Evidence map›Paper›PMID 34977421›Full record

ArticleContemporary clinical trials communications2022

Design of a randomized controlled trial of digital health and community health worker support for diabetes management among low-income patients.

Rory Harte, Lindsey Norton, Christina Whitehouse, Ilona Lorincz, Denerale Jones, Norma Gerald, Irene Estrada, Carolyn Sabini, Nandita Mitra, Judith A Long and 4 more

Abstract read
In one paragraph

Article in Contemporary clinical trials communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

14 authors.

Rory HarteDivision of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Lindsey NortonDivision of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Christina WhitehouseVillanova University M. Louise Fitzpatrick College of Nursing, Villanova, PA, USA.
Ilona LorinczHospital of the University of Pennsylvania, Philadelphia, PA, USA.
Denerale JonesPenn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA.
Norma GeraldPenn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA.
Irene EstradaPenn Center for Community Health Workers, Penn Medicine, Philadelphia, PA, USA.
Carolyn SabiniDivision of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Nandita MitraDepartment of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Judith A LongDivision of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Joseph CappellaAnnenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA.
Karen GlanzPerelman School of Medicine and School of Nursing, University of Pennsylvania, Philadelphia, PA, USA.
Kevin G VolppPenn Center for Health Incentives and Behavioral Economics, Departments of Medical Ethics and Health Policy and Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Shreya KangoviDivision of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInsulin-dependent diabetes is a challenging disease to manage and involves complex behaviors, such as self-monitoring of blood glucose. This can be especially challenging in the face of socioeconomic barriers and in the wake of the COVID-19 pandemic. Digital health self-monitoring interventions and community health worker support are promising and complementary best practices for improving diabetes-related health behaviors and outcomes. Yet, these strategies have not been tested in combination. This protocol paper describes the rationale and design of a trial that measures the combined effect of digital health and community health worker support on glucose self-monitoring and glycosylated hemoglobin.

methodsThe study population was uninsured or publicly insured; lived in high-poverty, urban neighborhoods; and had poorly controlled diabetes mellitus with insulin dependence. The study consisted of three arms: usual diabetes care; digital health self-monitoring; or combined digital health and community health worker support. The primary outcome was adherence to blood glucose self-monitoring. The exploratory outcome was change in glycosylated hemoglobin.

conclusionThe design of this trial was grounded in social justice and community engagement. The study protocols were designed in collaboration with frontline community health workers, the study aim was explicit about furthering knowledge useful for advancing health equity, and the population was focused on low-income people. This trial will advance knowledge of whether combining digital health and community health worker interventions can improve glucose self-monitoring and diabetes-related outcomes in a high-risk population.

Indexed as

Community health workerDiabetesDigital healthSelf-monitoring of blood glucose

Identifiers

PMID34977421
PMCPMC8688867

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Registered trials

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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.