application of machine learning approaches for activity … nguyen_ahmadi... · 2020. 11. 11. ·...

Application of Machine Learning Approaches

for Activity Recognition and Energy Expenditure

Prediction in Free Living Children and

Adolescents

Matthew Ahmadi

BS, MS

Submitted in fulfillment of the requirements for the degree of

Doctor of Philosophy

School of Exercise and Nutrition Sciences

Faculty of Health

Queensland University of Technology

2020

i

Funding

I wish to thank Queensland University of Technology and Professor Stewart G. Trost for

providing me with a living allowance, tuition fee sponsorship, supervisor scholarship,

and top up scholarship.

Keywords

Machine learning, physical activity, cerebral palsy, personalised, accelerometer,

preschool, children, adolescents, energy expenditure, activity classification, physical

activity recognition, wearable sensors, feature fusion, free-living, real-world

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Abstract

Regular participation in physical activity provides numerous health benefits for

children and adolescents such as improved cardiovascular fitness, mental health,

cognitive functioning, musculoskeletal health, and motor development. To date, a range

of methods have been used to measure physical activity (PA) across all age groups.

They can be broadly categorized as self-report and objective measures. Large-scale

epidemiological studies and clinical trials have traditionally relied on participant self-

reports of PA. However, given the limitations of self-reports, such as recall and social

desirability bias, objective measures are being used with increasing regularity. Due to its

robustness and unobtrusive size, accelerometer-based motion sensors have become

the preferred method for assessing physical activity in field-based research.

Accelerometer output in gravitational acceleration is not a measure of physical activity

and therefore needs to be processed into quantifiable physical activity outcomes. The

processing of accelerometer data into physical activity outcomes can be categorized

into two approaches: 1) threshold-based or cut-point methods and 2) machine learning

or pattern recognition approaches. Although the application of cut points to

accelerometer data continues to be widely used in the research world, the existence of

multiple and often conflicting sets of cut-points, along with the significant

misclassification error associated with this approach has prompted calls to adopt more

sophisticated approaches to accelerometer data reduction such as machine learning

(ML) or pattern recognition approaches. Although ML approaches may be potentially

better than cut points, there are currently knowledge gaps that preclude them from

being widely implemented. These include the generalizability of laboratory trained

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models under free-living conditions, specifically among children with unique movement

patterns such as preschool-age children and children that have motor impairments such

as cerebral palsy (CP), and the performance of group-based models compared to

personalised models that can account for the heterogeneity in movement patterns

between children.

The thesis addressed these significant knowledge gaps in the field of physical

activity measurement in five studies. The studies were designed to provide an

understanding of the generalizability of ML models among children with unique

movement patterns under free-living conditions. The studies also determined if activity

classification models developed from laboratory activity trials perform well when

implemented in a real-world environment. In addition, the studies explored the feasibility

and accuracy of personalised activity recognition models.

Study 1 and 2 developed and tested ML physical activity classification models for

ambulatory children with CP. Study 1 was the first study to develop ML physical activity

classification models among children with CP using Binary Decision Tree, Random

Forest, and Support Vector Machine. The study demonstrated that ML physical activity

classification models trained on features in the accelerometer signal from the wrist, hip,

and combined hip and wrist provide acceptable classification accuracy for recognition of

a range of activities commonly performed by ambulatory children with CP. Overall

accuracy ranged from 76.0% for the wrist Binary Decision Tree model to 90.0% for the

combined hip and wrist model Support Vector Machine model. In study 2, group-based,

group-personalised, and fully-personalised activity classification models were examined

in ambulatory children with CP using single, two-, and three-placement accelerometer

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placements on the least-affected wrist, hip, and ankle. The study showed that under

laboratory condition average accuracy for each placement ranged from 89.0% to 94.0%.

In addition, the group-based and group-personalised models performed similarly with

the fully-personalised models obtaining the highest classification accuracy. When these

models were implemented in a simulated free-living trial generalizability was poor and

overall accuracy decreased to between 50.0% to 62.0%. Additionally, results indicated

group-personalised and fully-personalised models may provide better detection of

walking among children with severe motor impairments under non-laboratory conditions.

During laboratory activity trials and the simulated free-living trial no increase in accuracy

was obtained from two- and three-placement models compared to a single placement

ankle model.

Studies 3, 4, and 5 sought to understand the generalizability of laboratory-trained

and free-living trained activity classification and energy expenditure prediction models

under free-living conditions among preschool-age children. In study 3, when ML activity

classification models trained on laboratory activity trials were implemented in a free-

living environment overall accuracy was between 60% for the wrist to 70% for the hip.

This represented a decrease of 10 to 20 percentage points compared to cross-

validation performance under laboratory conditions. The models detected walking with

an accuracy of only 9%-15% in a real-world environment. The study demonstrated

models trained on laboratory data do not generalize to free-living conditions and

identified several other key methodological issues that contributed to the

underperformance. These included: the 15 second length of the activity prediction

window was too long to capture pulsatile activity behaviours of preschool-age children;

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the occurrence of mixed activity windows unique to free-living conditions; and the

models used features from only one accelerometer placement and did not utilize

temporal information from adjacent activity windows. In study 4, when ML activity

classification models were trained on free-living data overall accuracy for the best

performing hip and wrist model was 85.0% and 80.0%, respectively. Detection of

walking increased to between 68.0% to 82.0%. In addition to using free-living data, the

inclusion of temporal information from lag and lead windows improved classification

accuracy. Overall accuracy incrementally increased from 1 second to 10 second

windows, however there was only minimal improvement between 10 second and 15

second windows. Finally, the implementation of a combined hip and wrist model did not

provide better accuracy than a single hip placement model. In study 5, the application of

energy expenditure prediction models trained on free-living data did not provide better

accuracy than models trained on laboratory activity trial data when implemented under

free-living conditions. Models trained on laboratory activity trial data displayed a root

mean square error (RMSE) of 0.58 to 0.71 kcals/min for the wrist and hip placement,

whilst free-living models had an RMSE of 0.61 to 0.73 kcals/min. The results indicated

that the laboratory-trained models had similar accuracy performance under free-living

conditions as they did under laboratory conditions with no further improvements attained

when using free-living data.

Collectively, this body of research has made significant contributions to the

application of machine learning approaches for activity recognition and energy

expenditure prediction for children and adolescents with unique movement patterns in a

real-world environment. The findings from this thesis conclude activity classification

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models trained on laboratory-based data fail to generalize to a real-world environment

and models trained on free-living data have superior accuracy. Moreover, in contrast to

activity classification models, energy expenditure prediction models trained on

laboratory-based data generalize to real world environments with no further

improvements attained when trained on free-living data. Among ambulatory children

with CP, the implementation of fully-personalized models provided better detection of

physical activities compared to group-based models under laboratory conditions.

