Evidence map›Paper›PMID 37000476›Full record

ArticleJMIR mHealth and uHealth2023

Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study.

Louise A C Millard, Laura Johnson, Samuel R Neaves, Peter A Flach, Kate Tilling, Deborah A Lawlor

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2023. 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
–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

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

  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

6 authors.

Louise A C MillardMedical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.ORCID 0000-0003-4787-8411
Laura JohnsonMedical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.ORCID 0000-0001-8985-3154
Samuel R NeavesMedical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.ORCID 0000-0002-4064-3794
Peter A FlachFaculty of Engineering, University of Bristol, Bristol, United Kingdom.ORCID 0000-0001-6857-5810
Kate TillingMedical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.ORCID 0000-0002-1010-8926
Deborah A LawlorMedical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.ORCID 0000-0002-6793-2262

Funding

British Heart Foundation AA/18/7/34219British Heart Foundation CH/F/20/90003Department of HealthMedical Research Council MC_UU_00011/3Medical Research Council MC_UU_00011/6
6 · The paper itself

Abstract

backgroundVoice-based systems such as Amazon Alexa may be useful for collecting self-reported information in real time from participants of epidemiology studies using verbal input. In epidemiological research studies, self-reported data tend to be collected using short, infrequent questionnaires, in which the items require participants to select from predefined options, which may lead to errors in the information collected and lack of coverage. Voice-based systems give the potential to collect self-reported information "continuously" over several days or weeks. At present, to the best of our knowledge, voice-based systems have not been used or evaluated for collecting epidemiological data.

objectiveWe aimed to demonstrate the technical feasibility of using Alexa to collect information from participants, investigate participant acceptability, and provide an initial evaluation of the validity of the collected data. We used food and drink information as an exemplar.

methodsWe recruited 45 staff members and students at the University of Bristol (United Kingdom). Participants were asked to tell Alexa what they ate or drank for 7 days and to also submit this information using a web-based form. Questionnaires asked for basic demographic information, about their experience during the study, and the acceptability of using Alexa.

resultsOf the 37 participants with valid data, most (n=30, 81%) were aged 20 to 39 years and 23 (62%) were female. Across 29 participants with Alexa and web entries corresponding to the same intake event, 60.1% (357/588) of Alexa entries contained the same food and drink information as the corresponding web entry. Most participants reported that Alexa interjected, and this was worse when entering the food and drink information (17/35, 49% of participants said this happened often; 1/35, 3% said this happened always) than when entering the event date and time (6/35, 17% of participants said this happened often; 1/35, 3% said this happened always). Most (28/35, 80%) said they would be happy to use a voice-controlled system for future research.

conclusionsAlthough there were some issues interacting with the Alexa skill, largely because of its conversational nature and because Alexa interjected if there was a pause in speech, participants were mostly willing to participate in future research studies using Alexa. More studies are needed, especially to trial less conversational interfaces.

Indexed as

FoodFeasibility StudiesFemaleHumansMaleSelf ReportSurveys and QuestionnairesUnited KingdomAmazon Alexadata collectiondigital healthfood and drinkself-reported datavoice-based approaches

Identifiers

PMID37000476
PMCPMC10132002

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.