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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

The risk of obesity during childhood can be significantly reduced through increased physical activity and decreased sedentary behavior. Recent technological advances have created opportunities for the real-time measurement of these behaviors. Mobile phones are ubiquitous and easy to use, and thus have the capacity to collect data from large numbers of people. The present study tested the feasibility, acceptability, and validity of an electronic Ecological Momentary Assessment (EMA) protocol using electronic surveys administered on the display screen of mobile phones to assess children's physical activity and sedentary behaviors. A total of 121 children (ages 9–13, 51% male, 38% at risk for overweight/overweight) participated in EMA monitoring from Friday afternoon to Monday evening during children's nonschool time, with 3–7 surveys/day. Items assessed current activity (e.g., watching TV/movies, playing video games, active play/sports/exercising). Children simultaneously wore an Actigraph GT2M accelerometer. EMA survey responses were time-matched to total step counts and minutes of moderate-to-vigorous physical activity (MVPA) occurring in the 30 min before each EMA survey prompt. No significant differences between answered and unanswered EMA surveys were found for total steps or MVPA. Step counts and the likelihood of 5+ min of MVPA were significantly higher during EMA-reported physical activity (active play/sports/exercising) vs. sedentary behaviors (reading/computer/homework, watching TV/movies, playing video games, riding in a car) (P < 0.001). Findings generally support the acceptability and validity of a 4-day EMA protocol using mobile phones to measure physical activity and sedentary behavior in children during leisure time.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Rising rates of overweight and obesity among US children are leading to concerns about diabetes, sleep apnea, hypertension, and other cardiovascular and metabolic disorders across the life course (1,2,3,4). Obesity risk can be significantly reduced through increased physical activity (e.g., sports and exercise) and decreased sedentary behaviors (e.g., watching TV) (5,6,7). However, recent estimates suggest that only 40–50% of children 6–11 years of age and 6–11% of children 12–15 years of age engage in ≥60 min/day of moderate-intensity activity on at least 5 out of the past 7 days (8). Also, children 6–15 years of age spend an average of 5–7 h/day in sedentary activity (9). Surveillance, epidemiological, and intervention studies seeking to reduce obesity risk among youth rely upon informative methods of measuring physical activity, sedentary behavior, and their correlates.

Precise physical activity assessment strategies are necessary to understand the dose-response relationship with various health outcomes, assess associations with environmental and psychosocial factors, and evaluate the effect of interventions and policies (10,11). However, developing reliable and valid self-report methods of measuring children's physical activity and sedentary behaviors is challenging due to recall errors and biases (12,13,14). Children often experience difficulties remembering the intensity and duration of activities after 24 h or more has passed since the behavior (15). Although objective methods of assessing physical activity are available such as accelerometers and pedometers, these devices are unable to provide information about specific activity type (e.g., watching TV vs. homework) and suffer from substantial missing data due to nonwear (16,17,18). Also, when used alone, accelerometers and pedometers are unable to measure mood during or the context of activities, which may be the important factors that influence behavior. These limitations can be overcome through technology-enabled real-time self-report assessment strategies.

Recent advances in mobile technology have created opportunities for the real-time self-report assessment of physical activity and sedentary behaviors in naturalistic settings (19,20). Common mobile phones are currently capable of running computer programs that trigger electronic surveys on the display screen of the device and store the recorded responses for future download. Mobile phones are ubiquitous and easy to use, and thus have the capacity to collect data from large numbers of people. This real-time self-report data capture strategy, known as Ecological Momentary Assessment (EMA), uses various sampling schedules to collect information (21,22). In event-contingent sampling, participants record information during or after a predetermined behavior such as a bout of physical activity. Interval-contingent sampling triggers survey responses according to a specific preset time frames (e.g., at 8 am and 12 noon everyday). Lastly, signal-contingent sampling schemes require participants to record data whenever they are prompted by the device, often at random times throughout the day (23,24). EMA can be used alone or in combination with accelerometers or other objective methods to measure physical activity and related factors such as mood and context (25,26,27,28,29,30,31).

