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Review
Accelerometer-based measures in physical activity surveillance: current practices and issues Željko Pedišić,1,2 Adrian Bauman1 1
Prevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Sydney, Australia 2 Faculty of Kinesiology, University of Zagreb, Zagreb, Croatia Correspondence to Professor Adrian Bauman, Prevention Research Collaboration, The Charles Perkins Centre, Level 6, The Hub, The University of Sydney, Sydney, NSW 2006, Australia; [email protected] Accepted 16 October 2014 Published Online First 4 November 2014
ABSTRACT Objective Self-reports of physical activity (PA) have been the mainstay of measurement in most noncommunicable disease (NCD) surveillance systems. To these, other measures are added to summate to a comprehensive PA surveillance system. Recently, some national NCD surveillance systems have started using accelerometers as a measure of PA. The purpose of this paper was specifically to appraise the suitability and role of accelerometers for population-level PA surveillance. Methods A thorough literature search was conducted to examine aspects of the generalisability, reliability, validity, comprehensiveness and between-study comparability of accelerometer estimates, and to gauge the simplicity, cost-effectiveness, adaptability and sustainability of their use in NCD surveillance. Conclusions Accelerometer data collected in PA surveillance systems may not provide estimates that are generalisable to the target population. Accelerometerbased estimates have adequate reliability for PA surveillance, but there are still several issues associated with their validity. Accelerometer-based prevalence estimates are largely dependent on the investigators’ choice of intensity cut-off points. Maintaining standardised accelerometer data collections in long-term PA surveillance systems is difficult, which may cause discontinuity in time-trend data. The use of accelerometers does not necessarily produce useful between-study and international comparisons due to lack of standardisation of data collection and processing methods. To conclude, it appears that accelerometers still have limitations regarding generalisability, validity, comprehensiveness, simplicity, affordability, adaptability, between-study comparability and sustainability. Therefore, given the current evidence, it seems that the widespread adoption of accelerometers specifically for large-scale PA surveillance systems may be premature.
INTRODUCTION
To cite: Pedišić Ž, Bauman A. Br J Sports Med 2015;49:219–223.
Non-communicable disease (NCD) surveillance includes the assessment and monitoring of chronic disease risk through regular populationrepresentative surveys of key behavioural and physiological antecedent factors and their determinants. Given that reducing physical inactivity is as important to global health as tobacco or obesity control,1 an international focus on NCD surveillance is provided through the WHO Global Monitoring Framework. This framework now incorporates a target to decrease physical inactivity by 10% in all participating countries by the year 2025.2 Increased efforts are being made to track the prevalence of physical activity (PA) internationally. For example, 87 countries have implemented the WHO STEPwise Approach to Surveillance measures
and protocols using the Global Physical Activity Questionnaire.3 Furthermore, a recent review has shown that 3 international and more than 30 national surveillance systems collect PA data in European Union member states, mostly utilising a variety of short questionnaires.4 In the UK, PA is assessed using at least 10 national and country-level surveillance systems.5–10 While information on some other health indicators, such as body mass index and tobacco use, is ascertained using reasonably standardised and comparable measures, the situation with PA is variable.4 11 Although they have recognised limitations in small-scale studies, self-report measures have been the method of choice in most population-based studies12 and, in many countries, time trends for PA and sedentary behaviour are reliant on decades of questionnaire-based assessment.4 Recently, technological improvements have led to enthusiasm for accelerometer use in several national NCD surveillance systems.4 5 13 14 However, there is a lack of studies specifically examining how well accelerometers conform to the principles of PA surveillance. Therefore, the purpose of this paper was to appraise the suitability of accelerometers for use in population-level PA surveillance. We did not aim to review issues associated with self-report measures, as it has been done elsewhere,12 15–18 but we focus here on the specific challenges of using accelerometers in PA surveillance systems. An appraisal of new technologies and methods in population surveillance systems is particularly important, given the difficulties in developing comprehensive and sustained monitoring systems in many countries. A gap in the accelerometer field has been a high level of focus on technical issues, without reflection on their usability as part of an integrated PA surveillance system. Specifically, we consider the generalisability, reliability, validity, comprehensiveness and between-study comparability of accelerometer estimates, and also their simplicity, cost-effectiveness, adaptability and potential sustainability in population PA surveillance.
