Development and implementation of a logic model: Occupational stress, physical activity, and sedentary behavior in the workplace

Work ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wendell C. Taylor ◽  
Bhibha M. Das ◽  
Raheem J. Paxton ◽  
Ross Shegog ◽  
Richard R. Suminski ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Occupational Stress ◽  
Logic Model

Development and implementation of a logic model: Occupational stress, physical activity, and sedentary behavior in the workplace

Work ◽  
2020 ◽  
Vol 67 (1) ◽  
pp. 203-213
Author(s):  
Wendell C. Taylor ◽  
Bhibha M. Das ◽  
Raheem J. Paxton ◽  
Ross Shegog ◽  
Richard R. Suminski ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Occupational Stress ◽  
Medical Center ◽  
Research Area ◽  
Logic Model ◽  
Workplace Stress ◽  
Sitting Time ◽  
Physiological Status ◽  
Intermediate Outcomes

BACKGROUND: An increasing level of occupational stress is a major problem in the workplace that requires innovative approaches and strategies. An understudied research area pertains to the effects that physical activity performed during the workday have on occupational stress. OBJECTIVE: To determine if and how an intervention that increases physical activity and reduces sedentary behavior affects workplace stress. The population of interest are employees at a large university medical center including supportive staff, healthcare professionals, physicians, and faculty members; the study design is longitudinal; the approach is the implementation of an innovative workplace program (i.e., the Booster Break). METHODS: We present a logic model promoting physical activity and reducing sitting time during the workday as a feasible and practical strategy to cope with occupational stress. RESULTS: The logic model approach emphasizes that funding, partnerships, and incentives are inputs to implementing program activities such as Booster Break sessions, weekly meetings, social support, and personal self-monitoring. Short-term outcomes were categorized as psychosocial, goal setting, organizational, and social; intermediate outcomes were behavioral and psychosocial; and long-term outcomes were health status and physiological status. CONCLUSIONS: This study is the first known effort to outline a comprehensive intervention based on changing physical activity and sedentary behavior during the workday and the concomitant effects on occupational stress. The findings of this study can be used to develop and implement interventions at workplaces to target increases in physical activity, decreases in sedentary time, and improvements in overall employee health.

More than a simple pastime? The potential of physical activity to moderate the relationship between occupational stress and burnout symptoms.

2020 ◽  
Vol 27 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Markus Gerber ◽  
René Schilling ◽  
Flora Colledge ◽  
Sebastian Ludyga ◽  
Uwe Pühse ◽  
...  
Keyword(s):  
Physical Activity ◽  
Occupational Stress ◽  
The Relationship

Comparisons of physical activity and sedentary behavior between owners and non-owners of commercial wearable devices

2021 ◽  
Vol 141 (2) ◽  
pp. 89-96
Author(s):  
Hsin-Yen Yen ◽  
Hao-Yun Huang
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Sedentary Behavior ◽  
Sedentary Time ◽  
Short Form ◽  
Wearable Devices ◽  
Wearable Device ◽  
World Health ◽  
World Health Organization Quality ◽  
Quality Of Life Scale

Aims: Wearable devices are a new strategy for promoting physical activity in a free-living condition that utilizes self-monitoring, self-awareness, and self-determination. The main purpose of this study was to explore health benefits of commercial wearable devices by comparing physical activity, sedentary time, sleep quality, and other health outcomes between individuals who used and those that did not use commercial wearable devices. Methods: The research design was a cross-sectional study using an Internet survey in Taiwan. Self-administered questionnaires included the International Physical Activity Questionnaire–Short Form, Pittsburgh Sleep Quality Index, Health-Promoting Lifestyle Profile, and World Health Organization Quality-of-Life Scale. Results: In total, 781 participants were recruited, including 50% who were users of wearable devices and 50% non-users in the most recent 3 months. Primary outcomes revealed that wearable device users had significantly higher self-reported walking, moderate physical activity, and total physical activity, and significantly lower sedentary time than non-users. Wearable device users had significantly better sleep quality than non-users. Conclusion: Wearable devices inspire users’ motivation, engagement, and interest in physical activity through habit formation. Wearable devices are recommended to increase physical activity and decrease sedentary behavior for promoting good health.

scholarly journals 2020 WHO guidelines on physical activity and sedentary behavior

2021 ◽  
Author(s):  
Anthony D. Okely ◽  
Anna Kontsevaya ◽  
Johan Ng ◽  
Chalchisa Abdeta
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Who Guidelines

scholarly journals Associations of working from home with occupational physical activity and sedentary behavior under the COVID‐19 pandemic

2021 ◽  
Vol 63 (1) ◽  
Author(s):  
Noritoshi Fukushima ◽  
Masaki Machida ◽  
Hiroyuki Kikuchi ◽  
Shiho Amagasa ◽  
Toshio Hayashi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Occupational Physical Activity ◽  
Working From Home

scholarly journals Can Health-Promoting Schools Contribute to Better Health Behaviors? Physical Activity, Sedentary Behavior, and Dietary Habits among Israeli Adolescents

2021 ◽  
Vol 18 (3) ◽  
pp. 1183
Author(s):  
Hila Beck ◽  
Riki Tesler ◽  
Sharon Barak ◽  
Daniel Sender Moran ◽  
Adilson Marques ◽  
...  
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Confidence Interval ◽  
Health Behaviors ◽  
Sedentary Behavior ◽  
Dietary Habits ◽  
Group Differences ◽  
Time Behavior ◽  
Health Promoting ◽  
The Mean

