Risk Factors for Low Physical Activity Levels in Preschool-aged Children in a Densely Populated Urban Community in Bangladesh
Conditions: Obesity, Childhood, Malnutrition, Anemia
Sponsor: The Hospital for Sick Children

RESEARCH PROTOCOL 
 
 
 
 
Risk factors for low physical activity levels in preschool-aged children in a 
densely populated urban community in Bangladesh 
 
 
 
 
 
Principal Investigator (Hospital for Sick Children): 
Dr. Daniel Roth 
Student Investigator (University of Toronto): 
Jessica Watterworth (MSc Candidate) 
Co-Investigator (Hospital for Sick Children):  
Jill Korsiak  
Co-Investigator (University of Toronto): 
 
Dr. Kelly Arbour-Nicitopolous  
Co-Investigator (ICDDR,B): 
 
 
 
Abdullah Al Mahmud 
Co-Investigator (ICDDR,B): 
 
 
 
Dr. Farhana Khanam Keya  
 
 
 
Lead Institutions: 
Hospital for Sick Children, Toronto 
 
Collaborating Organizations: 
University of Toronto, Toronto 
International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B) 
 
 
 
 
 
 
 
Protocol Version: #1.4 
Protocol Date: April 25, 2018  

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Table of Contents 
1. 
Study Summary .............................................................................................................. 3 
2. 
Title ................................................................................................................................ 4 
3. 
Protocol Version ............................................................................................................. 4 
4. 
Funding ........................................................................................................................... 4 
5. 
Roles and Responsibilities ............................................................................................... 4 
5.1. Names, affiliations, and roles of protocol contributors .................................................... 4 
5.2. 
Name and contact information for study sponsor ................................................................ 5 
6. 
Introduction .................................................................................................................... 5 
6.1. Background and Rationale .............................................................................................. 5 
7. 
Objectives ....................................................................................................................... 8 
8. 
Setting and Participants .................................................................................................. 9 
8.1. 
Inclusion Criteria .................................................................................................................. 9 
8.2. 
Exclusion Criteria .................................................................................................................. 9 
9. 
Methodology ................................................................................................................. 10 
9.1. 
Study Procedures ............................................................................................................... 10 
9.2. 
Recruitment ....................................................................................................................... 10 
9.3. 
Data Collection ................................................................................................................... 11 
9.3.1. 
Initial home visit: Consent (caregiver) ............................................................................ 11 
9.3.2. 
Initial home visit: Socioeconomic questionnaire (caregiver) ......................................... 11 
9.3.3. 
Initial home visit: Anthropometric measures (child and caregiver) .............................. 11 
9.3.4. 
Initial home visit: Home built environment audit (caregiver) ........................................ 14 
9.3.5. 
Accelerometry & Global Positioning System (GPS) logging (child) ................................ 15 
9.3.6. 
Daily phone calls (caregiver) ........................................................................................... 17 
9.3.7. 
Final visit: Home perception questionnaire (caregiver) ................................................. 17 
9.3.8. 
Final visit: Hemoglobin concentration collection (child) ................................................ 18 
9.3.9. 
Final visit: Collection of accelerometer and GPS logger ................................................. 19 
9.4. 
Expected study duration (for each participant) ................................................................... 19 
9.5. 
Participant removal criteria ................................................................................................ 20 
9.6. 
Sample size and power calculations ................................................................................... 20 
10. Analysis ......................................................................................................................... 20 
10.1. 
Outcome measure .............................................................................................................. 20 
10.2. 
Exposure variables ............................................................................................................. 23 
10.3. 
Other covariates ................................................................................................................ 24 
10.4. 
Statistical Analysis .............................................................................................................. 24 
10.4.1. 
Primary Analysis .............................................................................................................. 24 
10.4.2. 
Secondary Analysis .............................................................. Error! Bookmark not defined. 
11. Data Security and Protection of Subject Confidentiality ................................................. 29 
12. Consent Process and Documentation ............................................................................. 30 

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13. Risks .............................................................................................................................. 30 
14. Benefits ......................................................................................................................... 31 
15. Safety and Adverse Events ............................................................................................. 31 
16. References ..................................................................................................................... 32 
17. Appendix ....................................................................................................................... 36 
 
Table 1 - List of exposure variables and their derivation ............................................................. 36 
Table 2 - List of additional continuous covariates and their derivation ....................................... 39 
Table 3 - List of additional categorical covariates and their derivation ....................................... 41 
 
1. Study Summary 
Title 
Risk factors for low physical activity levels in preschool-aged children in 
a densely populated urban community in Bangladesh 
Short Title 
Preschooler Physical Activity Study (PresPA Study) 
Design 
Cross-sectional observational 
Study Duration 
1 year (August 2017 – August 2018) 
Study site 
Dhaka, Bangladesh  
Primary 
Objective 
To describe the physical activity levels in preschool-aged children in urban 
Bangladesh.  
Primary Outcome 
Preschooler physical activity levels (mean activity counts/15 seconds will 
categorize activity into: light and moderate-to-vigorous physical activity, 
and sedentary behavior based on predetermined cut-points). 
# of Participants 
60 
Main Inclusion 
Criteria 
• Child is between 34-38 months of age; 
• Child and primary caregiver reside in Dhaka or urban environs; 
• Parent/guardian provides written informed consent for study 
procedures. 
Main Exclusion 
Criteria 
• Child requires mobility assistance; 
• Child has been diagnosed with a neurological disorder affecting 
physical activity (e.g., Cerebral palsy) or a major chronic respiratory or 
cardiac disease that limits physical activity (e.g., severe asthma, 
congenital heart disease). 
Follow-up period 
N/A 

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Main Study 
Procedures  
1. Accelerometry & GPS logging 
2. Anthropometry 
3. Blood hemoglobin concentration test 
4. Home environment audit  
5. Home perception questionnaires  
6. Socioeconomic questionnaires 
2. Title 
 
Risk factors for low physical activity levels in preschool-aged children in a densely populated 
urban community in Bangladesh. 
 
Short title: Preschooler Physical Activity (PresPA) Study 
3. Protocol Version 
 
Version 1.0 – April 13, 2017 
Version 1.1 – July 18, 2017 
Version 1.2 – September 24, 2017 
Version 1.3 – October 2, 2017 
Version 1.4 – April 25, 2018 
4. Funding 
 
Restracomp (SickKids-University of Toronto Ontario Student Opportunity Trust Fund) 
Canadian Institutes for Health Research, Canadian Graduate Scholarship: Master’s (CGS) 
Canadian Institutes for Health Research, Bridge grant to PI 
Canadian Institutes for Health Research, Michael Smith Foreign Study Supplement (MSFSS) 
5. Roles and Responsibilities 
5.1. Names, affiliations, and roles of protocol contributors 
 
Role 
 
 
 
Name  
 
 
Institution 
Principal Investigator  
Daniel Roth 
 
 
Hospital for Sick Children 
Student Investigator  
Jessica Watterworth  
University of Toronto 
Co-Investigator 
 
Jill Korsiak 
 
 
Hospital for Sick Children 
Co-Investigator 
 
Kelly Arbour-Nicitopolous 
University of Toronto 
Co-Investigator 
 
Abdullah Mahmud 
 
icddr,b 

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Co-Investigator 
 
Farhana Khanam Keya 
icddr,b 
 
5.2. Name and contact information for study sponsor  
 
Principal Investigator: 
 
 
Daniel Roth 
Address: 
 
 
 
 
686 Bay Street Toronto, ON   M5G 0A4 
Email:  
 
 
 
 
daniel.roth@sickkids.ca 
Phone:  
 
 
 
 
416-813-7654 X328807 
Fax: 
 
 
 
 
 
416-813-6520 
 
Student Investigator:  
 
 
Jessica Watterworth 
Address: 
 
 
 
 
686 Bay Street Toronto, ON   M5G 0A4 
Email:  
 
 
 
 
jessica.watterworth@sickkids.ca 
Phone:  
 
 
 
 
416-813-7654 X309265 
Fax: 
 
 
 
 
 
416-813-6520 
 
 
Bangladesh Site Principal Investigator:  
Abdullah Al Mahmud 
Address: 
 
 
 
 
Centre for Child and Adolescent Health, icddr,b 
68, Shaheed Tajuddin Ahmed Sarani, Mohakhali 
Dhaka 1212, Bangladesh 
Email:  
 
 
 
 
mahmud@icddrb.org 
Phone:  
 
 
 
 
+880-2-9827001-10 Extension: 3807 OR 9827043 
6. Introduction 
6.1. Background and Rationale  
 
Physical Activity  
 
Physical activity (PA) in childhood is a known determinant of long-term health including bone 
and cardiometabolic health 1,2, mental health 3,4, and the development of cognitive and motor 
skills 5,6. It is especially important in the early years as it reflects the extent to which young 
children engage in exploratory behaviour (e.g. crawling or walking independently), a critical 
determinant of subsequent child development 7. It appears that PA is protective against obesity 
and overweight, and PA interventions among young children have been shown to reduce body 
mass index (BMI), an indicator conventionally used to identify individuals as obese or 
overweight 8,9. However, in young age groups, PA has been seen to play a stronger role in 
preventing rather than treating childhood obesity 10,11. Childhood obesity can track into 

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adulthood and ultimately increase the risk of non-communicable diseases such as type 2 
diabetes, gastrointestinal and hormone dependent cancers, and cardiovascular disease 12–14. In 
addition, it has been seen that habits pertaining to physical activity and inactivity also track 
from early childhood into adulthood 15. This is consistent with the Developmental Origins of 
Health and Disease theory that suggests that adult disease risk may be influenced by events 
that occur as early as pre-conception and into early childhood 16. This implies that early 
childhood is a critical time to intervene, teach, and encourage healthy lifestyle behaviours, such 
as participating in PA routinely. Although there has been substantial research done in high-
income countries exploring the associations between physical activity in children and rates of 
overweight and obesity 17,18, little is known about physical activity levels among young children 
living in low- and middle-income countries, or the environmental risk factors for low physical 
activity among children in resource-poor urban settings. 
 
