Design of a framework to promote physical activity for the elderly

Physical inactivity is estimated to be one of the leading risk factors for global mortality and it is associated with several illnesses, such as type 2 diabetes, cardiovascular diseases and various types of cancers. To tackle this issue and promote physical activity amongst the elderly, a system that computes automatically, in real-time, the score of a Boccia game was developed. The objective of this paper is to infer the best design possible for the User Interface (UI) that displays this information. To achieve this, two surveys were conducted involving 45 participants. In the first survey, the participants were asked what features they would like to see in the UI. Based on these remarks, the authors designed an UI, along with several variations. The preferences between these variations were afterwards evaluated in the second survey. Thus, the final design of the UI was validated before being shown to the elders.This article is a result of the project Deus ex Machina: NORTE – 01 – 0145 – FEDER - 000026, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)

Social, environmental and psychological factors associated with objective physical activity levels in the over 65s

Objective: To assess physical activity levels objectively using accelerometers in community dwelling over 65 s and to examine associations with health, social, environmental and psychological factors. Design: Cross sectional survey. Setting: 17 general practices in Scotland, United Kingdom. Participants: Random sampling of over 65 s registered with the practices in four strata young-old (65–80 years), old-old (over 80 years), more affluent and less affluent groups. Main Outcome Measures: Accelerometry counts of activity per day. Associations between activity and Theory of Planned Behaviour variables, the physical environment, health, wellbeing and demographic variables were examined with multiple regression analysis and multilevel modelling. Results: 547 older people (mean (SD) age 79(8) years, 54% female) were analysed representing 94% of those surveyed. Accelerometry counts were highest in the affluent younger group, followed by the deprived younger group, with lowest levels in the deprived over 80 s group. Multiple regression analysis showed that lower age, higher perceived behavioural control, the physical function subscale of SF-36, and having someone nearby to turn to were all independently associated with higher physical activity levels (R2 = 0.32). In addition, hours of sunshine were independently significantly associated with greater physical activity in a multilevel model. Conclusions: Other than age and hours of sunlight, the variables identified are modifiable, and provide a strong basis for the future development of novel multidimensional interventions aimed at increasing activity participation in later life.Publisher PDFPeer reviewe

Translations equations to compare ActiGraph GT3X and Actical accelerometers activity counts

Background: This study aimed to develop a translation equation to enable comparison between Actical and ActiGraph GT3X accelerometer counts recorded minute by minute. Methods: Five males and five females of variable height, weight, body mass index and age participated in this investigation. Participants simultaneously wore an Actical and an ActiGraph accelerometer for two days. Conversion algorithms and R2 were calculated day by day for each subject between the omnidirectional Actical and three different ActiGraph (three-dimensional) outputs: 1) vertical direction, 2) combined vector, and 3) a custom vector. Three conversion algorithms suitable for minute/minute conversions were then calculated from the full data set. Results: The vertical ActiGraph activity counts demonstrated the closest relationship with the Actical, with consistent moderate to strong conversions using the algorithm: y = 0.905x, in the day by day data (R2 range: 0.514 to 0.989 and average: 0.822) and full data set (R2 = 0.865). Conclusions: The Actical is most sensitive to accelerations in the vertical direction, and does not closely correlate with three-dimensional ActiGraph output. Minute by minute conversions between the Actical and ActiGraphvertical component can be confidently performed between data sets and might allow further synthesis of information between studies

Comparison of two different physical activity monitors

<p>Abstract</p> <p>Background</p> <p>Understanding the relationships between physical activity (PA) and disease has become a major area of research interest. Activity monitors, devices that quantify free-living PA for prolonged periods of time (days or weeks), are increasingly being used to estimate PA. A range of different activity monitors brands are available for investigators to use, but little is known about how they respond to different levels of PA in the field, nor if data conversion between brands is possible.</p> <p>Methods</p> <p>56 women and men were fitted with two different activity monitors, the Actigraph™ (Actigraph LLC; AGR) and the Actical™ (Mini-Mitter Co.; MM) for 15 days. Both activity monitors were fixed to an elasticized belt worn over the hip, with the anterior and posterior position of the activity monitors randomized. Differences between activity monitors and the validity of brand inter-conversion were measured by <it>t</it>-tests, Pearson correlations, Bland-Altman plots, and coefficients of variation (CV).</p> <p>Results</p> <p>The AGR detected a significantly greater amount of daily PA (216.2 ± 106.2 vs. 188.0 ± 101.1 counts/min, P < 0.0001). The average difference between activity monitors expressed as a CV were 3.1 and 15.5% for log-transformed and raw data, respectively. When a conversion equation was applied to convert datasets from one brand to another, the differences were no longer significant, with CV's of 2.2 and 11.7%, log-transformed and raw data, respectively.</p> <p>Conclusion</p> <p>Although activity monitors predict PA on the same scale (counts/min), the results between these two brands are not directly comparable. However, the data are comparable if a conversion equation is applied, with better results for log-transformed data.</p

