A Mobile Application for Easy Design and Testing of Algorithms to Monitor Physical Activity in the Workplace

This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like in modern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user’s physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets.

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Trends in Self-Reported Sitting Time by Physical Activity Levels Among US Adults, NHANES 2007/2008–2017/2018

Journal of Physical Activity and Health ◽

10.1123/jpah.2021-0221 ◽

2021 ◽

Vol 18 (S1) ◽

pp. S74-S83

Author(s):

Emily N. Ussery ◽

Geoffrey P. Whitfield ◽

Janet E. Fulton ◽

Deborah A. Galuska ◽

Charles E. Matthews ◽

...

Keyword(s):

Physical Activity ◽

Sedentary Behavior ◽

Physical Inactivity ◽

Premature Mortality ◽

Linear Increase ◽

Sitting Time ◽

Point Estimate ◽

Activity Levels ◽

Physical Activity Levels ◽

Active Adults

Background: High levels of sedentary behavior and physical inactivity increase the risk of premature mortality and several chronic diseases. Monitoring national trends and correlates of sedentary behavior and physical inactivity can help identify patterns of risk in the population over time. Methods: The authors used self-reported data from the National Health and Nutrition Examination Surveys (2007/2008–2017/2018) to estimate trends in US adults’ mean daily sitting time, overall, and stratified by levels of leisure-time and multidomain physical activity, and in the joint prevalence of high sitting time (>8 h/d) and physical inactivity. Trends were tested using orthogonal polynomial contrasts. Results: Overall, mean daily sitting time increased by 19 minutes from 2007/2008 (332 min/d) to 2017/2018 (351 min/d) (Plinear < .05; Pquadratic < .05). The highest point estimate occurred in 2013/2014 (426 min/d), with a decreasing trend observed after this point (Plinear < .05). Similar trends were observed across physical activity levels and domains, with one exception: an overall linear increase was not observed among sufficiently active adults. The mean daily sitting time was lowest among highly active adults compared with less active adults when using the multidomain physical activity measure. Conclusions: Sitting time among adults increased over the study period but decreased in recent years.

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Abstract MP92: Sedentary Behavior: Do Television-viewing and Total Time Sitting Measure the Same Component?

Circulation ◽

10.1161/circ.129.suppl_1.mp92 ◽

2014 ◽

Vol 129 (suppl_1) ◽

Author(s):

Heather A McGrane Minton ◽

Kelly Thevenet-Morrison ◽

I. Diana Fernandez

Keyword(s):

Physical Activity ◽

Health Outcomes ◽

Sedentary Behavior ◽

Television Viewing ◽

Weighted Kappa ◽

Sitting Time ◽

Viewing Time ◽

Kappa Statistics ◽

Sedentary Behaviors ◽

Tv Viewing

Background: Sedentary behaviors (SB) are activities associated with prolonged time periods of sitting, reclining, or laying down during waking hours. While the relation between SB and physical activity is complex, the common consensus is that SB is not the absence of physical activity and consists of its own determinants posing distinct health outcomes. These behaviors are of significant public health importance as the majority of Americans spend much of their days in SB and due to the increased risks of morbidity and mortality associated with SB. Adverse health outcomes associated with SB include cardiovascular disease, obesity, metabolic syndrome, hypertension and mortality. Television-viewing time and total sitting time have both been used widely to assess time spent in SB and therefore we hypothesize that TV-viewing time and total hours sitting will have high concordance and can be used interchangeably to represent sedentary behaviors. Methods: Using a sample (n = 2858) from the Images of a Healthy Worksite study, a group-randomized control trial involving nutrition and physical activity, the current study assessed how two different tools measured time spent in SB. Tertiles were created based upon the distribution of hours sitting and hours spent TV-vewing. Weighted Kappa statistics were used to measure concordance between hours of TV-viewing and total hours of time spent sitting for the entire sample and for subgroup analyses. Results: Weighted Kappa statistics for tertiles of hours sitting and tv hours were 0.0046, indicating little agreement on the television and the sitting items. Kappa w statistics for BMI categories also showed poor agreement (obese Kappa w = 0.02, overweight Kappa w = 0.002, and healthy subjects Kappa w = 0.006. The Kappa w statistics for males and females were -0.006 and 0.02, respectively. Kappa w statistics for the intervention group (Kappa w = 0.007) and for the control group (Kappa w = 0.0005) also showed little agreement. Conclusions: These results suggest that although commonly used, using television viewing time and total time spent sitting as interchangeable markers of SB, is not a valid assumption. We propose that total time spent sitting and hours spent television-viewing represent different domains within the construct of sedentary behavior. It is important for future researchers to use measures of sedentary behavior that capture the numerous domains involved in measuring SB to allow for the most sensitive measurement of this high-risk behavior.

