Factors associated with adolescent physical activity ... - SAGE Journals

EUROPEAN PHYSICAL EDUCATION REVIEW [DOI: 10.1177/1356336X09364722] Volume15(3):295–314:364722

EPER

Factors associated with adolescent physical activity during middle school physical education: A one-year case study

Terry Senne

David Rowe University of Strathclyde, UK

Boni Boswell and James Decker East Carolina University, USA

Texas Woman’s University, USA

and

Shaun Douglas Githens Middle School, North Carolina, USA Abstract The purpose of this descriptive component of a larger, exploratory case study was to examine associations among lesson contexts, teacher behaviors, and adolescent physical activity over a year of physical education (PE) at one school. Middle school students (n = 206) and their PE teachers (n = 4) were observed twice-weekly across one academic year. Data were collected using the System for Observing Fitness Instruction Time (SOFIT), Behavioral Evaluation Strategy and Taxonomy (BEST) software and Yamax SW-200 pedometers. Students spent 32.5 percent lesson time in at least moderate intensity activity, averaging 1542 steps per lesson. Higher activity levels were associated with lesson contexts of fitness activity, skill activity, and game play; while lowest activity levels occurred during free play. Higher activity levels were associated with teacher behaviors of promoting fitness, demonstrating fitness, and observing; lower activity levels were associated with teacher behaviors of general instruction and management. Key-words: adolescence • lesson contexts • pedometers • physical activity • SOFIT • teacher behavior

Introduction Although recommendations have been published promoting regular moderate to vigorous physical activity (MVPA) for children and adolescents, many individuals in the United States do not meet recommended guidelines (Lowry et al., 200l; Morgan et al., 2003; Nader, 2003). Numerous researchers have identified schools as the most appropriate setting for promoting physical activity (Burgeson et al., 2001; Morgan

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et al., 2003; Sallis et al., 1997). Major organizations such as the Centers for Disease Control and Prevention (1997), United States Department of Health and Human Services (2000), National Association for Sport and Physical Education (2004), and the American Academy of Pediatrics (2000) have suggested that students should participate in daily physical education (PE). However, the School Health Policies and Programs study (Burgeson et al., 2001) reported that only 8.0 percent of elementary, 6.4 percent of middle schools, and 5.8 percent of high schools provide daily PE. In particular, North Carolina public school students are not sufficiently physically active. The 2001 North Carolina Youth Risk Behavior Surveillance System documented a continuing dramatic decline in the physical activity of North Carolina children and adolescents as they matured. Levels of physical activity of North Carolina youths declined between the 9th and 12th grades, with the greater drop in activity being for girls, compared to boys. This problem is not limited to the USA as similar results have been reported for Australia (Barnett et al., 2002), Belgium (Cardon et al., 2004), Canada (Dwyer et al., 2003), and Hong Kong (MacFarlane and Kwong, 2003). In a review of her research over the past two decades, Lee (2002: 118) stated that current physical activity levels in schools appear ‘amazingly low’. In addition, low levels of physical activity during PE classes have been reported by Fairclough (2003) and McKenzie et al. (1996). As noted in Healthy People 2010 (US Dept of Health and Human Services, 2000), only 38 percent of students in grades 9–12 PE classes were sufficiently physically active for a minimum of 20 minutes. Similarly, Nader (2003) found that elementary school children were physically active less than 40 percent of PE class time. Although several observational studies in PE indicated that physical activity levels are low, few researchers have investigated factors associated with physical activity levels during PE lessons over an extended and prolonged period of time. Most observational studies of physical activity during PE have been of relatively short-term duration or involved observation for only a subset of classes from the overall PE curriculum. Before effective changes can be instigated, a deeper understanding of teacher behaviors and greater information about the contextual nature of PE classes is necessary (Lee, 2002). Furthermore, since the PE curriculum and context vary over the course of an academic year, it becomes necessary to examine and investigate physical activity within the school setting over this same period of time. Hence, this investigation serves as a foundational study for further experimental designs to investigate these voids in the literature. Therefore, the overarching purpose of the study was to conduct an in-depth exploratory case study to investigate patterns of physical activity (PA) and teacher perceptions in middle school PE classes twice-weekly within a single school context across one academic year, using a mixed method (quantitative and qualitative) research design. This article focuses on the investigation of patterns of physical activity in a middle school PE instruction across various lesson contexts and teacher behaviors. The second area of focus of the study (not addressed in this paper) examined

