Physical Activity, Sedentary Behavior in Parent-Child Pairs
Physical Activity, Sedentary Behavior in Parent-Child Pairs
The current study analyzed baseline data from a subgroup of children and parents participating in a larger 4-yr intervention trial (Healthy PLACES [Promoting Livable Active Community Environments]), which is investigating the effects of smart growth community design principles on the prevention of family obesity risk. Participants included fourth-through eighth-grade children (age 8–14 yr) and their parents. Families lived in Chino, CA, or surrounding communities within 30-min driving time from Chino (including Ontario, Pomona, Diamond Bar, Corona, and Yorba Linda/Mira Loma). Recruitment was through a variety of channels including informational flyers and letters distributed at community events, housing association meetings, residences, schools, clinics, churches, and community groups. In addition, study advertisements were placed in local newspapers, posters were displayed at community sites, and postcards were mailed to homes in the selected areas. All recruitment materials included the study recruitment hotline phone number and e-mail address. A telephone recruiter called all interested families and screened for eligibility. Inclusion criteria consisted of the following: a) a child currently enrolled in the fourth to eighth grade; b) living in Chino, CA, or a surrounding community; and c) annual household income less than $210,000. Upper income households were excluded because the goal of the study was to focus on children from low- to middle-income families who have higher obesity risk. Children who met the eligibility criteria were scheduled for a data collection appointment at a local community site or their home. Written informed consent and minor assent were obtained from participants. This research was reviewed and approved by the institutional review boards at the University of Southern California and the University of California, Berkeley.
Measurement occurred through a cross-sectional design. Objective physical activity, GPS, and survey data were collected from March 2009 to December 2010. No data collection took place from late July to August and during January because of typically adverse temperatures and weather conditions that limit outside activity in that part of Southern California. Within the parent–child pairs, both wore an accelerometer and GPS device during the same 7-d period.
Physical Activity The ActiGraph, Inc., GT2M model activity monitor (firmware v06.02.00, Pensacola, FL) provided an objective measure of physical activity. The device was worn on the right hip attached to an adjustable belt. A 30-s epoch was set for the recording of activity counts. Participants were asked to wear the accelerometers across seven continuous days. The devices were not worn when sleeping, bathing, or swimming. Cutpoints for MVPA were consistent with studies of national surveillance data. For adults, the MVPA threshold was 2020 counts per minute (equivalent to 3 METs). MVPA for children was defined using age-specific thresholds generated from the prediction equation of Freedson et al.. A threshold for moderate activity of 4 METs was used for children (as opposed to a 3-MET moderate activity cutoff for adults) to account for higher resting energy expenditure in children and youth. For both adults and children, light activity was greater than or equal to 100 counts per minute through the MVPA threshold. Sedentary activity was defined as less than 100 counts per minute.
Location Monitoring Portable GPS devices were used to assess locations in both children and parents. Geographic locations were logged for a 7-d period with the BT-335 Bluetooth GPS data logger device by GlobalSat Technology Corp. (New Taipei City, Taiwan) attached to a belt worn around the waist along with the accelerometer. The BT-335 (16 Mbit, 1575.42 MHz) consists of a GPS receiver and data logger with Bluetooth PC interface. This device records time, date, speed, altitude, and GPS location at preset intervals. It is WAAS/EGNOS/MSAS enabled and uses a SiRFstarIII chip set for accurate position tracking (up to 5-m accuracy outdoors) and improved indoor signal acquisition. The recording interval was set to a 30-s epoch to match the accelerometer specifications. After the GPS devices were returned, all recorded information was downloaded to a computer where the recorded longitudinal and latitudinal data and speed were downloaded to a CSV file format. Because the device has a battery life of 25 h, a battery charger was provided, and participants were instructed to recharge the battery each night. Linear distance between the parent and child for each 30-s epoch was calculated using geographic coordinates from the GPS.
Height and Weight Parents' and children's height and weight were measured in duplicate using an electronically calibrated digital scale (Tanita WB-110A Tanita, Arlington Heights, IL) and professional stadiometer (PE-AIM-101, Perspective Enterprises, Kalamazoo, MI) to the nearest 0.1 kg and 0.1 cm, respectively. BMI was calculated (kg·m). Children's weight status was classified according to Centers for Disease Control and Prevention age- and sex-specific BMI percentile cutoffs.
Demographic Variables Age, sex, and race/ethnicity were assessed through child and parent self-report surveys. Parents reported annual household income, which was divided into quartiles (less than $30,000, $30,000–$59,999, $60,000–$99,999, and $100,000 and above).
