This section provides a series of case studies or scenarios that show how to apply the insights from the Guide to identify and select assessment tools through the NCCOR Measures Registry. As described in other sections, the measurement considerations for physical activity related studies vary greatly depending on the nature of the research question and the relative need for accuracy and precision, as well as a number of other considerations. To highlight this, the case studies are developed to provide examples for each of the five categories of research described in the Behavioral Epidemiology Framework introduced in Section 2 and detailed in Section 6. Various ages and designs are used in the examples to demonstrate the unique measurement challenges involved in capturing data from children and youth of different ages. It is important to note that the examples are designed to be illustrative rather than prescriptive. Clearly, many alternative measures other than the ones indicated for each example are available, so emphasis should be placed on the types of decisions that must be made when selecting measures for a particular application.

Case Study 1: Examining the Independent and Joint Associations of Physical Activity and Sedentary Behavior on Body Mass Index Among Middle and High School Students

Background

The increased prevalence of childhood obesity among developed countries can be partially attributed to the increased availability of emerging technologies (e.g., smartphones) and lack of sufficient physical activity environments or opportunities. However, little is known about the independent contribution of sedentary behaviors and physical activity to obesity.

To address this, a school board planned a project to examine the independent and joint associations of physical activity and sedentary behavior on body mass index (BMI) among middle and high school students. The project team envisions categorizing youth based on compliance with existing guidelines for both physical activity (i.e., >60 min/day of moderate-to-vigorous intensity physical activity [MVPA]/day) and sedentary behavior (i.e., <2h/day). The study is to be conducted with all students at one school during the spring semester (i.e., a 3- to 4-month period).

Considerations

Given the nature of this study, the project team decides the design requires a measure appropriate for adolescents or middle school students. The measure also must be able to capture time spent in sedentary time and MVPA per week or compliance with the guidelines.

The team decides that it needs a measure that can provide precise estimates at the individual level. However, considering the large sample and the need to categorize individuals in either sedentary vs. non-sedentary and active vs. not-active groups, the team feels it should be satisfied with a measure that is accurate for groups of individuals.

This project involves assessing a large sample of adolescents. However, the study has some flexibility because data can be collected across an extended period of time (over a full semester). A key challenge in the design is the need to obtain measures of both physical activity and sedentary behavior outcomes. Rough categorical estimates from a large sample could be sufficient to detect associations, but more objective data from activity monitors could increase the accuracy of the estimates. Regardless of the measure chosen, expertise is needed to process and interpret the physical activity data in the large sample; immediate feedback is not required.

Measure Selection

With respect to the population, the lowest age range would be 12 years so any of the measures in the Measures Registry would be suitable.

Indirect calorimetry, direct observation, activity monitors, heart rate monitors, pedometers, self-reports, and diaries can all provide an estimate of compliance with physical activity guidelines (i.e., 60 min of MVPA/day). This same list of measures can be used to infer about sedentary time with the exception of pedometers, but direct observation, self-reports, and diaries are the only direct measures of sedentary behavior.

Indirect calorimetry and direct observation are not feasible for population studies, and activity monitors are the next most accurate in the possible list of measures. The few monitors that can provide accurate estimates of time spent in sedentary behavior are not accurate for MVPA. The team therefore needs two different measures and opts for a self-report to measure sedentary behavior.

Data are collected using activity monitors and self-reports (i.e., several participants at a time). The sample size is large but so is the timeline (i.e., 3 to 4 months). Therefore, the ratio of sample size to timeline is favorable.

A midsize to large project team is needed to assist in data collection. Staff with expertise in processing and interpreting accelerometer data also are needed. The team does not need to provide feedback to participants so this factor will not affect its decision.

Activity monitors and self-reports are a good choice for this study. The next step involves selecting a particular activity monitor model and choosing a self-report from the Measures Registry. Pilot testing is recommended to review the protocol and obtain training in handling activity monitor data.

Case Study 2: Determining Compliance with Physical Activity Recommendations Across Different Grade Levels

Background

Schools are considered the ideal setting to educate youth about physical activity and healthy lifestyles. National recommendations indicate that youth should accumulate at least 30 minutes/day of MVPA in the school-setting (150 minutes per week). However, it is difficult to determine compliance with these guidelines because of a lack of documented records. A team managing a large national research network plans a study to determine compliance with physical activity recommendations across different grade levels (i.e., elementary, middle, and high school). They are interested in determining what percentage of youth meet the public health goal of 60 minutes of MVPA per day and whether children are getting at least half of this activity at school. As a school-based project, they also want to ensure that the assessment provides an educational experience for both the students and the teachers.