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Physical activity, sedentary behavior, and incidence of obstructive sleep apnea in three prospective US cohorts

European Respiratory Journal ◽

10.1183/13993003.00606-2021 ◽

2021 ◽

pp. 2100606

Author(s):

Yue Liu ◽

Lin Yang ◽

Meir J. Stampfer ◽

Susan Redline ◽

Shelley S. Tworoger ◽

...

Keyword(s):

Physical Activity ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Sedentary Behavior ◽

Sitting Time ◽

Health Study ◽

Metabolic Factors ◽

Obstructive Sleep ◽

Watching Tv

Reduced physical activity and increased sedentary behavior may independently contribute to development of obstructive sleep apnea (OSA) through increased adiposity, inflammation, insulin resistance and body fluid retention. However, epidemiologic evidence remains sparse, and is primarily limited to cross-sectional studies.We prospectively followed 50 332 women from the Nurses’ Health Study (2002–2012), 68 265 women from the Nurses’ Health Study II (1995–2013), and 19 320 men from the Health Professionals Follow-up Study (1996–2012). Recreational physical activity (quantified by metabolic equivalent of task [MET]-hours/week) and sitting time spent watching TV and at work/away from home were assessed by questionnaires every 2–4 years. Physician-diagnosed OSA was identified by validated self-report. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for OSA incidence associated with physical activity and sedentary behavior.During 2 004 663 person-years of follow-up, we documented 8733 incident OSA cases. After adjusting for potential confounders, the pooled HR for OSA comparing participants with ≥36.0 versus <6.0 MET-hours/week of physical activity was 0.46 (95% CI: 0.43, 0.50; ptrend<0.001). Compared with participants spending <4.0 h/week sitting watching TV, the multivariable-adjusted HR (95% CI) was 1.78 (1.60, 1.98) for participants spending ≥28.0 h/week (ptrend<0.001). The comparable HR (95% CI) was 1.49 (1.38, 1.62) for sitting hours at work/away from home (ptrend<0.001). With additional adjustment for several metabolic factors including BMI and waist circumference, the associations with physical activity and sitting hours at work/away from home were attenuated but remained significant (ptrend<0.001), whereas the association with sitting hours watching TV was no longer statistically significant (ptrend=0.18).Higher levels of physical activity and fewer sedentary hours were associated with lower OSA incidence. The potential mediating role of metabolic factors in the association between sedentary behavior and OSA incidence may depend on type of sedentary behavior. Our results suggest that promoting an active lifestyle may reduce OSA incidence.

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Association between leisure-time physical activity and sedentary behavior with cardiometabolic health in the ELSA-Brasil participants

SAGE Open Medicine ◽

10.1177/2050312119827089 ◽

2019 ◽

Vol 7 ◽

pp. 205031211982708 ◽

Cited By ~ 4

Author(s):

Francisco José Gondim Pitanga ◽

Sheila Maria Alvim Matos ◽

Maria da Conceição C. Almeida ◽

Ana Luísa Patrão ◽

Maria del Carmen Bisi Molina ◽

...

Keyword(s):

Physical Activity ◽

Sedentary Behavior ◽

Screen Time ◽

Leisure Time ◽

Leisure Time Physical Activity ◽

Sitting Time ◽

Cardiometabolic Health ◽

Adult Health ◽

Cross Sectional ◽

Low Levels

Objectives: To assess associations, both individually and in combination, between leisure-time physical activity and sedentary behavior, and cardiometabolic health. Methods: This cross-sectional study included 13,931 civil servants participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Leisure-time physical activity was analyzed using the leisure-time domain of the long-form International Physical Activity Questionnaire, while questions related to cumulative sitting time and leisure-based screen time on a weekday and on one day on the weekend were used to establish sedentary behavior. Data analysis was performed using multivariate logistic regression. Results: Following adjustment for confounding variables, high levels of leisure-time physical activity and low levels of sedentary behavior were both associated with favorable cardiometabolic health markers in both genders. When these two factors were analyzed in conjunction, taking the combination of low levels of leisure-time physical activity and high levels of sedentary behavior as the reference, the inverse associations with cardiometabolic variables became even more significant. Conclusion: High levels of leisure-time physical activity and low levels of sedentary behavior were both inversely associated with the cardiometabolic variables analyzed; however, the two variables when evaluated in conjunction appear to produce more consistent associations, particularly when sedentary behavior is evaluated according to leisure-based screen time.

