Evidence map›Paper›PMID 36540855›Full record

ArticleApplied animal behaviour science2022

Evaluation of shelter dog activity levels before and during COVID-19 using automated analysis.

Sarah-Elizabeth Byosiere, Marcelo Feighelstein, Kristiina Wilson, Jennifer Abrams, Guy Elad, Nareed Farhat, Dirk van der Linden, Dmitrii Kaplun, Aleksandr Sinitca, Anna Zamansky

Open access · greenAbstract read
In one paragraph

Article in Applied animal behaviour science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.3field-weighted citation impact, top 19% of its field
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

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. 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

10 authors at 5 institutions in 4 countries.

Sarah-Elizabeth ByosiereThinking Dog Center, Department of Psychology, Hunter College, City University of New York, USA.
Marcelo FeighelsteinPrimrose, Tel Aviv, Israel.
Kristiina WilsonThinking Dog Center, Department of Psychology, Hunter College, City University of New York, USA.
Jennifer AbramsThinking Dog Center, Department of Psychology, Hunter College, City University of New York, USA.
Guy EladPrimrose, Tel Aviv, Israel.
Nareed FarhatInformation Systems Department, University of Haifa, Israel.
Dirk van der LindenDepartment of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne, UK.
Dmitrii KaplunDepartment of Automation and Control Processes, Saint Petersburg Electrotechnical University "LETI", St. Petersburg, Russia.
Aleksandr SinitcaDepartment of Automation and Control Processes, Saint Petersburg Electrotechnical University "LETI", St. Petersburg, Russia.
Anna ZamanskyInformation Systems Department, University of Haifa, Israel.
University of Haifa · ILHunter College · USSaint Petersburg State Electrotechnical University · RUCity University of New York · USNorthumbria University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Animal shelters have been found to represent stressful environments for pet dogs, both affecting behavior and influencing welfare. The current COVID-19 pandemic has brought to light new uncertainties in animal sheltering practices which may affect shelter dog behavior in unexpected ways. To evaluate this, we analyzed changes in dog activity levels before COVID-19 and during COVID-19 using an automated video analysis within a large, open-admission animal shelter in New York City, USA. Shelter dog activity was analyzed during two two-week long time periods: (i) just before COVID-19 safety measures were put in place (Feb 26-Mar 17, 2020) and (ii) during the COVID-19 quarantine (July 10-23, 2020). During these two periods, video clips of 15.3 second, on average, were taken of participating kennels every hour from approximately 8 am to 8 pm. Using a two-step filtering approach, a matched sample (based on the number of days of observation) of 34 dogs was defined, consisting of 17 dogs in each group (N1/N2 = 17). An automated video analysis of active/non-active behaviors was conducted and compared to manual coding of activity. The automated analysis validated by comparison to manual coding reaching above 79% accuracy. Significant differences in the patterns of shelter dog activity were observed: less activity was observed in the afternoons before COVID-19 restrictions, while during COVID-19, activity remained at a constant average. Together, these findings suggest that 1) COVID-19 lockdown altered shelter dog in-kennel activity, likely due to changes in the shelter environment and 2) automated analysis can be used as a hands-off tool to monitor activity. While this method of analysis presents immense opportunity for future research, we discuss the limitations of automated analysis and guidelines in the context of shelter dogs that can increase accuracy of detection, as well as reflect on policy changes that might be helpful in mediating canine stress in changing shelter environments.

Indexed as

Applied behaviorComputer visionCOVID-19Dog behaviorMachine learningShelter research

Identifiers

PMID36540855
PMCPMC9754327
OpenAlexW4221088464

What OpenQuestion holds

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Registered trials

None linked

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.