Evidence map›Paper›PMID 39445342›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)

Smartphone automated motor and speech analysis for early detection of Alzheimer's disease and Parkinson's disease: Validation of TapTalk across 20 different devices.

Renjie Li, Guan Huang, Xinyi Wang, Katherine Lawler, Lynette R Goldberg, Eddy Roccati, Rebecca J St George, Mimieveshiofuo Aiyede, Anna E King, Aidan D Bindoff and 3 more

Abstract read
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

13 authors.

Renjie LiWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0003-2374-5067
Guan HuangWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0002-9421-7344
Xinyi WangWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0003-2542-2026
Katherine LawlerWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0002-1484-1113
Lynette R GoldbergWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0002-8217-317X
Eddy RoccatiWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0002-7587-9051
Rebecca J St GeorgeSchool of Psychological Sciences University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0001-6206-1069
Mimieveshiofuo AiyedeWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0002-3823-0683
Anna E KingWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0003-1792-0965
Aidan D BindoffWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0002-0943-2702
James C VickersWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0001-5671-4879
Quan BaiSchool of ICT University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0003-1214-6317
Jane AltyWicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.ORCID https://orcid.org/0000-0002-5456-8676

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSmartphones are proving useful in assessing movement and speech function in Alzheimer's disease and other neurodegenerative conditions. Valid outcomes across different smartphones are needed before population-level tests are deployed. This study introduces the TapTalk protocol, a novel app designed to capture hand and speech function and validate it in smartphones against gold-standard measures.

methodsTwenty different smartphones collected video data from motor tests and audio data from speech tests. Features were extracted using Google Mediapipe (movement) and Python audio analysis packages (speech). Electromagnetic sensors (60 Hz) and a microphone acquired simultaneous movement and voice data, respectively.

resultsTapTalk video and audio outcomes were comparable to gold-standard data: 90.3% of video, and 98.3% of audio, data recorded tapping/speech frequencies within ± 1 Hz of the gold-standard measures. DISCUSSION: Validation of TapTalk across a range of devices is an important step in the development of smartphone-based telemedicine and was achieved in this study. Highlights: TapTalk evaluates hand motor and speech functions across a wide range of smartphones.Data showed 90.3% motor and 98.3% speech accuracy within +/-1 Hz of gold standards.Validation advances smartphone-based telemedicine for neurodegenerative diseases.

Indexed as

biomarkersdementiaMediapipemotor–cognitivepreclinical

Identifiers

PMID39445342
PMCPMC11496774

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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.