Preliminary evidence has been offered for the validity of electronic EMA as a method of assessing physical activity in adults and adolescents (30,31,32,33). However, less information is available about the feasibility and acceptability of electronic EMA strategies to measure active and sedentary behavior in children as young as 9 years old. Particular areas of concern for this age group are whether children will (i) carry the device with them during the entire day and voluntarily answer survey prompts (especially when exercising), and (ii) provide accurate and true responses pertaining to their behavior. To address these questions, the present study tested the feasibility, acceptability, and validity of a real-time EMA protocol using self-report electronic surveys on mobile phones to assess children's active and sedentary behaviors in naturalistic settings. The purpose of the EMA protocol was not to provide a measure of overall physical activity and sedentary behavior. Instead, the goal was to sample the occurrence of specific behaviors that can be linked to other time-intensive EMA measures such as mood and context.

Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Participants and recruitment

Participants included 4–8th grade children (ages 9–13 years) living in Chino, CA or surrounding communities within 30-min driving time from Chino (including Ontario, Pomona, Diamond Bar, Corona, and Yorba Linda/Mira Loma). The present study analyzed baseline data from a subgroup of children participating in a larger 4-year intervention trial (Healthy PLACES), which is investigating the effects of smart growth community design principles on the prevention of family obesity risk. Recruitment was through a variety of channels including informational flyers and letters distributed at community events, housing association meetings, residences, schools, clinics, churches, community groups. Inclusion criteria consisted of the following: (i) child currently enrolled in the 4–8th grade, (ii) living in Chino, CA or a surrounding community, (iii) annual household income <$165,000, and (iv) ability to complete questionnaires in English. Children who met the eligibility criteria were scheduled for a data collection appointment at a local community site or their home. This research was reviewed and approved by the institutional review board at the University of Southern California (Alhambra, CA).

Procedures

Eight children were recruited to participate in a pilot study consisting of EMA monitoring and an in-person qualitative follow-up interview lasting 20–30 min. The interview questions assessed children's reactions to carrying and using the mobile phone device, the obtrusiveness of the monitoring, reasons for missing survey prompts, and the types of technical problems that were encountered. After feedback from the pilot study was used to modify and improve the EMA protocol, a main trial was conducted with 121 children.

EMA

EMA data were collected using an HTC Shadow mobile phone (T-Mobile, Bellevue, WA) with a custom version of the MyExperience software installed (http:myexperience.sourceforge.net). The mobile phone calling, texting, and Internet capabilities were disabled. The software was programmed to display electronic question sequences and response choices on mobile phone screen (see Figure 1). Participants used the up and down arrows on the phone keypad to select a response choice from the options provided. Data were stored on the phone in an electronic file until downloaded by researchers. Verbal and written instructions were provided to the child and parent on how to use the device. Children completed a practice assessment in the presence of a research staff member and were given the opportunity to ask questions. Parents were taught how to use the device, so that they could assist the child if needed.

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Figure 1. Screen shots for Ecological Momentary Assessment (EMA) electronic survey items. Images display how EMA items and response choices appeared on the display screen of the mobile phone. Respondents used the keypad to toggle up/down and select their response. Only once response choice could be selected per screen. If a respondent selected “Other” on Screen 1, he/she was automatically directed to Screen 2. Thus, “Other” was not recoded as a stand-alone response choice.

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Children's discretionary, nonschool time was monitored across 4 days. EMA surveying occurred from Friday at 4 pm to Monday at 8:30 pm with 20 auditory prompts (3–7 random prompts during preprogrammed intervals each day). No prompting occurred between 8 am and 4 pm on Monday during school hours. Upon hearing the signal, children were instructed to stop their current activity and complete a short electronic question sequence. This process required 2–3 min. If a signal occurred during an incompatible activity (e.g., sleeping or bathing), participants were instructed to ignore it. If no entry was made, the phone emitted up to three reminder signals at 5-min intervals. After this point, the electronic survey became inaccessible until the next recording opportunity. During the monitoring period, children received one phone call and one SMS message from researchers to inquire about any technical problems and remind them to recharge the phone each night. A study hotline was also available to participants to report technical issues and request a replacement phone if necessary. Children were compensated up to $40 for participating in the study: $20 plus an additional $1 for each completed EMA survey entry (20 total) over the 4 days.

