As smartphone and wearable device ownership increase, interest in their utility to monitor physical activity has risen concurrently. Numerous examples of the application of wearables in clinical and epidemiological research settings already exist. However, whether these devices are all suitable for physical activity surveillance is open for debate. In this commentary, we respond to a commentary by Mair et al. () and discuss four key issues specifically relevant to surveillance that we believe need to be tackled before consumer wearables can be considered for this measurement purpose: representative sampling, representative wear time, validity and reliability, and compatibility between devices. A recurring theme is how to deal with systematic biases by demographic groups. We suggest some potential solutions to the issues of concern such as providing individuals with standardized devices, considering summary metrics of physical activity less prone to wear time biases, and the development of a framework to harmonize estimates between device types and their inbuilt algorithms. We encourage collaborative efforts from researchers and consumer wearable manufacturers in this area. In the meantime, we caution against the use of consumer wearable device data for inference of population-level activity without the consideration of these issues.
Considerations for the Use of Consumer-Grade Wearables and Smartphones in Population Surveillance of Physical Activity
Tessa Strain, Katrien Wijndaele, Matthew Pearce, and Søren Brage
Influence of Step Frequency on Movement Intensity Predictions with the CSA Accelerometer: A Field Validation Study in Children
Søren Brage, Niels Wedderkopp, Lars Bo Andersen, and Karsten Froberg
Four Computer Science and Applications (CSA, Model 7164) accelerometers were validated against speed and heart rate in a field trial, consisting of two walking and two preset running speeds, and 3 min of running at freely chosen speeds. Fifteen children (9–11 years) were recruited from a suburban school in Denmark. Mean CSA output was calculated and converted to acceleration by calibration to sinusoidal accelerations in a mechanical setup, the latter variable being independent of frequency-based filtering. Mean CSA output and estimated acceleration both correlated significantly with speed (r 2 = 0.55 and r 2 = 0.76, respectively) and heart rate (r 2 = 0.60 and r 2 = 0.81, respectively), controlled for gender. ANOVA post hoc test failed to show significant differences in accelerometer output between running speeds. Inter-individual variability of CSA output and acceleration could not be explained by differences in step frequency in walking but running values correlated significantly with step frequency (r = −0.86 and r = −0.47 for CSA output and acceleration, respectively). Conversion of CSA output to average acceleration provides more precise estimates of intensity with less inter-individual variability than raw CSA output. Different running intensities, however, are generally not well differentiated with vertical accelerometry.
Diurnal Profiles of Physical Activity and Postures Derived From Wrist-Worn Accelerometry in UK Adults
Ignacio Perez-Pozuelo, Thomas White, Kate Westgate, Katrien Wijndaele, Nicholas J. Wareham, and Soren Brage
Background: Wrist-worn accelerometry is the commonest objective method for measuring physical activity in large-scale epidemiological studies. Research-grade devices capture raw triaxial acceleration which, in addition to quantifying movement, facilitates assessment of orientation relative to gravity. No population-based study has yet described the interrelationship and variation of these features by time and personal characteristics. Methods: 2,043 United Kingdom adults (35–65 years) wore an accelerometer on the non-dominant wrist and a chest-mounted combined heart-rate-and-movement sensor for 7 days free-living. From raw (60 Hz) wrist acceleration, we derived movement (non-gravity acceleration) and pitch and roll (forearm) angles relative to gravity. We inferred physical activity energy expenditure (PAEE) from combined sensing and sedentary time from approximate horizontal arm angle coupled with low movement. Results: Movement differences by time-of-day and day-of-week were associated with forearm angles; more movement in downward forearm positions. Mean (SD) movement was similar between sexes ∼31 (42) mg, despite higher PAEE in men. Women spent longer with the forearm pitched >0°, above horizontal (53% vs 36%), and less time at <0° (37% vs 53%). Diurnal pitch was 2.5–5° above and 0–7.5°below horizontal during night and daytime, respectively; corresponding roll angles were ∼0° (hand flat) and ∼20° (thumb-up). Differences were more pronounced in younger participants. All diurnal profiles indicated later wake-times on weekends. Daytime pitch was closer to horizontal on weekdays; roll was similar. Sedentary time was higher (17 vs 15 hours/day) in obese vs normal-weight individuals. Conclusions: More movement occurred in forearm positions below horizontal, commensurate with activities including walking. Findings suggest time-specific population differences in behaviors by age, sex, and BMI.