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Physical Activity and Sitting Time Are Specifically Associated With Multiple Chronic Diseases and Medicine Intake in Brazilian Older Adults

Journal of Aging and Physical Activity ◽

10.1123/japa.2017-0271 ◽

2018 ◽

Vol 26 (4) ◽

pp. 608-613 ◽

Cited By ~ 1

Author(s):

Alex S. Ribeiro ◽

Luiz C. Pereira ◽

Danilo R.P. Silva ◽

Leandro dos Santos ◽

Brad J. Schoenfeld ◽

...

Keyword(s):

Physical Activity ◽

Sedentary Behavior ◽

Chronic Diseases ◽

Physical Inactivity ◽

Prevalence Ratio ◽

Sitting Time ◽

Older Individuals ◽

Independent Association ◽

Older Men And Women ◽

Multiple Chronic Diseases

The purpose of the study was to clarify the independent association between sedentary behavior and physical activity with multiple chronic diseases and medicine intake in older individuals. Sedentary behavior and physical activity were measured by questionnaires. Diseases and medication use were self-reported. Poisson’s regression was adopted for main analysis, through crude and adjusted prevalence ratio and confidence interval of 95%. For men, sedentary time >4 hr/day presented a 76% higher prevalence of ≥2 chronic diseases, while physical inactivity increases the likelihood of using ≥2 medicines in 95%. For women, sedentary behavior >4 hr/day presented an 82% and 43% greater prevalence for ≥2 chronic diseases and the intake of ≥2 medicines, respectively. Sedentary behavior represents an independent associated factor of multiple chronic diseases in older men and women. In addition, inactivity for men and sedentarism for women are associated with the amount of medicine intake.

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Workers’ physical activity data contribute to estimating maximal oxygen consumption: a questionnaire study to concurrently assess workers’ sedentary behavior and cardiorespiratory fitness

BMC Public Health ◽

10.1186/s12889-019-8067-4 ◽

2020 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Tomoaki Matsuo ◽

Rina So ◽

Masaya Takahashi

Keyword(s):

Physical Activity ◽

Oxygen Consumption ◽

Occupational Health ◽

Sedentary Behavior ◽

Cardiorespiratory Fitness ◽

Prediction Models ◽

Intraclass Correlation ◽

Maximal Oxygen Consumption ◽

Sitting Time ◽

Epidemiological Surveys

Abstract Background Sedentary behavior (SB) and cardiorespiratory fitness (CRF) are important issues in occupational health. Developing a questionnaire to concurrently assess workers’ SB and CRF could fundamentally improve epidemiological research. The Worker’s Living Activity-time Questionnaire (WLAQ) was developed previously to assess workers’ sitting time. WLAQ can be modified to evaluate workers’ CRF if additional physical activity (PA) data such as PA frequency, duration, and intensity are collected. Methods A total of 198 working adults (93 women and 105 men; age, 30–60 years) completed anthropometric measurements, a treadmill exercise test for measuring maximal oxygen consumption (VO2max), and modified WLAQ (m-WLAQ, which included questions about PA data additional to the original questions). Multiple regression analyses were performed to develop prediction equations for VO2max. The generated models were cross-validated using the predicted residual error sum of squares method. Among the participants, the data of 97 participants who completed m-WLAQ twice after a 1-week interval were used to calculate intraclass correlation coefficient (ICC) for the test–retest reliability analyses. Results Age (r = − 0.29), sex (r = 0.48), body mass index (BMI, r = − 0.20), total sitting time (r = − 0.15), and PA score (total points for PA data, r = 0.47) were significantly correlated with VO2max. The models that included age, sex, and BMI accounted for 43% of the variance in measured VO2max [standard error of the estimate (SEE) = 5.04 ml·kg− 1·min− 1]. These percentages increased to 59% when the PA score was included in the models (SEE = 4.29 ml·kg− 1·min− 1). Cross-validation analyses demonstrated good stability of the VO2max prediction models, while systematic underestimation and overestimation of VO2max were observed in individuals with high and low fitness, respectively. The ICC of the PA score was 0.87 (0.82–0.91), indicating excellent reliability. Conclusions The PA score obtained using m-WLAQ, rather than sitting time, correlated well with measured VO2max. The equation model that included the PA score as well as age, sex, and BMI had a favorable validity for estimating VO2max. Thus, m-WLAQ can be a useful questionnaire to concurrently assess workers’ SB and CRF, which makes it a reasonable resource for future epidemiological surveys on occupational health.