Nutrition Transition and the Double Burden 
 
As developing countries undergo economic advancement, individuals experience a shift in their 
dietary habits towards diets higher in processed and prepared foods that are generally 
micronutrient poor and energy dense 19,20. Factors that influence this nutrition transition 
include: income shifts, food prices and availability, as well as the modernization of the food 
industry and the influence of mass media 20. It has been predicted that many low- and middle-
income countries, including Bangladesh, will experience this nutrition transition across all 
income classes within the next decade 21. This is problematic considering that in 2014, it was 
reported that 36% of Bangladeshi children under five years were stunted and 12% were 
severely stunted 22. Since stunting is considered an indicator of malnutrition, these statistics 
indicate that Bangladeshi children may be subject to undernutrition and micronutrient 
deficiencies 23,24. Additionally, studies have identified an increasing trend in childhood obesity 
and overweight, particularly in urban settings in Bangladesh 25–27. This reveals that Bangladesh 
will experience an emerging nutritional ‘double burden’, an expression referring to the co-
occurrence of high rates of both over-nutrition and undernutrition27,28.  
 
Current Physical Activity levels in Bangladesh 
 
Along with the occurrence of the double burden in Bangladesh, low physical activity levels and 
high sedentary activities have been identified as major risk factors associated with overweight 
and obesity among urban school-aged children in Bangladesh 25,29. However, these studies did 
not use objective assessments of physical activity such as accelerometry, a measurement 
technique that quantifies frequency, intensity, and duration of human movement using digital 
devices worn by individuals for extended periods. 30. Furthermore, there are few data in 

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Bangladesh or other LMICs regarding physical activity levels of preschool-aged children. Prior 
research on physical activity assessment in young children has shown that objective measures 
using accelerometers have greater validity than parental reports of physical activity 31. We 
hypothesize that in densely-populated urban settings in LMICs (e.g., Dhaka, Bangladesh), 
preschool-aged children cared for predominantly in the home may be at particularly high risk of 
low physical activity due to space constraints indoors and limited opportunities for free, safe 
play in the outdoors. Therefore, characteristics of the physical home environment may have 
dominant effects on the physical activity levels of preschool-aged children in dense urban 
settings. In Brazil, a cross-sectional study found significant positive associations between infant 
gross motor development and the amount of space inside the home, as well as number of 
gross-motor toys available 32. However, few studies have addressed the determinants of 
physical activity levels in preschool-aged children in low- to middle-income countries and in 
particular, how physical activity is influenced by the home environment.  
 
We speculate that the preschool-aged children (~3 years of age) in our study population are 
cared for predominantly in the home, due to the limited number of daycare centres available in 
Dhaka. In a 2008 report on urban planning and women’s accessibility to urban facilities, it was 
established that there are only 13 government-facilitated and 23 private daycare facilities 
available in Dhaka (total population ~15 million), and the majority of them were reported to be 
inadequately structured and unsuitable for children 33. In addition, it was reported that of the 
41% of working mothers who participated in the survey, 94.3% rely on a maid servant to look 
after their children within the home, as opposed to enrolling them into daycare facilities 33. In a 
more recent report from 2015, women who were interviewed reported that the major barriers 
to obtaining childcare in Dhaka pertains to the unavailability of daycare/preschool centres near 
the workplace, as well as a lack of security for toddlers, hygienic and healthy environments, and 
space for a playground 34. These reports indicate that daycares are uncommon in this setting, 
and that children in Dhaka, especially preschool-aged children, spend their days within the 
home.  
 
Influence of Iron Deficiency on Physical Activity 
 
In addition to indoor and outdoor aspects of the built environment, undernutrition itself may 
adversely affect young children’s physical activity levels,35. Prior work has shown that 
undernourished infants, specifically those with iron deficiency, have lower levels of physical 
activity that may contribute to functional isolation and developmental delays 36. In an early 
study by Lozoff and colleagues, they found that iron-deficient anemic infants had lower 
endurance levels, excess fatigability, and showed indications of being less active 36. It was also 
seen in a subset of infants at risk of micronutrient deficiencies in rural Bangladesh that 

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administering weekly supplements of iron and zinc protected children from a decline in motor 
development and exploration 37. The effect sizes of children who were administered a 
supplement containing a 16 micronutrient mix that included iron and zinc were similar to those 
that just received zinc and iron, indicating that other micronutrients may not be as important in 
infant motor development as iron and zinc alone 37. Additionally, a case-control study 
conducted in Indonesia found that when compared to the non-anemic, non-iron deficient 
control children, anemic, iron-deficient infants who received iron supplements developed 
motor skills more quickly and displayed greater physical activity 38. Although these children may 
be experiencing motor advancements due to nutrient “catch-up”, this still indicates that iron-
deficiency anemia may be a potential risk factor for low physical activity levels in young 
children. Considering the high prevalence of undernutrition and anemia in young children in 
Bangladesh, these may be important factors to consider when evaluating physical activity levels 
within this population.  
 
Study Rationale 
 
As the double burden of nutrition emerges in Bangladesh and other low- to middle-income 
countries, it is imperative to develop strategies to mitigate the adverse effects associated with 
both undernutrition and obesity. Identifying levels of physical activity among children is a 
requisite step to developing and targeting interventions to promote physical activity among 
young populations. Furthermore, exploring the risk factors associated with physical activity 
levels in young children can help better inform policy makers surrounding a public health 
intervention. 
7. Objectives 
 
Goals 
The overall goal of our research is to generate new knowledge regarding the nutritional and 
environmental determinants of physical activity in young children living in a densely populated 
urban community in Bangladesh.  
 
Primary Objectives 
The specific objectives of this study are to: 
1. Describe physical activity levels in a sample of preschool-aged children in an inner-city 
community in Dhaka, Bangladesh, 
2. Estimate the associations between characteristics of the physical environment of the 
home (total area of available floor space inside of the home, number and presence of 

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physical hazards, and the number of gross motor activity-oriented items present) and 
the physical activity levels of preschoolers in Dhaka, and 
3. Estimate the associations between hemoglobin concentration (Hb) and preschooler 
physical activity level. 
4. Describe sleep quantity and quality in the sample of preschool-aged children in urban 
Bangladesh 
 
We hypothesize that low levels of preschooler physical activity are associated with a lack of 
play-oriented physical attributes (i.e., total area of indoor floor space, presence and count of 
unsafe physical hazards, and presence and count of stationary and portable gross motor 
activity-oriented items) within the homes in urban Bangladesh. We also speculate that low Hb 
may be associated with low physical activity levels in this population.  
8. Setting and Participants 
 
 
We propose to conduct a cross-sectional observational study of preschool-aged children 
between 34-38 months of age (n=60) selected from the ongoing Maternal Vitamin D for Infant 
Growth (MDIG) trial cohort in Dhaka, Bangladesh. The study will take place in urban Dhaka, 
Bangladesh. We will enroll a minimum of 60 participants with complete data sets, but will 
recruit up to 90 participants until this enrolment target is reached.  
 
8.1. Inclusion Criteria 
• Child is 34-38 months of age;  
• Child and primary caregiver reside in Dhaka or urban environs; 
• Parent/guardian provides written informed consent for study procedures. 
 
8.2. Exclusion Criteria 
• Child requires mobility assistance and/or has been diagnosed with a major neurological 
or orthopedic condition or disorder affecting physical activity (e.g., cerebral palsy, club 
foot, etc.). 
• Major physician-diagnosed chronic respiratory or cardiac disease that limits physical 
activity (e.g., severe asthma, currently taking medication for symptomatic congenital 
heart disease, etc.).  
 
 

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9. Methodology  
9.1. Study Procedures 
 
The majority of data collection will take place during the initial home visit, including collection 
of anthropometric measures and a home built environment audit. Accelerometers and GPS 
loggers will be provided during the initial home visit. The participant will then take part in 7 
days of accelerometry and GPS logging data collection consecutive or non-consecutively over a 
14 day time period. A final visit will take place at the end of the data collection period where 
data surrounding the perception of the home environment and household food security will be 
collected, as well as participant hemoglobin concentration will be measured. This final visit will 
occur at the clinic where the study physician and/or paramedics can safely measure 
hemoglobin collection. We will ask the caregiver of the participant to bring the accelerometers 
and GPS loggers to the clinic where they will be collected for analysis by study personnel.  
 
9.2. Recruitment  
 
Participants within the Maternal Vitamin D for Infant Growth (MDIG) trial (NCT01924013) 
cohort registry who have a) expressed interest in participating in future sub-studies, and b) 
have children within the target age range, will be contacted. Participants will be contacted prior 
to their anticipated study visit. A contact list will be updated each week to prospectively recruit 
participants in advance or during the age window in which children are eligible for enrolment. 
Each list will begin with the eldest children to reduce the number of missed opportunities to 
enroll children before they exit the eligible age window. The catchment area will include the 
following neighbourhoods in urban Dhaka: Kamrangir char, Azimpur, Lalbag, and Hazariba 
 
Local study personnel will make initial contact via telephone call. If a participant does not 
answer the phone call and therefore has not declined participation, they will continue to be 
contacted 2 additional times overall until a) the child is no longer within the eligible age range, 
b) contact is made and the participant enrolls in the study, or c) contact is made and the 
participant declines joining the study. Study personnel will follow the Screening Script to ask 
each caregiver a series of questions to determine if their child is eligible (Screening Form). Once 
a participant is successfully enrolled, ages out of the eligible age range or declines participating 
in the study, they will be removed from subsequent contact lists.  
 