S E N N E E T A L . : P H YS I C A L AC T I V I T Y I N M I D D L E S C H O O L P E

teacher perceptions concerning the importance of physical activity (PA) during classes, how these beliefs matched their classroom behaviors, in addition to potential barriers to increasing PA levels and/or strategies for increasing PA levels during PE instruction. Additionally, since previous studies conducted generally incorporated a single method of measuring the outcome variables, a secondary purpose of this study was to employ multiple methods to measure adolescent physical activity levels, lesson contexts, and teacher behaviors. Method Research design A single case-study design was used to examine physical activity in a rural middle school PE program located in North Carolina. Ninety middle school PE classes, grades 6–8, were observed twice-weekly across one academic year. The following outcome measures were examined: (a) System for Observing Fitness Instruction Time (SOFIT) data on lesson contexts, teacher behaviors, and student physical activity levels; (b) pedometer data on student step-counts during PE; and (c) Behavioral Evaluation Strategy and Taxonomy (BEST) data on lesson time spent in activity, instruction, management, and wait categories. The University and Medical Center Institutional Review Board granted approval to conduct the study, as did the county school system, school administration, and PE teachers prior to the start of the study. Informed consent was obtained from teacher participants, student participants, and their parents or guardians. Participants and setting The site selected for the case study was a rural K–8 school located in North Carolina. Sixty-eight percent of all middle school students (n = 206; 105 females and 101 males), grades 6–8, enrolled in PE classes, along with their PE teachers (2 females and 2 males), provided informed consent to participate in the study. Eighty-two percent of 6th graders (n = 78) and 75 percent and 49 percent of 7th (n = 72) and 8th graders (n = 56), respectively, participated in the study. The sample included 72 percent Caucasian and 28 percent minority students (including 18 percent African American and 5 percent Hispanic). All the PE teachers were Caucasian. Total school enrollment was 836, of which 305 were classified as middle school students. Twentyeight percent of total school population received free or reduced-priced lunches. Socio-Economic Status (SES) statistics were not available. Middle school PE was comprised of three 6th grade classes, three 7th grade classes, and two 8th grade classes each semester. Individual class sizes ranged from 13 to 35 students. An average class size of 16, 16, and 28 students were found for 6th, 7th, and 8th grade classes respectively. All classes in a given grade level received PE instruction during the same period of the school day.

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Of seven middle schools within a convenient distance, the site selected best met the following criteria: (a) PE teachers of both genders at varied levels of teaching experience, (b) a middle school PE schedule that allowed for observation of all PE classes on at least two days of the week (to meet time constraints of researchers for data collection), and (c) a willingness of school, administration, and physical educators to participate in the study. A total of four PE teachers (two males, two females) and one male student teacher taught PE for the duration of the study. Teaching experience ranged from 0 to 14 years. All teachers held a bachelor’s degree in PE and one held a master’s degree. A gymnasium was the only indoor facility provided for all PE classes. One to three middle school PE classes were taught daily in the gymnasium concurrently. Sometimes, weather permitting, all or some classes were conducted outside in order to reduce the number of students in the gymnasium. Indoor classes were team taught. Teachers interchanged roles from lead to support teacher throughout most PE lessons. Usually one class rotated to health education for two-week intervals while the other class(es) remained in PE. This rotation continued until the end of the semester. Middle school students were enrolled in health/PE for only one semester per academic year. Instructional units taught over the course of the study included fitness activities, basketball, scooter activities, aerobics, dodgeball, volleyball, softball, golf, flag football, leisure games, Pillo Polo, and soccer. Class periods were 45 minutes in length for 6th and 7th grade classes and 50 minutes for 8th grade. Students were provided five minutes changing time at the start and end of the lesson; thereby providing the potential of 35 minutes or 40 minutes for activity and/or fitness for 6th–7th grade and 8th grade classes, respectively. The average length of lesson time available for activity as documented with BEST data was 34 min 4s. Instrumentation The SOFIT instrument (McKenzie, 1999) is a direct observation instrument designed for quantifying children’s physical activity levels, lesson contexts, and teacher behaviors during PE lessons. One addition incorporated while recording the teacher behavior that occurred during each recording period, was to identify and document the teacher of influence with each teacher behavior recorded, as noted in the supplementary SOFIT rules of the training manual (McKenzie, 1999). Specifically, in a teamtaught lesson context, teacher of influence was defined as the teacher who had the most direct influence over the student being observed (based on proximity to the student, and direct verbal or visual communication with the student or the whole class) at that designated point in time. As such, the teacher of influence changed at various points throughout a single lesson. All other SOFIT components were recorded according to the established protocol (McKenzie, 1999). Observer reliability for SOFIT has been demonstrated to be high during field-based observation of middle school PE. Over 33 middle school PE lessons, McKenzie et al. (2000) reported agreement between raters of 89 percent (student physical activity level), 96 percent (lesson