SUITABILITY OF ACCELEROMETRY FOR PA SURVEILLANCE Generalisability Population PA measures need to be acceptable to most participants to ensure high adherence and provide generalisable data. In order to capture the between-day variability in PA and sedentary behaviour, participants are usually asked to wear accelerometers during waking hours for seven consecutive days. There is still an ongoing debate on how many days of monitoring are needed for reliable estimates of habitual PA,19–24 but valid data are usually defined as 10 or more hours of wear time on at
Pedišić Ž, et al. Br J Sports Med 2015;49:219–223. doi:10.1136/bjsports-2013-093407
1 of 8
Downloaded from http://bjsm.bmj.com/ on August 13, 2017 - Published by group.bmj.com
Review least 4 days.14 25–28 In large-scale population-based studies, between 6% and 32% participants (median 17.6%) do not meet this criterion, and are excluded from subsequent analyses.14 26–29 National Health and Nutrition Examination Survey (NHANES) 2003–2006 and Health Survey for England 2008 have shown that participants with valid and invalid accelerometer data significantly differ in a range of sociodemographic, lifestyle and health characteristics.29–31 This means that findings based on accelerometer data may not be generalisable to the target population, but only to those who adhere to the rigorous measurement requirements. Reweighting of the sample, such as was performed by Troiano et al,28 may help to reduce selection bias. Such reweighting may not necessarily eliminate the selection bias, because it is restricted only to standard auxiliary variables (ie, those whose population distributions are available, such as gender, age and race/ethnicity). Nonetheless, we recognise that missing data remains a substantial problem for self-report measures as well. For comparison, in a large-scale study among adults from 20 countries using the International Physical Activity Questionnaire—short form, between 0% and 7.4% (median 1.6%) of participants had missing PA data.32 Furthermore, in the National Health Interview Survey 2005, NHANES 2005–2006, and Behavioral Risk Factor Surveillance System 2005 self-reported PA data were missing in around 2.9%, 0.1% and 7.1% of participants, respectively.33 Hence, the percentages of participants with incomplete data may be lower in questionnaire-based than in accelerometer-based studies on national-representative samples.
Reliability Measures used for PA surveillance need to be sufficiently reliable to provide credible estimates at the population level, but not necessarily for individuals. Accelerometer reliability is subject to technical (eg, inconsistencies in capturing signals and processing data) and human-related (eg, accidental altering of the position of device) sources of random error. Accelerometers demonstrated high intrainstrument and interinstrument reliability in mechanical laboratory settings, with coefficients of variation (CV) mostly below 5% and 10%, respectively,34–43 and when assessing structured activities in controlled laboratory conditions, with most intraclass correlation coefficients (ICCs) ranging from 0.60 to 0.90.36 42 44–48 Interinstrument reliability of accelerometers in free-living conditions ranged from mediocre for RT3 accelerometers (CV 9.8–39.8%)49 to relatively high for Actigraph 7164 and GT1M models (CV 0.9–15.5%).49 50 A test–retest reliability study has shown high agreement between accelerometer data collected during two 7-day periods (1–4 weeks apart) in free-living conditions (ICCs 0.77–0.90).51 This evidence suggests that the reliability of accelerometer-based estimates of PA and sedentary behaviour may be sufficient for public health surveillance. In comparison, a recent comprehensive review of 89 PA and sedentary behaviour questionnaires showed that most of their test– retest reliability ICCs were slightly lower than for accelerometers, ranging from 0.59 to 0.84 (median 0.73).52
Validity PA surveillance measures need to provide valid estimates at the population level. Two recent reviews have shown moderate criterion validity of accelerometers.53 54 The pooled correlations with doubly labelled water were 0.39 (activity energy expenditure, AEE) and 0.52 (total energy expenditure, TEE) for uniaxial devices, and 0.59 (AEE) and 0.61 (TEE) for triaxial devices.54 On average, uniaxial accelerometers underestimated AEE by 24% and TEE by 12%, while triaxial devices 2 of 8
underestimated AEE by 21% and TEE by 7%.54 Validation studies using activPAL inclinometers as the criterion measure have shown that hip-mounted accelerometers provide reasonably accurate group estimates, but not individual estimates of sedentary behaviour.55–60 By comparison, in previous validation studies identified by six systematic reviews,52 61–65 most correlations between PA questionnaires and doubly labelled water estimates of AEE and TEE ranged from 0.21 to 0.45 (median 0.35)66–72 and 0.23 to 0.58 (median 0.37),66 70 71 73–80 respectively. AEE was underestimated by most questionnaires, while no clear pattern could be observed for TEE. Average absolute (non-negative) values of the differences between questionnaires and doubly labelled water estimates of AEE and TEE were 32%66–71 74 77 81 and 23%,66 70 74–80 82–86 respectively. The validity of accelerometer-based estimates of PA and sedentary behaviour is potentially compromised by four categories of concerns: (1) technical shortcomings, (2) significant amounts of non-wearing time, (3) possible participant’s interference with the results and (4) use of intensity cut-off points.
