Evidence map›Paper›PMID 42118603›Full record

SynthesisJournal of intellectual disability research : JIDR2026

Use of Wearable Sensors in Angelman Syndrome: A Systematic Review.

Veronika Vozka, Wei Siong Neo, Jane Kinkus Yatcilla, Carolyn E B McCormick, Roslyn Harold, Kimberly Gálvez-Ortega, Mihir Dey, Laura E Fitzpatrick, A J Schwichtenberg, Orlando S Hoilett and 2 more

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of intellectual disability research : JIDR, 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

12 authors.

Veronika VozkaDepartment of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA.ORCID https://orcid.org/0009-0004-0132-9382
Wei Siong NeoDepartment of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA.
Jane Kinkus YatcillaLibraries and School of Information Studies, Purdue University, West Lafayette, Indiana, USA.ORCID https://orcid.org/0000-0003-0660-4983
Carolyn E B McCormickDepartment of Human Development and Family Science, Purdue University, West Lafayette, Indiana, USA.
Roslyn HaroldDepartment of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA.
Kimberly Gálvez-OrtegaDepartment of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA.
Mihir DeyDepartment of Biomedical Engineering, University of Cincinnati, Cincinnati, Ohio, USA.
Laura E FitzpatrickDepartment of Biomedical Engineering, University of Cincinnati, Cincinnati, Ohio, USA.
A J SchwichtenbergDepartment of Human Development and Family Science, Purdue University, West Lafayette, Indiana, USA.ORCID https://orcid.org/0000-0002-8920-6798
Orlando S HoilettDepartment of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA.
Dan FotiDepartment of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA.
Bridgette L KelleherDepartment of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA.

Funding

Purdue Institute for Integrative Neuroscience
6 · The paper itself

Abstract

backgroundWearable sensors are a promising method for collecting clinical trial outcome data for people with Angelman syndrome (AS). However, there has yet to be a systematic probe into the ways in which wearable sensors have been successfully used in AS. The current study aims to provide a quantitative summary of wearable sensors used in AS, including contexts of use and psychometric properties, and to present key narrative highlights.

methodLiterature searches were performed in three electronic databases: APA PsycInfo, PubMed and Web of Science Core Collection. Data items were categorized into four categories: sample characteristics, study methodological details, wearable sensor characteristics and psychometric properties assessed. Sample characteristics included sample size, age, biological sex, race/ethnicity and cognitive/developmental functioning. Study methodological details were subdivided into study design and setting. Wearable sensor characteristics included sensor type, placement site, means of attachment, assessed construct and sensor-related data loss. Psychometric properties assessed included reliability and validity of sensor-derived data.

resultsWe identified 16 articles through our systematic review. Wearable sensors were used to study sleep (n = 10, 62.5%), language (n = 2, 12.5%), gait (n = 2, 12.5%), caregiver proximity (n = 1, 6.3%), EEG power (n = 1, 6.3%), and arousal (n = 1, 6.3%) in AS through actigraphs, vocalization recorders, inertial sensors, radio-frequency identification watches, wireless EEG caps, and functional near-infrared spectroscopy caps, respectively. Findings from these studies broadly indicate that wearable sensors are feasible, reliable and valid for assessing a range of behaviours relevant to AS.

conclusionsWearable sensors are a promising solution to enhance assessments in AS. However, with the small extant literature characterized by small sample sizes and restricted focus on a few relevant features in AS, there remains ample opportunities to explore the use of wearable sensors in people with AS. Additional studies will better inform clinical decision-making and ultimately improve the lives of people with AS and their families.

Indexed as

Angelman SyndromeOutcome Assessment, Health CarePsychometricsWearable Electronic DevicesDigital HealthHumansAngelmanend pointgenetic syndromeoutcome measurespersonalized medicinesystematic reviewwearables

Identifiers

PMID42118603
PMCPMC13553319

What OpenQuestion holds

Textmetadata
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.