To conduct data manipulation tasks before analysis, accelerometer and GPS files were imported into the R version 2.9.2 programming language interface. Date and time stamps to the nearest 30-s epoch were used to match all accelerometer and GPS records within each parent–child pair. In the numerous cases where concurrent accelerometer and GPS were unavailable for either the parent or the child, we used a missing data code (not applicable) for designating the accelerometer and/or GPS values for these epochs. Overnight (11 p.m. to 5 a.m.) and school (8 a.m. to 3 p.m. on weekdays) hours were removed from the analyses. Strings of consecutive readings of zero activity counts lasting 60 min or more were considered accelerometer nonwear and excluded from analyses. Activity outliers were identified as records with greater than 16,383 counts per 30-s epoch. Records with GPS speeds greater than 169 km·h (105 mph) were also considered outliers because normal driving speeds are well below this value. Motorized activity, which was identified by records with speeds greater than 32 km·h because typical bicycling speeds range from 15 to 30 km·h (9.32 to 18.64 mph), was also excluded from the analyses. Once these records were removed, parent–child pairs were determined to have sufficient data for inclusion in the analysis if they had a minimum of two valid days (any combination of weekdays or weekend days) of matched available data—where a valid weekday was defined as a minimum of 2 h of matched available accelerometer and GPS data points for the pair and a valid weekend day was defined as a minimum of 4 h of matched available accelerometer and GPS data points for the pair. "Joint" or "together" behaviors were defined as activities of the same intensity level (sedentary or MVPA) that occurred at the same time and in the same location (<50 m apart). A maximum separation of less than 50 m between the parent and child was selected because this distance is approximately equivalent to the length of a ball court (e.g., basketball, volleyball, racquetball) or large residential yard.
Using the parent–child pair as the unit of analysis, multiple linear regression models were fit for the following outcomes: 1) average daily minutes of MVPA performed by parents and children together during nonschool waking hours, 2) the percent of MVPA performed by parents and children together during nonschool waking hours out of children's total MVPA during this period, 3) the percent of MVPA performed by parents and children together during nonschool waking hours out of parents' total MVPA during this period, 4) average daily minutes of sedentary behavior performed by parents and children together during nonschool waking hours, 5) the percent of sedentary behavior performed by parents and children together during nonschool waking hours out of children's total sedentary behavior during this period, and 6) the percent of sedentary behavior performed by parents and children together during nonschool waking hours out of parents' total sedentary behavior during this period. Predictors in the models include parent's and child's sex, age, BMI, ethnicity/race, and family income. To examine differences in daily MVPA and sedentary minutes performed together by parents and children between weekdays and weekend days, multilevel models were fit using day-level data and controlled for clustering of observation within each parent–child pair. All analyses were conducted using SAS Version 9.2 (SAS Institute Inc., Cary, NC).
Methods
Sample
The current study analyzed baseline data from a subgroup of children and parents participating in a larger 4-yr intervention trial (Healthy PLACES [Promoting Livable Active Community Environments]), which is investigating the effects of smart growth community design principles on the prevention of family obesity risk. Participants included fourth-through eighth-grade children (age 8–14 yr) and their parents. Families lived in Chino, CA, or surrounding communities within 30-min driving time from Chino (including Ontario, Pomona, Diamond Bar, Corona, and Yorba Linda/Mira Loma). Recruitment was through a variety of channels including informational flyers and letters distributed at community events, housing association meetings, residences, schools, clinics, churches, and community groups. In addition, study advertisements were placed in local newspapers, posters were displayed at community sites, and postcards were mailed to homes in the selected areas. All recruitment materials included the study recruitment hotline phone number and e-mail address. A telephone recruiter called all interested families and screened for eligibility. Inclusion criteria consisted of the following: a) a child currently enrolled in the fourth to eighth grade; b) living in Chino, CA, or a surrounding community; and c) annual household income less than $210,000. Upper income households were excluded because the goal of the study was to focus on children from low- to middle-income families who have higher obesity risk. Children who met the eligibility criteria were scheduled for a data collection appointment at a local community site or their home. Written informed consent and minor assent were obtained from participants. This research was reviewed and approved by the institutional review boards at the University of Southern California and the University of California, Berkeley.
Study Design
Measurement occurred through a cross-sectional design. Objective physical activity, GPS, and survey data were collected from March 2009 to December 2010. No data collection took place from late July to August and during January because of typically adverse temperatures and weather conditions that limit outside activity in that part of Southern California. Within the parent–child pairs, both wore an accelerometer and GPS device during the same 7-d period.
Measures
Physical Activity The ActiGraph, Inc., GT2M model activity monitor (firmware v06.02.00, Pensacola, FL) provided an objective measure of physical activity. The device was worn on the right hip attached to an adjustable belt. A 30-s epoch was set for the recording of activity counts. Participants were asked to wear the accelerometers across seven continuous days. The devices were not worn when sleeping, bathing, or swimming. Cutpoints for MVPA were consistent with studies of national surveillance data. For adults, the MVPA threshold was 2020 counts per minute (equivalent to 3 METs). MVPA for children was defined using age-specific thresholds generated from the prediction equation of Freedson et al.. A threshold for moderate activity of 4 METs was used for children (as opposed to a 3-MET moderate activity cutoff for adults) to account for higher resting energy expenditure in children and youth. For both adults and children, light activity was greater than or equal to 100 counts per minute through the MVPA threshold. Sedentary activity was defined as less than 100 counts per minute.