Schools with health-promoting school (HPS) frameworks are actively committed to enhancing healthy lifestyles. This study explored the contribution of school participation in HPS on students’ health behaviors, namely, physical activity (PA), sedentary behavior, and dieting. Data from the 2018/2019 Health Behavior in School-aged Children study on Israeli adolescents aged 11–17 years were used. Schools were selected from a sample of HPSs and non-HPSs. Between-group differences and predictions of health behavior were analyzed. No between-group differences were observed in mean number of days/week with at least 60 min of PA (HPS: 3.84 ± 2.19 days/week, 95% confidence interval of the mean = 3.02–3.34; non-HPS: 3.93 ± 2.17 days/week, 95% confidence interval of the mean = 3.13–3.38). Most children engaged in screen time behavior for >2 h/day (HPS: 60.83%; non-HPS: 63.91%). The odds of being on a diet were higher among more active children (odds ratio [OR] = 1.20), higher socio-economic status (OR = 1.23), and female (OR = 2.29). HPS did not predict any health behavior. These findings suggest that HPSs did not contribute to health behaviors more than non-HPSs. Therefore, health-promoting activities in HPSs need to be improved in order to justify their recognition as members of the HPS network and to fulfill their mission.

scholarly journals Associations between objectively measured physical activity, sedentary behaviour and time in bed among 75+ community-dwelling Danish older adults

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Li-Tang Tsai ◽  
Eleanor Boyle ◽  
Jan C. Brønd ◽  
Gry Kock ◽  
Mathias Skjødt ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Sedentary Behavior ◽  
Sedentary Behaviour ◽  
Sleep Duration ◽  
Activity Level ◽  
Time In Bed ◽  
Objectively Measured ◽  
Objectively Measured Physical Activity ◽  
Vector Magnitude

Abstract Background Older adults are recommended to sleep 7–8 h/day. Time in bed (TIB) differs from sleep duration and includes also the time of lying in bed without sleeping. Long TIB (≥9 h) are associated with self-reported sedentary behavior, but the association between objectively measured physical activity, sedentary behavior and TIB is unknown. Methods This study was based on cross-sectional analysis of the Healthy Ageing Network of Competence (HANC Study). Physical activity and sedentary behaviour were measured by a tri-axial accelerometer (ActiGraph) placed on the dominant wrist for 7 days. Sedentary behavior was classified as < 2303 counts per minute (cpm) in vector magnitude and physical activity intensities were categorized, as 2303–4999 and ≥ 5000 cpm in vector magnitude. TIB was recorded in self-reported diaries. Participants were categorized as UTIB (usually having TIB 7–9 h/night: ≥80% of measurement days), STIB (sometimes having TIB 7–9 h/night: 20–79% of measurement days), and RTIB (rarely having TIB 7–9 h/night: < 20% of measurement days). Multinominal regression models were used to calculate the relative risk ratios (RRR) of being RTIB and STIB by daily levels of physical activity and SB, with UTIB as the reference group. The models were adjusted for age, sex, average daily nap length and physical function. Results Three hundred and fourty-one older adults (median age 81 (IQR 5), 62% women) were included with median TIB of 8 h 21 min (1 h 10 min)/day, physical activity level of 2054 (864) CPM with 64 (15) % of waking hours in sedentary behavior. Those with average CPM within the highest tertile had a lower RRR (0.33 (0.15–0.71), p = 0.005) for being RTIB compared to those within the lowest tertile of average CPM. Accumulating physical activity in intensities 2303–4999 and ≥ 5000 cpm/day did not affect the RRR of being RTIB. RRR of being RTIB among highly sedentary participants (≥10 h/day of sedentary behavior) more than tripled compared to those who were less sedentary (3.21 (1.50–6.88), p = 0.003). Conclusions For older adults, being physically active and less sedentary was associated with being in bed for 7–9 h/night for most nights (≥80%). Future longitudinal studies are warranted to explore the causal relationship sbetween physical activity and sleep duration.

scholarly journals Physical activity, sedentary behavior and risk of coronary artery disease, myocardial infarction and ischemic stroke: a two-sample Mendelian randomization study

2021 ◽  
Author(s):  
Martin Bahls ◽  
Michael F. Leitzmann ◽  
André Karch ◽  
Alexander Teumer ◽  
Marcus Dörr ◽  
...  
Keyword(s):  
Physical Activity ◽  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Ischemic Stroke ◽  
Coronary Artery ◽  
Sedentary Behavior ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Artery Disease

Abstract Aims Observational evidence suggests that physical activity (PA) is inversely and sedentarism positively related with cardiovascular disease risk. We performed a two-sample Mendelian randomization (MR) analysis to examine whether genetically predicted PA and sedentary behavior are related to coronary artery disease, myocardial infarction, and ischemic stroke. Methods and results We used single nucleotide polymorphisms (SNPs) associated with self-reported moderate to vigorous PA (n = 17), accelerometer based PA (n = 7) and accelerometer fraction of accelerations > 425 milli-gravities (n = 7) as well as sedentary behavior (n = 6) in the UK Biobank as instrumental variables in a two sample MR approach to assess whether these exposures are related to coronary artery disease and myocardial infarction in the CARDIoGRAMplusC4D genome-wide association study (GWAS) or ischemic stroke in the MEGASTROKE GWAS. The study population included 42,096 cases of coronary artery disease (99,121 controls), 27,509 cases of myocardial infarction (99,121 controls), and 34,217 cases of ischemic stroke (404,630 controls). We found no associations between genetically predicted self-reported moderate to vigorous PA, accelerometer-based PA or accelerometer fraction of accelerations > 425 milli-gravities as well as sedentary behavior with coronary artery disease, myocardial infarction, and ischemic stroke. Conclusions These results do not support a causal relationship between PA and sedentary behavior with risk of coronary artery disease, myocardial infarction, and ischemic stroke. Hence, previous observational studies may have been biased. Graphic abstract

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