Evidence map›Paper›PMID 35786370›Full record

ArticleExperimental aging research

Time-Varying Effect Models for Examining Age-Dynamic Associations in Gerontological Research.

Darcé M Costello, Terrence E Murphy

Abstract read
In one paragraph

Article in Experimental aging research. 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
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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

2 authors.

Darcé M CostelloDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Terrence E MurphyDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

Funding

The National Health and Trends StudyU01AG032947 · NIA · JOHNS HOPKINS UNIVERSITY · PI VICKI A. FREEDMAN, JENNIFER ANN SCHRACK · 2008 to 2026
$169.4M
Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Lauren Ferrante · 2002 to 2026
$37.9M
NIA NIH HHS P30 AG021342NIA NIH HHS U01 AG032947
6 · The paper itself

Abstract

objectivesDynamic processes unfolding over later adulthood are of prime interest to gerontological researchers. Time-varying effect modeling (TVEM) accommodates dynamic change trajectories, but its use in gerontological research is limited. We introduce and demonstrate TVEM with an empirical example based on the National Health and Aging Trends Study (NHATS).

methodsWe examined (a) age-varying prevalence of past month elevated symptoms of depression and anxiety and (b) age-varying associations between older adults' elevated symptoms of depression and anxiety and needing help with basic activities of daily living and educational attainment.

resultsThe proportion of participants reporting elevated symptoms of depression and anxiety in the past month increased gradually from 23-29% across the ages 70-92. Individuals needing help with ADLs had higher odds of reporting elevated symptoms of depression and anxiety, however the association was strongest for those in their 60s versus 80s. Across all ages, adults with lower education levels had higher odds of reporting elevated symptoms of depression and anxiety, an association that also varied by age.

conclusionWe demonstrated TVEM's value for studying dynamic associations that vary across chronological age. With the recent availability of free, user-friendly software for implementing TVEM, gerontological researchers have a new tool for exploring complex change processes that characterize older adults' development.

Indexed as

Activities of Daily LivingAgingAdultAgedAnxietyDepressionHumans

Identifiers

PMID35786370
PMCPMC9807687

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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.