Technical shortcomings The wrist-worn or hip-worn accelerometers do not capture common activities such as cycling, resistance and static exercise, and carrying loads. Additionally, non-waterproof accelerometers cannot be used to assess aquatic activities. For example, accelerometer activity counts during cycling are underestimated by approximately 73%.87 This may limit assessment of ‘active travel’ in countries where cycling is prevalent, such as China, Denmark and the Netherlands.88 89 Furthermore, different accelerometer models underestimate energy expenditure of a range of other daily living and leisure-time activities, with the highest bias determined for ascending stairs and playing tennis.90
Non-wearing time Across different studies and age groups, between 27% and 74% of participants who satisfied the inclusion criteria (eg, 4 days×10 h), did not have valid accelerometer data for all days of measurement14 25 26 28 91 92 and their average wearing time per valid day was between 13 and 15 h.14 25–27 28 93 This shows that some awake time is not monitored. Unfortunately, suggested methods of missing data imputation94 95 are not without shortcomings and were often not used in large-scale accelerometer-based studies.14 25–28 93 96 Nonetheless, there are encouraging indications that the compliance is higher for wristworn when compared with hip-mounted accelerometers.97
Non-objectivity Accelerometers are considered objective devices as their assessment of acceleration is independent of human-related factors. However, participants can influence accelerometer data collection in free-living conditions by intentional non-wearing, altering their habitual behaviour, and changing the position or shaking the device. Children as well as adults occasionally report aesthetic issues and physical discomfort as reasons for the occasional non-wearing of accelerometers.98 99 Besides, more than 40% of adolescents found it disturbing to wear the accelerometer during physical activities and were worried about losing or breaking the device.100 Furthermore, awareness that PA is being monitored might influence habitual behaviour. Although the Hawthorne effect has been recognised as a potential limitation of accelerometry,15 101–103 empirical evidence on its magnitude is equivocal and remains scarce.104 105 Furthermore, Kowalski et al,106 Kowalski et al107 and Pate et al108 detected Pedišić Ž, et al. Br J Sports Med 2015;49:219–223. doi:10.1136/bjsports-2013-093407
Downloaded from http://bjsm.bmj.com/ on August 13, 2017 - Published by group.bmj.com
Review tampering with accelerometers in 8%, 33% and 1%, respectively, of schoolchildren/adolescents. The latter study used CSA 7164 (no display), while the former two used older Caltrac models (with display, but taped in a holster to prevent tampering). Tampering with devices is less likely among adults, but evidence in this area is still limited. Furthermore, one of the main arguments for utilisation of objective measures in PA surveillance is to avoid the social desirability response bias associated with self-reports.109–112 Unlike some objective measures of PA, it seems that accelerometry may occasionally be related to measures of social desirability.69 113 Adams et al69 reported that a social desirability score was not related to PA estimates from doubly labelled water (r=−0.02, p=0.860), while it was significantly related to accelerometer counts (r=−0.29, p=0.009). Interestingly, in the same study, none of the three tested self-report measures exhibited significant bivariate correlation with the social desirability scale.69 Another study has shown a negative relationship between social desirability in adolescence and accelerometer-assessed sedentary time in adulthood.113 Such social desirability bias is not a widespread problem with accelerometers, but from an ‘independent scientific perspective’, the questioning and challenging of all dimensions of and potential biases in measurement is worthy of consideration.
