Evidence map›Paper›PMID 29671754›Full record

ReviewPhysiological measurement2018

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses.

Erik Reinertsen, Gari D Clifford

Registry-linked trialAbstract readReview
In one paragraph

Review in Physiological measurement, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05810636 (Hyperactivity Assessment in Children With Attention-deficit Hyperactivity Disorder), which is not on this map. Cited by 62 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 2 pooled it
–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.

NCT05810636 enrolling by invitationnot on this mapstarted 2022, after this paper: background citation

Hyperactivity Assessment in Children With Attention-deficit Hyperactivity Disorder

Typeobservational_patient_registrySponsorNational Taiwan University HospitalRan2022 to 2026Enrolled200ConditionsAttention Deficit Disorder With HyperactivityArmsGarmin Vivosmart wearable device
3 · Its place in the literature

Who cites it

62 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Guideline
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Automated Monitoring and Clinical Notifications of Patient State for Neuropsychiatric Neuromodulation Studies.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Navigating the Intersection of Technology and Depression Precision Medicine.Advances in experimental medicine and biology · 2024
    Review
  19. Review
  20. An exploration of actigraphy in the context of ketamine and treatment-resistant depression.International journal of methods in psychiatric research · 2023
    Article

2 more citing papers are in PubMed but not listed here.

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

2 authors.

Erik ReinertsenDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America.ORCID 0000-0001-7214-9611
Gari D CliffordDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM008169 · NIGMS · EMORY UNIVERSITY · PI GROSS, ROBERT E · 1987 to 2021
$20.3M
Morehouse Cardiovascular Research Center of ExcellenceP50HL117929 · NHLBI · MOREHOUSE SCHOOL OF MEDICINE · PI TAYLOR, HERMAN ALFRED · 2013 to 2018
$6.7M
Sleep Disturbance as a Mechanism for Ischemic Heart Disease in PTSDR01HL136205 · NHLBI · EMORY UNIVERSITY · PI VACCARINO, VIOLA · 2017 to 2020
$3.1M
Deep Learning and Streaming Analytics for Prediction of Adverse Events in the ICUK01ES025445 · NIEHS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NEMATI, SHAMIM · 2015 to 2019
$951k
NHLBI NIH HHS P50 HL117929NHLBI NIH HHS R01 HL136205NIEHS NIH HHS K01 ES025445NIGMS NIH HHS T32 GM008169
6 · The paper itself

Abstract

Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.

Indexed as

BehaviorElectrocardiography, AmbulatoryHumansMental DisordersMonitoring, PhysiologicSmartphoneWearable Electronic Devices

Identifiers

PMID29671754
PMCPMC5995114

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.