Further research among ambulatory children with CP is needed to confirm these

findings under real world conditions.

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Table of contents Funding ........................................................................................................................... i

Keywords ........................................................................................................................ i

Abstract .......................................................................................................................... ii

Table of contents......................................................................................................... vii

List of figures .............................................................................................................. xii

Study 1 (Chapter 4) ............................................................................................................... xii





List of tables ............................................................................................................... xiii

Study1 (Chapter 4) ............................................................................................................... xiii

Study 2 (Chapter 5) .............................................................................................................. xiv




List of thesis publications .......................................................................................... xv

Publications included in this thesis ........................................................................................ xv

Other publications during candidature ................................................................................... xv

Statement of original authorship ............................................................................. xvii

Chapter 1. Introduction ................................................................................................. 1

1.1 Research problem ............................................................................................................ 3

1.2 Overall study objectives .................................................................................................... 4

1.2.1 Research aims ........................................................................................................... 5

Chapter 2. Literature review ......................................................................................... 7

2.1 Physical activity and health in children and adolescents ................................................... 7

2.1.1 Physical activity guidelines for children and adolescents ............................................ 8

2.2 Prevalence of meeting physical activity guidelines in children and youth .........................17

2.3 Measurement of physical activity in children and adolescents .........................................25

2.3.1 Self-report physical activity measurement .................................................................25

2.3.2 Objective physical activity measurement ...................................................................32

2.4 Accelerometer data processing methods .........................................................................39

2.4.1 Cut-point approaches ................................................................................................39

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2.4.2 Machine learning approaches to accelerometer data processing in youth .................68

2.4.3 Summary of studies implementing machine learning approaches .............................94

2.5 Children and adolescents with cerebral palsy ..................................................................96

2.5.1 Habitual physical activity levels in children with cerebral palsy ..................................97

2.5.2 Measurement of habitual physical activity in children with cerebral palsy ..................98

Chapter 3. Research design and methods .............................................................. 103

3.1 Study 1 design and methodology ................................................................................... 104


3.3 Study 3 and 4 design and methodology ......................................................................... 107


3.5 Ethics ............................................................................................................................ 109

Chapter 4. Machine learning algorithms for activity recognition in ambulant

children and adolescents with Cerebral Palsy ....................................................... 111

4.1 Publication status .......................................................................................................... 111

4.2 Statement of contribution ............................................................................................... 111

4.3 Abstract ......................................................................................................................... 113

4.4 Background ................................................................................................................... 115

4.5 Methods ........................................................................................................................ 117

4.5.1 Participants ............................................................................................................. 117

4.5.2 Data collection ........................................................................................................ 118

4.5.3 Instrumentation ....................................................................................................... 119

4.5.4 Data pre-processing and feature extraction ............................................................. 119

4.5.5 Machine learning algorithms ................................................................................... 120

4.5.6 Model training and cross-validation ......................................................................... 122

4.5.7 Statistical analysis ................................................................................................... 123

4.6 Results .......................................................................................................................... 123

4.7 Discussion ..................................................................................................................... 125

4.7.1 Conclusion .............................................................................................................. 130

4.8 Abbreviations ................................................................................................................. 130

4.9 Declarations .................................................................................................................. 131

4.10 References .................................................................................................................. 133

4.11 Figures ........................................................................................................................ 138

4.12 Tables ......................................................................................................................... 140

4.13 Supplemental information ............................................................................................ 147

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Chapter 5. Machine learning to quantify physical activity in children with cerebral

palsy: comparison of group, group-personalised, and fully-personalised activity

classification models ................................................................................................ 150



5.3 Abstract ......................................................................................................................... 152

5.4 Introduction .................................................................................................................... 153

5.5 Materials and methods .................................................................................................. 156

5.5.1 Participants ............................................................................................................. 156

5.5.2 Individual activity trials ............................................................................................ 158


5.5.4 Machine learning activity classification models ........................................................ 159


5.5.6 Model training and cross-validation ......................................................................... 160

5.5.7 Simulated free-living evaluation ............................................................................... 161

5.5.8 Statistical evaluation ............................................................................................... 161

5.6 Results .......................................................................................................................... 162

5.6.1 Leave one out cross-validation ................................................................................ 162

5.6.2 Simulated free-living trial ......................................................................................... 167

5.7 Discussion ..................................................................................................................... 175

5.7.1 Conclusions ............................................................................................................ 180

5.9 References .................................................................................................................... 182

5.10 Supplemental information ............................................................................................ 186

Chapter 6. Free-living Evaluation of Laboratory-based Activity Classifiers in

Preschoolers ............................................................................................................. 201



6.3 Abstract ......................................................................................................................... 203

6.4 Introduction .................................................................................................................... 205

6.5 Methods ........................................................................................................................ 207

6.5.1 Participants ............................................................................................................. 207

6.5.2 Free-living play session ........................................................................................... 208


6.5.4 Direct observation coding procedure ....................................................................... 209

6.5.5 Accelerometer data processing ............................................................................... 209

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6.5.6 Classifier evaluation ................................................................................................ 210

6.6 Results .......................................................................................................................... 210

6.7 Discussion ..................................................................................................................... 213

6.8 Acknowledgements ....................................................................................................... 221

6.9 References .................................................................................................................... 222

6.10 Figures ........................................................................................................................ 226

6.11 Tables ......................................................................................................................... 227

Chapter 7. Machine learning models for classifying physical activity in free living

preschool children .................................................................................................... 231



7.3 Abstract ......................................................................................................................... 233

7.4 Introduction .................................................................................................................... 234

7.5 Methods ........................................................................................................................ 237

7.5.1 Participants ............................................................................................................. 237

7.5.2 Free-living play session ........................................................................................... 237


7.5.4 Direct observation coding procedure ....................................................................... 238

7.5.6 Statistical analysis ................................................................................................... 241

7.6 Results .......................................................................................................................... 241

7.7 Discussion ..................................................................................................................... 244

7.7.1 Conclusions ............................................................................................................ 251

7.8 Author Contributions: ..................................................................................................... 252

7.9 Funding: ........................................................................................................................ 252

7.10 Acknowledgments: ...................................................................................................... 252

7.11 Conflicts of Interest: ..................................................................................................... 253

7. 12 References ................................................................................................................. 254

7.13 Figures ........................................................................................................................ 261

7.14 Tables ......................................................................................................................... 262

7.15 Supplemental Information ............................................................................................ 265

Chapter 8. Laboratory-based and free-living algorithms for energy expenditure

estimation in preschool children: a free-living evaluation .................................... 268



8.3 Abstract ......................................................................................................................... 270

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8.4 Introduction .................................................................................................................... 272

8.5 Methods ........................................................................................................................ 275

8.5.1 Participants and setting ........................................................................................... 275

8.5.2 Study Design ........................................................................................................... 275