Intensity cut-off points The use of ‘cut-off points’ has been the most common method for defining the intensity of PA in large-scale accelerometerbased studies.14 25–28 93 96 114 Experts in the field recommend not using cut-off points,115 116 and suggest that other solutions such as pattern recognition may provide better estimates of moderate to vigorous physical activity (MVPA). In most calibration studies, cut-off points were developed by analysing the relationship between accelerometer counts and objectively measured energy expenditure during a set of activities using regression analyses or receiver operating characteristic curves.117 118 Possible issues that can affect the validity of PA and sedentary behaviour estimates when such cut-off points are applied in public health surveillance systems are: (1) non-representativeness of the calibration study sample, (2) non-representativeness of the set of activities used in the calibration study, (3) difference between the accelerometer models used for surveillance and in the calibration study, (4) non-representativeness of the sample of accelerometers used in the calibration study and (5) variability of true individual intensity thresholds around the universal group-based cut-off points (ie, SE of regression or false-positive and false-negative rates in the calibration study). Furthermore, for some accelerometer models, different sets of cut-off points have been developed and recommended.119–122 Across 15 calibration studies of Actigraph accelerometers, the lower threshold for MVPA for adults ranged from 191 to 3285 counts per minute (cpm).121 122 Application of different cut-off points can result in significantly different prevalence estimates and intensity-specific PA levels.111 122–127 The accelerometry-defined prevalence of ‘insufficient PA’ among US adults, US youth and European youth ranged from 2.4% to 95.3%,121 40.7% to 93.8%121 and 0% to 97%,114 respectively, depending on which cut-off points were applied. It has been shown that the choice of cut-off points can also influence the association between estimated PA and various health outcomes.121 128 Despite this, the use of cut-off points remains the method of choice for the estimation of intensity-specific PA levels. By contrast, it seems that large-scale studies were more consistent in the choice of cut-off points for sedentary behaviour, with
Review
Accelerometer-based measures in physical activity surveillance: current practices and issues Željko Pedišić,1,2 Adrian Bauman1 1
Prevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Sydney, Australia 2 Faculty of Kinesiology, University of Zagreb, Zagreb, Croatia Correspondence to Professor Adrian Bauman, Prevention Research Collaboration, The Charles Perkins Centre, Level 6, The Hub, The University of Sydney, Sydney, NSW 2006, Australia; [email protected] Accepted 16 October 2014 Published Online First 4 November 2014
ABSTRACT Objective Self-reports of physical activity (PA) have been the mainstay of measurement in most noncommunicable disease (NCD) surveillance systems. To these, other measures are added to summate to a comprehensive PA surveillance system. Recently, some national NCD surveillance systems have started using accelerometers as a measure of PA. The purpose of this paper was specifically to appraise the suitability and role of accelerometers for population-level PA surveillance. Methods A thorough literature search was conducted to examine aspects of the generalisability, reliability, validity, comprehensiveness and between-study comparability of accelerometer estimates, and to gauge the simplicity, cost-effectiveness, adaptability and sustainability of their use in NCD surveillance. Conclusions Accelerometer data collected in PA surveillance systems may not provide estimates that are generalisable to the target population. Accelerometerbased estimates have adequate reliability for PA surveillance, but there are still several issues associated with their validity. Accelerometer-based prevalence estimates are largely dependent on the investigators’ choice of intensity cut-off points. Maintaining standardised accelerometer data collections in long-term PA surveillance systems is difficult, which may cause discontinuity in time-trend data. The use of accelerometers does not necessarily produce useful between-study and international comparisons due to lack of standardisation of data collection and processing methods. To conclude, it appears that accelerometers still have limitations regarding generalisability, validity, comprehensiveness, simplicity, affordability, adaptability, between-study comparability and sustainability. Therefore, given the current evidence, it seems that the widespread adoption of accelerometers specifically for large-scale PA surveillance systems may be premature.
INTRODUCTION
To cite: Pedišić Ž, Bauman A. Br J Sports Med 2015;49:219–223.
