Health Promotion in the Workplace: Assessing Stress and Lifestyle With an Intranet Tool

Autor: Lucini, Daniela; Solaro, Nadia; Lesma, Alessandro; Gillet, Veronique Bernadette; Pagani, Massimo
Sprache: Englisch
Veröffentlicht: 2011
Quelle: Directory of Open Access Journals: DOAJ Articles
Online Zugang: http://www.jmir.org/2011/4/e88/
https://doaj.org/toc/1438-8871
1438-8871
doi:10.2196/jmir.1798
https://doaj.org/article/dc687b4f2f954e46a1d3084b3b525ccb
https://doi.org/10.2196/jmir.1798
https://doaj.org/article/dc687b4f2f954e46a1d3084b3b525ccb
Erfassungsnummer: ftdoajarticles:oai:doaj.org/article:dc687b4f2f954e46a1d3084b3b525ccb

Zusammenfassung

BackgroundChronic noncommunicable conditions, particularly cardiovascular and metabolic diseases, are the major causes of death and morbidity in both industrialized and low- to middle-income countries. Recent epidemiological investigations suggest that management of lifestyle factors, such as stress and lack of physical activity, could have an important value in cardiometabolic conditions, while information technology tools could play a significant facilitatory role. ObjectivesThe objective of our study was to verify the feasibility of using a private website, directed to the workers of a major Italian company, to describe their health profile and lifestyle and work habits using an ad hoc self-administered questionnaire. MethodsWe administered anonymous multiple choice Web-based questionnaires to 945 participants (683 completed the task) as part of an ongoing health promotion program in a multinational company. Qualitative and quantitative data were synthesized with nonlinear principal component analysis to construct indicators (ie, variables) for stress, control, and lifestyle domains. Considering in addition absenteeism, the Calinski-Harabasz statistic and cluster analysis jointly differentiated seven clusters, which displayed different distributions of standardized classification variables. The final step consisted in assessing the relationship of the resulting seven subject typologies with personal data, illnesses, and metabolic syndrome status, carried out for the most part with descriptive methods. ResultsStatistical analyses singled out two not-overlapping domains of stress and control, as well as three not-overlapping domains of physical activity, smoking, and alcohol habits. The centroids of the seven clusters generated by the procedure were significantly (P < .001) different considering all possible 21 comparisons between couples of groups. Percentage distributions of variables describing personal information (gender, age group, work category, illness status, or metabolic syndrome) within ...