Sit-Stand Desk Software Can Now Monitor and Prompt Office Workers to Change Health Behaviors.


Journal

Human factors
ISSN: 1547-8181
Titre abrégé: Hum Factors
Pays: United States
ID NLM: 0374660

Informations de publication

Date de publication:
08 2019
Historique:
pubmed: 9 10 2018
medline: 11 4 2020
entrez: 9 10 2018
Statut: ppublish

Résumé

To determine the effectiveness of a computer-based intervention designed to increase sit-stand desk usage and help reverse workplace physical inactivity. Sit-stand desks have been successful in reducing workplace sedentary behavior, but the challenge remains for an effective method to increase the usage in order to experience the health and productivity benefits. Data collection (1-year field study with 194 workers) used a novel method of computer software that continuously recorded objective electric sit-stand desk usage, while taking into account the time a worker spends away from their desk (breaks, meetings). During the baseline period, all workers' desk usage was recorded by the software, and the intervention period consisted of software reminders and real-time feedback to all workers to change desk positions. Pooled means were calculated to determine desk usage patterns, and effect sizes and pairwise mean differences were analyzed to test for intervention significance. The intervention doubled desk usage by increasing ~1 change to ~2 changes per work day. There was a 76% reduction in workers who never used the sit-stand function of the desk. Medium to large effect sizes from the intervention were observed in all three primary outcome measures (desk in sitting/standing position and desk position changes per work day). These findings demonstrate an effective intervention that increased postural transitioning and interrupted prolonged inactivity while remaining at the workstation. The methods and results in this research study show that we can quantify an increase in desk usage and collect aggregate data continuously.

Sections du résumé

OBJECTIVE
To determine the effectiveness of a computer-based intervention designed to increase sit-stand desk usage and help reverse workplace physical inactivity.
BACKGROUND
Sit-stand desks have been successful in reducing workplace sedentary behavior, but the challenge remains for an effective method to increase the usage in order to experience the health and productivity benefits.
METHOD
Data collection (1-year field study with 194 workers) used a novel method of computer software that continuously recorded objective electric sit-stand desk usage, while taking into account the time a worker spends away from their desk (breaks, meetings). During the baseline period, all workers' desk usage was recorded by the software, and the intervention period consisted of software reminders and real-time feedback to all workers to change desk positions. Pooled means were calculated to determine desk usage patterns, and effect sizes and pairwise mean differences were analyzed to test for intervention significance.
RESULTS
The intervention doubled desk usage by increasing ~1 change to ~2 changes per work day. There was a 76% reduction in workers who never used the sit-stand function of the desk. Medium to large effect sizes from the intervention were observed in all three primary outcome measures (desk in sitting/standing position and desk position changes per work day).
CONCLUSION
These findings demonstrate an effective intervention that increased postural transitioning and interrupted prolonged inactivity while remaining at the workstation.
APPLICATION
The methods and results in this research study show that we can quantify an increase in desk usage and collect aggregate data continuously.

Identifiants

pubmed: 30296174
doi: 10.1177/0018720818807043
pmc: PMC6637722
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

816-824

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Auteurs

Pankaj Parag Sharma (PP)

Texas A&M University, College Station, USA.

Ranjana K Mehta (RK)

Texas A&M University, College Station, USA.

Adam Pickens (A)

Texas A&M University, College Station, USA.

Gang Han (G)

Texas A&M University, College Station, USA.

Mark Benden (M)

Texas A&M University, College Station, USA.

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Classifications MeSH