8.5.4 Indirect calorimetry .................................................................................................. 276

8.5.5 Accelerometer ......................................................................................................... 277

8.5.6 Development and evaluation of free-living EE models ............................................. 277


8.5.8 Feature selection..................................................................................................... 278

8.5.9 Model training and testing ....................................................................................... 278

8.5.10 Comparison with laboratory-based EE models ...................................................... 279

8.5.11 Statistical analysis ................................................................................................. 279

8.6 Results .......................................................................................................................... 279

8.6.1 LOSO cross-validation ............................................................................................ 280

8.6.2 Evaluation in hold-out sample ................................................................................. 282

8.6.3 Prediction of free play total EE ................................................................................ 283

8.7 Discussion ..................................................................................................................... 284

8.7.1 Conclusions ............................................................................................................ 288

8.8 Acknowledgements ....................................................................................................... 288

8.9 References .................................................................................................................... 289

8.10 Figures ........................................................................................................................ 292

8.11 Supporting information ................................................................................................. 296

Chapter 9. General discussion ................................................................................. 298

9.1 Summary of key outcomes ............................................................................................ 299

9.2 Significance of key outcomes ........................................................................................ 304

9.3 Strengths and limitations ............................................................................................... 305

9.4 Future directions and contextualisation .......................................................................... 308

9.4.1 Habitual physical activity in clinical populations ....................................................... 308

9.4.2 Behaviour change interventions and determinants of physical activity ..................... 309

9.4.3 mHealth and eHealth .............................................................................................. 310

9.4.4 24-hour activity behaviours and sensor fusion ......................................................... 311

9.4.5 Translation of physical activity epidemiology into health policy ................................ 311

9.4.6 Summary of future directions and contextualisation ................................................ 312

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Chapter 10. Bibliography .......................................................................................... 314

Appendix 1. Statement of contribution of co-authors ............................................ 335

List of figures

Figure 1. Flex Heart Rate Method ......................................................................................................... 34

Figure 2: Overview of implementing machine learning techniques to attain physical activity

predictions ................................................................................................................................................. 70

Figure 3: Supervised, unsupervised, and semi-supervised machine learning approaches using

labelled and unlabelled accelerometer data ........................................................................................ 74

Figure 4: Overview of group-based, group-personalized, and fully-personalized model scheme.

.................................................................................................................................................................... 75

Figure 5: Overview of research design and methodology for each thesis study .......................... 104

Study 1 (Chapter 4)

Figure 4- 1: Overall accuracy for hip, wrist, and combined hip and wrist classifiers ................... 138

Figure 4- 2: F-score for hip, wrist, and combined hip and wrist classifiers ................................... 139

Study 2 (Chapter 5)

Figure 5- 1. Overall Accuracy by Placement for Cross Validation Results ................................... 162

Figure 5- 2. Overall Accuracy for Model Type by GMFCS level for LOO-CV ............................... 163

Figure 5- 3. Overall accuracy statistics by accelerometer placement when evaluated under

simulated free-living condition. ............................................................................................................. 168

Figure 5- 4. Overall accuracy for group, group personalised, and fully personalised RF

classifiers, by GMFCS level during the simulated free-living evaluation. ...................................... 168

Study 3 (Chapter 6)

Figure 6- 1. Activity class accuracy for all windows and with mixed windows removed ............. 226

Study 4 (Chapter 7)

Figure 7- 1: Interaction plots summarizing the effect of window size and feature set on adjusted

F-scores for models trained on wrist, hip, and combined hip and wrist accelerometer data. .... 261

Study 5 (Chapter 8)

Figure 8- 1. Results for the free-living, retrained laboratory, and off the shelf laboratory models

for the hip placement in the hold-out validation sample. .................................................................. 292

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Figure 8- 2. Results for the free-living, retrained laboratory, and off the shelf laboratory models

for the wrist placement in the hold-out validation sample ................................................................ 293

Figure 8- 3. Bland Altman plots depicting regression line and 95% prediction intervals for off the

shelf laboratory, retrained laboratory, and free-living models for hip placement. ........................ 294

Figure 8- 4. Bland Altman plots depicting regression line and 95% prediction intervals for off the

shelf laboratory, retrained laboratory, and free-living models for wrist placement....................... 295

List of tables

Table 1: Youth Physical Activity Guidelines ......................................................................................... 10

Table 2: Youth Physical Activity National Surveys .............................................................................. 20

Table 3: Regression calibration studies for hip-worn accelerometers among school-aged

children ...................................................................................................................................................... 42

Table 4 Regression calibration studies for wrist-worn accelerometers among school-aged

children ...................................................................................................................................................... 45

Table 5 Regression calibration studies for hip-worn accelerometers among preschool-aged

children ...................................................................................................................................................... 45

Table 6: Receiver Operating Characteristic Curve calibration studies for hip-worn

accelerometers among school-aged children ...................................................................................... 48


accelerometers among clinical population of school-aged children ................................................. 51

Table 8: Receiver Operating Characteristic Curve calibration studies for wrist-worn

accelerometers among school-aged children ...................................................................................... 54


accelerometers among preschool-aged children ................................................................................ 58



Table 11: Receiver Operating Characteristic Curve calibration studies for wrist-worn


Table 12: Machine learning activity classification studies among school age children ................. 80

Table 13: Machine learning activity classification studies among preschool aged children ......... 86

Table 14: Machine learning energy expenditure prediction studies among school aged children

.................................................................................................................................................................... 89

Table 15: Machine learning energy expenditure prediction studies among preschool aged

children ...................................................................................................................................................... 92

Study1 (Chapter 4)

Table 4- 1. Participants Characteristics .............................................................................................. 140

Table 4- 2: Confusion Matrices for Binary Decision Tree, Random Forest, and Support Vector

Machine classifiers trained on wrist data. ........................................................................................... 141


Machine classifiers trained on hip data............................................................................................... 143

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Machine classifiers trained on combined hip and wrist data. .......................................................... 145

Study 2 (Chapter 5)

Table 5- 1. Participants Characteristics .............................................................................................. 157

Table 5- 2. LOOCV Overall and Activity Class Accuracy for Group, GMFCS-specific, and

Personalized classification models ...................................................................................................... 166

Table 5- 3. Hip placement activity class recognition for group, group-personalised, and fully-

personalized classification models during the simulated free-living trial. ...................................... 172

Table 5- 4. Ankle placement activity class recognition for group, group-personalised, and fully-

personalized classification models during the simulated free-living trial. ...................................... 173

Table 5- 5. Combined hip and ankle placement activity class recognition for group, group-

personalised, and fully-personalized classification models during the simulated free-living trial.

.................................................................................................................................................................. 174

Study 3 (Chapter 6)

Table 6- 1. List of Activity Classes and Activity Types ..................................................................... 227

Table 6- 2. Descriptive statistics for time spent in activity class and activity type during active

play sessions........................................................................................................................................... 228

Table 6- 3. Extended confusion matrix for activity class and activity type for the wrist classifiers.