Measures

EMA items. Each EMA question sequence measured current main activity type (“What were you DOING right before the beep went off (Choose your main activity)?”) (see Figure 1). Responses indicating “Active Play/Sports/Exercising” were coded as physical activity and those indicating “Reading/Computer/Homework,” “Watching TV/Movies,” “Playing video games,” and “Riding in a car” were coded as sedentary behavior. Other items assessed social company, physical location, mood, and enjoyment. However, the results for these additional items are presented elsewhere (28). The questions were administered in English.

Physical activity. The Actigraph (Pensacola, FL), GT2M model activity monitor (firmware v06.02.00) provided an objective measure of physical activity. The device was worn on the right hip attached to an adjustable belt. Participants were asked to wear the accelerometers across seven continuous days (encompassing the 4 days of EMA monitoring). The devices were not worn when sleeping, bathing, or swimming. All accelerometer recordings were time-stamped in order to be linked with EMA data captured on the mobile phone. Outcome variables consisted of the total number of (i) steps and (ii) moderate-to-vigorous physical activity (MVPA) minutes within the 30 min before each EMA electronic survey. MVPA was defined using age-specific thresholds generated from the Freedson prediction equation (≥4 metabolic equivalents) (34). Due to positive skew, the continuous MVPA minutes variable was dichotomized as less than (coded 0) or greater than/equal to (coded 1) 5 min of MVPA within the 30-min interval. EMA entries with a total of zero activity counts in the 30 min before the survey prompt were considered accelerometer nonwear and excluded from analyses.

Height and weight. Children's height and weight were measured in duplicate using an electronically calibrated digital scale (Tanita WB-110A; Tanita, Tokyo, Japan) and professional stadiometer (PE-AIM-101) to the nearest 0.1 kg and 0.1 cm, respectively. BMI was calculated (kg/m2). Children's weight status was classified according to CDC age- and gender-specific BMI percentile cutoffs.

Demographic and time variables. Participants' age, sex, and ethnicity were assessed through a child self-report survey. Parents reported annual household income, which was divided into quartiles (<$45,000; $45,000–$79,999; $80,000–$99,999; and $100,000 and above). Each entry was also coded for the time of day that it began (i.e., morning (8:30 am–11:59 am), afternoon (12:00 pm–5:59 pm), or evening (6:00 pm–8:30 pm)).

Data analyses

Multilevel analyses were conducted using SUDAAN 10.0 (RTI International, Research Triangle Park, NC). The without replacement design statement was used, as it is the most appropriate setting for implementing Generalized Estimating Equation model-fitting techniques on clustered data (see http:www.rti.orgsudaanpage.cfmSUDAAN_Design_Options). Modeling employed a robust variance estimation method (SEMETHOD=Zeger) to adjust for the clustering of EMA observations (level 1) within each child (level 2) (35).