If a participant meets all inclusion criteria and does not meet any exclusion criteria, an initial 
home visit will be scheduled at the convenience of the participant and their caregiver. Informed 
consent and study enrolment will occur at the beginning of the Initial Home Visit, prior to any 

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data collection procedures.  
 
9.3. Data Collection  
9.3.1. Initial home visit: Consent (caregiver) 
 
The home visit will begin by having the participant’s caregiver review and sign the PresPA Study 
Consent Form (A); this is necessary to obtain parental permission. In Bangladesh, enrolment is 
permissible with only one legal guardian providing consent. A verbal reading of the consent 
form will be provided if the caregiver of the participant is illiterate. Once the participant’s 
caregiver has signed or thumb stamped the consent form, data collection can begin. 
 
 
9.3.2. Initial home visit: Socioeconomic questionnaire (caregiver) 
 
A primary caregiver (usually mother) of the preschooler participants will be asked to participate 
in the collection of socioeconomic data, including education level and questions pertaining to 
wealth (which will be used to develop the overall wealth index; Form 1). Due to the low literacy 
rates within the target population, this will be done in a structured interview format. 
 
9.3.3. Initial home visit: Anthropometric measures (child and caregiver) 
 
Next, the preschooler participant and their caregiver will take part in anthropometric measures 
(Form 2). Using a portable lengthboard/stadiometer (ShorrBoardTM, Shorr Productions, Olney, 
MD, USA), duplicate measures of height will be collected from both child and mother. Child 
height will be collected during the initial home visit, whereas maternal height will be collected 
during the final clinic visit using a stadiometer (Seca-217, Seca Corp., Hamburg, Germany). If 
there is a discrepancy of 1 cm between the first and second measure, another set of 
measurements will be collected. If a child does not cooperate in standing during this measure, 
study personnel will be instructed to lay the lengthboard/stadiometer on the ground to obtain 
a recumbent length measurement. Height measurements obtained from a recumbent length 
measurement will be adjusted for by subtracting 0.7 cm before plotting on the World Health 
Organization (WHO) growth curves 39.  
 
Weight measurements will be collected using a portable scale (Seca-874, Seca Scale Corp., 
Hamburg, Germany). Duplicate measures of weight will be collected from both child and 
mother. If there is a discrepancy of 50 g between the first and second measure, another set of 
measurements will be collected. If a child is unable to stand still during the weight 
measurement, their caregiver can be measured while holding the participant. The caregiver’s 

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body weight will then be subtracted from the combined measure (participant and caregiver) to 
obtain the participant’s body weight.  
 
Preschooler participant and caregiver waist circumference will be collected next using a non-
stretch, compression spring tape measure (Gulick II measuring tape, Country Technology Inc., 
Gays Mills, WI, USA). As per the recommended protocol from Statistics Canada, NHANES, and 
the Canadian Health Measures Survey, waist circumference will be measured at the top of the 
iliac crest 40. These measures will be taken in duplicate; if there is a discrepancy of more than 
0.5 cm between the first and second measure, a second set of measurements will be collected. 
Measurements will be taken at the end of a normal expiration (after expiration, before 
inspiration).  
 
Lastly, we will collect bioelectrical impedance (BIA) measures from both child and caregiver 
using the Quantum II – Body Composition AnalyzerTM (RJL Systems, Clinton Township, MI, USA). 
This single-frequency, portable device utilizes the hand-to-foot technique, in which 4 electrodes 
are attached to the body, two on the hand and two on the foot, while lying supine. BIA 
estimates total body water (TBW) by sending a low voltage electric current through the body 
between the electrodes, in which resistance and reactance of the current are measured. The 
current runs quickly through water present in hydrated muscle tissue (fat free mass; FFM), but 
will experience resistance in fat mass (FM). Therefore, the more FFM, the more body water one 
has and the less resistance the current will experience.  Resistance and reactance values 
collected from this device will then be inputted into a validated equation to properly convert 
TBW to FFM. Participants must be lying supine and relatively still in order to obtain precise 
measures. If a child does not follow these procedures, their caregiver can lay supine with them, 
so long as they do not touch the child’s body; soothing the child by caressing their head is 
acceptable, as it will not affect the precision of the measurements (since the current runs only 
through the hand, up the arm, down the torso and towards the foot on the right side; between 
electrodes). If an individual does not comply and refuses to lie still, fluctuation in resistance and 
reactance values will arise. Any protocol deviations, including lack of cooperation by the 
participants and/or their caregiver during this measure, will be recorded (seen in Form 2) and 
the appropriate measures will be excluded from analyses.  
 
For the preschool-aged participants, we will use an equation validated against 18O dilution in 
well and malnourished young children developed by Fjeld and colleagues 41: 
 
 
TBW = 0.76 + 0.18H2/R50 + 0.39W  
 

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where TBW is in kg; H, height in cm; W, weight in kg; R, resistance in ohms at 50 KHz. This 
equation was developed for both boys and girls and therefore is not sex-specific. Although this 
equation was developed and validated in a population from Peru 41, it was validated again in a 
population of preschool-aged children in urban India 32. They found that the FFM values that 
this equation predicted was in good agreement with those derived from TBW measured by D2O 
dilution 42,43. This equation was chosen for the PresPA study due similar characteristics between 
urban Indian preschoolers and urban Bangladeshi preschoolers and due to the fact that no 
equations validated in Bangladeshi children exist to date.  
 
To determine FM from TBW, the hydration constants developed by Fomon and colleagues will 
be used, as they remain the reference standards for preschoolers: 0.775 (boys, 3 years), 0.779 
(girls, 3 years) 44. TBW will be divided by these factors to calculate FFM (kg). FM (kg) is then 
determined by calculating the difference between total body weight (kg) and FFM (kg). 
 
To evaluate the body composition of the female primary caregivers (usually mothers) of the 
preschoolers, we will use an equation validated in Bangladeshi postpartum women against 2H20 
dilution criterion method:  
 
TBW = 4.297 + 0.190*W + 0.349*H2/R50 
 
where TBW is in kg; H, height in cm; W, weight in kg; R50, resistance in ohms at 50kHz 45. FFM 
(kg) can then be determined by dividing TBW by 0.732, assuming that this population’s FFM has 
a hydration factor of 0.732 45. FM (kg) is then determined by calculating the difference between 
total body weight (kg) and FFM (kg).  
 
BIA measures will be collected in duplicate. If the second resistance and/or reactance value has 
a discrepancy of greater than 5% from the first value taken, a second set of measures will be 
collected. Participants and their caregivers will be asked to remove any metal material (i.e., belt 
clasps, underwire bras, etc.), as this will disrupt the pathway of the electrical current. 
Additionally, caregivers will be asked if they have a pacemaker (Form 2). Those who agree to 
having a pacemaker will automatically be opted out of providing BIA measures, as the current 
may interrupt its functionality. Child participants will be screened for cardiac conditions, 
including having a pacemaker, prior to the initial visit during the recruitment process (Screening 
Form).  
 
Participants and their caregivers can choose to opt out of any of the anthropometric measures 
without penalty. Mothers who are pregnant will be automatically opted out of waist 
circumference and BIA measures.  

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If any anthropometric measures are not collected during the initial home visit (e.g., measuring 
device is not functioning properly, participant does not co-operate, etc.), measures may instead 
be collected during the final clinic visit. 
 
9.3.4. Initial home visit: Home built environment audit (caregiver) 
 
Before the home environment audit can begin, study personnel will obtain permission from a 
full-time resident of the household for which measurements are requested. The PresPA Study 
Consent Form (B) allows for photography to be used to capture the built physical environment 
of the home. Once consent is provided, the home audit can begin. If the owner of the home 
refuses to sign Consent Form (B), the home built environment audit will still proceed; however, 
no photography will be conducted in the home. The participant and their caregiver are still 
eligible to participate in all other study procedures, with the exception of the collection of 
photographs. 
 
Once consent has been given, study personnel will ask the caregiver if there are any restricted 
areas within the indoor home environment where their child is not allowed access. These areas 
will not be included in total available floor space (m2) of the indoor space. The study personnel 
will then draw the layout of the indoor space (Form 3). Then, using a laser measuring device 
(DistoTM D1, Leica Geosystems, Switzerland), the study personnel will measure the dimensions 
of the space, collecting measures in duplicate. They will include any furniture that is greater 
than 1 m2, including each piece of furniture in the drawing of layout, as well as measuring the 
dimensions of furniture in duplicate using a standard tape measure. Study personnel will not 
include any beds or sofas in the drawings, as they will not be subtracted from the available 
indoor floor space (since it is common for children to use beds or sofas as a play space in 
Dhaka). They will also be asked to take at least 2 photographs of each room within the home, 
consent permitting, to accompany the area measurements. These pictures will not include any 
individuals and will strictly capture the physical built environment of the indoor space.  
 