context), and 80 percent (teacher behavior). From the same data, the interobserver intraclass correlation was R = .96 for proportion of lesson time spent in MVPA. Physical activity ratings from SOFIT in PE settings have been validated against heart rate and accelerometry measures of activity intensity. From controlled physical activity trials in 173 children in grades 1–6, Rowe et al. (1997) demonstrated that SOFIT levels 2, 3, 4, and 5 correspond to significantly different heart rates and that heart rates are similar at levels 1 and 2 (lying, sitting). From the same heart rate data, it was also shown that levels 4 and 5 correspond to MVPA intensities. A graduate research assistant was trained in the SOFIT procedures (McKenzie, 1999) by the first author. Initially, videotaped middle school PE lessons were used to practice recording SOFIT data, with the research assistant and researcher discussing what each had recorded and why it was categorized as such to assist in establishment of inter-observer reliability using SOFIT categories of teacher behaviors, lesson contexts, levels of physical activity, and for determining teacher of influence. A CD with a 10-second observation and 10-second recording interval was used to record SOFIT data at the appropriate time intervals. After this initial orientation to the procedures, both individuals observed videotaped PE lessons using the SOFIT instrument recording data independently of one another. An inter-observer reliability of 84 percent agreement was established. Next, the on-site researcher and assistant independently recorded SOFIT data on six live middle school PE lessons at the school site. An inter-observer reliability of 91 percent agreement was obtained for live, field-based observations. Inter-observer reliability was established for both videotaped lessons and live on-site observations based on SOFIT protocols (McKenzie, 1999); whereby percentage agreement for each category of student activity, lesson context, and teacher behavior were determined, followed by calculation of percentage agreement during the entire lesson. The BEST software package can be custom-designed to record various types of direct observational data. We used BEST (Sharpe and Koperwas, 1999) software to record the percentage of time spent in lesson context categories of activity (motorengaged time), instruction, and management. A wait category was also added. The lesson context was classified as ‘activity’ if 50 percent or more of the students were motor-engaged. If the teacher was providing information specific to a lesson focus and less than 50 percent of students were motor-engaged (e.g. teacher demonstrating how a skill was to be performed and commenting on correct mechanics to perform the skill) the lesson context was categorized as ‘instruction’. Management was recorded when transitioning from one learning activity to another, changing equipment, changing organization of students, relocating to another area, and similar types of situations. The wait category was used when the regular activities of the lesson were interrupted due to an external influence (e.g. listening to a message over the school intercom system). Typically, motor-engaged activities would cease during wait time until the interruption was resolved. A duration recording strategy was used to track the length of time spent in the various lesson contexts. These categories were configured on a keyboard such that each was coded by using the first letter in each of the