To examine EMA survey compliance patterns, multilevel logistic regression analyses tested whether the likelihood of survey nonresponse (unanswered vs. answered) varied as a function of day of the week, time of day, sex, age, race/ethnicity, annual household income, and weight status. Analyses also investigated whether children were willing to answer the survey during physical activity. Whether EMA survey nonresponse was related to concurrent physical activity levels (i.e., steps and MVPA measured by accelerometer) was tested through linear and logistic regression, respectively. Multilevel models also compared the total number of steps (linear regression) and likelihood at least 5 min of MVPA (logistic regression) during the 15-min interval before and 15-min interval after each survey response. The construct validity of EMA survey reports of the main current activity was tested through multilevel linear regression analyses with the EMA-reported activity (9-level categorical variable with active play/sports/exercise as the reference group) as the independent variable and concurrent objectively measured physical activity level (i.e., number of steps assessed by accelerometer) as the dependent variable. A focused contrast compared the likelihood of attaining at least 5 min of MVPA (measured by accelerometer) between EMA surveys reporting physical activity vs. sedentary behavior using multilevel logistic regression. All models were initially stratified by weight status (at risk for underweight, underweight, normal weight vs. at risk for overweight and overweight). When no group differences were found, results are presented for the entire sample with the groups combined and weight status included as covariate. All models also controlled for sex, age, annual household income, and race/ethnicity (at level 2). Adjusted Wald F statistics and associated P values were used to determine the statistical significance of each factor in the regression analyses. Predicted margins (i.e., standardized proportions adjusting for all of the other model covariates) were generated from the logistic regressions (36).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Feasibility and acceptability

Results from the open-ended follow-up interviews in the pilot group (N = 8) suggested that the device and protocol were feasible and acceptable to children in this age group. Although none of the children owned their own phone, seven out of eight children had previously used a similar type of mobile phone device. Six of the children felt that the device was easy to use and unobtrusive. Six of the children were able to remember to carry the phone with them each day and recharge the phone each night (with some assistance from parents). However, four children reported that they preferred to carry the phone in their pockets instead of wearing it on the belt that was provided. Three children indicated that they were not allowed to wear the phone during organized sports (i.e., soccer games and track meet). All of the children reported that they would be willing to participate in a future EMA study of this nature. Frequently mentioned reasons for missing prompts included sleeping, not being able to hear the prompts, and forgetting to wear the mobile phone. Reported technical issues included not being able to initiate the survey sequence when prompted, receiving error messages, and the battery door coming open.

Data availability

Figure 2 displays the flow chart for data availability within the main trial (N = 121). One participant was removed from the analysis due to irretrievable data (i.e., missing memory card upon return of that child's mobile phone). No other mobile phones or accelerometers were lost or severely damaged. One child received a replacement phone midway through the 4-day monitoring period due to technical problems with his/her original phone. A total of seven children who either (i) received <50% of the electronic survey prompts due to major technological problems with the phone software or (ii) responded to <50% of the electronic survey prompts due to an unusual weekend schedule (e.g., out of town, physical illness) were asked to participate in a retrial. Data from the retrial (not the original trial) are used for these participants. A total of 124 (5%) of 2,400 total surveys were unprompted due to technical issues (e.g., software crash, phone powered off, battery drain). Twelve survey prompts (0.5%) were not received because children obtained the mobile phone after the first programmed prompt (between 4 and 6 pm) on Friday. Accelerometer data were unavailable for six children due to problems with initializing and downloading the devices (resulting in 120 unmatched prompts). χ2-analyses conducted at the person level suggested that children missing accelerometer data did not differ from the full sample in terms of gender, race/ethnicity, household income, or weight status (P < 0.05). However, they were significantly older than children not missing accelerometer data (P = 0.003). Also, accelerometers were not worn (i.e., 0 total activity counts for the 30-min interval before the EMA survey) during 376 of the survey prompts. Three participants who did not wear accelerometers during any of the survey prompts were removed. One-way ANOVA's conducted at the person level showed that the percent of data lost per child due to accelerometer nonwear was unrelated to gender, age, race/ethnicity, household income, and weight status. However, accelerometer nonwear was more common during the first prompt of the day on Saturday and Sunday (8:30–10 am) (27% nonwear) than any of the other prompting intervals (14–15% nonwear) (P < 0.001). Of the 1,788 survey prompts that were matched with accelerometer data, a total of 326 prompts were unanswered. In the main trial, the display screen for one mobile phone was cracked. None of the devices were lost or stolen.

