Evidence map›Paper›PMID 41424497›Full record

ArticleFrontiers in sleep2024

Virtual first: implementation of a novel sleep telehealth platform in the United States military.

Emerson M Wickwire, Jacob Collen, Vincent F Capaldi, Samson Z Assefa, Rachell Jones, Scott G Williams, Connie L Thomas, Daniel C Williams, Jennifer S Albrecht

Abstract read
In one paragraph

Article in Frontiers in sleep, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Emerson M WickwireSleep Disorders Center, Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.
Jacob CollenSleep Disorders Center, Walter Reed National Military Medical Center, Bethesda, MD, United States.
Vincent F CapaldiSleep Disorders Center, Walter Reed National Military Medical Center, Bethesda, MD, United States.
Samson Z AssefaSleep Disorders Center, Alexander T. Augusta Military Medical Center, Fort Belvoir, VA, United States.
Rachell JonesBehavioral Biology Branch, Center for Military Psychiatry and Neuroscience, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
Scott G WilliamsCenter for Military Psychiatry and Neuroscience, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
Connie L ThomasBehavioral Biology Branch, Center for Military Psychiatry and Neuroscience, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
Daniel C WilliamsDepartment of Family and Community Medicine, University of New Mexico School of Medicine, Albuquerque, NM, United States.
Jennifer S AlbrechtDepartment of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There is a gross shortage of sleep specialist providers within the military health system. Telehealth and mobile health represent promising approaches to increase access to high quality, cost-effective care in the U.S military. Objectives: This paper reports findings from a mixed-methods clinical implementation study of a novel sleep telehealth platform at two military treatment facilities in the National Capitol Region. The platform includes a mobile app and integrated wearable sensors (i.e., a commercial off-the-shelf sleep tracker [Fitbit]). The primary purpose was to evaluate the implementation of a 10-day remote monitoring assessment and provision of evidence-based sleep treatment recommendations to patients and providers. In addition, we sought to observe, in an exploratory manner, subsequent engagement with the app during 5 days of personalized sleep education and training. Methods: Patients with sleep problems completed an intensive 10-day remote monitoring assessment that included a baseline intake questionnaire, daily sleep diaries, twice daily symptom surveys, and Fitbit. Based on this assessment, patients received personalized assessment results. Concurrently, a provider report was generated that included provisional diagnoses and evidence-based treatment recommendations. Next, participants gained access to personalized sleep education and trainings within the mobile app. Within an established implementation science framework, outcomes were assessed via behavioral adherence (engagement with the app) and separate questionnaires for patients and providers. Last, we conducted four focus groups with patients and 12 key informant interviews with primary care managers (PCMs) and economic stakeholders to seek feedback and recommendations for future directions. Results: Two hundred and seventy patients participated in the study. Using validated research questionnaires, participants reported high-risk for obstructive sleep apnea (65.6%), moderate to severe insomnia (38.2%), and moderate to severe daytime sleepiness (38.5%), and moderate to severe anxiety (14.1%) and depressive (20.4%) symptoms. Total sleep time was 6.6 (SD = 1.8) h based on sleep diaries and 6.1 (SD = 1.8) h based on Fitbit. Regarding implementation, reach, effectiveness, adoption, implementation, and maintenance were all notably high, based on quantitative and qualitative data from participants and PCMs. Conclusions: Sleep telehealth and mobile health represent promising approaches to increase access to cost-effective, evidence-based care for sleep disorders in the U.S. military.

Indexed as

implementationinsomniamilitarysleepsleep apneatelehealth

Identifiers

PMID41424497
PMCPMC12713946

What OpenQuestion holds

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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.