Non-communicable disease (NCD) surveillance includes the assessment and monitoring of chronic disease risk through regular populationrepresentative surveys of key behavioural and physiological antecedent factors and their determinants. Given that reducing physical inactivity is as important to global health as tobacco or obesity control,1 an international focus on NCD surveillance is provided through the WHO Global Monitoring Framework. This framework now incorporates a target to decrease physical inactivity by 10% in all participating countries by the year 2025.2 Increased efforts are being made to track the prevalence of physical activity (PA) internationally. For example, 87 countries have implemented the WHO STEPwise Approach to Surveillance measures
and protocols using the Global Physical Activity Questionnaire.3 Furthermore, a recent review has shown that 3 international and more than 30 national surveillance systems collect PA data in European Union member states, mostly utilising a variety of short questionnaires.4 In the UK, PA is assessed using at least 10 national and country-level surveillance systems.5–10 While information on some other health indicators, such as body mass index and tobacco use, is ascertained using reasonably standardised and comparable measures, the situation with PA is variable.4 11 Although they have recognised limitations in small-scale studies, self-report measures have been the method of choice in most population-based studies12 and, in many countries, time trends for PA and sedentary behaviour are reliant on decades of questionnaire-based assessment.4 Recently, technological improvements have led to enthusiasm for accelerometer use in several national NCD surveillance systems.4 5 13 14 However, there is a lack of studies specifically examining how well accelerometers conform to the principles of PA surveillance. Therefore, the purpose of this paper was to appraise the suitability of accelerometers for use in population-level PA surveillance. We did not aim to review issues associated with self-report measures, as it has been done elsewhere,12 15–18 but we focus here on the specific challenges of using accelerometers in PA surveillance systems. An appraisal of new technologies and methods in population surveillance systems is particularly important, given the difficulties in developing comprehensive and sustained monitoring systems in many countries. A gap in the accelerometer field has been a high level of focus on technical issues, without reflection on their usability as part of an integrated PA surveillance system. Specifically, we consider the generalisability, reliability, validity, comprehensiveness and between-study comparability of accelerometer estimates, and also their simplicity, cost-effectiveness, adaptability and potential sustainability in population PA surveillance.
SUITABILITY OF ACCELEROMETRY FOR PA SURVEILLANCE Generalisability Population PA measures need to be acceptable to most participants to ensure high adherence and provide generalisable data. In order to capture the between-day variability in PA and sedentary behaviour, participants are usually asked to wear accelerometers during waking hours for seven consecutive days. There is still an ongoing debate on how many days of monitoring are needed for reliable estimates of habitual PA,19–24 but valid data are usually defined as 10 or more hours of wear time on at
Pedišić Ž, et al. Br J Sports Med 2015;49:219–223. doi:10.1136/bjsports-2013-093407
1 of 8
Downloaded from http://bjsm.bmj.com/ on August 13, 2017 - Published by group.bmj.com
Review least 4 days.14 25–28 In large-scale population-based studies, between 6% and 32% participants (median 17.6%) do not meet this criterion, and are excluded from subsequent analyses.14 26–29 National Health and Nutrition Examination Survey (NHANES) 2003–2006 and Health Survey for England 2008 have shown that participants with valid and invalid accelerometer data significantly differ in a range of sociodemographic, lifestyle and health characteristics.29–31 This means that findings based on accelerometer data may not be generalisable to the target population, but only to those who adhere to the rigorous measurement requirements. Reweighting of the sample, such as was performed by Troiano et al,28 may help to reduce selection bias. Such reweighting may not necessarily eliminate the selection bias, because it is restricted only to standard auxiliary variables (ie, those whose population distributions are available, such as gender, age and race/ethnicity). Nonetheless, we recognise that missing data remains a substantial problem for self-report measures as well. For comparison, in a large-scale study among adults from 20 countries using the International Physical Activity Questionnaire—short form, between 0% and 7.4% (median 1.6%) of participants had missing PA data.32 Furthermore, in the National Health Interview Survey 2005, NHANES 2005–2006, and Behavioral Risk Factor Surveillance System 2005 self-reported PA data were missing in around 2.9%, 0.1% and 7.1% of participants, respectively.33 Hence, the percentages of participants with incomplete data may be lower in questionnaire-based than in accelerometer-based studies on national-representative samples.