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Figure 2. Flow chart for data availability. Level 1 represents the number of electronic EMA surveys, and level 2 represents the number of participants. Surveys programmed = the number of electronic EMA surveys that each participant should have received as programmed. Surveys received = the number of electronic EMA surveys that were actually received by the participant. Surveys matched = the number of electronic EMA surveys that could be matched to valid accelerometer data. Matched surveys-answered = the number of electronic surveys (matched to accelerometer data) that were answered by the participant. Accelerometer nonwear was defined as a total of zero activity counts in the 30-min interval before the electronic survey prompt. EMA, Ecological Momentary Assessment.

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Descriptive statistics

Demographic characteristics for the full sample enrolled in the main trial (N = 121) are shown in Table 1. Overall, the proportion of EMA survey prompts reporting each type of main activity were as follows: reading, computer, homework (12.8%); watching TV/movies (20.2%); playing video games (6.3%); active play, sports, or exercise (16.0%); eating or drinking (8.1%); talking or on the phone (1.9%); chores (2.8%); riding in a car (8.7%); and something else (23.8%).

Table 1.  Demographic characteristics of children enrolled in the main trial (N = 121)
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Unanswered EMA surveys

On average, children responded to 80% (range 7–100%) of survey prompts that could be matched to accelerometer data. Multilevel logistic regression analysis found that the percentage of answered (vs. unanswered) survey prompts was greater among children with a white racial/ethnic background (89%) as compared with African-American (79%), Asian (78%), mixed/biracial (78%), and other (75%) (Adj. Wald F = 2.41, df = 5, P = 0.04). The likelihood of answering a survey prompt did not significantly differ by day of the week, time of day, sex, age, income, or weight status (underweight/at risk for underweight/normal weight vs. at risk for overweight/overweight).

When examining the 30 min before each survey prompt, the average number of steps was marginally greater for unanswered (M = 321.13, s.e. = 23.93) as compared with answered (M = 278.27, s.e. = 11.86) survey prompts (Adj. Wald F = 3.09, df = 1, P = 0.08). However, the percentage of EMA survey prompts with at least 5 min of MVPA in the proceeding 30 min was did not differ between unanswered (10%) as compared with answered (7%) surveys (Adj. Wald F = 2.53, df = 1, P = 0.11). These patterns of results were similar across weight status groups.

Extent to which EMA surveys disrupted activity

Physical activity (steps and MVPA measured by accelerometer) were compared between the 15-min interval before and the 15-min interval after each answered survey prompt to determine whether the act of answering the survey itself disrupted the child's activity. When examining the survey prompts where children reported sedentary behavior (i.e., playing video games; reading, computer, homework; riding in a car; watching TV/movies) as the main activity, results showed that the total number of steps did not differ during the 15-min interval before (M = 85.35, s.e. = 5.80) as compared to the 15-min interval after (M = 87.59, s.e. = 5.87) the answered survey prompt (Adj. Wald F = 0.15, df = 1, P = 0.70). Results were similar for survey prompts reporting physical activity (i.e., active play, sports, exercise) as the main activity, with a comparable number of total steps before (M = 249.41, s.e. = 18.25) and after (M = 224.17, s.e. = 15.65) the answered survey prompt (Adj. Wald F = 2.29, df = 1, P = 0.13). The logistic regression models comparing the likelihood of attaining at least 5 min of MVPA in the 15-min before vs. the 15-min after the answered survey prompts were initially overparameterized, and the following covariates were removed: day of the week, time of day, race/ethnicity, and income. The final model for sedentary behavior found that the likelihood of attaining at least 5 min of MVPA minutes did not significantly differ between the 15-min interval before (1%) and the 15-min interval after (2%) answered survey prompts (Adj. Wald F = 1.49, df = 1, P = 0.22). However, for survey prompts reporting physical activity as the main activity, children who were at risk for overweight or overweight were marginally less likely to attain at least 5 min of MVPA during the 15-min after (10%) as compared to the 15-min before (15%) answered survey prompts (Adj. Wald F = 3.55, df = 1, P = 0.06). The likelihood of MVPA did not differ before (15%) as compared with after (16%) survey prompts reporting physical activity for underweight, at risk for underweight, or normal weight children (Adj. Wald F = 0.05, df = 1, P = 0.82).

