Evidence map›Paper›PMID 33792556›Full record

ArticleJMIR mHealth and uHealth2021

Voice Interface Technology Adoption by Patients With Heart Failure: Pilot Comparison Study.

Lida Anna Apergi, Margret V Bjarnadottir, John S Baras, Bruce L Golden, Kelley M Anderson, Jiling Chou, Nawar Shara

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  17. A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.International journal of environmental research and public health · 2022
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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.

Lida Anna ApergiRobert H. Smith School of Business, University of Maryland, College Park, MD, United States.ORCID 0000-0002-3347-6481
Margret V BjarnadottirRobert H. Smith School of Business, University of Maryland, College Park, MD, United States.ORCID 0000-0003-2955-1992
John S BarasInstitute for Systems Research, University of Maryland, College Park, MD, United States.ORCID 0000-0002-4955-8561
Bruce L GoldenRobert H. Smith School of Business, University of Maryland, College Park, MD, United States.ORCID 0000-0002-5270-6094
Kelley M AndersonGeorgetown University, Washington, DC, United States.ORCID 0000-0002-4405-2991
Jiling ChouMedstar Health Research Institute, Hyattsville, MD, United States.ORCID 0000-0002-0552-0246
Nawar SharaGeorgetown University, Washington, DC, United States.ORCID 0000-0002-9005-252X

Funding

Maternal Morbidity and Mortality: Risk Factors, Early Detection and Personalized InterventionUL1TR001409 · NCATS · GEORGETOWN UNIVERSITY · PI GONDRE-LEWIS, MARJORIE C, MELLMAN, THOMAS A · 2015 to 2024
$37.8M
NCATS NIH HHS UL1 TR001409
6 · The paper itself

Abstract

backgroundHeart failure (HF) is associated with high mortality rates and high costs, and self-care is crucial in the management of the condition. Telehealth can promote patients' self-care while providing frequent feedback to their health care providers about the patient's compliance and symptoms. A number of technologies have been considered in the literature to facilitate telehealth in patients with HF. An important factor in the adoption of these technologies is their ease of use. Conversational agent technologies using a voice interface can be a good option because they use speech recognition to communicate with patients.

objectiveThe aim of this paper is to study the engagement of patients with HF with voice interface technology. In particular, we investigate which patient characteristics are linked to increased technology use.

methodsWe used data from two separate HF patient groups that used different telehealth technologies over a 90-day period. Each group used a different type of voice interface; however, the scripts followed by the two technologies were identical. One technology was based on Amazon's Alexa (Alexa+), and in the other technology, patients used a tablet to interact with a visually animated and voice-enabled avatar (Avatar). Patient engagement was measured as the number of days on which the patients used the technology during the study period. We used multiple linear regression to model engagement with the technology based on patients' demographic and clinical characteristics and past technology use.

resultsIn both populations, the patients were predominantly male and Black, had an average age of 55 years, and had HF for an average of 7 years. The only patient characteristic that was statistically different (P=.008) between the two populations was the number of medications they took to manage HF, with a mean of 8.7 (SD 4.0) for Alexa+ and 5.8 (SD 3.4) for Avatar patients. The regression model on the combined population shows that older patients used the technology more frequently (an additional 1.19 days of use for each additional year of age; P=.004). The number of medications to manage HF was negatively associated with use (-5.49; P=.005), and Black patients used the technology less frequently than other patients with similar characteristics (-15.96; P=.08).

conclusionsOlder patients' higher engagement with telehealth is consistent with findings from previous studies, confirming the acceptability of technology in this subset of patients with HF. However, we also found that a higher number of HF medications, which may be correlated with a higher disease burden, is negatively associated with telehealth use. Finally, the lower engagement of Black patients highlights the need for further study to identify the reasons behind this lower engagement, including the possible role of social determinants of health, and potentially create technologies that are better tailored for this population.

Indexed as

Heart FailureTelemedicineHumansMaleMiddle AgedPilot ProjectsSelf CareTechnologyartificial intelligenceconversational agentheart failuremobile phonesocial determinants of healthtelehealthvoice interfacewireless technology

Identifiers

PMID33792556
PMCPMC8050751

What OpenQuestion holds

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LicenceCC BY
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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.