.................................................................................................................................................................. 229

Table 6- 4. Extended Confusion Matrix for Activity Class and Activity Type for the hip classifiers

.................................................................................................................................................................. 230

Study 4 (Chapter 7)

Table 7- 1: Description of the five activity classes ............................................................................ 262

Table 7- 2: F-scores for the five activity classes and the weighted average F-score for each

model ........................................................................................................................................................ 263

Table 7- 3: Confusion matrices for PA classification from the wrist, hip, and combined hip and

wrist placement for lag/lead 10 s and 15 s window models............................................................. 264

Study 5 (Chapter 8)

Table 8- 1. Features selected for free-living models ........................................................................ 280

Table 8- 2: Leave one subject out cross-validation results for the free-living models and

retrained lab model ................................................................................................................................ 282

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List of thesis publications

Publications included in this thesis

1) Ahmadi MN., O’Neil M., Fragala-Pinkha M., Lennon N., Trost SG. Machine Learning algorithms for activity recognition in ambulant children and adolescents with Cerebral Palsy; Journal of NeuroEngineering and Rehabilitation 2018;15:105 - (Incorporated as Chapter 4)

2) Ahmadi MN., O’Neil ME., Baque E., Boyd RN., Trost SG. Machine learning to quantify physical activity in children with cerebral palsy: comparison of group, group-personalised, and fully-personalised activity classification models; Sensors (Special Issue: Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice) 2020; 20 (14): 3976– (Incorporated as Chapter 5)

3) Ahmadi MN., Brookes D., Chowdhury A., Pavey T., Trost SG. Free-living evaluation of laboratory-based activity classifiers in preschoolers; Medicine & Science in Sports & Exercise 2020; 52 (5): 1227-1234 – (Incorporated as Chapter 6)

4) Ahmadi MN., Pavey T., Trost SG. Machine learning models for classifying physical activity in free living preschool children; Sensors (Special Issue: Sensors for Human Physical Behaviour Monitoring) 2020; 20 (16); 4364 – (Incorporated as Chapter 7)

5) Ahmadi MN., Chowdhury A, Pavey T, Trost SG. Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation. PLoS One. 2020; 15 (5):e0233229 – (Incorporated as Chapter 8)

Other publications during candidature

6) Greever, C., Ahmadi, M., Sirard, J., Alhassan, S. Associations among physicalactivity, screen time, and sleep in low socioeconomic status urban girls;Preventative Medicine Report, 2017;5: 275-278

7) Trost, SG., Cliff DP., Ahmadi MN., Tuc NV., Hagenbuchner M. Sensor-enabledActivity Class Recognition in Preschoolers: Hip versus Wrist Data; Medicine &Science in Sports & Exercise, 2018; 50 (3): 634-641

8) Alhassan S., Nwaokelemeh O., Greever CJ., Burkart S., Ahmadi M., St. LaurentCW., Barr-Anderson DJ. Effect of a culturally-tailored mother-daughter physicalactivity intervention on pre-adolescent African-American girls’ physical activitylevels; Preventative Medicine Report, 2018; 11: 7-14

xvi

9) Alhassan S., St. Laurent CW., Burkart S., Greever CJ., Ahmadi MN. Feasibility of Integrating Physical Activity Into Early Education Learning Standards on Preschooler’s Physical Activity Levels; Journal of Physical Activity and Health, 2019; 16 (2): 101-107

10) Ahmadi MN., Trost SG. Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data; Journal of Sport Sciences 2020; 38 (4): 399-404

11) Ahmadi MN., Pfeiffer KA, Trost SG. Random Forest and Regularized Logistic Regression Models for Activity Classification in Youth Using Raw Accelerometer Data from the Hip; Measurement in Physical Education and Exercise Science. 2020; 24 (2): 129-136

12) Goodlich B., Armstrong E., Ahmadi MN., Horan S., Baque E., Carty C., Trost SG. A novel method to quantify habitual physical activity in children GMFCS levels III and IV; Developmental Medicine & Child Neurology 2020; 62 (9): 1054-1060

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Statement of original authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

October 24, 2020

QUT Verified Signature

1

Chapter 1. Introduction

The relationship between physical activity (PA) and health in children and

adolescents is well established. This extensive scientific evidence base has prompted

global health authorities, scientists, health specialists, and policy makers to develop

evidence-based physical activity guidelines for children and adolescents. Most

guidelines recommend preschool age children accumulate 180 mins of PA of which 60

mins should be energetic play, and school age children obtain 60 mins of MVPA.

Currently it is estimated about 80% of adolescents are not meeting these guidelines (1,

2). There are no comparable global data for younger children and children with

disabilities due to the lack of large-scale national surveillance data across countries in

this population. A review of 40 studies found high variability in prevalence estimates of

preschool-age children’s physical activity levels (3). The high variability is partly

attributable to a lack of a consensus in an operational definition of meeting guidelines

and measurement methodologies across studies. Among children with disabilities,

based on retrospective cohort studies from national surveillance data in the United

States of America, it is estimated that between 81% - 88% of children and adolescents

with disabilities do not meet physical activity guidelines and have lower odds (adjusted

OR = 0.75) of being sufficiently active compared to their typically developing peers (4–

6).

In response to concerns about childhood inactivity, researchers are actively

exploring strategies to promote physical activity and reduce sedentary behaviour in

youth. However, the efficacy of these strategies are dependent on the accurate

measurement of physical activity. Traditionally, self-report measurements have been

2

used to assess PA; however, they are susceptible to recall and social desirability bias.

Objective measures such as accelerometer-based motion sensors and heart-rate

monitors provide researchers with methods to assess PA that avoid these limitations.

Due to their lightweight, unobtrusive size, low cost, and ability to record data for several

weeks, accelerometer-based motion sensors have become the preferred method to

measure PA in children and adolescents (7–9). However, there continues to be

challenges in the processing of accelerometer data into physical activity outcomes. Prior

approaches to processing accelerometer data have been reliant on threshold-based

approaches to classify PA intensity, but this approach is subject to significant

measurement error. Threshold-based approaches are intrinsically prone to

misclassification due to activities of differing intensities producing similar acceleration

values therefore causing intensity thresholds to be dependent on the activities

performed during calibration (10, 11). Moreover, when accelerometers are worn on

body placements that are not affixed to the body’s center of mass such as the wrist,

thresholds-based approaches cannot differentiate whole-body movements that result in

increased intensity from movements that occur during sedentary or stationary activities

(12). An emerging approach is the use of machine learning (ML) methods to predict PA

type and/or energy expenditure. Machine learning methods using pattern recognition

techniques predict physical activity outcomes based on previous knowledge or

recognizable features in the acceleration data (13–15). However, currently there are a

number of significant knowledge gaps related to the implementation of ML methods to

accelerometer data in children and adolescents.