Validity of EMA activity responses

The construct validity of EMA activity responses was tested by examining differences in the mean number of steps (measured by accelerometer) across EMA-reported activity categories (see Figure 3). Across both weight status groups, steps were significantly higher for EMA surveys reporting active play, sports, or exercise than any other type of activity (Adj Wald F = 22.16, df = 8, P < 0.001). In addition, the mean number of steps recorded while talking on the phone, chores, riding in a car, and something else were significant greater than mean steps recorded while reading, computer use, homework, watching TV/movies, and playing video games (P < 0.05). Although underweight, at risk of underweight, and normal weight children recorded more steps than at risk for overweight and overweight children within each type of activity (with the exception of watching TV/movies), these differences were not statistically significant. Also, children were more likely to engage in at least 5 min of MVPA within the 30-min interval before EMA surveys reporting physical activity (i.e., active play, sports, or exercise (26% of surveys) as compared sedentary behavior (i.e., playing video games; reading, computer, homework; riding in a car; watching TV/movies) (4% of surveys) as the main activity (Adj Wald F = 69.18, df = 1, P < 0.001).

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Figure 3. Mean steps (measured by accelerometer) by activity categories self-reported through Ecological Momentary Assessment (EMA). Steps were recorded by accelerometer in the 30-min window before each EMA survey prompt. Values represent the predicted marginal means generated through multilevel linear regressions, which adjusted for day of the week, time of day, sex, age, race/ethnicity, and annual household income. s.e. bars are shown. Nonoverlapping s.e. bars indicate a statistically significant difference between means at P < 0.05. Underweight/at risk for underweight/normal weight (<85th age- and sex-specific percentile for BMI). At risk for overweight/overweight (≥85th age- and sex-specific percentile for BMI).

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Assessing physical activity and sedentary behavior using real-time electronic surveys displayed on mobile phones can help us to understand the extent to which obesity prevention effects are working in children. The present study found that a 4-day EMA protocol consisting of 3–7 electronic surveys/day during leisure time was acceptable in this population. Children answered ∼80% of the surveys that could be matched to accelerometer data. Objectively measured activity levels did not differ between answered and unanswered EMA surveys prompts, suggesting that children were willing to complete surveys during physical activity. Overall, the construct validity of EMA-reported activities was supported by matching accelerometer data that differentiated between activity types. Overall, no phones were lost and only a few were damaged. Taken together, these findings suggest that electronic EMA with mobile phones may be a promising approach to understanding children's physical activity and sedentary behaviors.

Results indicated that there are numerous sources of data loss when using conducting electronic EMA using mobile phones. Although data loss due to software problems (e.g., software crashes) is thought to occur at random, hardware damage (e.g., screen breakage) could be more common in children who are more physically active or behaviorally disruptive. Due to the rare occurrence of phone damage in the present study, the latter hypothesis could not be tested. Overall rates of data loss due to children not answering the electronic surveys are comparable to other EMA studies (37,38,39). However, EMA response rates were higher among white children. It is possible that children from racial/ethnic minority populations may encounter challenges to EMA compliance such as lack of reminders and assistance from parents due to decreased familiarity with technology and research procedures. Future research should seek to better understand the barriers that children from minority backgrounds may face when participating in EMA studies.

An important objective of this study was to determine whether children were willing and able to respond to EMA survey prompts while engaging in physical activity. Overall, steps and MVPA did not significantly differ between answered and unanswered survey prompts—suggesting that survey nonresponse was not necessarily more common while children were physically active. However, these data only included instances when children wore the accelerometer belt. We cannot rule out the possibility that EMA survey nonresponse was more common during physical activity when the accelerometer belt was removed. Results also suggest that the act of responding to the EMA survey may interrupt higher intensity activities for at risk of overweight and overweight children, as shown by lower levels of MVPA after as compared to before survey prompts reporting physical activity. To address these concerns, future EMA studies measuring children's physical activity could program a follow-up survey to take place an hour after the unanswered survey to assess the nature of the missing activity through short-term recall.

