Evidence map›Paper›PMID 32442129›Full record

ArticleJournal of medical Internet research2020

Effect of Speech Recognition on Problem Solving and Recall in Consumer Digital Health Tasks: Controlled Laboratory Experiment.

Jessica Chen, David Lyell, Liliana Laranjo, Farah Magrabi

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 21% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. 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

4 authors at 1 institution in 1 country.

Jessica ChenCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, North Ryde, Australia.ORCID 0000-0002-1798-905X
David LyellCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, North Ryde, Australia.ORCID 0000-0002-2695-0368
Liliana LaranjoCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, North Ryde, Australia.ORCID 0000-0003-1020-3402
Farah MagrabiCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, North Ryde, Australia.ORCID 0000-0002-8426-5588
Australian Institute of Health and Welfare · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health applications and few studies have evaluated the efficacy of conversational agents in the hands of consumers. In other consumer-facing tools, cognitive load has been observed to be an important factor affecting the use of speech recognition technologies in tasks involving problem solving and recall. Users find it more difficult to think and speak at the same time when compared to typing, pointing, and clicking. However, the effects of speech recognition on cognitive load when performing health tasks has not yet been explored.

objectiveThe aim of this study was to evaluate the use of speech recognition for documentation in consumer digital health tasks involving problem solving and recall.

methodsFifty university staff and students were recruited to undertake four documentation tasks with a simulated conversational agent in a computer laboratory. The tasks varied in complexity determined by the amount of problem solving and recall required (simple and complex) and the input modality (speech recognition vs keyboard and mouse). Cognitive load, task completion time, error rate, and usability were measured.

resultsCompared to using a keyboard and mouse, speech recognition significantly increased the cognitive load for complex tasks (Z=-4.08, P<.001) and simple tasks (Z=-2.24, P=.03). Complex tasks took significantly longer to complete (Z=-2.52, P=.01) and speech recognition was found to be overall less usable than a keyboard and mouse (Z=-3.30, P=.001). However, there was no effect on errors.

conclusionsUse of a keyboard and mouse was preferable to speech recognition for complex tasks involving problem solving and recall. Further studies using a broader variety of consumer digital health tasks of varying complexity are needed to investigate the contexts in which use of speech recognition is most appropriate. The effects of cognitive load on task performance and its significance also need to be investigated.

Indexed as

AdolescentAdultConsumer Health InformaticsFemaleHumansLaboratoriesMaleMiddle AgedProblem SolvingSpeech Recognition SoftwareYoung Adultconsumer health informaticsergonomicsspeech recognition software

Identifiers

PMID32442129
PMCPMC7296411
OpenAlexW3023602036

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.