Reliability Measures used for PA surveillance need to be sufficiently reliable to provide credible estimates at the population level, but not necessarily for individuals. Accelerometer reliability is subject to technical (eg, inconsistencies in capturing signals and processing data) and human-related (eg, accidental altering of the position of device) sources of random error. Accelerometers demonstrated high intrainstrument and interinstrument reliability in mechanical laboratory settings, with coefficients of variation (CV) mostly below 5% and 10%, respectively,34–43 and when assessing structured activities in controlled laboratory conditions, with most intraclass correlation coefficients (ICCs) ranging from 0.60 to 0.90.36 42 44–48 Interinstrument reliability of accelerometers in free-living conditions ranged from mediocre for RT3 accelerometers (CV 9.8–39.8%)49 to relatively high for Actigraph 7164 and GT1M models (CV 0.9–15.5%).49 50 A test–retest reliability study has shown high agreement between accelerometer data collected during two 7-day periods (1–4 weeks apart) in free-living conditions (ICCs 0.77–0.90).51 This evidence suggests that the reliability of accelerometer-based estimates of PA and sedentary behaviour may be sufficient for public health surveillance. In comparison, a recent comprehensive review of 89 PA and sedentary behaviour questionnaires showed that most of their test– retest reliability ICCs were slightly lower than for accelerometers, ranging from 0.59 to 0.84 (median 0.73).52
Validity PA surveillance measures need to provide valid estimates at the population level. Two recent reviews have shown moderate criterion validity of accelerometers.53 54 The pooled correlations with doubly labelled water were 0.39 (activity energy expenditure, AEE) and 0.52 (total energy expenditure, TEE) for uniaxial devices, and 0.59 (AEE) and 0.61 (TEE) for triaxial devices.54 On average, uniaxial accelerometers underestimated AEE by 24% and TEE by 12%, while triaxial devices 2 of 8
underestimated AEE by 21% and TEE by 7%.54 Validation studies using activPAL inclinometers as the criterion measure have shown that hip-mounted accelerometers provide reasonably accurate group estimates, but not individual estimates of sedentary behaviour.55–60 By comparison, in previous validation studies identified by six systematic reviews,52 61–65 most correlations between PA questionnaires and doubly labelled water estimates of AEE and TEE ranged from 0.21 to 0.45 (median 0.35)66–72 and 0.23 to 0.58 (median 0.37),66 70 71 73–80 respectively. AEE was underestimated by most questionnaires, while no clear pattern could be observed for TEE. Average absolute (non-negative) values of the differences between questionnaires and doubly labelled water estimates of AEE and TEE were 32%66–71 74 77 81 and 23%,66 70 74–80 82–86 respectively. The validity of accelerometer-based estimates of PA and sedentary behaviour is potentially compromised by four categories of concerns: (1) technical shortcomings, (2) significant amounts of non-wearing time, (3) possible participant’s interference with the results and (4) use of intensity cut-off points.
