Evidence map›Paper›PMID 42529009›Full record

ArticleFrontiers in human neuroscience2026

Wearable monitoring during music-based interventions in dementia: physiological and behavioral observations from a pilot study.

Haoran Zhou, Nan Jiang, Santiago Bernheim, Peter Paik, Qiqi Zhou, Katsuo Kurabayashi, Kendra Ray

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2026. 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
0cells of the map it votes in
0citing 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

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

7 authors.

Haoran ZhouDepartment of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, United States.
Nan JiangDepartment of Electrical and Computer Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.
Santiago BernheimDepartment of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, United States.
Peter PaikDepartment of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, United States.
Qiqi ZhouDepartment of Rehabilitation Medicine, NYU Langone Health, New York, NY, United States.
Katsuo KurabayashiDepartment of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, United States.
Kendra RayDepartment of Rehabilitation Medicine, NYU Langone Health, New York, NY, United States.

Funding

Developing a therapeutic, music-based mobile application to combat neuropsychiatric symptoms in people living with Alzheimer's disease and related dementiasR41AT012152 · NCCIH · AUTOTUNE ME LLC · PI KURABAYASHI, KATSUO, RAY, KENDRA · 2024 to 2025
$295k
NCCIH NIH HHS R41 AT012152
6 · The paper itself

Abstract

Introduction: Music-based interventions (MBIs) are widely used in dementia care, but objective methods for characterizing participant responses during intervention sessions remain limited. Synchronized datasets combining wearable physiological signals and behavioral observations are particularly scarce. Methods: We conducted a pilot feasibility study involving five individuals with Alzheimer's disease and related dementias (ADRD) who participated in 13 formal MBI sessions. Physiological signals, including photoplethysmography (PPG), electrodermal activity (EDA), skin temperature (TEMP), and accelerometry (ACC), were collected using a wrist-worn wearable sensor and synchronized with intervention playlists and time-stamped behavioral observations. Exploratory analyses examined physiological responses across intervention phases, participant-specific response patterns, time-of-day effects, and music-preference effects. Results: The dataset contains 13 intervention sessions, 99 music segments, and 248 behavioral observations. PPG, ACC, TEMP, and behavioral observations were available for all sessions, while EDA quality varied because of sensor-contact challenges. Behavioral responses were highly heterogeneous across participants, with engagement and calm behaviors observed most frequently. Physiological responses also showed substantial inter-individual variability, and case studies demonstrated that physiological and behavioral responses were not always concordant. Conclusion: This study demonstrates the feasibility of collecting synchronized physiological, behavioral, and intervention-context data during MBIs in people living with dementia. The resulting publicly available multimodal dataset provides a foundation for future investigations of participant-specific responses and adaptive music-based interventions.

Indexed as

Alzheimer's disease and related dementiasbehavioral observationdementia caremultimodal datasetmusic-based interventionphysiological monitoringwearable sensing

Identifiers

PMID42529009
PMCPMC13416265

What Socratic 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 Socratic graph.