Evidence map›Paper›PMID 41213544›Full record

ArticlePsychology and aging2026

Longitudinal profiles of affect dynamics in middle and later life: A latent transition analysis.

Sun Ah Lee, Dahlia Mukherjee, Stephanie T Lanza, David M Almeida

Abstract read
In one paragraph

Article in Psychology and aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Sun Ah LeeDepartment of Human Development and Family Studies, Pennsylvania State University.ORCID 0000-0002-4237-7012
Dahlia MukherjeeDepartment of Psychiatry and Behavioral Health, Penn State College of Medicine.ORCID 0000-0003-1007-2094
Stephanie T LanzaCenter for Healthy Aging, Pennsylvania State University.ORCID 0000-0002-6101-8381
David M AlmeidaDepartment of Human Development and Family Studies, Pennsylvania State University.ORCID 0000-0002-5233-8148

Funding

U19 MIDlife in the United States-Diversity Supplement-WellsU19AG051426 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI CAROL D. RYFF · 2016 to 2026
$74.8M
Integrative Pathways to Cognitive, Affective, and Brain HealthU01AG077928 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Sterling C Johnson, MARGIE E LACHMAN · 2022 to 2026
$17.7M
Promoting Rapid Uptake of Multilevel Latent Class Modeling via Best Practices: Investigating Heterogeneity in Daily Substance Use PatternsR01DA057588 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI STEPHANIE T LANZA · 2023 to 2026
$2.7M
John D. and Catherine T. MacArthur FoundationNIA NIH HHS U01 AG077928NIA NIH HHS U19 AG051426NIDA NIH HHS R01 DA057588NIH HHSPennsylvania State University; Social Science Research Institute
6 · The paper itself

Abstract

Affect dynamics are key indicators of health and well-being across adulthood, yet little is known about their longitudinal changes and correlates. Combining mean levels and daily variability, this study examined latent profiles of affect dynamics and patterns of profile transitions over a 10-year span among middle-aged and older adults. We examined longitudinal data from the Midlife in the United States study, consisting of the second and third waves of daily diary assessments (National Study of Daily Experiences). The analytic sample included 950 U.S. adults with 7,222 days for Wave 2 and 7,580 days for Wave 3 (mean age at Wave 2 = 53 years, range: 34-81; 58% women; 86% White). Two affect dynamic indicators-affective variability and affect mean levels-were derived from eight daily dairy interviews at each wave. Latent profile analyses identified three profiles of affect dynamics:

Identifiers

PMID41213544
PMCPMC12944671

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Registered trials

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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.