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categories (i.e. A = activity time; I = instruction time; M = management time; W = wait time). They were configured as ‘toggle’ keys; meaning that time was recorded for a designated category when the observer initially hit the relevant key, and time for that event concluded as the observer hit the same key again. The first author was previously trained in BEST for these specific categories, using it on a frequent basis over several years, and she conducted all BEST data collection during the study. When the lesson observation was completed, BEST software was then used to obtain mean percentage of time and absolute time (seconds) spent in each category. In previous research, no significant differences were found between time spent in MVPA as calculated with SOFIT and continuous data collected using BEST (Heath et al., 2002). Yamax SW-200 pedometers were also used to measure middle school students’ physical activity levels. Yamax pedometers have been shown to yield reliable data in children (Barfield et al., 2004; Rowe et al., 2004; Vincent and Pangrazi, 2002). Additionally, the Yamax is among the most accurate models for measuring steps taken and distance covered during ambulatory physical activity (Bassett et al., 1996; Schneider et al., 2003). In recent studies, Yamax pedometers have been used to set step rate criteria associated with moderate activity recommendations in PE lessons of elementary (Scruggs et al., 2003, 2005) and middle school (Scruggs, 2005, 2007) children. In studies of physical activity, a single type of instrument is often used (e.g. accelerometer, heart rate monitor, direct observation). Each type of instrument has its own strengths and limitations. In the current study, multiple methods were used in order to provide multiple sources of information to address the research questions. The major strengths of the SOFIT method are that it provides very rich data at the individual student level, and can be highly objective in trained observers (and has been used in several validation studies as a gold standard comparison method). The limitations of this method are that it is labor-intensive both in terms of data collection and data entry and when used in real-time (as opposed to rating videotaped lessons) can only be used to measure physical activity in one child at a time. The BEST method has similar strengths and limitations to the SOFIT method, but has the added advantages of enabling a rating of class-level data, facilitating electronic data entry, and enabling more accurate time stamping (i.e. not being restricted to 20 s time periods). The main limitation of the BEST method as used in the current study is that it did not provide individual-level data nor the cross-referencing of data on several variables (lesson context, teacher behavior, student activity) simultaneously. Pedometers provide the advantages of being highly objective and enabling both individual-level data and through data aggregation, summary class-level data, but do not provide data on activity intensity, mode, or context. Data collection protocols During each lesson, students were identified by the use of colored and numbered scrimmage vests. All students wore scrimmage vests during every lesson (even though


not all students were observed), in order that the observed students were unaware that they were being observed. In this way, we hoped to avoid reactivity (a change in behavior due to awareness of being watched). Additionally, prior to each lesson, numbered pedometers were placed on each participant’s scrimmage vest so that pedometer data could be linked to the appropriate student after each lesson. Pedometers were worn on the waistband, in the midline of the right leg. A two-week familiarization period preceded data collection, to ensure that students and teachers followed the appropriate procedures, and that any initial change in physical activity behavior due to the novelty of wearing a pedometer had worn off. Students were instructed that they were participating in a study, and they were not to open the pedometer case at any time during the lesson. At the end of each lesson, scrimmage vests and pedometers were turned in, pedometer scores were recorded by the research assistant, and pedometers were reset to zero and put in place ready for the next PE class. For each lesson observed, SOFIT data were recorded by the research assistant adhering to the SOFIT manual protocol (McKenzie, 1999). Four students were selected to be observed on a rotating basis throughout each lesson based on SOFIT protocol as well. Physical activity level of selected students, lesson context, teacher behavior, and the teacher of influence were coded on a 20-second interval throughout the PE lesson. Coding began when 51 percent of the students in the class entered the instructional area and concluded when 51 percent departed at the end of the lesson. Total number of students present for the lesson was also recorded. Student physical activity was coded using SOFIT according to the following: 1 = lying down; 2 = sitting; 3 = standing; 4 = walking; and 5 = very active, as outlined in the manual (McKenzie, 1999). Lesson contexts included: (a) general content or management-related contexts; (b) general knowledge of rules, strategy, social behavior, or skill technique; (c) physical fitness knowledge; (d) fitness or focus on healthrelated fitness activities; (e) skill practice or activity time devoted to practice of skills (sport skills, dance skills, gymnastics skills, etc.) with a primary goal of skill development; (f) game play or activity time dedicated to application of skills in a game setting with little or no teacher intervention, including dances, gymnastics routines, etc.; and (g) other/free play indicated when free play time occurs during which PE instruction is not intended. Teacher behaviors were associated with what the teacher was doing during the prescribed observation interval using a hierarchical order coding system. Teacher behaviors coded are presented in hierarchical order: (a) promotes fitness, (b) demonstrates fitness, (c) instructs generally, (d) manages, (e), observes, or (f) other task such as attending to events not related to class responsibilities (e.g. leaves the instructional area, reads the newspaper, makes phone calls). BEST data collection was conducted by the first author to record the amount of class time spent in activity, instruction, management, and wait categories. Data collection for BEST began when at least 51 percent of the students had entered the gymnasium and concluded when at least 51 percent of the students departed at the conclusion of the lesson.