Effects of reallocating time in different activity intensities on health and fitness: a cross sectional study

BACKGROUND: The effects of replacing time in specific activity categories for other categories (e.g. replacing sedentary time with light activity) on health and fitness are not well known. This study used isotemporal substitution to investigate the effects of substituting activity categories in an equal time exchange fashion on health and fitness in young people. METHODS: Participants were drawn from schools in Camden, London (n = 353, mean age 9.3 ± 2.3 years). Time sedentary, in light and in moderate-to-vigorous activity (MVPA) was measured via accelerometry. The effects of substituting time in activity categories (sedentary, light and MVPA) with equivalent time in another category on health and fitness were examined using isotemporal substitution. RESULTS: In single and partition models, MVPA was favourably associated with body fat %, horizontal jump distance and flexibility. Time sedentary and in light activity were not associated with health and fitness outcomes in these models. In substitution models, replacing one hour of sedentary time with MVPA was favourably associated with body fat % (B = -4.187; 95% confidence interval (CI), -7.233, -1.142), horizontal jump distance (B = 16.093; 95% CI, 7.476, 24.710) and flexibility (B = 4.783; 95% CI, 1.910, 7.656). Replacing time in light activity with MVPA induced similar benefits but there were null effects for replacing sedentary with light intensity. CONCLUSION: Substituting time sedentary and in light activity with MVPA was associated with favourable health and fitness. Time in sedentary behaviour may only be detrimental to health and fitness when it replaces time in MVPA in young people

Should physical activity recommendations be ethnicity-specific? Evidence from a cross-sectional study of south Asian and European men

Background Expert bodies and health organisations recommend that adults undertake at least 150 min.week−1 of moderate-intensity physical activity (MPA). However, the underpinning data largely emanate from studies of populations of European descent. It is unclear whether this level of activity is appropriate for other ethnic groups, particularly South Asians, who have increased cardio-metabolic disease risk compared to Europeans. The aim of this study was to explore the level of MPA required in South Asians to confer a similar cardio-metabolic risk profile to that observed in Europeans undertaking the currently recommended MPA level of 150 min.week−1.&lt;p&gt;&lt;/p&gt; Methods Seventy-five South Asian and 83 European men, aged 40–70, without cardiovascular disease or diabetes had fasted blood taken, blood pressure measured, physical activity assessed objectively (using accelerometry), and anthropometric measures made. Factor analysis was used to summarise measured risk biomarkers into underlying latent ‘factors’ for glycaemia, insulin resistance, lipid metabolism, blood pressure, and overall cardio-metabolic risk. Age-adjusted regression models were used to determine the equivalent level of MPA (in bouts of ≥10 minutes) in South Asians needed to elicit the same value in each factor as Europeans undertaking 150 min.week−1 MPA.&lt;p&gt;&lt;/p&gt; Findings For all factors, except blood pressure, equivalent MPA values in South Asians were significantly higher than 150 min.week−1; the equivalent MPA value for the overall cardio-metabolic risk factor was 266 (95% CI 185-347) min.week−1.&lt;p&gt;&lt;/p&gt; Conclusions South Asian men may need to undertake greater levels of MPA than Europeans to exhibit a similar cardio-metabolic risk profile, suggesting that a conceptual case can be made for ethnicity-specific physical activity guidance. Further study is needed to extend these findings to women and to replicate them prospectively in a larger cohort.&lt;p&gt;&lt;/p&gt

Uptake and effectiveness of the Children's Fitness Tax Credit in Canada: the rich get richer

<p>Abstract</p> <p>Background</p> <p>The Government of Canada implemented a Children's Fitness Tax Credit (CFTC) in 2007 which allows a non-refundable tax credit of up to $500 to register a child in an eligible physical activity (PA) program. The purposes of this study were to assess whether the awareness, uptake, and perceived effectiveness of this tax credit varied by household income among Canadian parents.</p> <p>Methods</p> <p>An internet-based panel survey was conducted in March 2009 with a representative sample of 2135 Canadians. Of those, parents with children aged 2 to 18 years of age (<it>n </it>= 1004) were asked if their child was involved in organized PA programs (including dance and sports), the associated costs to register their child in these programs, awareness of the CFTC, if they had claimed the CFTC for the tax year 2007, and whether they planned to claim it in the upcoming year. Parents were also asked if they believed the CFTC has lead to their child being more involved in PA programs.</p> <p>Results</p> <p>Among parents, 54.4% stated their child was in organized PA and 55.5% were aware of the CFTC. Parents in the lowest income quartile were significantly less aware and less likely to claim the CFTC than other income groups. Among parents who had claimed the CFTC, few (15.6%) believed it had increased their child's participation in PA programs.</p> <p>Conclusions</p> <p>More than half of Canadian parents with children have claimed the CFTC. However, the tax credit appears to benefit the wealthier families in Canada.</p

