Evidence map›Paper›PMID 39079375›Full record

ArticlePsychiatry research2024

From mood to use: Using ecological momentary assessments to examine how anhedonia and depressed mood impact cannabis use in a depressed sample.

Amanda C Collins, Damien Lekkas, Cara A Struble, Brianna M Trudeau, Abi D Jewett, Tess Z Griffin, Matthew D Nemesure, George D Price, Michael V Heinz, Subigya Nepal and 5 more

Abstract read
In one paragraph

Article in Psychiatry research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

15 authors.

Amanda C CollinsCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States. Electronic address: Amanda.C.Collins@dartmouth.edu.
Damien LekkasCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States.
Cara A StrubleCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Brianna M TrudeauCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Abi D JewettCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Tess Z GriffinCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Matthew D NemesureDigital Data Design Institute, Harvard Business School, Boston, MA, United States.
George D PriceCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States.
Michael V HeinzCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States.
Subigya NepalDepartment of Computer Science, Dartmouth College, Hanover, NH, United States.
Arvind PillaiDepartment of Computer Science, Dartmouth College, Hanover, NH, United States.
Daniel M MackinCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Andrew T CampbellCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Computer Science, Dartmouth College, Hanover, NH, United States.
Alan J BudneyCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States.
Nicholas C JacobsonCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States; Department of Computer Science, Dartmouth College, Hanover, NH, United States.

Funding

Treatment Development & Evaluation CoreP30DA029926 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2011 to 2026
$21.5M
Training in the Science of Co-Occurring DisordersT32DA037202 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2014 to 2026
$4.7M
Personalized Deep Learning Models of Rapid Changes in Major Depressive Disorder Symptoms using Passive Sensor Data from Smartphones and Wearable DevicesR01MH123482 · NIMH · DARTMOUTH COLLEGE · PI JACOBSON, NICHOLAS CHARLES · 2020 to 2024
$2.6M
NIDA NIH HHS P30 DA029926NIDA NIH HHS T32 DA037202NIMH NIH HHS R01 MH123482
6 · The paper itself

Abstract

Anhedonia and depressed mood are two cardinal symptoms of major depressive disorder (MDD). Prior work has demonstrated that cannabis consumers often endorse anhedonia and depressed mood, which may contribute to greater cannabis use (CU) over time. However, it is unclear (1) how the unique influence of anhedonia and depressed mood affect CU and (2) how these symptoms predict CU over more proximal periods of time, including the next day or week (rather than proceeding weeks or months). The current study used data collected from ecological momentary assessment (EMA) in a sample with MDD (N = 55) and employed mixed effects models to detect and predict weekly and daily CU from anhedonia and depressed mood over 90 days. Results indicated that anhedonia and depressed mood were significantly associated with CU, yet varied at daily and weekly scales. Moreover, these associations varied in both strength and directionality. In weekly models, less anhedonia and greater depressed mood were associated with greater CU, and directionality of associations were reversed in the models looking at any CU (compared to none). Findings provide evidence that anhedonia and depressed mood demonstrate complex associations with CU and emphasize leveraging EMA-based studies to understand these associations with more fine-grained detail.

Indexed as

AffectAnhedoniaDepressionEcological Momentary AssessmentMajor Depressive DisorderAdultFemaleHumansMaleMarijuana UseMiddle AgedYoung AdultAnhedoniaCannabis useComorbidityDepressionEcological momentary assessment

Identifiers

PMID39079375
PMCPMC11398046

What Socratic holds

Textmetadata
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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 Socratic graph.