Location Monitoring Portable GPS devices were used to assess locations in both children and parents. Geographic locations were logged for a 7-d period with the BT-335 Bluetooth GPS data logger device by GlobalSat Technology Corp. (New Taipei City, Taiwan) attached to a belt worn around the waist along with the accelerometer. The BT-335 (16 Mbit, 1575.42 MHz) consists of a GPS receiver and data logger with Bluetooth PC interface. This device records time, date, speed, altitude, and GPS location at preset intervals. It is WAAS/EGNOS/MSAS enabled and uses a SiRFstarIII chip set for accurate position tracking (up to 5-m accuracy outdoors) and improved indoor signal acquisition. The recording interval was set to a 30-s epoch to match the accelerometer specifications. After the GPS devices were returned, all recorded information was downloaded to a computer where the recorded longitudinal and latitudinal data and speed were downloaded to a CSV file format. Because the device has a battery life of 25 h, a battery charger was provided, and participants were instructed to recharge the battery each night. Linear distance between the parent and child for each 30-s epoch was calculated using geographic coordinates from the GPS.
Height and Weight Parents' and children's height and weight were measured in duplicate using an electronically calibrated digital scale (Tanita WB-110A Tanita, Arlington Heights, IL) and professional stadiometer (PE-AIM-101, Perspective Enterprises, Kalamazoo, MI) to the nearest 0.1 kg and 0.1 cm, respectively. BMI was calculated (kg·m). Children's weight status was classified according to Centers for Disease Control and Prevention age- and sex-specific BMI percentile cutoffs.
Demographic Variables Age, sex, and race/ethnicity were assessed through child and parent self-report surveys. Parents reported annual household income, which was divided into quartiles (less than $30,000, $30,000–$59,999, $60,000–$99,999, and $100,000 and above).
Data Merging and Processing
To conduct data manipulation tasks before analysis, accelerometer and GPS files were imported into the R version 2.9.2 programming language interface. Date and time stamps to the nearest 30-s epoch were used to match all accelerometer and GPS records within each parent–child pair. In the numerous cases where concurrent accelerometer and GPS were unavailable for either the parent or the child, we used a missing data code (not applicable) for designating the accelerometer and/or GPS values for these epochs. Overnight (11 p.m. to 5 a.m.) and school (8 a.m. to 3 p.m. on weekdays) hours were removed from the analyses. Strings of consecutive readings of zero activity counts lasting 60 min or more were considered accelerometer nonwear and excluded from analyses. Activity outliers were identified as records with greater than 16,383 counts per 30-s epoch. Records with GPS speeds greater than 169 km·h (105 mph) were also considered outliers because normal driving speeds are well below this value. Motorized activity, which was identified by records with speeds greater than 32 km·h because typical bicycling speeds range from 15 to 30 km·h (9.32 to 18.64 mph), was also excluded from the analyses. Once these records were removed, parent–child pairs were determined to have sufficient data for inclusion in the analysis if they had a minimum of two valid days (any combination of weekdays or weekend days) of matched available data—where a valid weekday was defined as a minimum of 2 h of matched available accelerometer and GPS data points for the pair and a valid weekend day was defined as a minimum of 4 h of matched available accelerometer and GPS data points for the pair. "Joint" or "together" behaviors were defined as activities of the same intensity level (sedentary or MVPA) that occurred at the same time and in the same location (<50 m apart). A maximum separation of less than 50 m between the parent and child was selected because this distance is approximately equivalent to the length of a ball court (e.g., basketball, volleyball, racquetball) or large residential yard.
Data Analyses
Using the parent–child pair as the unit of analysis, multiple linear regression models were fit for the following outcomes: 1) average daily minutes of MVPA performed by parents and children together during nonschool waking hours, 2) the percent of MVPA performed by parents and children together during nonschool waking hours out of children's total MVPA during this period, 3) the percent of MVPA performed by parents and children together during nonschool waking hours out of parents' total MVPA during this period, 4) average daily minutes of sedentary behavior performed by parents and children together during nonschool waking hours, 5) the percent of sedentary behavior performed by parents and children together during nonschool waking hours out of children's total sedentary behavior during this period, and 6) the percent of sedentary behavior performed by parents and children together during nonschool waking hours out of parents' total sedentary behavior during this period. Predictors in the models include parent's and child's sex, age, BMI, ethnicity/race, and family income. To examine differences in daily MVPA and sedentary minutes performed together by parents and children between weekdays and weekend days, multilevel models were fit using day-level data and controlled for clustering of observation within each parent–child pair. All analyses were conducted using SAS Version 9.2 (SAS Institute Inc., Cary, NC).
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