Technical shortcomings The wrist-worn or hip-worn accelerometers do not capture common activities such as cycling, resistance and static exercise, and carrying loads. Additionally, non-waterproof accelerometers cannot be used to assess aquatic activities. For example, accelerometer activity counts during cycling are underestimated by approximately 73%.87 This may limit assessment of ‘active travel’ in countries where cycling is prevalent, such as China, Denmark and the Netherlands.88 89 Furthermore, different accelerometer models underestimate energy expenditure of a range of other daily living and leisure-time activities, with the highest bias determined for ascending stairs and playing tennis.90
Non-wearing time Across different studies and age groups, between 27% and 74% of participants who satisfied the inclusion criteria (eg, 4 days×10 h), did not have valid accelerometer data for all days of measurement14 25 26 28 91 92 and their average wearing time per valid day was between 13 and 15 h.14 25–27 28 93 This shows that some awake time is not monitored. Unfortunately, suggested methods of missing data imputation94 95 are not without shortcomings and were often not used in large-scale accelerometer-based studies.14 25–28 93 96 Nonetheless, there are encouraging indications that the compliance is higher for wristworn when compared with hip-mounted accelerometers.97
Non-objectivity Accelerometers are considered objective devices as their assessment of acceleration is independent of human-related factors. However, participants can influence accelerometer data collection in free-living conditions by intentional non-wearing, altering their habitual behaviour, and changing the position or shaking the device. Children as well as adults occasionally report aesthetic issues and physical discomfort as reasons for the occasional non-wearing of accelerometers.98 99 Besides, more than 40% of adolescents found it disturbing to wear the accelerometer during physical activities and were worried about losing or breaking the device.100 Furthermore, awareness that PA is being monitored might influence habitual behaviour. Although the Hawthorne effect has been recognised as a potential limitation of accelerometry,15 101–103 empirical evidence on its magnitude is equivocal and remains scarce.104 105 Furthermore, Kowalski et al,106 Kowalski et al107 and Pate et al108 detected Pedišić Ž, et al. Br J Sports Med 2015;49:219–223. doi:10.1136/bjsports-2013-093407
Downloaded from http://bjsm.bmj.com/ on August 13, 2017 - Published by group.bmj.com
Review tampering with accelerometers in 8%, 33% and 1%, respectively, of schoolchildren/adolescents. The latter study used CSA 7164 (no display), while the former two used older Caltrac models (with display, but taped in a holster to prevent tampering). Tampering with devices is less likely among adults, but evidence in this area is still limited. Furthermore, one of the main arguments for utilisation of objective measures in PA surveillance is to avoid the social desirability response bias associated with self-reports.109–112 Unlike some objective measures of PA, it seems that accelerometry may occasionally be related to measures of social desirability.69 113 Adams et al69 reported that a social desirability score was not related to PA estimates from doubly labelled water (r=−0.02, p=0.860), while it was significantly related to accelerometer counts (r=−0.29, p=0.009). Interestingly, in the same study, none of the three tested self-report measures exhibited significant bivariate correlation with the social desirability scale.69 Another study has shown a negative relationship between social desirability in adolescence and accelerometer-assessed sedentary time in adulthood.113 Such social desirability bias is not a widespread problem with accelerometers, but from an ‘independent scientific perspective’, the questioning and challenging of all dimensions of and potential biases in measurement is worthy of consideration.
Intensity cut-off points The use of ‘cut-off points’ has been the most common method for defining the intensity of PA in large-scale accelerometerbased studies.14 25–28 93 96 114 Experts in the field recommend not using cut-off points,115 116 and suggest that other solutions such as pattern recognition may provide better estimates of moderate to vigorous physical activity (MVPA). In most calibration studies, cut-off points were developed by analysing the relationship between accelerometer counts and objectively measured energy expenditure during a set of activities using regression analyses or receiver operating characteristic curves.117 118 Possible issues that can affect the validity of PA and sedentary behaviour estimates when such cut-off points are applied in public health surveillance systems are: (1) non-representativeness of the calibration study sample, (2) non-representativeness of the set of activities used in the calibration study, (3) difference between the accelerometer models used for surveillance and in the calibration study, (4) non-representativeness of the sample of accelerometers used in the calibration study and (5) variability of true individual intensity thresholds around the universal group-based cut-off points (ie, SE of regression or false-positive and false-negative rates in the calibration study). Furthermore, for some accelerometer models, different sets of cut-off points have been developed and recommended.119–122 Across 15 calibration studies of Actigraph accelerometers, the lower threshold for MVPA for adults ranged from 191 to 3285 counts per minute (cpm).121 122 Application of different cut-off points can result in significantly different prevalence estimates and intensity-specific PA levels.111 122–127 The accelerometry-defined prevalence of ‘insufficient PA’ among US adults, US youth and European youth ranged from 2.4% to 95.3%,121 40.7% to 93.8%121 and 0% to 97%,114 respectively, depending on which cut-off points were applied. It has been shown that the choice of cut-off points can also influence the association between estimated PA and various health outcomes.121 128 Despite this, the use of cut-off points remains the method of choice for the estimation of intensity-specific PA levels. By contrast, it seems that large-scale studies were more consistent in the choice of cut-off points for sedentary behaviour, with