Validation of an Internet-Based Long Version of the International Physical Activity Questionnaire in Danish Adults Using Combined Accelerometry and Heart Rate Monitoring
Andreas Wolff Hansen, Inger Dahl-Petersen, Jørn Wulff Helge, Søren Brage, Morten Grønbæk, and Trine Flensborg-Madsen
The International Physical Activity Questionnaire (IPAQ) is commonly used in surveys, but reliability and validity has not been established in the Danish population.
Among participants in the Danish Health Examination survey 2007–2008, 142 healthy participants (45% men) wore a unit that combined accelerometry and heart rate monitoring (Acc+HR) for 7 consecutive days and then completed the IPAQ. Background data were obtained from the survey. Physical activity energy expenditure (PAEE) and time in moderate, vigorous, and sedentary intensity levels were derived from the IPAQ and compared with estimates from Acc+HR using Spearman’s correlation coefficients and Bland-Altman plots. Repeatability of the IPAQ was also assessed.
PAEE from the 2 methods was significantly positively correlated (0.29 and 0.49; P = 0.02 and P < 0.001; for women and men, respectively). Men significantly overestimated PAEE by IPAQ (56.2 vs 45.3 kJ/kg/day, IPAQ: Acc+HR, P < .01), while the difference was nonsignificant for women (40.8 vs 44.4 kJ/kg/day). Bland-Altman plots showed that the IPAQ overestimated PAEE, moderate, and vigorous activity without systematic error. Reliability of the IPAQ was moderate to high for all domains and intensities (total PAEE intraclass correlation coefficient = 0.58).
This Danish Internet-based version of the long IPAQ had modest validity and reliability when assessing PAEE at population level.
Objective and Self-Reported Physical Activity and Risk of Falling Among Community-Dwelling Older Adults From Southern Brazil
Renata M. Bielemann, Ricardo Oliveira, Andréa Dâmaso Bertoldi, Elaine Tomasi, Flávio Fernando Demarco, Maria Cristina Gonzalez, Andrea Wendt Bohm, Soren Brage, and Ulf Ekelund
This study evaluated prospective associations between self-reported and objectively measured physical activity (PA) and risk of falls among older adults. A cohort study started in 2014 with 1,451 community-dwelling older adults living in Pelotas, Brazil. Leisure-time PA was obtained by the International Physical Activity Questionnaire and 7-day raw accelerometer data evaluated for total, light PA, and moderate to vigorous PA. In 2016–2017, participants recorded their falls in the previous 12 months. Around 23% of the 1,161 participants followed-up in 2016–2017 experienced a fall in the last 12 months. Participants who did not spend any time in self-reported leisure-time PA at baseline had on average 34% higher risk of falls, and individuals in the lowest tertile for moderate to vigorous PA had on average 51% higher risk of falls compared to those in the highest tertile. Low levels of self-reported and objectively measured moderate to vigorous PA were related to higher risk of falling among Brazilian older adults.
Network Harmonization of Physical Activity Variables Through Indirect Validation
Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham, and Søren Brage
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.
Objectively Measured Physical Activity and Polypharmacy Among Brazilian Community-Dwelling Older Adults
Renata M. Bielemann, Marysabel P.T. Silveira, Bárbara H. Lutz, Vanessa I.A. Miranda, Maria Cristina Gonzalez, Soren Brage, Ulf Ekelund, and Andréa Dâmaso Bertoldi
Background: Previous observations regarding association between physical activity (PA) and use of medicines among older adults are derived from self-reported PA. This study aimed to evaluate the association between objectively measured PA and polypharmacy among older adults with multimorbidity in Southern Brazil. Methods: This study included 875 noninstitutionalized older people, aged ≥60 years. Prescribed medicines used in the 15 days prior to the interview, socioeconomic data, and the presence of comorbidities were self-reported. Accelerometers were used to evaluate PA following the interview. Results: Prevalence of polypharmacy (≥5 medicines) was 38.3% (95% confidence interval, 35.0–41.5); those belonging to the lowest tertile of PA used more medicines. The authors observed a significant inverse association for polypharmacy between men belonging to the second and third tertiles of PA for objectively measured overall PA and light PA compared with the most inactive tertile. For women, the association between PA and polypharmacy was significant for overall, light, and moderate to vigorous PA only in the third tertile. Conclusions: Overall, light and moderate to vigorous PA were inversely associated to polypharmacy and differed by gender. Promotion of PA in older adults may be an effective intervention to reduce the number of medicines used independent of the number of comorbidities.
