Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Digital phenotyping and digital monitoring technologies for relapse detection in mental health: a systematic review.

BMC psychiatry - 7 Apr 2026

Dormechele William, Addo Isaac Yeboah, Boadi Caleb, Bonsu Emmanuel Osei, Adams Mercy Oseiwah

Abstract excerpt

BACKGROUND: Mental health relapse remains a major challenge in the long-term management of psychiatric disorders. Conventional monitoring approaches rely primarily on periodic clinical assessments and self-report measures, which may fail to capture early behavioural changes preceding symptom deterioration. Digital phenotyping, which refers to the continuous collection and analysis of behavioural and physiological...

Topics

Humans
Digital Health
Mental Disorders
Phenotype
Recurrence
Mental Health

Source

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Identifiers

PMID: 41947072
DOI: 10.1186/s12888-026-08033-w
PMCID: PMC13188289

Genes

SMS

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 29 Aug 2026. Prepared record publication-41947072-2026-08-29.1. Manifest publication-graph-cohort-2026-08-29.1.

Source receipt: PubMed via Helena Literature Corpus, retrieved 29 Aug 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Digital phenotyping and digital monitoring technologies for relapse detection in mental health: a systematic review.PMID 41947072Read ↗

Digital phenotyping for predicting relapse in psychiatric disorders: a systematic review of passive sensing approaches.Semantic proximity Read ↗

Using Smartphone-Based Digital Phenotyping to Predict Relapse in Serious Mental Disorders Among Slum Residents in Dhaka, Bangladesh: Protocol for a Machine Learning Study.Semantic proximity Read ↗

More than one piece of the puzzle: considering non-clinical factors for personalisation in digital phenotypingSemantic proximity Read ↗

The Current State/ Trends in Digital Phenotyping for Mental Health Research and Care.Semantic proximity Read ↗

Digital phenotyping of depression: A multi-modal passive sensing approach to identifying novel behavioral and physiological markers of treatment response.Semantic proximity Read ↗

Anomaly detection to predict relapse risk in schizophrenia.Semantic proximity Read ↗

From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression.Semantic proximity Read ↗

Early signs of depression and anxiety disorders in the young using digital biomarkers – Study protocol of a multi-method measurement burst studySemantic proximity Read ↗

Early signs of depression and anxiety disorders in the young using digital biomarkers – Study protocol of a multi-method measurement burst studySemantic proximity Read ↗

Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol.Semantic proximity Read ↗

From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depressionSemantic proximity Read ↗

Passive Digital Phenotyping for Identifying Dynamic Symptom Patterns: Toward Precision Monitoring in PsychiatrySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.