Evidence map›Paper›PMID 37147664›Full record

ArticleThe international journal of behavioral nutrition and physical activity2023

Modelling count, bounded and skewed continuous outcomes in physical activity research: beyond linear regression models.

Muhammad Akram, Ester Cerin, Karen E Lamb, Simon R White

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Article in The international journal of behavioral nutrition and physical activity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

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0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

19 citing papers in PubMed.

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  12. Determinants of time to positivity in bloodstream infections: an analysis of a population-based cohort in Queensland, Australia.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025
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  16. Mapping Kansas City cardiomyopathy, Seattle Angina, and minnesota living with heart failure to the MacNew-7D in patients with heart disease.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Muhammad AkramMary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia. Muhammad.Akram@acu.edu.au.ORCID 0000-0003-1073-3614
Ester CerinMary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.ORCID 0000-0002-7599-165X
Karen E LambCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.ORCID 0000-0001-9782-8450
Simon R WhiteMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0001-8642-7037

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInference using standard linear regression models (LMs) relies on assumptions that are rarely satisfied in practice. Substantial departures, if not addressed, have serious impacts on any inference and conclusions; potentially rendering them invalid and misleading. Count, bounded and skewed outcomes, common in physical activity research, can substantially violate LM assumptions. A common approach to handle these is to transform the outcome and apply a LM. However, a transformation may not suffice.

methodsIn this paper, we introduce the generalized linear model (GLM), a generalization of the LM, as an approach for the appropriate modelling of count and non-normally distributed (i.e., bounded and skewed) outcomes. Using data from a study of physical activity among older adults, we demonstrate appropriate methods to analyse count, bounded and skewed outcomes.

resultsWe show how fitting an LM when inappropriate, especially for the type of outcomes commonly encountered in physical activity research, substantially impacts the analysis, inference, and conclusions compared to a GLM.

conclusionsGLMs which more appropriately model non-normally distributed response variables should be considered as more suitable approaches for managing count, bounded and skewed outcomes rather than simply relying on transformations. We recommend that physical activity researchers add the GLM to their statistical toolboxes and become aware of situations when GLMs are a better method than traditional approaches for modeling count, bounded and skewed outcomes.

Indexed as

ExerciseAgedHumansLinear ModelsBounded dataCount dataGeneralized linear modelLinear regression modelPhysical activitySkewed dataTransformations

Identifiers

PMID37147664
PMCPMC10163772

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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.