3

1.1 Research problem

Although ML methods have enormous potential for sensor-based measurement

of PA in youth, it is important to note that ML models have predominantly been

developed and evaluated using data from laboratory-based activity trials in which

participants complete a series of structured physical activity trials of fixed duration. In

adult studies, ML activity classification algorithms trained on accelerometer data from

simulated activity trials exhibited a decrease in accuracy of between 32-47% when

implemented in a free-living environment (16). Therefore, if ML models are to be

implemented in field-based studies, it is critically important to evaluate the performance

of models in a free-living environment among children and adolescents. Further, the

performance of these models should be compared to ML models trained on

accelerometer data collected under real world conditions.

A second major gap in the research literature is a lack of studies exploring

personalised ML models. Prior ML studies involving youth have only developed and

tested group models. Group based models are trained using data from a representative

group of end users and deployed as an “off the shelf” algorithm for making predictions in

previously unseen end users. Conversely, personalised models are trained using data

from one person thereby allowing the model to match the idiosyncrasies of the

individual (17). To date, no studies have investigated the feasibility and relative

accuracy of personalized ML models compared to group-based models in children and

adolescents.

Finally, studies exploring ML methods in youth have been conducted in children

and adolescents with typical development. The utility of ML models for accelerometer-

4

based assessments of physical activity in youth with movement challenges has not

been previously investigated. The most common physical disability among children and

adolescents is cerebral palsy with a prevalence of 1.4/1000 live births (18). Notably,

children and adolescents with cerebral palsy have varying degrees of motor

impairments, which leads to movement asymmetries and atypical gait patterns, as well

as anatomical and neuromotor diagnosis (19, 20) The heterogeneity of motor

impairments and decreased mechanical efficiency results in different movement

patterns, and relationships with activity and energy expenditure within this population.

This creates unique challenges for investigators trying to assess physical activity and

health outcomes from movement data. The implementation of personalised ML models

has the potential to provide accurate physical activity estimates by accounting for the

heterogeneity in motor impairment severity in this population. Currently, there is a

critical need to develop and test ML algorithms that provide accurate estimates of

habitual physical activity in children and adolescents with cerebral palsy.

1.2 Overall study objectives

This thesis will provide an understanding of significant knowledge gaps in the

field of physical activity measurement that are preventing widespread uptake of ML

methods among researchers. The studies conducted as part of the thesis will apply ML

methods to develop and test activity classification and energy expenditure prediction

models in children and adolescents. The studies will explore the potential utility of ML

methods in children with unique movement patterns. The studies will also determine if

activity classification models developed from activity trials perform well when

implemented in a real-world environment. In addition, the studies will explore the

5

feasibility and accuracy of personalised activity recognition models compared to group-

based activity recognition models. Moreover, the thesis will expand on the current

methodological approaches to physical activity measurement studies among children

with unique movement patterns by comparing classification models developed from

laboratory-based activity trials to models developed from activities performed in a real-

world setting, as well as the performance of personalised and group classification

models in a real-world setting

1.2.1 Research aims

To address these research gaps, this thesis was conducted in five studies. In

study 1, several activity recognition models were developed in a sample of ambulant

children and adolescents with CP. The primary aim was to develop and test machine

learning models for the automatic identification of physical activity in children with CP

across a range of functional mobility levels. Study 2, evaluated and compared the

accuracy of group-based, group-personalised, and fully-personalized machine learning

physical activity classification models for ambulant children with CP. To assess the

validity of the models under conditions that more closely replicated real world

conditions, classification accuracy was further evaluated in a simulated free-living trial.

A secondary aim was to examine the effects of accelerometer placement and

combination of placements. Study 3 sought to determine the generalizability of activity

recognition models trained on laboratory-based activity data to real world conditions for

preschool-aged children. In study 4 machine learning activity classification models were

trained on true free-living accelerometer data for preschool-aged children. The effects of

window size, inclusion of temporal information, and combination of monitor placement

6

for the hip and wrist on accuracy when implemented in real world conditions was

assessed. In study 5 the generalizability of hip and wrist laboratory trained machine

learning energy expenditure models to real-world conditions was evaluated and

benchmarked against the accuracy of machine learning energy expenditure models

trained on free-living data.

7

Chapter 2. Literature review

The purpose of this chapter is to provide a comprehensive review of the research

evidence relevant to the health benefits of physical activity in children and adolescents,

and the approaches used to quantify physical activity and sedentary behaviour in this

population. The chapter is divided into five sections. Section 2.1 provides a brief

overview of the health benefits of physical activity in children and adolescents, and

reviews the current youth physical activity guidelines. Section 2.2 summarizes the

descriptive epidemiological literature related to compliance with physical activity

guidelines. Section 2.3 summarizes the different methodologies used to measure

physical activity in children and adolescents. Section 2.4 extends the discussion of

measurement methodology by reviewing cut-point and ML methods used to quantify

physical activity from accelerometer output. Section 2.5 gives an overview of the use of

accelerometers to measure physical activity in children and adolescents with CP.

2.1 Physical activity and health in children and adolescents

Regular participation in physical activity (PA) provides several important health

benefits in children and adolescents, such as improved cardiovascular health (lipids,

lipoproteins, blood pressure, aerobic fitness) , mental health (anxiety, depression, self-

concept), cognitive functioning, musculoskeletal health (bone mineral density, muscular

strength, muscular endurance), and motor development (21–23).

Physical activity and its benefits on acute and chronic health is well documented.

Accordingly, promoting physical activity has become a global public health priority, and

several national organizations have formed their own advisory committees, issued

8

resolutions and guidelines for youth physical activity. To track the compliance with these

guidelines, measures of physical activity have been incorporated into several public

health surveillance systems.

2.1.1 Physical activity guidelines for children and adolescents

Table 1 displays the PA guidelines for children and adolescents from four

countries along with the World Health Organization (WHO). The guidelines were based

on systematic reviews and stakeholder consultations and consensus (24–34). The

guidelines provide recommendations for parents, teachers, caregivers, coaches, policy

makers, and health care providers, in addition to children and adolescents. Although

each organization followed rigorous guideline development procedures, there are slight

inconsistencies in the PA guidelines. Additionally, there is some inconsistency in the

guidelines for preschool and infant age children. Although all 5 organizations have

similar guidelines for school-age children, the lower and upper bound cutoff year for this

age group differs. Notwithstanding the slight differences in the age groupings of the PA

guidelines, all are consistent in the amount, intensity, and frequency of PA

recommendation. Given the consistency and rigorous approach to develop the PA

guidelines, it has been advised that it would not be prudent to revise the uniform

recommendations unless new evidence dictated (22).