Next, the study personnel will ask about any outdoor spaces that are accessible to the child. 
Only outdoor spaces that have clear perimeters (e.g., fenced in backyard, railings or walls 
surrounding a rooftop, railings surrounding a balcony) will be measured using the laser-
measuring device. If the laser does not operate in the outdoor environment (e.g., weather 
conditions do not permit the proper lighting standards necessary to obtain a reading), a 
standard tape measure will be used. Open streets, alleyways, large parks or green spaces, or 
outdoor spaces that do not have a clear perimeter will not be measured; however, they will be 
documented and physical characteristics will be described by study personnel in the 

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appropriate sections of Form 3. Study personnel will be asked to photograph all outdoor 
spaces, consent permitting, as long as they do not include any individuals within the pictures.  
 
Once the indoor, and if applicable, outdoor area has been measured, the study personnel will 
assess the home environment for physical hazards. Using the audit provided (Form 3), they will 
assess the presence and safety surrounding stairwells, accessibility to stovetops and open 
sewage drains, as well as the presence of unfinished construction and common household 
hazards (e.g., boti, a common household cutting device in Bangladesh). A check list will be 
provided to the caregiver at the final visit which will indicate if any of the 5 hazards that we are 
assessing are present in their home. In addition, we will provide them with strategies to 
increase safety of their children (e.g., put up railing around stairwells, keep boti in a space that 
isn’t accessible to the child, etc.).  
 
Finally, study personnel will take an inventory of any gross-motor activity-facilitating items 
within the home (Form 3). They will ask the caregiver to show/bring them items with which 
they allow their child to play on/with. Study personnel will then document each item on the 
audit form (From 3) and take photographs of each item (up to 10 items). These photographs 
will not include individuals, but will strictly capture the gross-motor activity-facilitating items 
present in the home. During data input, the student investigator will evaluate these items and 
place them into specific categories: fine-motor activity item, stationary/fixed gross-motor 
activity item, portable gross-motor activity item (ball play equipment, push/pull equipment, 
riding equipment, rocking and twisting equipment) 46,47.  
 
9.3.5. Accelerometry & Global Positioning System (GPS) logging (child) 
 
At the end of the initial home visit, the study personnel will provide the participant with their 
activity belt. The belt will consist of a tri-axial accelerometer (ActiGraph GT3X-BT, ActiGraph 
Corp., Pensacola, FL, USA) that will be positioned at the right hip, and a passive GPS logger 
(iTrail H6000, KJB Security Products Inc., TN, USA) that will be either positioned at the left hip of 
the child, or on the arm of the caregiver. The placement of the GPS logging device will depend 
on the participant’s comfortability with wearing the device on the activity belt, otherwise it is 
acceptable for the caregiver to wear it on their person. Both the accelerometer and GPS logger 
will be charged and initialized prior to the home visit. The study personnel will then provide the 
caregiver with instructions on how the belt is positioned on the child and explain that the child 
is to wear the belt 24 hours each day. The belt should only be removed during periods of 
bathing and swimming. As mentioned above, to improve child comfort during the day or while 
sleeping, the caregiver may remove the GPS device from the pouch attached to the belt at 
anytime and wear it on their person (a separate, additional belt will be provided to the 

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caregiver during the initial home visit), so long as they are with their children at all times during 
the day. Caregivers can remove the belt while sleeping if they find it uncomfortable, but must 
reattach it each morning.   
 
The accelerometer will objectively measure all movement of the device-wearer continuously. 
The device is to be worn continuously for 7 days; however, the child can wear the device for 7 
days non-consecutively within a 14-day time period if the caregiver identifies that the child is 
travelling away from home, is ill (e.g., illness that parent perceives as greatly affecting PA, such 
as fever, major injury, etc.), bedridden, or hospitalized. Accelerometry and GPS data will not be 
included in analyses during those specified days. This will continue until the caregiver indicates 
that the participant has returned home from travelling or is no longer ill, bedridden, or 
hospitalized, when accelerometry collection can resume. Study personnel will gather this 
information via the daily telephone calls with the participant’s caregiver (section 9.3.6).  
 
The accelerometer device will be set to collect data at 30 Hz (i.e., it will take readings 30 times 
per second) to ensure high-quality data. Data can only be viewed after the device has been 
collected from the participant and uploaded into the software program (ActiLife v10). Activity is 
measured in counts, and is analyzed over specified time periods. Once the number of counts 
has been analyzed over a specified time period, the data point is then referred to as an ‘epoch’. 
Data will be analyzed in 15 second epochs (counts per 15 seconds; cp15s).  
 
The GPS logger will objectively measure the coordinates of each location that the participant 
travels. As with the accelerometer, the GPS logger device is to be worn continuously for 7 days, 
or non-consecutively within a 14-day time period. The collection of location data will occur 
simultaneous to the collection of accelerometry data. To ensure quality location data, the 
device will be set to take readings every 15 seconds, as this will directly correspond to the 15 
second epochs calculated from the processed accelerometry data. This data will be used in 
combination with the accelerometry data to gain insight on the location of preschooler activity 
(e.g., home, park, relative’s home, street, etc.). Similarly, as with the accelerometry data, data 
collected from the GPS logger can only be viewed after the device has been collected from the 
participant and downloaded into the software program (iTrail GPS Mapping and Trip Analysis 
Software). This passive GPS device does not offer remote real-time tracking; therefore, it 
cannot provide real-time location data and data can only be collected once the device has been 
returned.  
 
The data collected from the GPS logger in combination with the accelerometry data will 
substantially increase the measured accuracy, sensitivity and objectivity of an individual’s 
exposure to environments while they engage in active behaviour 48. Together, the will 

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objectively, and accurately, measure the types of environments that individuals are exposed to, 
the duration they were there for, and what kind of active behaviours they participated in 48.  
 
The combination of accelerometers and GPS logging devices have been used in a few studies 
that measure the risk factors surround PA of preschool-aged children specifically 49–51. Each of 
these studies utilizes an activity belt system on which we have modeled our design (e.g., 
accelerometer positioned on the right hip; GPS logger positioned on the left hip).  
 
9.3.6. Daily phone calls (caregiver) 
 
Every afternoon, study personnel will call the caregiver and go through a daily recall of (a) what 
time the participant woke up in the morning, (b) if, and when, the activity belt was removed 
during the participant’s waking hours, (c) what time the participant went to sleep, and (d) if the 
participant left the home during the day (including location and duration away from home). 
This information will be documented in the Accelerometry & GPS Log each day. If a participant’s 
caregiver cannot be reached on any particular day, study personnel will attempt to call 2 
additional times that day. If contact has not been made, study personnel will contact the 
participant’s caregiver the next morning and ask them to recall the previous day, then call again 
in the afternoon to recall the current day; a normal calling schedule will resume. If contact has 
not been made within 3 days, which has not been previously agreed upon between the 
participant’s caregiver and study personnel (e.g., the participant is travelling, sick, etc.), the 
participant will be removed from the study and study personnel will visit the participant’s home 
to collect the activity belt.  
 
9.3.7. Final visit: Home perception questionnaire (caregiver) 
 
Once the caregiver has indicated that the child has completed their 7 days of wearing the 
activity belt, study personnel will schedule a clinic visit at the convenience of the participant 
and their caregiver. If the participant is unable to travel to the clinic, a home visit can be 
arranged instead. During the final visit, the participant’s caregivers will participate in the Home 
Perception Questionnaire (Form 4) in the form of an interview conducted by study personnel. 
This questionnaire consists of questions pertaining to the caregiver’s perception of the 
adequacy of facilitating physical activity, safety, and cleanliness inside and outside of the home. 
This questionnaire will also capture the known physical activity risk factor: screen time 52,53. In 
addition, this questionnaire will collect information on food security, a potential risk factor for 
low hemoglobin levels, and iron supplement usage. The Home Perception Questionnaire will be 
conducted during the final visit as opposed to the initial visit in order to avoid influencing the 
caregiver and effect how the participant wears the activity belt during the accelerometry data 
collection period.  

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The caregivers of the participants can opt out of the home perception questionnaire, if they 
choose to, without penalty.  
In addition, child participants within the study will have the opportunity to participate in a 
general physical checkup during the final clinic visit by our study physician. Participation in the 
general physical is not mandatory, but is offered as a health incentive to participate in the final 
visit of the study. 
 
9.3.8. Final visit: Hemoglobin concentration collection (child)  
 
Hemoglobin concentration will be collected from the child participant one time during the final 
visit in the clinic (or the home if needed). The study physician or paramedics will ensure that 
the child is in a seated position before proceeding to clean the child’s left index finger with an 
alcohol wipe. They will proceed to prick participant’s left index finger with a spring-loaded 
needle. Squeezing from the base of the finger to the tip, the research personnel with then 
encourage blood to flow out of the participant’s finger. In order to prevent an inaccurate 
hemoglobin reading, the first drop of blood will be wiped away with the alcohol swap and the 
squeezing process will repeat. Once a second drop of blood emerges from the participant’s 
finger, a hemoglobinometer (HemoCue, Helsingborg, Sweden) will be used to collect the drop 
of blood and provide a hemoglobin concentration reading (to be documented in Form 4).  
 
If an individual has a reading <110 g/L, therefore indicating mild anemia, the study physician 
will be informed. The individual will then be referred to the local clinic to be evaluated by a 
physician. The Physician Referral Log will keep track of all anemic participants, date of referral, 
whether the participant accepted or declined the referral, date of physician appointment, if the 
participant attended appointment, treatment dosage, and reimbursement amount. If iron 
supplements are prescribed, we will reimburse the participant the amount spend on the 
treatment. Detailed instructions will also be provided to the caregiver on the use and safe 
storage of the supplements. 
 
