Evidence map›Paper›PMID 32042264›Full record

ArticleBMC nursing2020

Designing a multifaceted telehealth intervention for a rural population using a model for developing complex interventions in nursing.

Stephen M Davis, Amanda Jones, Margaret E Jaynes, Kori N Woodrum, Marcus Canaday, Lindsay Allen, Jennifer A Mallow

Open access · goldAbstract read
In one paragraph

Article in BMC nursing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
2.2field-weighted citation impact, top 12% of its field
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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Stephen M Davis1Department of Health Policy, Management, and Leadership, Robert C. Byrd Health Sciences Center, School of Public Health, West Virginia University, PO Box 9190, Morgantown, WV 26506-9190 USA.ORCID 0000-0003-0392-3364
Amanda Jones1Department of Health Policy, Management, and Leadership, Robert C. Byrd Health Sciences Center, School of Public Health, West Virginia University, PO Box 9190, Morgantown, WV 26506-9190 USA.
Margaret E Jaynes3Departments of Neurology & Pediatrics, West Virginia University, PO Box 9214, Morgantown, WV 26506 USA.
Kori N Woodrum1Department of Health Policy, Management, and Leadership, Robert C. Byrd Health Sciences Center, School of Public Health, West Virginia University, PO Box 9190, Morgantown, WV 26506-9190 USA.
Marcus CanadayTake Me Home, West Virginia, Bureau for Medical Services, Charleston, WV 25301 USA.
Lindsay Allen1Department of Health Policy, Management, and Leadership, Robert C. Byrd Health Sciences Center, School of Public Health, West Virginia University, PO Box 9190, Morgantown, WV 26506-9190 USA.
Jennifer A Mallow5Adult Health Department, School of Nursing, West Virginia University, PO Box 9600, Morgantown, WV 26506 USA.
West Virginia University · USWest Virginia Bureau of Senior Services · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTelehealth interventions offer an evidenced-based approach to providing cost-effective care, education, and timely communication at a distance. Yet, despite its widespread use, telehealth has not reached full potential, especially in rural areas, due to the complex process of designing and implementing telehealth programs. The objective of this paper is to explore the use of a theory-based approach, the Model for Developing Complex Interventions in Nursing, to design a pilot telehealth intervention program for a rural population with multiple chronic conditions.

methodsIn order to develop a robust, evidenced based intervention that suits the needs of the community, stakeholders, and healthcare agencies involved, a design team comprised of state representatives, telehealth experts, and patient advocates was convened. Each design team meeting was guided by major model constructs (i.e., problem identification, defining the target population and objectives, measurement theory selection, building and planning the intervention protocol). Overarching the process was a review of the literature to ensure that the developed intervention was congruent with evidence-based practice and underlying the entire process was scope of practice considerations.

resultsTen design team meetings were held over a six-month period. An adaptive pilot intervention targeting home and community-based Medicaid Waiver Program participants in a rural environment with a primary objective of preventing re-institutionalizations was developed and accepted for implementation. To promote intervention effectiveness, asynchronous (i.e., remote patient monitoring) and synchronous (i.e., nursing assessment of pain and mental health and care coordination) telehealth approaches were selected to address the multiple comorbidities of the target population. An economic evaluation plan was developed and included in the pilot program to assess intervention cost efficiency.

conclusionsThe Model for Developing Complex Interventions in Nursing provided a simple, structured process for designing a multifaceted telehealth intervention to minimize re-institutionalization of participants with multiple chronic conditions. This structured process may promote efficient development of other complex telehealth interventions in time and resource constrained settings. This paper provides detailed examples of how the model was operationalized.

Indexed as

Chronic diseaseIntervention designMedicaidNursingRuralTelehealth

Identifiers

PMID32042264
PMCPMC7001246
OpenAlexW3009322511

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.