In June 2016, Canada released the first 24 hour movement guidelines for their 5-

17 year old age group that include sleep and sedentary behaviour in addition to the

updated PA guidelines (24). In 2019, Australia released similar 24-hour movement

guidelines for 5-17 year old children that were harmonized from the Canadian

guidelines. In 2017, both Canada and Australia released 24 hour movement guidelines

9

for early childhood, followed by WHO in 2019 (25, 27, 29). The United Kingdom

proposed 24 hour movement guidelines for children under 5 years, however current

guidelines are still focused on PA (28). Additionally, advisory committees for the United

States of America are in the process of formulating movement guidelines that will

encompass an entire 24 hour day to include recommendations for sleep, PA, and SB.

Previously, the relationship between each of these three behaviours with health

outcomes were investigated in isolation of each other and, as evident in the literature,

the evidence base for these three behaviours are constructed as such (35). However,

moving forward, the implementation of 24-hour movement guidelines dictates these

three behaviours will be viewed in combination with each other. There is a growing body

of cross-sectional research that has begun to address the combined effects of these

three behaviours on health markers and warrants further longitudinal observation and

interventional research (36–39).

10

Table 1: Youth Physical Activity Guidelines

Organization Age Group (yrs) Recommendation

Australian (25, 30)

11

and storytelling with a caregiver is

encouraged

Sleep: 11-14 hours of good quality

sleep, including naps, with consistent

sleep and wake-up times

3-5

PA: At least 180minutes spent in a

variety of physical activities, of which at

least 60 minutes is energetic play,

spread throughout the day; more is

better

Sedentary behaviour: Not being

restrained for more than 1 hour at a

time (prams, strollers, etc.) or sitting for

extended periods. Sedentary screen

time should be no more than 1 hour;

less is better. When sedentary,

engaging in pursuits such as reading


encouraged


sleep, which may include a nap, with

consistent sleep and wake-up times

5-17

PA: 60 minutes of MVPA daily

involving a variety of aerobic activities

-VPA and muscle and bone

strengthening activities at least 3

days/week. Several hours of a variety

of structured and unstructured light

physical activities

Sedentary behaviour: No more than 2

hours per day of recreational screen

12

time; limited sitting for extended

periods

Sleep: Uninterrupted 9 to 11 hours of

sleep per night for ages 5-13 yrs. 8 to

10 hours of sleep per night for ages 14-

17 yrs, with consistent bed and wake-

up times

Canadian 24 Hour

Movement

Guidelines(24, 29)

13

younger than 2 years, sedentary

screen time is not recommended. For

those aged 2 years, sedentary screen

time should be no more than 1 hour;

less is better. When sedentary,



encouraged

Sleep: 11-14 hours of good-quality

sleep, including naps, with consistent

bedtimes and wake-up times.

3-4

PA: At least 180 minutes spent in a

variety of physical activieis spread

throughout the day, of which at least 60

minutes is energetic play; more is

better

Sedentary behaviour: Not being

restrained for more than 1 hour at a

time (eg. In a stroller or high chair) or

sitting for extended periods. Sedentary

screen time should be no more than 1

hour; less is better. When sedentary,



encouraged

Sleep: 10-13 hours of good-quality


consistent bedtimes and wake-up

times..

5-17

PA: 60 minutes of MVPA daily

involving a variety of aerobic activities

-VPA and muscle and bone


days/week. Several hours of a variety

14

of structured and unstructured light

physical activities

Sedentary behaviour: No more than 2

hours per day of recreational screen

time; limited sitting for extended

periods

Sleep: Uninterrupted 9 to 11 hours of

sleep per night for ages 5-13 yrs. 8 to

10 hours of sleep per night for ages 14-

17 yrs, with consistent bed and wake-

up times

UK(28, 32)

Infants who cannot

walk

PA should be encouraged from birth,

particularly through floor-based play

and water-based activities in safe

environments

-minimise the amount of time being

sedentary

Pre-school age

who are capable of

walking unaided

180 minutes of activity throughout the

day


sedentary

5-18

-At least 60 minutes of MVPA daily

- VPA and muscle and bone


days/week


sedentary

USA(31, 33, 34)

15

-placed in safe settings that facilitate

PA and do not restrict movement

-PA should promote the development

of movement skills

-Should have an environment that

meets safety standards for performing

large muscle activities

-Individuals responsible for the well-

being of infants should be aware of the

importance of physical activity and

facilitate the infant’s movement skills

1-3

-At least 30 minutes of structured PA

daily

-At least 60 minutes of unstructured PA

daily, and should not be sedentary for

more than 60 minutes at a time

-Toddlers should develop movement

skills that are building blocks for more

complex movement tasks

- Should have an environment that



- Individuals responsible for the well-




3-5

-At least 60 minutes of structured PA

daily

-At least 60 minutes of unstructured PA

daily, and should not be sedentary for

more than 60 minutes at a time

-Preschoolers should develop

competence in movement skills that

are building blocks for more complex

movement tasks

16

-Should have an environment that



-Individuals responsible for the well-




6-17

-At least 60 minutes of PA daily

-Most of the 60 minutes should be

MVPA aerobic PA

-VPA, muscle strengthening, and bone-

strengthening should be apart of the 60

minutes at least 3 days per week

WHO(26, 27)

17

strollers, etc.). For those aged 1 yr,

sedentary screen time is not

recommended. For those aged 2 yrs,

no more than 1 hour of sedentary

screen time


sleep, including naps with regular sleep

and wake-up times

3-4

PA: 180 minutes of PA of which at

least 60 minutes is MVPA

Sedentary behaviour: Not be restrained

for more than 1 hour at a time (prams,

strollers, etc.). No more than 1 hour

sedentary screen time is not

recommended.



regular sleep and wake-up times

5-17

-Accumulate at least 60 minutes of

MVPA daily. Most of the daily PA

should be aerobic

-VPA, muscle strengthening, and bone-

strengthening should be incorporated

at least 3 days per week.

2.2 Prevalence of meeting physical activity guidelines in children and

youth

Considering the documented health benefits of physical activity, it is important to

routinely monitor and report on the proportion of children meeting recommended

18

guidelines. Based on the latest World Health Organization report, 80% of adolescent

children were not meeting guidelines (40). With the growing importance to advance

physical activity-based research and develop strategies to promote physical activity, the

Active Kids Global Alliance was formed in 2014. Members of the alliance include

researchers, health professionals, and stakeholders representing 49 countries on 6

continents. Assessment of each countries youth physical activity scores are released in

the form of report cards. The aggregate score of the 2018 report cards for the 49

countries had 27-33% of the children meeting PA guidelines which represents a D

grade for overall physical activity. The report cards for individual countries reveals

considerably different scores between countries and within countries for the same age

groups. Although the differences between countries can be attributed to environmental

and social factors, the high variance within countries cannot be explained by these

factors.