If an individual has a reading <90 g/L, therefore indicating moderate or severe anemia, the 
study physician will be informed. As stated above, the individual will then be referred to the 
local clinic to be evaluated by a physician. All cases of anemia will be documented on Form 4 
and will be brought to the supervisor’s attention, then included in the Physician Referral Log. 
The Student Investigator will ensure that the study physician follows up with these participants, 
and  provides reimbursement for any iron supplements prescribed for treatment. 
 

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This measure was designed to be collected post accelerometry collection in order to prevent 
any potential influence iron supplements may have on the participant’s physical activity, 
specifically for those identified with anemia.  
 
An individual has the right to refuse participating in the hemoglobin concentration collection, 
and will not be penalized for declining this aspect of the study. They will not be included in 
analyses involving individual hemoglobin concentrations. 
 
9.3.9. Final visit: Collection of accelerometer and GPS logger 
 
Prior to the final visit, study personnel will remind the caregivers of the participants to bring the 
activity belt(s) to the clinic for their appointment. They will then collect the activity belts 
(including the accelerometer and GPS logger) from the participants. Data from both devices will 
then be downloaded and analyzed accordingly (see Section 9.3.5). Once the activity belt has 
been received, compensation will be provided to the participant’s caregiver for their time, 
travel, and participation in the study. As compensation for the participants for participating in 
the study, we will then be translating their PA data into a child-friendly, graphics-based activity 
report that each participant will receive post-analysis.  
 
This activity report will be explained to the parents. Based on the report, study personnel will 
either inform the caregiver to either encourage child physical activity levels or maintain current 
levels for optimal child health.   
 
9.4. Expected study duration (for each participant) 
 
The caregivers of the participants will be contacted for recruitment prior to their anticipated 
home visit. Study personnel will arrange a convenient time for the participant and their 
caregiver to conduct the initial home visit.  
 
Each participant will have a minimum study duration of 5 days and a maximum of 15 days. This 
will include: the initial home visit on Day 1; between 7-14 consecutive or non-consecutive days 
of wearing the activity belt (accelerometry and GPS logging); and the final visit in the clinic, 
which will occur once the participant has worn the activity belt for at least 4 days. 
 
The initial home visit is expected to last between 1-2 hours. Each daily phone call is expecting to 
be between 5-15 minutes each day. The final home visit is expected to last between 1-1.5 hours 
minutes.  
 

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9.5. Participant removal criteria 
 
Participants will be removed from the study if any of the following occur: 
1. Participant refuses to participate in accelerometry collection (as this is the primary 
outcome measure of the study); 
2. Contact with the participant’s caregiver has not been made in over 3 days; 
3. Caregiver withdraws participant consent. 
 
9.6. Sample size and power calculations  
 
Given 126 screened children, we expect to enroll up to 75 children in the study. For our primary 
objective, we will need 60 enrolled children with complete accelerometric data to be able to 
describe the mean counts/15 sec (cp15s) with a margin of error of ±2.45 cp15s, assuming a 
standard deviation of 9.7 cp15s 54.  
10. Analysis 
10.1. 
Outcome measure 
 
The primary measure of physical activity will be a continuous variable that represents the 
average total physical activity level of each child, expressed as a mean counts per 15 seconds 
(cp15s) averaged over all of the child’s eligible wear time on all days that meet criteria for 
inclusion.  
 
Due to the young age of the participants, compliance for a full 7 days may prove to be difficult; 
therefore we will use 3 days of data as the minimum number of valid days per child 31. If a child 
has more than 7 days of eligible wear time, we will include all additional days with sufficient 
wear time.   
 
Accelerometry data is collected at 30 Hz, meaning that data measures are collected 30 times 
per second. Activity counts collected within the 30 measures/second are averaged to provide 
an activity count for that particular second. We will be analyzing the data in sections of 15 
seconds, therefore we will be averaging activity counts within 15 second intervals (average 15 
data points of activity counts). These 15 second epochs (cp15s intervals that have been 
analyzed) will be used to analyze physical activity levels by categorizing them into sedentary 
behaviour (SED; sitting, laying down, etc.), light physical activity (LPA; walking, crawling, etc.), 
and moderate-to-vigorous physical activity (MVPA; running, jumping, etc.) using the cut-points 
chosen 55.  
 

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We will use the cut-points established by Trost and colleagues: £48 cp15s for sedentary 
behaviour (SED), 49-418 cp15s for light physical activity (LPA), and >418 cp15s for moderate-to-
vigorous physical activity (MVPA) 55. These cut-points were chosen because they were derived 
from direct observation of a sample of toddler and preschool-aged children while wearing the 
ActiGraph GT1M accelerometer 55. Videos of the children playing were coded using a modified 
version of the Children’s Activity Rating Scale before receiver operating characteristic curve 
analyses were conducted to determine the corresponding cut-points to the 3 categories of 
interest: SED, LPA, and MVPA 55. These results were found to exhibit excellent classification 
accuracy 55. They were then cross-validated against pre-existing cut-points that are established 
for toddlers and preschoolers, which were not found to be significantly different than the pre-
established cut points established by Pate and colleagues 56 and those established by National 
Health and Nutrition Examination Survey 55. 
 
We will then be able to calculate total time (minutes) as well as proportion of time (%) spent in 
each activity category (SED, LPA, MVPA). The overall mean cp15s for each child will be based on 
all 15-second epochs that occurred during eligible wear periods from the time that the 
caregiver indicated the child woke up until the time that the caregiver indicated the child went 
to sleep at. If this data is missing, we will use eligible wear periods between 8am to 8pm. This 
mean will exclude non-wear periods as defined above and when the accelerometer was not 
being worn by the child (based on daily logs collected from the caregivers via afternoon 
telephone call; Section 9.3.7.). 
 
For each child, we will calculate the mean number of minutes per day and the proportions of 
the child’s total eligible wear time that are classified as SED, LPA and MVPA. We will also 
determine the proportion of days on which each child reached or exceeded 180 minutes of 
activity classified as LPA or MVPA, and the proportion of days on which each child reached or 
exceeded 60 minutes of activity classified as MVPA. These cut-offs are based on the Canadian 
preschooler physical activity guidelines established by Tremblay and colleagues 57, as there are 
no Bangladeshi standards available at present. If Bangladeshi guidelines are released before 
data analysis and reporting is completed, we will also apply those standards in our analyses.  
 
For each child, we will establish the following parameters related to accelerometry: 
• Total duration (minutes) of the potential data collection period on all days on which the 
accelerometer was instructed to be worn; 
• Total eligible wear time (minutes), including data collected while the child was awake 
(from the time that their caregiver indicated that they woke up until the time that they 
went to sleep). If this data is not provided, total eligible wear time will be measured 
between 8am and 8pm. Non-wear periods will be excluded based on the following 

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criteria: ≥20 minutes of 0 cp15s allowing for up to 2 minutes of 0-100 cp15s in that 
period 31. A given day will be considered eligible for inclusion if there are at least 10 
hours/day (600 min) between the indicated time period that the child was awake at, or 
between 8am to 8pm of wear time for those with missing data, after excluding non-
wear periods; 
• Total indoor eligible wear time (minutes; excluding non-wear periods as previously 
defined), based on maternal report; 
• Total eligible wear time in or around the home (minutes; excluding non-wear periods as 
previously defined), based on combination of maternal report and GPS logging. “Around 
the home” will be defined as occurring in the same building or property of the home 
(e.g., rooftop, neighbouring apartment in the same building, common areas of the 
apartment building, private courtyard areas that are on the same property as the home 
in which the child resides). 
 
For purposes of secondary and sensitivity analyses, we will also estimate for each child:  
• Mean cp15s for indoor activity 
• Mean cp15s for activity in or around the home  
• Mean cp15s on a day that is typically a work day (Sunday through Thursday) 
• Mean cp15s on a Friday and/or Saturday (weekend)  
 
For each of the above measures, we will also classify the activity in terms of average daily 
minutes and proportion of total time spent in SED state, LPA state, and MVPA state.  In 
sensitivity analyses, we will compare the average daily minutes and proportion of total time 
spend in each activity category across different cut-off points that have been established by 
various research groups:  
• Proportion of time spent in activity categories established by Pate and colleagues (SED: 
≤199 cp15s; LPA: 200-419 cp15s; MVPA≥420 cp15s)56  
• Proportion of time spent in activity categories established by Sirard and colleagues (SED: 
≤301 cp15s; LPA: 302-614 cp15s; MVPA ≥615 cp15s)58Proportion of time spent in 
activity categories established by Van Cauwenberge and colleagues (SED: ≤372 cp15s; 
LPA: 373-584 cp15s; MVPA ≥585 cp15s)59 
 
To address aim 4, we will analyze sleep quality and quantity directly on the nighttime 
accelerometry wear periods. Sleep data will be measured in 60-second epochs. This is obtained 
from the same accelerometry file that the activity data is derived from; however, data will be 
re-integrated into a 60-second epoch file (analyzes number of activity counts in every 60-
second period).  
 