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Assessment of Sedentary Behavior With the International Physical Activity Questionnaire

Journal of Physical Activity and Health ◽

10.1123/jpah.5.s1.s30 ◽

2008 ◽

Vol 5 (s1) ◽

pp. S30-S44 ◽

Cited By ~ 173

Author(s):

Dori E. Rosenberg ◽

Fiona C. Bull ◽

Alison L. Marshall ◽

James F. Sallis ◽

Adrian E. Bauman

Keyword(s):

Physical Activity ◽

Sedentary Behavior ◽

Physical Inactivity ◽

International Physical Activity Questionnaire ◽

Population Surveillance ◽

Sitting Time ◽

Time Activity ◽

Physical Activity Questionnaire ◽

The Relationship ◽

Activity Questionnaire

Purpose:This study explored definitions of sedentary behavior and examined the relationship between sitting time and physical inactivity using the sitting items from the International Physical Activity Questionnaire (IPAQ).Methods:Participants (N = 289, 44.6% male, mean age = 35.93) from 3 countries completed self-administered long- and short-IPAQ sitting items. Participants wore accelero-meters; were classified as inactive (no leisure-time activity), insufficiently active, or meeting recommendations; and were classified into tertiles of sitting behavior.Results:Reliability of sitting time was acceptable for men and women. Correlations between total sitting and accelerometer counts/min <100 were significant for both long (r = .33) and short (r = .34) forms. There was no agreement between tertiles of sitting and the inactivity category (kappa = .02, P = .68).Conclusion:Sedentary behavior should be explicitly measured in population surveillance and research instead of being defined by lack of physical activity.

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Physical Activity Monitoring in Extremely Obese Adolescents From the Teen-LABORATORIES Study

Journal of Physical Activity and Health ◽

10.1123/jpah.2013-0006 ◽

2015 ◽

Vol 12 (1) ◽

pp. 132-138 ◽

Cited By ~ 1

Author(s):

Renee M. Jeffreys ◽

Thomas H. Inge ◽

Todd M. Jenkins ◽

Wendy C. King ◽

Vedran Oruc ◽

...

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Sedentary Behavior ◽

Activity Monitor ◽

Walk Test ◽

Female Age ◽

Activity Monitors ◽

Physical Activity Monitoring ◽

Sedentary Bouts ◽

Intermittent Activity

Background:The accuracy of physical activity (PA) monitors to discriminate between PA, sedentary behavior, and nonwear in extremely obese (EO) adolescents is unknown.Methods:Twenty-five subjects (9 male/16 female; age = 16.5 ± 2.0 y; BMI = 51 ± 8 kg/m2) wore 3 activity monitors (StepWatch [SAM], Actical [AC], Actiheart [AH]) during a 400-m walk test (400MWT), 2 standardized PA bouts of varying duration, and 1 sedentary bout.Results:For the 400MWT, percent error between observed and monitor-recorded steps was 5.5 ± 7.1% and 82.1 ± 38.6% for the SAM and AC steps, respectively (observed vs. SAM steps: −17.2 ± 22.2 steps; observed vs. AC steps: −264.5 ± 124.8 steps). All activity monitors were able to differentiate between PA and sedentary bouts, but only SAM steps and AH heart rate were significantly different between sedentary behavior and nonwear (P < .001). For all monitors, sedentary behavior was characterized by bouts of zero steps/counts punctuated by intermittent activity steps/counts; nonwear was represented almost exclusively by zero steps/counts.Conclusion:Of all monitors tested, the SAM was most accurate in terms of counting steps and differentiating levels of PA and thus, most appropriate for EO adolescents. The ability to accurately characterize PA intensity in EO adolescents critically depends on activity monitor selection.

