Evidence map›Paper›PMID 34937601›Full record

ArticlePsychological medicine2023

Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach.

Joshua E Curtiss, David Mischoulon, Lauren B Fisher, Cristina Cusin, Szymon Fedor, Rosalind W Picard, Paola Pedrelli

Open access · greenAbstract read
In one paragraph

Article in Psychological medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

11 citing papers in PubMed, 26 citations in OpenAlex.

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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 3 institutions in 1 country.

Joshua E CurtissDepression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA.
David MischoulonDepression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA.
Lauren B FisherDepression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA.
Cristina CusinDepression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA.
Szymon FedorThe Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Rosalind W PicardThe Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Paola PedrelliDepression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA.
Harvard University · USMassachusetts Institute of Technology · USMassachusetts General Hospital · US

Funding

Leveraging Artificial Intelligence for the assessment of severity of depressive symptomsR01MH118274 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI PEDRELLI, PAOLA, PICARD, ROSALIND W · 2019 to 2023
$2.5M
NIMH NIH HHS R01 MH118274
6 · The paper itself

Abstract

backgroundPredicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain 'early warning signals' (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether

methodsThirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms.

resultsResults suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (

conclusionsThis study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.

Indexed as

DepressionMajor Depressive DisorderHumansPsychopathologySystems AnalysisTime FactorsDepressiondynamicsearly warning signalstime series

Identifiers

PMID34937601
PMCPMC10606954
OpenAlexW4200447927

What OpenQuestion holds

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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.