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Using the manufacturer’s add-on to the ActiLife software (ActiLife 6 Full sleep Features 
Upgrade), we will be able to detect the number of sleep periods as well as the duration and 
quality of the sleep periods within the accelerometry file for each participant. The software will 
also detect and describe: 
• Time in bed/out of bed (am/pm); duration in bed (hours; min) 
• Sleep onset (when sleep commenced; am/pm) 
• Total Sleep Time (TST; hours, min) 
• Wake After Sleep Onset (WASO; describes how many minutes after sleep onset the 
participant woke up; min) 
• Awakenings (number of awakenings during sleep onset) 
• Average awakenings (average duration of awakenings during sleep onset; min) 
• Total activity counts during sleep period (cp60s) 
• Movement Index (MI) 
• Fragmentation Index (FI) 
• Sleep Fragmentation Index (SFI) 
• Sleep Efficiency (%) 
 
For descriptive statistics, these measures will be averaged over the number of eligible nighttime 
wear periods for each participant. In extended analyses, each eligible nighttime wear period 
will be considered as the unit of analysis.  
 
The accelerometry data will either be processed using the software supplied by the 
manufacturer (ActiLife ver6) or using a published code to analyze raw accelerometer data 60. 
 
10.2. 
Exposure variables  
 
1) Total area of available floor space inside the home (m2) 
2) Frequency that child plays outdoors  
3) Total number of physical hazards in the home (presence of open stairwells, stove 
height) 
4) Total number of fine-motor activity items available to the child  
5) Presence of gross-motor activity items available to the child across each category 
(e.g., fixed, portable: ball equipment, push/pull equipment, riding equipment, rocking 
and twisting equipment) 
6) Hemoglobin concentration (g/L).  
 
Descriptions and derivations of each of these variables can be found in Table 1 (Appendix).  

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10.3. 
Other covariates 
 
Other covariates will include: preschooler sex, preschooler anthropometric measures (e.g., 
height-for-age z-score, BMI-for-age z-score, waist circumference, and body fat percentage), 
maternal anthropometric measures (e.g., BMI, waist circumference, and body fat percentage), 
maternal and paternal socio-economic factors (educational level and household assets), and 
household food security status. Descriptions and derivations of each of these variables can be 
found in Table 2 (continuous variables) and Table 3 (categorical variables) (Appendix).  
 
10.4. 
Statistical Analysis 
10.4.1. Primary Analysis 
 
Statistical analyses will be performed using STATA (College Station, TX). Data from Forms 1, 2, 3, 
4, and the Accelerometry and GPS Log will be double entered in real time independently by 
research personnel. Data forms will be de-identified, such that only a participant ID number will 
be used to identify participants. Any discrepancies between data enterers will be flagged and 
reconciled by the student investigator. Data reconciliation will be conducted by the student 
investigator and will consist of reviewing the original hardcopy of the document and/or 
discussing the measure with the research assistant who had originally recorded it. Data review 
will consist of creating histograms, boxplots, and normal quantile-quantile (Q-Q) plots of each 
continuous variable to identify outliers and potentially implausible data.  
 
Each day, the student investigator will assess the hardcopies of the data collection forms for 
missing data. In the case that data that can be collected over the telephone is missing (e.g., 
socioeconomic information, age, home perception questionnaire measures, etc.), if the missing 
data is flagged within 60 days of the date of initial collection, study personnel will reach out to 
the participant and their caregiver via telephone call to collect the measure of interest. If the 
missing data is flagged outside of the 60-day window of re-collection, the participant will be 
automatically opted out of that particular measure. In the case that data that must be collected 
in person is missing (e.g., body composition measures, dimensions of the home environment, 
hemoglobin concentration, etc.), if the missing data is flagged within 21 days of the date of 
initial collection, study personnel will reach out to the participant and their caregiver to 
schedule another home visit to collect the measure of interest (e.g., if measures are missing 
from the initial home visit, they may be collected in person during the final visit). In the case 
that the participant or their caregiver has opted out of a particular measure (e.g., hemoglobin 
concentration), they will not be included in analyses that involve that measure of interest.  
 

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Objective #1: Describe physical activity levels of the sample of preschoolers in urban Dhaka. 
 
To address our first objective, we will first create a histogram of the distribution of the data to 
assess the spread and shape of the frequency distribution of each of the categories of all of the 
physical activity data (average cp15s across all days for each participant). The number of bins 
within the histogram will be determined using the equation 1 + 3.3log10(n). A boxplot will then 
be created to assess the spread, as measured by the median; interquartile range (IQR); range of 
observations; presence of outliers; and provide additional information about the shape of the 
data. Finally, a Q-Q plot will be created to compare the frequency distribution of the data to a 
normal distribution. Using the shape and direction of the curvature, we will assess any 
deviation from normality. 
 
We will estimate the mean and 95% confidence interval of cp15s for total physical activity of 
the sample of children, overall and stratified into the following groups: 
• Boys and girls 
• BMI categories (underweight, normal, overweight and obese) 
• Household food security status (food secure, moderately food insecure; severely food 
insecure) 
• Anemic and non-anemic 
 
We will then summarize the distributions of the average amount (# of minutes) and proportions 
of wear time spent engaging in SED, LPA, and MVPA, over the measurement period. 
Distributions of each time spent in each level of activity will be shown graphically (histograms, 
kernel density plots) and summarized using conventional descriptive statistics: mean and 
standard deviation if normally distributed, or median and interquartile range if not normally 
distributed.  
 
We will then estimate the frequency of children who met Canadian physical activity 
recommendations on any day, and the average number of days on which children met the 
recommendations (180 minutes of activity classified as LPA or MVPA, working towards 60 
minutes of activity classified as MVPA)57. Bangladeshi recommended guidelines for preschool-
aged children will be used if they are released before statistical analyses begin. These data will 
be compared to published results in similar international, preschooler populations. 
 
The above analyses will be repeated using data restricted to indoor wear time, in and around 
the household wear time, on weekdays, and on weekends.    
 

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In addition, we will be using the GPS logging data to create ‘heat maps’ for each of the 
participants’ physical activity locations. This will first be done at the individual level using kernel 
density estimation and smoothing. These individual ‘heat maps’ can be combined in a master 
map that will create a visual piece that can aid in describing study findings. We will then use the 
heat maps for each individual to estimate the average diameter of the ‘sphere’ of activity (km) 
for the entire study population.  
 
Objective #2: Estimate the associations between characteristics of the physical environment of 
the home environment (total area of available floor space inside of the home; frequency that 
child plays outdoors; number of physical hazards present within the home; number of stationary 
gross-motor activity-facilitating items present; and number of portable gross-motor activity-
facilitating items present) and preschooler activity level. 
 
To address our second objective, we will first graph each of the variables of interest to assess 
the data for outliers or implausible data. We will generate histograms, box-plots, and Q-Q plots 
to assess the following continuous variable: 
• Total area of available floor space inside of the home (m2); Height of stove inside the 
home (m); 
We will generate medians and IQRs for the following discrete, continuous variables: 
• Open stairwells in the home (no railing); 
• Presence of fine-motor activity-facilitating items; 
• Presence of gross-motor activity-facilitating items (for each category).  
 
These discrete, continuous variables may be categorized and treated as categorical variables 
depending on the data. To assess the linearity between these continuous exposures and 
outcome measure (using each parameter defined in Section 10.1.), we will create scatterplots 
and generate the correlation coefficient (Pearson correlation) for each relationship. A locally 
weighted scatterplot smoother (LOWESS) technique will also be used to check for departures 
from linearity. If the association between the exposure and the outcome is linear, we will 
proceed with the linear regression. However, if the association is non-linear, we will proceed 
with a linear spline model.   
 
Descriptive statistics: 
 We will calculate the mean and standard deviation of the following continuous measures: 
• Area of available floor space indoors;  
• Population density of indoor space (number of people dwelling in the home/total 
available area of indoor floor space);  
• Height of stairwell railings (of those that had a railings); and 

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• Height of stove (of those who had stoves) 
We will estimate the median and interquartile range of the following discrete measures: 
• Number of rooms within the homes;  
• Number of people who live in the home 
We will generate tabular descriptions (frequencies and percents) of the following measures: 
• Homes that had open stairwells (no railings); 
• Homes that that had minor (<1m2) unfinished construction (of those who had unfinished 
construction); 
• Those who had major (>1m2) unfinished construction (of those who had unfinished 
construction); 
• Building materials that the activity-facilitating items in the home are made of (e.g., clay, 
plastic, metal, wood, cloth, other). 
 
We will use a mixed linear model with a random child-specific intercept. This is due to the 
repeated measures collected over the 7-day period for each participant. The physical activity 
data will be analyzed in activity counts per 15 second epochs, however to create multiple 
observations, we will nest the data by averaging the epochs over each of the 7 days to create 7 
separate observations in each of the participant’s datasets. This method was chosen since it will 
appropriately model the means and standard errors as well as properly handle missing data. 
We will use this model to estimate the unadjusted and multivariable-adjusted associations 
between each of the exposure variables (specified above) and PA level (counts/15s). Covariates 
that will be included in multivariable models will be pre-determined using a hypothesized 
directed acyclic graph (DAG).  
 
Objective #3: Estimate the associations between haemoglobin concentration (Hb) and 
preschooler physical activity level. 
 
 
To address our third objective, summary statistics (including mean, standard deviation, median, 
range, interquartile range) will be generated for hemoglobin (as a continuous variable) to help 
identify outliers and biologically implausible data.  
 
Mixed linear modeling with a random intercept will then be used to determine the unadjusted 
and multivariable-adjusted association between infant hemoglobin and physical activity (mean 
counts/15s). Similar to objective #2, a priori covariates to be included in the multivariable 
model will be determined through the development of a DAG.  
 
Using the same procedures identified in Objective #2, we will assess model assumptions and fit.   
 

