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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Voice Diaries as Digital Biomarkers: A Scoping Review.

Elizabeth Nemeti1, Aravind Venkatachalam2, Tricia Park1

  • 1Department of Biomedical Informatics, Emory University, Atlanta, GA, USA.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Voice diaries show promise as digital biomarkers, but current computational analysis is early-stage and primarily text-based. More research is needed for transparent methods and rigorous validation of audio and multimodal data.

Keywords:
acoustic featuresmental healthspeech analysisvoice diaries

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Area of Science:

  • Digital health
  • Biomedical informatics
  • Computational linguistics

Background:

  • Voice diaries are increasingly explored as a source of digital biomarkers.
  • Objective assessment of health conditions through vocal biomarkers is a growing field.
  • Understanding current methodologies is crucial for advancing voice diary applications.

Purpose of the Study:

  • To review the utilization of voice diaries as digital biomarkers.
  • To summarize existing computational pipelines for voice diary analysis.
  • To identify methodological gaps and limitations in current research.

Main Methods:

  • A systematic literature search was conducted across seven databases.
  • The Population-Concept-Context framework guided the search strategy.
  • Included studies were analyzed for data types (text, audio, multimodal) and computational approaches.

Main Results:

  • Most studies (7/10) analyzed voice diary transcripts, with fewer analyzing raw audio (3/10).
  • One study utilized a multimodal approach combining text, audio, and sensor data.
  • Computational methods included lexicon-based, machine learning, and large language model (LLM) approaches, with inconsistent validation.

Conclusions:

  • Computational analysis of voice diaries as digital biomarkers is a promising but nascent field.
  • Current methods are predominantly text-based, highlighting a need for audio and multimodal data analysis.
  • Transparent methodologies and rigorous validation are essential for future advancements.