Objective vs. Self-Reported Physical Activity and Sedentary Time: Effects of Measurement Method on Relationships with Risk Biomarkers

&lt;p&gt;&lt;b&gt;Purpose:&lt;/b&gt; Imprecise measurement of physical activity variables might attenuate estimates of the beneficial effects of activity on health-related outcomes. We aimed to compare the cardiometabolic risk factor dose-response relationships for physical activity and sedentary behaviour between accelerometer- and questionnaire-based activity measures.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methods:&lt;/b&gt; Physical activity and sedentary behaviour were assessed in 317 adults by 7-day accelerometry and International Physical Activity Questionnaire (IPAQ). Fasting blood was taken to determine insulin, glucose, triglyceride and total, LDL and HDL cholesterol concentrations and homeostasis model-estimated insulin resistance (HOMAIR). Waist circumference, BMI, body fat percentage and blood pressure were also measured.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; For both accelerometer-derived sedentary time (&#60;100 counts.min−1) and IPAQ-reported sitting time significant positive (negative for HDL cholesterol) relationships were observed with all measured risk factors – i.e. increased sedentary behaviour was associated with increased risk (all p&#8804;0.01). However, for HOMAIR and insulin the regression coefficients were &#62;50% lower for the IPAQ-reported compared to the accelerometer-derived measure (p&#60;0.0001 for both interactions). The relationships for moderate-to-vigorous physical activity (MVPA) and risk factors were less strong than those observed for sedentary behaviours, but significant negative relationships were observed for both accelerometer and IPAQ MVPA measures with glucose, and insulin and HOMAIR values (all p&#60;0.05). For accelerometer-derived MVPA only, additional negative relationships were seen with triglyceride, total cholesterol and LDL cholesterol concentrations, BMI, waist circumference and percentage body fat, and a positive relationship was evident with HDL cholesterol (p = 0.0002). Regression coefficients for HOMAIR, insulin and triglyceride were 43–50% lower for the IPAQ-reported compared to the accelerometer-derived MVPA measure (all p&#8804;0.01).&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusion:&lt;/b&gt; Using the IPAQ to determine sitting time and MVPA reveals some, but not all, relationships between these activity measures and metabolic and vascular disease risk factors. Using this self-report method to quantify activity can therefore underestimate the strength of some relationships with risk factors.&lt;/p&gt

Cross-sectional associations between sleep duration, sedentary time, physical activity, and adiposity indicators among Canadian preschool-aged children using compositional analyses

Abstract Background Sleep duration, sedentary behaviour, and physical activity are three co-dependent behaviours that fall on the movement/non-movement intensity continuum. Compositional data analyses provide an appropriate method for analyzing the association between co-dependent movement behaviour data and health indicators. The objectives of this study were to examine: (1) the combined associations of the composition of time spent in sleep, sedentary behaviour, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) with adiposity indicators; and (2) the association of the time spent in sleep, sedentary behaviour, LPA, or MVPA with adiposity indicators relative to the time spent in the other behaviours in a representative sample of Canadian preschool-aged children. Methods Participants were 552 children aged 3 to 4 years from cycles 2 and 3 of the Canadian Health Measures Survey. Sedentary time, LPA, and MVPA were measured with Actical accelerometers (Philips Respironics, Bend, OR USA), and sleep duration was parental reported. Adiposity indicators included waist circumference (WC) and body mass index (BMI) z-scores based on World Health Organization growth standards. Compositional data analyses were used to examine the cross-sectional associations. Results The composition of movement behaviours was significantly associated with BMI z-scores (p = 0.006) but not with WC (p = 0.718). Further, the time spent in sleep (BMI z-score: γ sleep  = −0.72; p = 0.138; WC: γ sleep  = −1.95; p = 0.285), sedentary behaviour (BMI z-score: γ SB  = 0.19; p = 0.624; WC: γ SB  = 0.87; p = 0.614), LPA (BMI z-score: γ LPA  = 0.62; p = 0.213, WC: γ LPA  = 0.23; p = 0.902), or MVPA (BMI z-score: γ MVPA  = −0.09; p = 0.733, WC: γ MVPA  = 0.08; p = 0.288) relative to the other behaviours was not significantly associated with the adiposity indicators. Conclusions This study is the first to use compositional analyses when examining associations of co-dependent sleep duration, sedentary time, and physical activity behaviours with adiposity indicators in preschool-aged children. The overall composition of movement behaviours appears important for healthy BMI z-scores in preschool-aged children. Future research is needed to determine the optimal movement behaviour composition that should be promoted in this age group