Evidence map›Paper›PMID 35632028›Full record

ArticleSensors (Basel, Switzerland)2022

Perceptions of and Experiences with Consumer Sleep Technologies That Use Artificial Intelligence.

Esther Oh, William Kearns, Megan Laine, George Demiris, Hilaire J Thompson

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Esther OhBiobehavioral Nursing and Health Informatics, School of Nursing, University of Washington, Seattle, WA 98195-7266, USA.
William KearnsBiobehavioral Nursing and Health Informatics, School of Nursing, University of Washington, Seattle, WA 98195-7266, USA.
Megan LaineBiobehavioral Nursing and Health Informatics, School of Nursing, University of Washington, Seattle, WA 98195-7266, USA.
George DemirisSchools of Nursing and Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0002-6318-5829
Hilaire J ThompsonBiobehavioral Nursing and Health Informatics, School of Nursing, University of Washington, Seattle, WA 98195-7266, USA.ORCID 0000-0002-5472-478X

Funding

Center for Innovation in Sleep Self-Management- Informatics CoreP30NR016585 · NINR · UNIVERSITY OF WASHINGTON · PI HEITKEMPER, MARGARET MCLEAN, WARD, TERESA M · 2016 to 2020
$2.4M
NINR NIH HHS P30 NR016585NINR NIH HHS P30NR016585
6 · The paper itself

Abstract

This study aims to assess the perspectives and usability of different consumer sleep technologies (CSTs) that leverage artificial intelligence (AI). We answer the following research questions: (1) what are user perceptions and ideations of CSTs (phase 1), (2) what are the users' actual experiences with CSTs (phase 2), (3) and what are the design recommendations from participants (phases 1 and 2)? In this two-phase qualitative study, we conducted focus groups and usability testing to describe user ideations of desires and experiences with different AI sleep technologies and identify ways to improve the technologies. Results showed that focus group participants prioritized comfort, actionable feedback, and ease of use. Participants desired customized suggestions about their habitual sleeping environments and were interested in CSTs+AI that could integrate with tools and CSTs they already use. Usability study participants felt CSTs+AI provided an accurate picture of the quantity and quality of sleep. Participants identified room for improvement in usability, accuracy, and design of the technologies. We conclude that CSTs can be a valuable, affordable, and convenient tool for people who have issues or concerns with sleep and want more information. They provide objective data that can be discussed with clinicians.

Indexed as

Artificial IntelligenceTechnologyFocus GroupsHumansQualitative ResearchSleepadultsartificial intelligencesleepuser-centered design

Identifiers

PMID35632028
PMCPMC9145650

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.