The individual report card grades are determined from the accumulation of

several representative national and state surveys. Table 2 displays the results from

each of the national surveys used to determine activity report cards for Australia,

England, Canada, and the United States of America. A lack of standardised

methodological approach results in the use of different measurement devices and

techniques and can lead to vastly different outcomes across each survey for similar age

groups within a country. For example, the U.S uses two national surveys that employ

two different PA measurement techniques. Among adolescents, the two surveys report

activity values that differ by more than 40%. Additionally, Australia uses self-report

questionnaires, pedometry, and accelerometry for primary and secondary school

19

children, and obtained PA estimations that lack agreement across all three techniques.

Further, in Australia, PA levels for children 0-5 years is evaluated using proxy self-report

questionnaires, which will make it inappropriate to evaluate PA trends across childhood

as the children enter primary school and their PA levels will be assessed with a different

technique. Importantly, assessment of all physical activity constructs (i.e: Active

Transportation, Activity Type, Organized Sport Participation, etc.) are typically

measured using several devices. The use of multiple devices increases the burden for

the participants as well as the researchers who handle the data. Recent technological

advances has the potential to provide the opportunity for one electronic device to

assess all the constructs of physical activity, thereby mitigating the burden and

expediting data evaluation.

20

Table 2: Youth Physical Activity National Surveys

Country Survey Name Year of Survey

Age (years)

% Meeting PA Guidelines

Minutes per day* Measurement Method

Australia

Australian Health Survey

2011-2012

2-4 72% Boys: 382 7-day Parent

report Girls: 359

5-17 19%

Boys: 97

7-day Parent or Self-report

Girls: 84

5-8 yrs: 120

9-11 yrs: 95

12-14: 78

15-17: 62

5-8 22% 10,147 steps

Pedometer

9-11 24.4% 10,075 steps

12-14 12.6% 8,943 steps

15-17 17.1% 7371 steps

5-17 Boys: 25% Boys: 9,654 steps

Girls: 8% Girls: 8,625 steps

National Health Survey

2017-2018 15-17 10% Boys: 96 7-day Parent or

Self-report Girls: 57

National Secondary

Students’ Diet and Activity

Survey

2018 12-17

Boys: 24%

NA 7-day Self-

report

Girls: 9%

England Health

Behaviour in 2018 11 11 yrs: 20% NA

7-day Self-report

21

School-aged Children Boys: 22%

Girls: 18%

13

13 yrs: 14%

Boys: 16%

Girls: 9%

15

15 yrs: 11%

Boys: 15%

Girls: 7%

Health Survey for England

2015

2-4

2-4 yrs: 9%

NA 7-day Parent or

Self-report

Boys: 10%

Girls: 9%

5-7 Boys: 30%

Girls: 26%

8-10 Boys: 25%

Girls: 24%

11-12 Boys: 21%

Girls:11%

13-15 Boys: 19%

Girls: 7%

5-15

5-15 yrs: 21%

Boys: 24%

Girls: 18%

What about YOUth Survey

2014 15

15 yrs: 14%

NA 7-day Self-

report Boys: 18%

Girls: 9%

Canada

Canadian Health

Measures Survey

2019

5-11 47% 3-5 yrs: 72

Accelerometer 12-17 31%

6-11 yrs: 62

Boys: 71

Girls: 52

22

5-17

5-17: 39% 12-17 yrs:61

Boys: 52% Boys: 77

Girls: 26% Girls: 43

Canadian Physical Activity Levels Among Youth Survey

2016

5-10 Boys: 10% Boys: 12,200 steps

Pedometer


11-14 Boys: 6% Boys: 11,700 steps

Girls:3% Girls: 10,200 steps

15-19 Boys: 2% Boys: 10,000 steps


5-19 5% 5-19: 11,000 steps

5-19† 41%† NA

Canadian Assessment of

Physical Literacy

2018 8-12 Boys: 27% Girls: 14%

8 yrs Boys:12,890 steps

Pedometer

8 yrs Girls:11,450 steps









8-12 Boys:12,355 steps

23

8-12 Girls:10,779 steps

United States of America

The Child and Adolescent

Health Measurement

Initiative

2016 6-17

6-17 yrs: 23%

NA 7-day Self-

report Boys: 28%

Girls: 20%

Youth Risk Behavior

Surveillance System

2017 Grades

9-12

Grade 9: 31%

NA 7-day Self-

report

Grade 10: 26%

Grade 11: 25%

Grade 12: 23%

Grade 9-12: 26%

Boys: 35%

Girls: 18%

National Health and Nutrition Examination

Survey

2008

6-11

6-11 yrs: 42% Boys: 95

Accelerometer

Boys: 49% Girls: 75

Girls: 35%

12-15

12-15 yrs: 8% Boys: 45

Boys: 12% Girls: 25

Girls: 3%

16-19

16-19 yrs: 7% Boys: 33

Boys: 10% Girls: 20

Girls: 5%

*Minutes per day toward meeting guideline. †Used age and gender specific step thresholds

24

The majority of large-scale national surveys have relied on self-report methods of

physical activity whilst a few have used either pedometers or accelerometers. The

effects of using different methods to measure physical activity can result in a lack of

consistency in physical activity estimation and affect the comparability between studies.

Two evident observations from these reports is that there is inconsistency in the

estimation of children in the same age group meeting guidelines within the same

country, and this can be attributed to the accuracy of different devices and techniques

used to assess physical activity in children. An example of this can be observed in the

United States of America where less than 11% of high school boys and 6% of high

school girls met physical activity guidelines when accelerometry was used. Conversely,

when a self-report measure was used, these estimates increased more than 4-fold.

However, a direct comparison between these surveys is difficult because they were

administered close to 10 years apart. In addition, an important limitation of pedometer

use in the national surveys is the choice of step thresholds to determine if physical

activity guidelines of 60 minutes to moderate-vigorous physical activity was met.

Canadian surveys used a daily cumulative12,000-step threshold and Australian surveys

used a daily cumulative13,000-step threshold for boys and 11,000-step threshold for

girls. However, these step thresholds do not consider the frequency or duration

(cadence) of stepping and were not validated against criterion measures for energy

expenditure and physical activity intensity. Rather, these thresholds were correlated

with 60 minutes of daily of moderate-vigorous physical activity estimated by

accelerometer cut points or arbitrarily determined based on an aggregation of

pedometer measured steps/day in a representative sample of children (41, 42).

25

Regardless of the measurement method used in different surveys, it is resoundingly

evident that across countries a large percentage of youth are not meeting physical

activity guidelines, with physical activity decreasing as age increases, and boys are

consistently more active than girls at each age group. Furthermore, there is a significant

lack of physical activity information on young children and future national surveillance

efforts should include this age group.