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How Sedentary Are Older People? A Systematic Review of the Amount of Sedentary Behavior

Journal of Aging and Physical Activity ◽

10.1123/japa.2014-0164 ◽

2015 ◽

Vol 23 (3) ◽

pp. 471-487 ◽

Cited By ~ 198

Author(s):

Juliet A. Harvey ◽

Sebastien F.M. Chastin ◽

Dawn A. Skelton

Keyword(s):

Physical Activity ◽

Older Adults ◽

Sedentary Behavior ◽

Successful Aging ◽

Large Scale ◽

Well Being ◽

Self Report ◽

Sitting Time ◽

Scale Population ◽

Data Source

Background/objectives:Sedentary behavior (SB), defined as sitting (nonexercising), reclining, and lying down (posture), or by low energy expenditure, is a public health risk independent to physical activity. The objective of this systematic literature review was to synthesize the available evidence on amount of SB reported by and measured in older adults.Data source:Studies published between 1981 and 2014 were identified from electronic databases and manual searching. Large-scale population studies/surveys reporting the amount of SB (objective/subjective) in older adults aged ≥ 60 years of age were included. Appraisal and synthesis was completed using MOOSE guidelines.Results:349,698 adults aged ≥ 60 within 22 studies (10 countries and 1 EU-wide) were included. Objective measurement of SB shows that older adults spend an average of 9.4 hr a day sedentary, equating to 65–80% of their waking day. Self-report of SB is lower, with average weighted self-reports being 5.3 hr daily. Within specific domains of SB, older adults report 3.3 hr in leisure sitting time and 3.3 hr watching TV. There is an association with more time spent in SB as age advances and a trend for older men to spend more time in SB than women.Conclusion/implications:Time spent sedentary ranges from 5.3–9.4 hr per waking day in older adults. With recent studies suggesting a link between SB, health, and well-being, independent of physical activity, this is an area important for successful aging.Limitations:Different methodologies of measurement and different reporting methods of SB made synthesis difficult. Estimated SB time from self-report is half of that measured objectively; suggesting that most self-report surveys of SB will vastly underestimate the actual time spent in SB.

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Correlations of Subjective and Social Well-Being With Sedentary Behavior and Physical Activity in Older Adults—A Population-Based Study

The Journals of Gerontology Series A ◽

10.1093/gerona/glab065 ◽

2021 ◽

Author(s):

Shuyun Chen ◽

Amaia Calderón-Larrañaga ◽

Marguerita Saadeh ◽

Ing-Mari Dohrn ◽

Anna-Karin Welmer

Keyword(s):

Physical Activity ◽

Social Support ◽

Older Adults ◽

Positive Affect ◽

Sedentary Behavior ◽

Social Participation ◽

Oldest Old ◽

Well Being ◽

Sitting Time ◽

National Study

Abstract Background Subjective and social well-being, avoiding sedentary behavior (SB), and engaging in physical activity (PA) are important factors for health in older adults, but the extent to which they are related to each other remains unclear. We aimed to investigate these correlations, and whether they differ by age. Method A cross-sectional study was carried out in 595 people aged 66 years and older, from the Swedish National study on Aging and Care in Kungsholmen. Subjective and social well-being (life satisfaction, positive and negative affect, social connections, social support, and social participation) were assessed through validated questionnaires and activPAL3 accelerometers provided information on SB and PA. Data were analyzed using multi-adjusted quantile regression models. Results Higher positive affect was significantly associated with less daily sitting time (β = −27.08, 95% confidence interval [CI]: −47.77, −6.39) and higher levels of light PA (LPA) (β = 40.67, 95% CI: 21.06, 60.28). Higher levels of social support and social participation were associated with less daily sitting time (β = −22.79, 95% CI: −39.97, −5.62; and β = −21.22, 95% CI: −39.99, −2.44) and more time in LPA (β = 23.86, 95% CI: 4.91, 42.81; and β = 25.37, 95% CI: 6.27, 44.47). Stratified analyses suggested that the associations of positive affect and social participation were strongest for individuals aged 80 years and older. Conclusions Our results suggest that older adults with higher levels of subjective and social well-being spend less time sitting and engage more in PA. This was especially evident among the oldest-old individuals. Future research should longitudinally investigate the directionality of these correlations.

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