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Objective #4: Describe sleep quantity and quality in the sample of preschool-aged children in 
urban Bangladesh 
 
To address our fourth objective, we will create histograms of the distribution of the data to 
assess the spread and shape of the frequency distribution of the various available measures of 
sleep quantity and quality. The number of bins within the histogram will be determined using 
the equation 1 + 3.3log10(n). A boxplot will then be created to assess the spread, as measured 
by the median; interquartile range (IQR); range of observations; presence of outliers; and 
provide additional information about the shape of the data. Finally, a Q-Q plot will be created 
to compare the frequency distribution of the data to a normal distribution. Using the shape 
and direction of the curvature, we will assess any deviation from normality. 
 
We will then create summary statistics (including mean/standard deviation or 
median/range/interquartile range) for: 
• Time spent in bed (hours)  
• Time “to bed”, time “out of bed”  
• Sleep quantity  
o Time at which “sleep onset” 
o Total Sleep Time (TST) (hours; min) 
o Total activity counts (cp60s) 
• Sleep quality 
o Wake After Sleep Onset (WASO) (min) 
o Awakenings (number/sleep period); Avg awakenings (avg number/sleep period) 
o Movement Index (MI) 
o Fragmentation Index (FI) 
o Sleep Fragmentation Index (SFI) 
o Sleep efficiency (%) 
 
We will also compare distributions across groups stratified by: 
• Boys and girls 
• BMI categories (underweight, normal, overweight and obese) 
• Household food security status (food secure, moderately food insecure; severely food 
insecure) 
• Anemic and non-anemic 
• Other maternal and infant characteristics 
 
We will then estimate the frequency of children who met Canadian physical activity 24 Hour 
Movement Guidelines recommendations on any day, and the average number of days on which 
children met the recommendations for sleep duration (10-13 hours/day)57. Bangladeshi 
recommended guidelines for preschool-aged children will be used if they are available at the 

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time of statistical analyses. These data will be compared to published results in similar 
international, preschooler populations. 
 
This will be done using the same data files collected from the participant; however, we will only 
use data from non-waking hours. We will use the ActiLife software to analyze all nighttime 
activity data and derive variables pertaining to sleep quantity and quality.  
 
We will also compare objectively measured sleep periods (based on accelerometry) with parent 
reported bedtimes and waking times. This will be done using Individual Manual Inspection (IMI) 
by comparing bedtimes and waking times reported in the Accelerometry & GPS Log to the 
computed activity and sleep graphs created in the ActiLife software.  
 
10.4.2. Sensitivity analyses 
 
To account for the participants’ ‘adjustment time’ to wearing the activity belts, we will exclude 
the first day of wear-time from analyses. 
 
We will be comparing the wear-time location data from parent-report measures to the data 
from the GPS logger to evaluate whether parent-report alone provides enough information on 
the participant’s location information. We will assess the GPS data for each participant, 
specifically looking for substantial coordinate (latitude and longitude) changes. Any deviations 
from the home coordinates will be cross-referenced to the mother’s report by comparing the 
coordinate’s corresponding time stamp and the time indicated by the mother. We will also 
assess any coordinate changes if the parent indicated that the child left the home.  
 
We will run the same model described in objective 2, however we will stratify by preschooler 
sex.  
 
We will be comparing the mean cp15s of the Trost and colleagues cut-points 55 to the cut-points 
established by Pate and colleagues 56, Sirard and colleagues 58, and Van Cauwenberge and 
colleagues 59. We will conduct an ANOVA to establish if there is a difference among strata listed 
above.   
11. Data Security and Protection of Subject Confidentiality  
 
Data will be entered and accessed using RedCap (Sick Kids version) with an encrypted laptop 
and managed on a secure server at the iccdr,b in Dhaka. This laptop will be accessed by the 
student investigator during the study collection period in Dhaka, and then transported back to 
Toronto when the student returns post-data collection period. If any data are collected after 
the student investigator returns to Toronto, data will be securely transferred to Toronto via a 

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secure web portal. All participants will be assigned a unique study ID number, which will be 
used to ensure that transferred data is de-identified.  
 
All photographs will be organized, stored, and transferred using the participant’s ID to ensure 
subject confidentiality. Individuals will not be captured in the photographs; however, if any 
faces or identifiable characteristics are captured accidentally, these images will be blacked out 
accordingly.  
 
Double data entry by different study personnel and in-built range/consistency checks will be 
used to optimize data quality. Discrepancies between data enterers will be flagged by RedCap 
and adjudicated by the student investigator using hardcopies of the data collection forms. 
12. Consent Process and Documentation 
 
Written consent will be obtained from a primary caregiver (typically the mother) for all study 
procedures (Consent Form A for study procedures; Consent Form B for home audit and 
photographs of the built environment inside and outside of the home). During the initial home 
visit, the consent form will be read to the participant’s caregiver. If all terms are agreed to, the 
caregiver can either sign the consent form or thumb stamp it using their left thumb. No data 
collection procedures will commence until written, informed consent is collected.  
 
The study physician will assess the parent/caregiver’s capacity to provide consent. This will be 
based on the study physician’s ample experience working with this particular study population. 
13. Risks 
 
This is a minimal risk study. There is a low risk of infection following the collection of 
hemoglobin concentration via finger prick; appropriate study procedures, including the proper 
cleaning of the finger using an alcohol swab will help to minimize this risk. 
 
The only potential risk/discomfort associated with wearing the accelerometer and GPS logger is 
the minimal discomfort and weight of the activity belt. During the daily phone calls, study 
personnel will ask the caregiver of the participant if there are any problems or concerns with 
their child wearing the devices. This ensures that if there are any compliance issues surrounding 
wearing the devices, study personnel will be aware of this immediately. If a participant feels 
uncomfortable wearing the accelerometer, the study personnel will inform their supervisor, 
and make appropriate changes to increase compliance and decrease participant burden (e.g., 
encouraging the participant to wear the belt over/under clothes, depending on preference; 

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attaching stickers to the devices to make them more visually appealing to children; providing 
the caregiver with a pedometer to model in front of the child while they wear the activity belt). 
If after making appropriate changes, the participant still does not feel comfortable wearing the 
activity belt, they can choose to opt-out of the remainder of the study and still receive 
compensation, if a minimum of 4 days of accelerometer data collection has been obtained.  
 
If a participant feels uncomfortable wearing the GPS logger specifically, we will allow for the 
caregiver (or whoever is supervising the child) to carry the device on their person, so long as 
they are with the child during waking hours. In addition, if the caregiver does not feel 
comfortable disclosing specific locations that their child visits, they can opt to remove the GPS 
logger from the pouch attached the activity belt for that specific time period. The device will 
then be returned to the pouch once the participant has left that specific location.  
 
Research ethics approval will be obtained from both the Research Ethics Board at the Hospital 
for Sick Children in Toronto and the Research Review Committee and Ethical Review Committee 
at icddr,b in Dhaka.  
14. Benefits 
 
Although there are no major health benefits for participating in the study, there is an 
opportunity to receive medical treatment to individuals who are below the hemoglobin 
concentration cut-off for anemia.  
 
Additionally, all participants will receive a graphics-based, child-friendly activity report of their 
data collected from wearing the accelerometers (e.g., average number of daily steps taken, 
average time spent being active, average time spent being sedentary, etc.). All activity reports 
will congratulate the participants on their achievements throughout the week and thank them 
for participating in the study. The activity reports will be hand delivered to the participants 
after the accelerometry data has been analyzed. This will also be explained to caregivers to 
provide them with additional information on increasing or maintaining their child’s physical 
activity levels to promote optimal health.  
 
Financial compensation will be provided to caregivers for their time and participation in the 
study, with a minimum of 4 days of activity data collection.  
15. Safety and Adverse Events 
 
Adverse events are not predicted to occur in this study. 

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55. 
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17. Appendix 
 
Table 1 - List of exposure variables and their derivation 
Variable 
Unit/Categories; 
Type of variable 
Derivation  
Total available 
floor space 
inside the 
home 
m2; continuous 
Data required (Form 3: Q9b-c): 
• Averages of the 2 length measures from each 
room/space inside the home (m) 
• Averages of the 2 width measure from each 
room/space inside the home (m) 
• Averages of the 2 length measures from each large 
piece of furniture (>1m2) from inside the home (m) 
• Averages of the 2 width measure from each large 
piece of furniture (>1m2) from inside the home (m) 
Area of each room/space within the home will be 
calculated by multiplying the averages of the 2 length 
by width measures for each room/space in the home 
(m2). Area of each large piece of furniture within the 
home will be calculated by multiplying the averages of 
the 2 length by width measures for each large piece of 
furniture in the home (m2). Areas from any beds will 
also be calculated and identified.  
The total area of available space indoors (m2) will be 
calculated by summing all of the areas of living space 
dimensions within the home. The total area of large 
furniture (m2) will be calculated by summing all of the 
areas of furniture obstructions within the home, 
excluding the areas from any beds.  
Finally, the area of total available floor space inside of 
the home (m2) will be calculated by subtracting the 
total area of large furniture (m2) from the total area of 
available space indoors (m2). Areas of the beds will be 
included as available indoor floor space, as they are 
often used as a play area for children in this setting.  
Total available 
floor space 
inside and 
m2; continuous 
Data required (Form 3: Q11b-c): 
• Area of the total available floor space inside the 
home (as described above); 