Considerations

This is a large project that will require a measure that is equally appropriate for children and adolescents. The measure needs to capture the frequency, duration, and intensity of physical activity and address most of the physical activity domains, particularly physical activity occurring at school.

The team recognizes that it needs a measure that is highly feasible but decides it can sacrifice individual-level accuracy so long as it has a measure that is relatively accurate at the group level. Feasibility is a priority in surveillance research (and in this particular project) because the team needs to rely on teachers to perform the assessments, while ensuring that data can be collected within a similar period of time (e.g., spring semester) to allow for direct comparisons between schools.

The team needs to include staff responsible for coordinating all the contacts with schools and facilitate the assessments. It also needs a measure that can provide immediate feedback because the annual assessment was intentionally designed to educate students about the importance of physical activity and how to self-monitor their activity behaviors.

Measure Selection

All the measures are suitable for children and adolescents but the team needs to make a careful choice if they decide to use report-based measures because these can present challenges when administered to young children.

Indirect calorimetry, direct observation, activity monitors, heart rate monitors, self-reports, and diaries can all provide an estimate of time spent in MVPA. However, only self-reports and diaries can capture the context of physical activity and specifically partition daily activity accumulated at school and out-of-school. Activity at school can be estimated with activity monitors or heart rate measures if additional information is collected (e.g., school schedule).

Self-reports can easily be shared with schools throughout the country for assessment and be sent along with assessment instructions for teachers. A web-based self-report would be a particularly good option, while diaries can be less accurate and be difficult for young children to complete.

The scale of the project creates some challenges for resources and time. Self-reports allow for the collection of large amounts of data within a short time period so this is an advantage. This particular study will not require great human power to administer the self-report because the data will be collected by school staff (e.g., physical education teachers). Self-reports also can provide immediate feedback and therefore have unique educational value.

A wide variety of self-reports is available for children and adolescents, but they can be narrowed down once the Measures Registry filters are applied to include context-related physical activity and those that have demonstrated validity across different age groups (ages 8 to 18 years). A web-based version would be preferred and applying this filter would narrow the available self-reports even further. Pilot testing would require testing the web-based tool to determine, for example, how scores are saved (e.g., how the server saves the data) and how feedback is provided.

Case Study 3: Identifying Predisposing Factors for Active Commuting in Elementary School Children Who Live in Urban and Suburban Settings

Background

Promoting walking and active transportation is an important public health strategy for the whole population, but specific efforts have been made to increase the percentage of children who walk or bike to school. To make it easier to promote this behavior, it is important to better understand factors that influence adoption of this strategy and the barriers that must be overcome to promote it on a large scale.

A project team therefore plans an evaluation to identify predisposing factors for active commuting in elementary school children who live in urban and suburban settings. Although some children may bike to school, the focus was on understanding walking behavior and barriers to walking. In addition to walking behaviors, the team also plans to collect information on parents’ perceptions of neighborhood safety and benefits of physical activity, along with detailed mapping of distance between home and school, and availability of sidewalks and crosswalks.

Considerations

This project needs to use measures that capture activity primarily in elementary school children. The indicator of active commuting is defined as walking behaviors and, therefore, the team’s measure needs to include an indicator of number of steps accumulated per week.

The team is particularly interested in exploring the associations between walking and predisposing family and environmental factors for active commuting. A reasonable degree of accuracy is needed to ensure that the measures are sensitive enough to capture differences in walking due to the hypothesized factors.

A team of individuals will be needed to help collect the data and to evaluate the school and community factors that may influence active transportation.

Measure Selection

All measures can be adjusted for possible use, but pedometers are a logical choice for this purpose. Considering the age of the sample, it would be difficult to get accurate estimates from report-based measures.

Direct observation, activity monitors, pedometers, self-reports, and diaries can provide a measure of steps. Report-based measures would be particularly useful to obtain information about the context, or parents’ perceptions of neighborhood safety and benefits of physical activity. However, this case study focuses on selecting a measure of physical activity and not related barriers.

