Evidence map›Paper›PMID 36601285›Full record

ReviewDigital health

Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers.

Manuel Spitschan, Karin Smolders, Benjamin Vandendriessche, Brinnae Bent, Jessie P Bakker, Isaac R Rodriguez-Chavez, Céline Vetter

Open access · goldAbstract readReview
In one paragraph

Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 3% of its field
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

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.

  1. Guideline
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Identifying chest-worn light logger adherence: a validation study.medRxiv : the preprint server for health sciences · 2025
    Article
  13. Predicting visual field boundaries from head features.Journal of the Optical Society of America. A, Optics, image science, and vision · 2025
    Article
  14. Article
  15. Developing digital health technologies for frontotemporal degeneration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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 at 4 institutions in 3 countries.

Manuel SpitschanTranslational Sensory & Circadian Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.ORCID https://orcid.org/0000-0002-8572-9268
Karin SmoldersHuman-Technology Interaction Group, Eindhoven University of Technology, Eindhoven, The Netherlands.
Benjamin VandendriesscheByteflies, Antwerp, Belgium.
Brinnae BentEdge Analytics Inc., Campbell, CA, USA.
Jessie P BakkerSignifier Medical Technologies, Needham, MA, USA.
Isaac R Rodriguez-ChavezCenter for Decentralized Clinical Trials and Digital Medicine, ICON plc, Blue Bell, PA, USA.
Céline VetterDepartment of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA.
Case Western Reserve University · USEindhoven University of Technology · NLMax Planck Institute for Biological Cybernetics · DEUniversity of Colorado Boulder · US

Funding

Wellcome Trust 204686/Z/16/Z
6 · The paper itself

Abstract

Background: Light exposure is an important driver and modulator of human physiology, behavior and overall health, including the biological clock, sleep-wake cycles, mood and alertness. Light can also be used as a directed intervention, e.g., in the form of light therapy in seasonal affective disorder (SAD), jetlag prevention and treatment, or to treat circadian disorders. Recently, a system of quantities and units related to the physiological effects of light was standardized by the International Commission on Illumination (CIE S 026/E:2018). At the same time, biometric monitoring technologies (BioMeTs) to capture personalized light exposure were developed. However, because there are currently no standard approaches to evaluate the digital dosimeters, the need to provide a firm framework for the characterization, calibration, and reporting for these digital sensors is urgent. Objective: This article provides such a framework by applying the principles of Results: This article describes opportunities for the use of digital dosimeters for basic research, for monitoring light exposure, and for measuring adherence in both clinical and non-clinical populations to light-based interventions in clinical trials.

Indexed as

circadian rhythmselectronic < generalhealth < generallight exposureneurology < medicineneuroscience < medicineprevention < diseasesleepwearables < personalized medicine

Identifiers

PMID36601285
PMCPMC9806438
OpenAlexW4312415452

What OpenQuestion 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 OpenQuestion graph.