ArticleBrain sciences2022
Continuous Neurophysiologic Data Accurately Predict Mood and Energy in the Elderly.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- A video intervention reduces racial bias in a representative sample of US adults: A brain as predictor study.PloS one · 2026Article
- Virtual Reality Education Increases Neurologic Immersion and Empathy in Nursing Students.Nursing reports (Pavia, Italy) · 2025Article
- The contagion of neurologic Immersion predicts retail purchases.Frontiers in neuroscience · 2025Article
- Continuous remote monitoring of neurophysiologic Immersion accurately predicts mood.Frontiers in digital health · 2024Article
- Prevalence and correlates of post-traumatic stress disorder and depression among welfare- and justice-involved adolescents in Nigeria.European journal of psychotraumatology · 2024Article
- Accurately predicting hit songs using neurophysiology and machine learning.Frontiers in artificial intelligence · 2023Article
- Why people keep watching: neurophysiologic immersion during video consumption increases viewing time and influences behavior.Frontiers in behavioral neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The elderly have an elevated risk of clinical depression because of isolation from family and friends and a reticence to report their emotional states. The present study explored whether data from a commercial neuroscience platform could predict low mood and low energy in members of a retirement community. Neurophysiologic data were collected continuously for three weeks at 1Hz and averaged into hourly and daily measures, while mood and energy were captured with self-reports. Two neurophysiologic measures averaged over a day predicted low mood and low energy with 68% and 75% accuracy. Principal components analysis showed that neurologic variables were statistically associated with mood and energy two days in advance. Applying machine learning to hourly data classified low mood and low energy with 99% and 98% accuracy. Two-day lagged hourly neurophysiologic data predicted low mood and low energy with 98% and 96% accuracy. This study demonstrates that continuous measurement of neurophysiologic variables may be an effective way to reduce the incidence of mood disorders in vulnerable people by identifying when interventions are needed.
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Registered trials
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.