A high degree of precision is not needed, but the nature of the study necessitates a reasonably accurate estimate of activity from a large sample of youth. Pedometers are viewed as the most valid measure for steps and as being very feasible (i.e., affordable) when compared to activity monitors. It also would be challenging to estimate the number of steps (or alternatively, distance covered) using direct observation or report-based measures such as self-reports or diaries. Report-based measures could be a good alternative if project staff are interested only in determining the mode of transportation/commuting (e.g., walking vs. car).

The team can divide data collection across several weeks. However, if it chooses to use pedometers, it will need to expand the project team in order to have sufficient personnel to distribute the pedometers and record the number of steps on each day of the week once participants arrive at school. It still will be necessary to ask participants to reset their pedometer (i.e., number of steps) at the beginning of each day before going to school.

The team wants to omit immediate feedback so that its associations are not confounded by motivation as a result of self-monitoring. This can be accomplished with sealed monitors.

The project team chooses to use pedometers but is aware of the implications and adjustments that it will need to include in its physical activity measurement protocol. The team now selects the most appropriate pedometer model by consulting the Measures Registry and testing its properties in a pilot test.

Case Study 4: Testing the Potential of a New Recess-Based Physical Activity Program Designed to Increase the Time Children Spend in MVPA During Recess

Background

Public health agencies have advocated for coordinated efforts to promote physical activity across the school day. Activity breaks during classroom time are encouraged along with enhancement of opportunities and programming during recess. Evidence-based strategies to promote physical activity during recess are needed because it has proven difficult to create engaging opportunities that promote physical activity in youth without constraining natural free play. An intervention study is planned to test the potential of a new recess-based physical activity program designed to increase the time children spend in MVPA during recess. Schools in a Midwestern state of the United States are randomly assigned to either a 1-year intervention or control group. The design calls for physical activity to be assessed at baseline, 6 months (post-intervention), and 1-year follow-up.

Considerations

The intervention requires a measure that is appropriate for children. The project team also needs a measure that can capture the frequency, duration, and intensity of physical activity to quantify the volume of MVPA.

The measure needs to be relatively accurate at the individual level to capture small variations in physical activity associated with the intervention (i.e., assuming physical activity is likely to increase by 15 to 20 minutes per week if the intervention is successful), but be cost effective because the team needs to buy several devices so that it can assess physical activity within a short amount of time (i.e., assuming all the schools need to be assessed within a 3- to 4-week period).

Additionally, the project team needs a diverse group of people who are able to collect a variety of data within a short time and who have the expertise to handle physical activity output. The team does not need to have immediate feedback so the more detailed data processing can be done after all data are collected.

Measure Selection

Most tools are suitable for young children, but report-based measures would likely not be a good option because either recall or recording activity at these younger ages is very challenging.

Indirect calorimetry, direct observation, activity monitors, heart rate monitors, self-reports, and diaries can provide an indicator of volume or MVPA. Only direct observation, self-reports, and diaries can be used to obtain information on context (e.g., recess). Monitor-based measures, such as activity monitors or heart rate monitors, also can be used if the measurement protocol is adjusted and additional information is collected to define the context where activity occurs (i.e., time periods when recess takes place and recess environment).

Indirect calorimetry is not feasible for this study but the team strongly considers direct observation (the next and most accurate measure of physical activity in the continuum) because it would allow them to combine both MVPA and context-related information, such as the availability and use of recess equipment. Activity monitors and heart rate monitors also could be an option and could provide more interpretable outcomes when compared to direct observation. The choice of report-based tools would not be ideal given that the tools have considerable error at the individual level (particularly in young children) and it would be very challenging to capture any intervention effects.

Working with several schools will create some challenges and require that the team collect large amounts of data in a short window of time. This will require a comprehensive project team to collect the data and undergo additional training to ensure that observation data are collected in a standardized way. The team does not need immediate feedback so this should not affect its decision on measures.

The team chooses to use both direct observation and activity monitors concurrently to provide a comprehensive and interpretable measure of physical activity. It now navigates through the Measures Registry to decide which direct observation and activity monitor to use. The team then follows up with staff training followed by pilot testing to ensure that direct observation data are collected with accuracy.