Evidence map›Paper›PMID 30907738›Full record

ArticleJMIR mHealth and uHealth2019

A Smartphone App to Assess Alcohol Consumption Behavior: Development, Compliance, and Reactivity.

Antoinette Poulton, Jason Pan, Loren Richard Bruns, Richard O Sinnott, Robert Hester

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

5 authors.

Antoinette PoultonMelbourne School of Psychological Sciences, University of Melbourne, Parkville, Australia.ORCID 0000-0001-9291-6128
Jason PanMelbourne School of Psychological Sciences, University of Melbourne, Parkville, Australia.ORCID 0000-0003-1710-8125
Loren Richard BrunsMelbourne eResearch Group, School of Computing and Information Services, University of Melbourne, Parkville, Australia.ORCID 0000-0003-2198-2173
Richard O SinnottMelbourne eResearch Group, School of Computing and Information Services, University of Melbourne, Parkville, Australia.ORCID 0000-0001-5998-222X
Robert HesterMelbourne School of Psychological Sciences, University of Melbourne, Parkville, Australia.ORCID 0000-0003-0982-8026

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere are disadvantages-largely related to cost, participant burden, and missing data-associated with traditional electronic methods of assessing drinking behavior in real time. This potentially diminishes some of the advantages-namely, enhanced sample size and diversity-typically attributed to these methods. Download of smartphone apps to participants' own phones might preserve these advantages. However, to date, few researchers have detailed the process involved in developing custom-built apps for use in the experimental arena or explored methodological concerns regarding compliance and reactivity.

objectiveThe aim of this study was to describe the process used to guide the development of a custom-built smartphone app designed to capture alcohol intake behavior in the healthy population. Methodological issues related to compliance with and reactivity to app study protocols were examined. Specifically, we sought to investigate whether hazard and nonhazard drinkers would be equally compliant. We also explored whether reactivity in the form of a decrease in drinking or reduced responding ("yes") to drinking behavior would emerge as a function of hazard or nonhazard group status.

methodsAn iterative development process that included elements typical of agile software design guided the creation of the CNLab-A app. Healthy individuals used the app to record alcohol consumption behavior each day for 21 days. Submissions were either event- or notification-contingent. We considered the size and diversity of the sample, and assessed the data for evidence of app protocol compliance and reactivity as a function of hazard and nonhazard drinker status.

resultsCNLab-A yielded a large and diverse sample (N=671, mean age 23.12). On average, participants submitted data on 20.27 (SD 1.88) out of 21 days (96.5%, 20.27/21). Both hazard and nonhazard drinkers were highly compliant with app protocols. There were no differences between groups in terms of number of days of app use (P=.49) or average number of app responses (P=.54). Linear growth analyses revealed hazardous drinkers decreased their alcohol intake by 0.80 standard drinks over the 21-day experimental period. There was no change to the drinking of nonhazard individuals. Both hazard and nonhazard drinkers showed a slight decrease in responding ("yes") to drinking behavior over the same period.

conclusionsSmartphone apps participants download to their own phones are effective and methodologically sound means of obtaining alcohol consumption information for research purposes. Although further investigation is required, such apps might, in future, allow for a more thorough examination of the antecedents and consequences of drinking behavior.

Indexed as

AdolescentAdultAlcohol DrinkingFemaleHumansMaleMiddle AgedMobile ApplicationsProgram DevelopmentSmartphoneSoftware DesignTreatment Adherence and Compliancealcohol drinkingcompliancemobile phonereactivityresearch app developmentsmartphonesmartphone apps

Identifiers

PMID30907738
PMCPMC6452287

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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.