To date, efforts to evaluate and promote PA in children have been hindered by a

lack of valid and reliable measures of PA. Valid and reliable measures of PA are a

necessity in studies designed to: 1) assess the distribution and determinants of PA in

defined population groups; 2) evaluate the impact of programs and policies to increase

PA; and 3) determine the dose-response relationship between PA and health (7).

2.3 Measurement of physical activity in children and adolescents

To date, a range of methods have been used to measure PA across all age

groups. They can be broadly categorized as self-report and objective measures. Large

scale epidemiological studies and clinical trials have traditionally relied on self-report

measures to assess PA due to their low participant burden and relatively inexpensive

cost.

2.3.1 Self-report physical activity measurement

Physical activity self-report has been used for numerous purposes. Investigators

interested in describing patterns of children’s physical activity, relating children’s

physical activity to physiological or behavioural variables, and evaluating physical

activity promotion programs have used self-report instruments to assess the mode,

26

intensity, frequency, duration, and context of physical activity. Investigators have

developed physical activity self-reports that suit study objectives and reflects the

diversity of physical activity research. Physical activity self-reports vary in the period of

time covered by the report and whether data are reported as ratings, activity scores with

arbitrary units, time, calories expended, or other summary scores (43, 44). Self-report

measures rely solely on information from the participant and include self-administered

questionnaires, interviewer-administered questionnaires, proxy-reports, and activity logs

and diaries. Each of these measures is dependent on the respondent’s ability to provide

accurate information about their past activity behaviours. This is pure cognitive work

and involves the manner in which memories are stored at the time PA occurs, and the

retrieval of these memories. The following sections provide a summary of different self-

report methods to measure physical activity among children and adolescents. Section

2.3.1.1 provides a summary of self-administered questionnaires. Section 2.3.1.2

summarizes interviewer-administered questionnaires. Section 2.3.1.3 gives a brief

overview of proxy-reports. Section 2.3.1.4 provides an overview for the use of activity

diaries to measure physical activity. Section 2.3.1.5 summarizes self-report physical

activity measurement methods.

2.3.1.1 Self-administered questionnaires

Sallis et al (43, 44), Sirard et al (45), and Kohl et al. (46), provide comprehensive

reviews of the validity for self-administered questionnaires when administered to

children and adolescents. These reviews report the validity correlation coefficients for

self-administered questionnaires range from as low as r= -0.10 to as high as r=0.88

when assessed against criterion measures such as direct observation and heart rate

27

monitors. There are several factors that explain the discrepancy in validity. Notably the

age-group targeted, content of items, and length of recall period are the primary factors

that affect the validity of a questionnaire. Studies have demonstrated that the reliability

of a questionnaire systematically increases with age and validity increases with shorter

recall periods (47–50). Pre-school age and pre-adolescent children have lower cognitive

functioning than adolescent children and affects their ability to recall the duration,

frequency, intensity of PA (49). Additionally, because of an inadequate understanding of

the concept of physical activity, young children may only associate physical activity with

participation in organized sports or fitness activities (51). Thus, when young children

self-report past activity behaviour, important sources of physical activity, such as

household chores and active video gaming, may be erroneously excluded (52).

Furthermore, the content of the question also has an important role in validity. For

instance, the 3-day sweat recall questionnaire (53) uses sweating as an indicator of PA

intensity level and displayed moderate validity (r= 0.46-0.51) and low reliability (r=.30).

Aside from environmental factors, that can affect perspiration, sweat rates and

thermoregulation efficiency increase with age effecting the body’s heat production and

dissipation (54). There are, however, ways in which the validity of self-reports can be

enhanced. Multimedia and computer based questionnaires have been shown to

enhance recall and increase the validity of self-reports in youth (55–57). Furthermore,

the data can be processed electronically without manual data entry, thereby reducing

researcher burden. Three such computerized instruments that have been used among

children are the Multimedia Activity Recall for Children and Adolescents (MARCA),

Computer Delivered Physical Activity Questionnaire (CDPAQ), and Patterns of Eating

28

and Activity at Teesside (peas@tees); all three have shown good content, construct,

concurrent, and convergent validity (56–59). An advantage of computerized instruments

is the integration of 1 day recall and segmented day format, both of which have been

shown to optimize activity recall among youth(49, 60). The segmented day format

separates a day into landmarks and is ordered logically, both of which have been shown

to successfully act as cues for children to remember more activities and identify the

context the activity was performed (48, 60, 61). Additionally, computerized instruments

can utilize pictures and graphic user interfaces that can assist children to conceptualize

intensity and duration of activities (56, 59).

Although there are limitations in children’s ability to recall PA, the use of self-

report to obtain data on PA domains such as activity type, organized sport, indoor vs

outdoor play is vital in providing context to how activity is achieved.

2.3.1.2 Interviewer-administered questionnaires

Another type of self-report requires a trained administrator to deliver the

questionnaire in the form of a one-on-one interview (43). The advantage of this

approach is the administrator can aid in the child’s comprehension and interpretation of

the questions and the retrieval of past activities and events. Further, the administrator

can ask probing questions to facilitate recall and thereby obtain activity information

which otherwise might not be reported. This is evident in the validation studies where

interviewer-administered questionnaires had moderate to strong correlations (r= .61-.89)

and agreement (75%) with criterion measures (48, 62). Additionally, a review of self-

report questionnaires for youth by Sallis et al. (44), indicated that two-thirds of

interviewer-administered questionnaires had validity correlations above r=0.5, compared

29

to one-third of self-administered questionnaires. However, interviewer-administered

questionnaires increases the burden on the research staff. Furthermore, it is vital that

the administrators receive rigorous training to avoid any potential response bias (43,

63). Notably, similar to self-administered questionnaires, validity scores were higher

among shorter recall time periods. This is due, in part, to questionnaires which ask

participants to recall activities over a week, month, or year. Recall of activities across

time periods may be inappropriate for children because of imprecise cognitive

processing, memory errors, and variability in physical activity over time that may

compound recall biases (44). Consequently, for both self- and interviewer-administered

questionnaires, single day recalls of –physical activity from the previous day are more

accurate since they overcome many problems related to children’s inability to recall past

activity behaviours. (60).

2.3.1.3 Proxy-reports

A method that avoids limitations in a child’s cognitive abilities is the use of Proxy-

reports. In this self-report approach, parents or teachers report the child’s activity.

Proxy-reports are required when assessing the PA of infants, toddlers, and preschool

age children, who are too young to report their own behaviour. However, relative to

young children, adolescent children have more independence and may engage in

activities that the proxy-respondent has no knowledge or false knowledge of and thus

would lead to inaccurate responses (64). In addition, preconceived perceptions of a

child’s activity by the proxy respondent can introduce bias into responses (64–66). The

validity (-0.19-0.77) and reliability coefficients (0.27-0.88) reported in the res

application of machine learning approaches for activity … nguyen_ahmadi... · 2020. 11. 11. ·...

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