Evidence map›Paper›PMID 37166964›Full record

ArticleJMIR research protocols2023

Assessing a Sleep Interviewing Chatbot to Improve Subjective and Objective Sleep: Protocol for an Observational Feasibility Study.

Ting Su, Rafael A Calvo, Melanie Jouaiti, Sarah Daniels, Pippa Kirby, Derk-Jan Dijk, Ciro Della Monica, Ravi Vaidyanathan

Abstract read
In one paragraph

Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Ting SuDepartment of Mechanical Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-8559-5143
Rafael A CalvoDyson School of Design Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-2238-0684
Melanie JouaitiDepartment of Mechanical Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-6402-0623
Sarah DanielsCare Research & Technology Centre, UK Dementia Research Institute, London, United Kingdom.ORCID https://orcid.org/0000-0002-7415-1907
Pippa KirbyCare Research & Technology Centre, UK Dementia Research Institute, London, United Kingdom.ORCID https://orcid.org/0000-0001-5289-3347
Derk-Jan DijkCare Research & Technology Centre, UK Dementia Research Institute, London, United Kingdom.ORCID https://orcid.org/0000-0001-5314-1008
Ciro Della MonicaCare Research & Technology Centre, UK Dementia Research Institute, London, United Kingdom.ORCID https://orcid.org/0000-0003-0422-4872
Ravi VaidyanathanDepartment of Mechanical Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-9625-4544

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSleep disorders are common among the aging population and people with neurodegenerative diseases. Sleep disorders have a strong bidirectional relationship with neurodegenerative diseases, where they accelerate and worsen one another. Although one-to-one individual cognitive behavioral interventions (conducted in-person or on the internet) have shown promise for significant improvements in sleep efficiency among adults, many may experience difficulties accessing interventions with sleep specialists, psychiatrists, or psychologists. Therefore, delivering sleep intervention through an automated chatbot platform may be an effective strategy to increase the accessibility and reach of sleep disorder intervention among the aging population and people with neurodegenerative diseases.

objectiveThis work aims to (1) determine the feasibility and usability of an automated chatbot (named MotivSleep) that conducts sleep interviews to encourage the aging population to report behaviors that may affect their sleep, followed by providing personalized recommendations for better sleep based on participants' self-reported behaviors; (2) assess the self-reported sleep assessment changes before, during, and after using our automated sleep disturbance intervention chatbot; (3) assess the changes in objective sleep assessment recorded by a sleep tracking device before, during, and after using the automated chatbot MotivSleep.

methodsWe will recruit 30 older adult participants from West London for this pilot study. Each participant will have a sleep analyzer installed under their mattress. This contactless sleep monitoring device passively records movements, heart rate, and breathing rate while participants are in bed. In addition, each participant will use our proposed chatbot MotivSleep, accessible on WhatsApp, to describe their sleep and behaviors related to their sleep and receive personalized recommendations for better sleep tailored to their specific reasons for disrupted sleep. We will analyze questionnaire responses before and after the study to assess their perception of our proposed chatbot; questionnaire responses before, during, and after the study to assess their subjective sleep quality changes; and sleep parameters recorded by the sleep analyzer throughout the study to assess their objective sleep quality changes.

resultsRecruitment will begin in May 2023 through UK Dementia Research Institute Care Research and Technology Centre organized community outreach. Data collection will run from May 2023 until December 2023. We hypothesize that participants will perceive our proposed chatbot as intelligent and trustworthy; we also hypothesize that our proposed chatbot can help improve participants' subjective and objective sleep assessment throughout the study.

conclusionsThe MotivSleep automated chatbot has the potential to provide additional care to older adults who wish to improve their sleep in more accessible and less costly ways than conventional face-to-face therapy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45752.

Indexed as

automated chatbotbehavior analysisconversational agentsolder adultssleep disorderssleep interview

Identifiers

PMID37166964
PMCPMC10214111

What OpenQuestion holds

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LicenceCC BY
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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.