Activity Determinants for Midlife WomenK23HL136657 · NHLBI · UNIVERSITY OF SCRANTON · PI ARIGO, DANIELLE · 2018 to 2022
$762k
NHLBI NIH HHS K23 HL136657
6 · The paper itself

Abstract

Digital technologies offer unique opportunities for health research. For example, Twitter posts can support public health surveillance to identify outbreaks (eg, influenza and COVID-19), and a wearable fitness tracker can provide real-time data collection to assess the effectiveness of a behavior change intervention. With these opportunities, it is necessary to consider the potential risks and benefits to research participants when using digital tools or strategies. Researchers need to be involved in the risk assessment process, as many tools in the marketplace (eg, wellness apps, fitness sensors) are underregulated. However, there is little guidance to assist researchers and institutional review boards in their evaluation of digital tools for research purposes. To address this gap, the Digital Health Checklist for Researchers (DHC-R) was developed as a decision support tool. A participatory research approach involving a group of behavioral scientists was used to inform DHC-R development. Scientists beta-tested the checklist by retrospectively evaluating the technologies they had chosen for use in their research. This paper describes the lessons learned because of their involvement in the beta-testing process and concludes with recommendations for how the DHC-R could be useful for a variety of digital health stakeholders. Recommendations focus on future research and policy development to support research ethics, including the development of best practices to advance safe and responsible digital health research.

Indexed as

ChecklistCOVID-19Ethics Committees, ResearchHumansRetrospective StudiesSARS-CoV-2accessbehavioral medicinebioethicsdata managementdigital healthinstitutional review boardIRBmHealthmobile phoneprivacyresearch ethicsrisks and benefitssocial mediausabilitywearable sensors

Identifiers

PMID34941548
PMCPMC8734920

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.