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Sarah M. Nusser, Nicholas K. Beyler, Gregory J. Welk, Alicia L. Carriquiry, Wayne A. Fuller and Benjamin M.N. King

Background:

Physical activity recall instruments provide an inexpensive method of collecting physical activity patterns on a sample of individuals, but they are subject to systematic and random measurement error. Statistical models can be used to estimate measurement error in activity recalls and provide more accurate estimates of usual activity parameters for a population.

Methods:

We develop a measurement error model for a short-term activity recall that describes the relationship between the recall and an individual’s usual activity over a long period of time. The model includes terms for systematic and random measurement errors. To estimate model parameters, the design should include replicate observations of a concurrent activity recall and an objective monitor measurement on a subsample of respondents.

Results:

We illustrate the approach with preliminary data from the Iowa Physical Activity Measurement Study. In this dataset, recalls tend to overestimate actual activity, and measurement errors greatly increase the variance of recalls relative to the person-to-person variation in usual activity. Statistical adjustments are used to remove bias and extraneous variation in estimating the usual activity distribution.

Conclusions:

Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.

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William L. Haskell

For the scientific domain of physical activity and public health research to advance its agenda of health promotion and disease prevention continued development of measurement methodologies is essential. Over the past 50 years most data supporting a favorable relationship between habitual physical activity and chronic disease morbidity and mortality have been obtained using self-report methods, including questionnaires, logs, recalls, and diaries. Many of these instruments have been shown to have reasonable validity and reliability for determining general type, amount, intensity, and bout duration, but typically do better for groups than individuals with some instruments lacking the sensitivity to detect change in activity. During the past decade the objective assessment of physical activity using accelerometer-based devices has demonstrated substantial potential, especially in documenting the pattern of light-, moderate-, and vigorous-intensity activity throughout the day. However, these devices do not provide information on activity type, location or context. Research that combines the strengths of both self-report and objective measures has the potential to provide new insights into the benefits of physical activity and how to implement successful interventions.

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Steven P. Hooker, Janet Fulton and Lanay M. Mudd

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Barbara E. Ainsworth, Carl J. Caspersen, Charles E. Matthews, Louise C. Mâsse, Tom Baranowski and Weimo Zhu

Context:

Assessment of physical activity using self-report has the potential for measurement error that can lead to incorrect inferences about physical activity behaviors and bias study results.

Objective:

To provide recommendations to improve the accuracy of physical activity derived from self report.

Process:

We provide an overview of presentations and a compilation of perspectives shared by the authors of this paper and workgroup members.

Findings:

We identified a conceptual framework for reducing errors using physical activity self-report questionnaires. The framework identifies 6 steps to reduce error: 1) identifying the need to measure physical activity, 2) selecting an instrument, 3) collecting data, 4) analyzing data, 5) developing a summary score, and 6) interpreting data. Underlying the first 4 steps are behavioral parameters of type, intensity, frequency, and duration of physical activities performed, activity domains, and the location where activities are performed. We identified ways to reduce measurement error at each step and made recommendations for practitioners, researchers, and organizational units to reduce error in questionnaire assessment of physical activity.

Conclusions:

Self-report measures of physical activity have a prominent role in research and practice settings. Measurement error may be reduced by applying the framework discussed in this paper.

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Richard P. Troiano, Kelley K. Pettee Gabriel, Gregory J. Welk, Neville Owen and Barbara Sternfeld

Context:

Advances in device-based measures have led researchers to question the value of reported measures of physical activity or sedentary behavior. The premise of the Workshop on Measurement of Active and Sedentary Behaviors: Closing the Gaps in Self-Report Methods, held in July 2010, was that assessment of behavior by self-report is a valuable approach.

Objective:

To provide suggestions to optimize the value of reported physical activity and sedentary behavior, we 1) discuss the constructs that devices and reports of behavior can measure, 2) develop a framework to help guide decision-making about the best approach to physical activity and sedentary behavior assessment in a given situation, and 3) address the potential for combining reported behavior methods with device-based monitoring to enhance both approaches.

Process:

After participation in a workshop breakout session, coauthors summarized the ideas presented and reached consensus on the material presented here.

Conclusions:

To select appropriate physical activity assessment methods and correctly interpret the measures obtained, researchers should carefully consider the purpose for assessment, physical activity constructs of interest, characteristics of the population and measurement tool, and the theoretical link between the exposure and outcome of interest.

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Laurel A. Borowski and Heather R. Bowles

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Louise C. Mâsse and Judith E. de Niet

Background:

Over the years, self-report measures of physical activity (PA) have been employed in applications for which their use was not supported by the validity evidence.

Methods:

To address this concern this paper 1) provided an overview of the sources of validity evidence that can be assessed with self-report measures of PA, 2) discussed the validity evidence needed to support the use of self-report in certain applications, and 3) conducted a case review of the 7-day PA Recall (7-d PAR).

Results:

This paper discussed 5 sources of validity evidence, those based on: test content; response processes; behavioral stability; relations with other variables; and sensitivity to change. The evidence needed to use self-report measures of PA in epidemiological, surveillance, and intervention studies was presented. These concepts were applied to a case review of the 7-d PAR. The review highlighted the utility of the 7-d PAR to produce valid rankings. Initial support, albeit weaker, for using the 7-d PAR to detect relative change in PA behavior was found.

Conclusion:

Overall, self-report measures can validly rank PA behavior but they cannot adequately quantify PA. There is a need to improve the accuracy of self-report measures of PA to provide unbiased estimates of PA.

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Barbara Sternfeld and Lisa Goldman-Rosas

Context:

Numerous instruments to measure self-reported physical activity (PA) exist, but there is little guidance for determining the most appropriate choice.

Objective:

To provide a systematic framework for researchers and practitioners to select a self-reported PA instrument.

Process:

The framework consists of 2 components: a series of questions and a database of instruments. The questions encourage users to think critically about their specific needs and to appreciate the strengths and limitations of the available options. Instruments for the database were identified through existing literature and expert opinion.

Findings:

Ten questions, ranging from study aim and study design to target population and logistical consideration, guide the researcher or practitioner in defining the criteria for an appropriate PA instruments for a given situation. No one question on its own determines the optimal choice, but taken together, they narrow the potential field. The database currently includes 38 different self-reported PA instruments, characterized by 18 different parameters.

Conclusions:

The series of questions presented here, in conjunction with a searchable database of self-report PA instruments, provides a needed step toward the development of guiding principles and good practices for researchers and practitioners to follow in making an informed selection of a self-reported PA instrument.

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Ana Henderson and Christine R. Fry

Background:

Improving parks in low income and minority neighborhoods may be a key way to increase physical activity and decrease overweight and obesity prevalence among children at the greatest risk. To advocate effectively for improved recreation infrastructure, public health advocates must understand the legal and policy landscape in which public recreation decisions are made.

Methods:

In this descriptive legal analysis, we reviewed federal, state, and local laws to determine the authority of each level of government over parks. We then examined current practices and state laws regarding park administration in urban California and rural Texas.

Results:

We identified several themes through the analysis: (1) multiple levels of governments are often involved in parks offerings in a municipality, (2) state laws governing parks vary, (3) local authority may vary substantially within a state, and (4) state law may offer greater authority than local jurisdictions use.

Conclusions:

Public health advocates who want to improve parks need to (1) think strategically about which levels of government to engage; (2) identify parks law and funding from all levels of government, including those not typically associated with local parks; and (3) partner with advocates with similar interests, including those from active living and school communities.