ReviewPerspectives on behavior science2024
The Use of Nonmonetary Outcomes in Health-Related Delay Discounting Research: Review and Recommendations.
Review in Perspectives on behavior science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- A Behavioral Economic Characterization of the Impact of the COVID-19 Quarantine on Reinforcer Diversity, Behavior, and Health.Perspectives on behavior science · 2026Review
- Exploring risk tolerance among individuals who use opioids.Experimental and clinical psychopharmacology · 2026Article
- Domain-specific discounting of health and money: implications for health technology assessment.The European journal of health economics : HEPAC : health economics in prevention and care · 2026Article
- Delay, Doubt and Dollars: Exploring Delay and Probability Discounting of Free and Paid HIV Vaccines among Sexual and Gender Minorities.AIDS and behavior · 2026Article
- Why health apps fail: the role of smartphone proficiency in mHealth resistance.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Delay discounting (DD) refers to the tendency to devalue an outcome as a function of its delay. Most contemporary human DD research uses hypothetical money to assess individual rates of DD. However, nonmonetary outcomes such as food, substances of misuse, and sexual outcomes have been used as well, and have advantages because of their connections to health. This article reviews the literature on the use of nonmonetary outcomes of food, drugs, and sexual outcomes in relation to health and reinforcer pathologies such as substance use disorders, obesity, and sexual risk behaviors, respectively, and makes a case for their use in discounting research. First, food, substances, and sex may be more ecologically valid outcomes than money in terms of their connections to health problems and reinforcer pathologies. Second, consistent trends in commodity-specific (i.e., domain) effects, in which nonmonetary outcomes are discounted more steeply than money, enhance variation in discounting values. Third, commodity-specific changes in discounting with treatments designed to change health choices are described. Finally, methodological trends such as test-retest reliability, magnitude effects, the use of hypothetical versus real outcomes, and age-related effects are discussed in relation to the three outcome types and compared to trends with monetary discounting. Limitations that center around individual preferences, nonsystematic data, and deprivation are discussed. We argue that researchers can enhance their DD research, especially those related to health problems and reinforcer pathologies, with the use of nonmonetary outcomes. Recommendations for future directions of research are delineated.
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Registered trials
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.