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outside of the 
home 
• Averages of the 2 length measures from each 
enclosed space outside the home (m) 
• Averages of the 2 width measure from each 
enclosed space outside the home (m) 
• Averages of the 2 length measures from each large 
obstruction (>1m2) within the enclosed space 
outside of the home (m) 
• Averages of the 2 width measures from each large 
obstruction (>1m2) within the enclosed space 
outside of the home (m) 
Area of the enclosed outdoor space will be calculated 
by multiplying the averages of the 2 length by width 
measures for each outdoor space (m2). Area of each 
large obstruction outdoors will be calculated by 
multiplying the averages of the 2 length by width 
measures for each large obstruction within the enclosed 
outdoor space (m2). 
The total area of enclosed outdoor space (m2) will be 
calculated by summing all of the averages of enclosed 
outdoor space. The total area of large obstructions (m2) 
will be calculated by summing all of the areas of large 
obstructions within the enclosed outdoor space outside 
of the home. Finally, the area of total available floor 
space outside of the home (m2) will be calculated by 
subtracting the total area of large outdoor obstructions 
(m2) from the total area of enclosed outdoor space 
(m2). 
Finally, the summation of the total available floor space 
inside of the home (m2) and the total available floor 
space outside of the home (m2) will provide the total 
available floor space inside and outside of the home 
(m2).  
Total number 
of physical 
hazards in or 
around the 
home 
Count (all 
integers between  
0 and 5); discrete 
Data required: 
• Presence of unsafe stairwell (No = 0; Yes = 1) 
o Form 3: Questions 12a, 12b, 12c, and 12d 
o “No” (will receive a score of 0) if:  
§ Q12a = 1 

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§ Q12a = 2; Q12b = 2; Q12c >0.9 m; 
Q12d = 1 
§ Q12a = 2; Q12b = 2; Q12c >0.9 m; 
Q12d = 2 
o “Yes” (will receive a score of 1) if:  
§ Q12a = 2; Q12c <0.9 m; Q12d = 1 
§ Q12a = 2; Q12c <0.9 m; Q12d = 2 
§ Q12a = 2; Q12c >0.9 m; Q12d = 1 
• Presence of stove that is accessible to child (No = 0; 
Yes = 1) 
o Form 3: Questions 13a and 13b 
o “No” (will receive a score of 0) if: 
§ Q13a = 1 
§ Q13a = 2; Q13b >0.9 m 
o “Yes” (will receive a score of 1) if: 
§ Q13a = 2; Q13b <0.9 m 
• Presence of unfinished construction (No = 0; Yes = 
1) 
o Form 3: Question 14 
o “No” (will receive a score of 0) if: 
§ Q14 = 1 
o “Yes” (will receive a score of 1) if: 
§ Q14 = 2 
§ Q14 = 3 
• Presence of uncovered sewage drain, accessible to 
the child (No = 0; Yes = 1) 
o Form 3: Question 15 
o “No” (will receive a score of 0) if: 
§ Q15 = 1 
o “Yes” (will receive a score of 1) if: 
§ Q15 = 2 
• Presence of boti in home and storage of item is 
accessible to the child (No = 0; Yes = 1) 
o Form 3: Questions 16a and 16b 
o “No” (will receive a score of 0): 
§ Q16a = 1 
§ Q16a = 2; Q16b = 2 
o “Yes” (will receive a score of 1): 

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§ Q16a = 2; Q16b = 1 
Each score from each of the 5 categories will be added 
to calculate the combined total of hazards present in 
the home.  
Total number 
of stationary 
gross-motor 
activity items  
Count (all 
integers between 
0 and 10); 
discrete 
Data required: 
• Form 3: Questions 17b-c, 18b-c, 19b-c, 20b-c, 21b-c, 
22b-c, 23b-c, 24b-c, 25b-c, 26b-c 
• The number of incidences where both the ‘b’ 
question = 2 AND the ‘c’ section = 2 (e.g., 17b = 2 
AND 17c = 2, this indicates that this item is a 
stationary gross-motor activity item) 
The number of items that satisfy the above rules will be 
added together to provide this score. The minimum 
score is 0 items, with a maximum score of 10 items. 
Total number 
of portable 
gross-motor 
activity items  
Count (all 
integers between  
0 and 10); 
discrete 
Data required: 
• Form 3: Questions 17b-c, 18b-c, 19b-c, 20b-c, 21b-c, 
22b-c, 23b-c, 24b-c, 25b-c, 26b-c 
• The number of incidences where both the ‘b’ 
question = 1 AND the ‘c’ section = 2 (e.g., 17b = 1 
AND 17c = 2, this indicates that this item is a 
portable gross-motor activity item) 
The number of items that satisfy the above rules will be 
added together to provide this score. The minimum 
score is 0 items, with a maximum score of 10 items.  
Hemoglobin 
concentration 
g/L; continuous 
Hemoglobin concentration data collecting in Form 4: 
Q24.  
 
 
Table 2 - List of additional continuous covariates and their derivation 
Variable 
Unit 
Derivation  
Body mass index 
(BMI) (caregiver) 
kg/m2 
Data required (Form 2: Q10-13): 
• Height (m; average of 2 closest height measures) 
• Weight (kg; average of 2 closest weight measures) 
BMI will be calculated using the formula:  
BMI = weight/height2 

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Body mass index 
(BMI) z-score  
(child participant) 
(z-score) 
Data required (Form 2: Q1-4): 
• Height (m; average of 2 closest height measures, value 
will be adjusted by subtracting 0.7 cm if recumbent 
length was measured) 
• Weight (kg; average of 2 closest weight measures) 
Height and weight will be plotted on the WHO body mass 
index-for-age (BMI-for-age) chart (sex specific; ages 2 to 5 
years) to determine participant BMI z-score.  
Waist circumference 
cm 
Average of the 2 closest waist circumference measures 
(Form 2: Q5,6,14,15) 
Body fat percentage 
(female caregiver) 
% 
Data required (Form 2: Q10, 11, 12, 13, 17, 18): 
• Height (m; average of 2 closest height measures) 
• Weight (kg; average of 2 closest weight measures) 
• Resistance (ohms measured at 50 kHz; average of the 2 
closest resistance measures) 
1) Total body water (TBW; kg) will be calculated using the 
following equation:  
TBW = 4.297 + 0.190*Weight + 0.349*Height2/Resistance 
2) To determine fat free mass (FFM; kg), TBW will be 
divided by a hydration factor of 0.732. 
3) Fat mass (FM; kg) will be calculated by subtracting FFM 
from total weight.  
4) Body fat percentage will be calculated using the following 
formula: Body fat % = (FM/Weight)*100% 
Body fat percentage 
(child participant) 
% 
Data required (Form 2: Q1, 2, 3, 4, 7, 8): 
• Height (m; average of 2 closest height measures, value 
will be adjusted by subtracting 0.7 cm if recumbent 
length was measured) 
• Weight (kg; average of 2 closest weight measures) 
• Resistance (ohms measured at 50 kHz; average of the 2 
closest resistance measures) 
1) Total body water (TBW; kg) will be calculated using the 
following equation:  
TBW = 0.76 + 0.18*Height2/Resistance + 0.39*Weight 
2) To determine fat free mass (FFM; kg), TBW will be 
divided by a hydration factor of 0.775 (males) or 0.779 
(females). 

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3) Fat mass (FM; kg) will be calculated by subtracting FFM 
from total weight.  
4) Body fat percentage will be calculated using the following 
formula: Body fat % = (FM/Weight)*100% 
Number of children 
living in the home 
Any 
integer 
Data required: 
• Form 3: Question 3 
Household density  
People/
m2 
Data required: 
• Number of adults living in home (Form 3; Question 2) 
• Number of children living in home (Form 3; Question 3) 
• Total available floor space inside of the home (m2, see 
above in Table 1) 
The total number of individuals living in the home will be 
calculated by summing the number of adults living in the 
home with the number of children living in the home. The 
total number of individuals living in the home will then by 
the total available floor space inside of the home to 
determine household density.  
Number of rooms in 
home 
Any 
integer 
Data required: 
• Form 3: Question 4 
Number of screens 
available to child 
Any 
integer 
Data required: 
• Form 3: Question 8 
 
Table 3 - List of additional categorical covariates and their derivation 
Variable 
Categories 
Derivation  
Sex 
Male; Female 
Screening Form. 
Maternal 
education 
No schooling; some or 
completed primary 
education; some or 
completed secondary 
education; some or 
completed tertiary 
education 
Data required: 
• Form 1: Q3-4 
Household 
wealth 
Wealth quintiles  
Information on household characteristics and 
ownership of specific household items will be collected 
in Form 1. Using principle analysis, participants will be 
categorized into 5 separate categories. The first 
principle component will be used to assign each 

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individual an asset score, with lower scores reflecting 
ownership of fewer items (i.e., less relative wealth) and 
higher scores indicative of greater relative wealth. 
Using the distribution of asset scores in the study 
population, quintiles will be formed and participants 
will be categorized appropriately. 
Food 
security 
status 
Food secure; 
moderately food 
insecure; severely 
food insecure 
Data required: 
• Question 18 (Form 4; 1-item food insecurity 
screening tool; NutriSTEPTM)61 
A score of 4 indicates food secure; a score between 2-3 
indicates moderately food insecure; a score of 1 
indicates severely food insecure.  
• Question 19 & 20 (Form 4; 2-item food insecurity 
screening tool; Hager and colleagues, 2010)62 
A combined score of 6 indicates food secure; a 
combined score of 4-5 indicates moderately food 
insecure; a combined score of 1-3 indicates severely 
food insecure.  
More than 
1 family 
living in the 
home 
Yes; No 
Data required: 
• Form 3: Question 6a