Physical Behaviors and Their Association With Adiposity in Men and Women From a Low-Resourced African Setting
Amy E. Mendham, Julia H. Goedecke, Nyuyki Clement Kufe, Melikhaya Soboyisi, Antonia Smith, Kate Westgate, Soren Brage, and Lisa K. Micklesfield
Background : We first explored the associations between physical behaviors and total and regional adiposity. Second, we examined how reallocating time in different physical behaviors was associated with total body fat mass in men and women from a low-income South African setting. Methods : This cross-sectional study included a sample of 692 participants (384 men and 308 women) aged 41–72 years. Physical behaviors were measured using integrated hip and thigh accelerometry to estimate total movement volume and time spent in sleeping, sitting/lying, standing, light physical activity, and moderate to vigorous physical activity (MVPA). Total body fat mass and regional adiposity were measured using dual-energy X-ray absorptiometry. Results : The associations between total movement volume and measures of regional obesity were mediated by total body adiposity. In men, reallocating 30 minutes of sitting/lying to 30 minutes of MVPA was associated with 1.0% lower fat mass. In women, reallocation of 30 minutes of sitting/lying to MVPA and 30 minutes of standing to MVPA were associated with a 0.3% and 1.4% lower fat mass, respectively. Conclusions : Although the association between physical behaviors and fat mass differed between men and women, the overall public health message is similar; reallocating sedentary time to MVPA is associated with a reduction in fat mass in both men and women.
Activity Behaviors in British 6-Year-Olds: Cross-Sectional Associations and Longitudinal Change During the School Transition
Kathryn R. Hesketh, Soren Brage, Hazel M. Inskip, Sarah R. Crozier, Keith M. Godfrey, Nicholas C. Harvey, Cyrus Cooper, and Esther M.F. Van Sluijs
Background: To explore activity behaviors at school entry, we describe temporal/demographic associations with accelerometer-measured physical activity in a population-based sample of British 6-year-olds, and examine change from ages 4 to 6. Methods: A total of 712 six-year-olds (308 at both ages) wore Actiheart accelerometers for ≥3 (mean 6.0) days. We derived minutes per day sedentary (<20 cpm) and moderate to vigorous physical activity (MVPA, ≥460 cpm), also segmented across mornings (06:00 AM to 09:00 AM), school (09:00 AM to 3:00 PM), and evenings (3:00 PM to 11:00 PM). Using mixed effects linear regression, we analyzed associations between temporal/demographic factors and children’s activity intensities at age 6, and change between ages 4 and 6. Results: Six-year-old children engaged in MVPA (mean [SD]): 64.9 (25.7) minutes per day (53% met UK guidelines). Girls did less MVPA than boys, particularly during school hours. Children were less active on weekends (vs weekdays) and more active on spring/summer evenings (vs winter). Longitudinally, 6-year-old children did less light physical activity (−43.0; 95% confidence interval, −47.5 to −38.4 min/d) but were more sedentary (29.4; 24.6 to 34.2), and engaged in greater MVPA (7.1; 5.2 to 9.1) compared to when they were aged 4. Conclusion: Half of 6-year-old children met current activity guidelines; MVPA levels were lower in girls and at weekends. UK children became more sedentary but did more MVPA as they entered formal schooling. Physical activity promotion efforts should capitalize on these changes in MVPA, to maintain positive habits.
Validation of activPAL Defined Sedentary Time and Breaks in Sedentary Time in 4- to 6-Year-Olds
Xanne Janssen, Dylan P. Cliff, John J. Reilly, Trina Hinkley, Rachel A. Jones, Marijka Batterham, Ulf Ekelund, Soren Brage, and Anthony D. Okely
This study examined the classification accuracy of the activPAL, including total time spent sedentary and total number of breaks in sedentary behavior (SB) in 4- to 6-year-old children. Forty children aged 4–6 years (5.3 ± 1.0 years) completed a ~150-min laboratory protocol involving sedentary, light, and moderate- to vigorous-intensity activities. Posture was coded as sit/lie, stand, walk, or other using direct observation. Posture was classified using the activPAL software. Classification accuracy was evaluated using sensitivity, specificity and area under the receiver operating characteristic curve (ROC-AUC). Time spent in each posture and total number of breaks in SB were compared using paired sample t-tests. The activPAL showed good classification accuracy for sitting (ROC-AUC = 0.84) and fair classification accuracy for standing and walking (0.76 and 0.73, respectively). Time spent in sit/lie and stand was overestimated by 5.9% (95% CI = 0.6−11.1%) and 14.8% (11.6−17.9%), respectively; walking was underestimated by 10.0% (−12.9−7.0%). Total number of breaks in SB were significantly overestimated (55 ± 27 over the course of the protocol; p < .01). The activPAL performed well when classifying postures in young children. However, the activPAL has difficulty classifying other postures, such as kneeling. In addition, when predicting time spent in different postures and total number of breaks in SB the activPAL appeared not to be accurate.