Also of interest in this study was whether children were truthful and/or accurately reported the nature of their current activity through the electronic survey on the mobile phone. Results found that time-matched objective activity data (measured by accelerometer) corresponded with children's EMA self-reports of current activity. Total step counts and the likelihood of at least 5 min of MVPA in the 30-min window surrounding each EMA prompt were greater for surveys reporting physical activity (i.e., active play, sports, exercise) than those reporting sedentary behavior (e.g., watching TV, playing video games, computer or Internet use). These findings reduce concerns that children do not understand the activity choices in the electronic survey, report activities that they have previously done (instead of their current activity), have given the mobile friend to someone else to complete the electronic surveys, and/or purposely report activities incorrectly out of social desirability or humor. Overall, results provide evidence for the validity of real-time data capture techniques to measure physical activity and sedentary behaviors through self-report in youth.

This study had a few limitations. First, the protocol only monitored behavior during children's leisure time. Physical activity and sedentary behavior taking place at school and while traveling to and from school were not assessed. Second, the current main activity item does not distinguish between productive (i.e., related to homework) and leisure-time computer use, which may be differentially related to health outcomes. Third, it is possible that 4 days of monitoring does not represent children's usual behavior. However, longer monitoring periods could impose participant burden and reduce compliance. Fourth, these data do not indicate the intensity or duration of activities. Lastly, the results may not be applicable to children from high-income households as they were excluded from the study.

Another important issue to consider with real-time data capture strategies is the cost-to-benefit trade-off. Often an experienced computer programmer and several rounds of piloting testing are needed to develop EMA programs to run on mobile phones. Furthermore, the mobile phone devices themselves can cost up to $200. Also, the number of and training necessary for research personnel to oversee EMA studies can cause some financial burden. Staff members often need to be available on a 24-h basis to respond to and solve technical concerns and conduct phone calls to participants during the monitoring period to encourage compliance. These costs should be weighed against the enhanced data quality available through EMA methods. As more adolescents carry mobile phones, future studies may be able to capitalize upon phones that children already own by downloading study software onto the phones, reducing cost. Alternatively, SIM cards from personal phones may be swapped into study phones so participants can make and receive personal calls on it during the study. Both strategies might further improve compliance carrying the device. Moreover, use of the phone's data network for remote data monitoring may also reduce the administrative cost of running the EMA studies.

Overall, this study found that an electronic EMA strategy to measure active and sedentary behavior appeared to be feasible and acceptable in children as young as 9 years old. Data loss might have been minimized through the use of monetary incentives for each electronic survey completed, reminder calls, and SMS messages from project staff, and the establishment of a hotline for participants to seek assistance with technical problems. However, the effects of these procedures on compliance rates were not tested experimentally. Despite concerns over the difficulty of answering survey prompts during sports and exercise, no evidence for significant data loss during these behaviors was found. Children's self-reported types of behavior on the real-time electronic surveys corresponded with activity levels objectively assessed through accelerometer. Therefore, electronic EMA strategies to measure children's active and sedentary behaviors may serve as a valid alternative to recall-based instruments and accelerometry, especially when researchers are specifically interested in activity type, context, and accompanying mood.

ACKNOWLEDGEMENT

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References

Funded by Active Living Research grant #RWJF 65837 (G.F.D., PI) and National Cancer Institute grant #R01-CA-123243 (M.P., PI). The authors thank Jennifer Beaudin, S.M. of the Massachusetts Institute of Technology for programming the Ecological Momentary Assessment (EMA) protocols used in this study and making modifications to the MyExperience tool. We also would like to acknowledge Keito Kawabata of the University of Southern California for his assistance with participant recruitment and data collection. We also thank the Active Living Research Accelerometer Loan Program.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. ACKNOWLEDGEMENT
  8. DISCLOSURE
  9. References
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