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Pedometer step-counts were recorded for each student participant on recording sheets by the research assistant after the lesson concluded and students had departed from the gymnasium. If a student entered class more than 10 minutes into the lesson or departed class more than 10 minutes prior to the end of the lesson, the step-count for that student was not recorded. Data management and screening All SOFIT record sheets were copied to a series of Excel spreadsheets. SOFIT activity scores were recorded using a spreadsheet structure in which teacher behaviors were nested within lesson contexts. For example, if a student was observed at an activity level of 4 (‘walking’) during a ‘fitness activity’ lesson context (context F), and the predominant teacher behavior was ‘promotes fitness’ (behavior P), the student’s activity score would be entered in spreadsheet FP, next to the child’s name. All data were entered by two people, to maintain data accuracy. After all data were entered, preliminary data screening revealed that none of the data entries were outside of the acceptable data range (i.e. all scores were within a range of 1 to 5). Individual pedometer data were entered for each lesson in a similar way, i.e. one person read out the data while a second person entered the data in spreadsheets separated by grade (6 through 8) and semester (fall and spring). Class dates were recorded in order to link pedometer data with the BEST software data. Researchers expected the data would be unaffected by reactivity (e.g. resetting the pedometer to zero, or shaking the pedometer) because of the controlled nature of the data collection process (i.e. during a PE lesson supervised by at least one teacher, with two adults observing). However, some unexplained low and high scores were observed. Using another data set obtained under a highly controlled PE condition (Barfield et al., 2004), we determined that any score below 300 steps or above 5,000 steps should be deleted. Subsequently 36 scores (2 percent of all data points) were deleted. Mean pedometer scores for each class were calculated for input into the final data analysis, and entered into a separate spreadsheet. Only classes in which at least 10 useable data points were recorded were used for the final data analysis. Subsequently, 15 classes were omitted based on this criterion. BEST output data were transcribed to the same spreadsheet as the class pedometer averages, for the following variables: date, grade, teacher(s), unit of instruction, time (length of lesson), and percentage time spent in activity, instruction, management, and wait categories. Results Descriptive statistics Over all classes observed for this study, 7,829 SOFIT observation periods were recorded. From BEST data records, common instructional content was basketball


(17 percent of all lessons observed), flag football (15 percent), golf (11 percent), soccer (11 percent), leisure games (10 percent), and fitness (8 percent). Average pedometer steps ranged from 520 steps to 2676 steps, with an average across all classes of 1542 (SD ± 454) steps, and an average step rate of 49 (SD ± 25) steps/min. Lowest step averages were associated with scooter activities, possibly attributable to the lower sensitivity of pedometers for measuring low-impact physical activity movements (Bassett et al., 1996). Across all classes, 20.0 percent of all observations were recorded as walking (SOFIT level 4), and 12.5 percent as very active (SOFIT level 5), totaling 32.5 percent of all observations spent in MVPA. The remaining observations were 28.0 percent standing, 38.7 percent sitting, and 0.8 percent lying down. Tables 1 and 2 show percentage lesson time spent in various lesson contexts and teacher behaviors, respectively. Approximately 48 percent of lesson time was spent in contexts associated with motor activity (i.e. fitness, skill, game play, and free play), and 52 percent of lesson time was spent in lesson contexts not typically associated with motor activity (i.e. management, general knowledge, and fitness knowledge). Interestingly, data from the BEST software supported these results, indicating approximately 47 percent activity time and a total of 53 percent spent in non-activity contexts (i.e. management, instruction, and wait time). Teacher behaviors recorded using SOFIT indicated teachers spent the majority of lesson time either in general instruction (46.9 percent of observations) or management (26.7 percent of observations) behaviors. Table 1

Percent activity time spent in different lesson contexts

Lesson context

Time

Management (M) General knowledge (K) Fitness knowledge (P) Fitness activity (F) Skill activity (S) Game play (G) Other (free play) (O)

25.6% 23.0% 3.3% 19.5% 7.0% 20.4% 1.3%

Note: time = % of all SOFIT observation periods (N = 7,829)

Table 2

Percent activity time spent in different teacher behaviors

Teacher behavior

Time

Promotes fitness (P) Demonstrates fitness (D) Instructs generally (I) Manages (M) Observes (O) Other (T)

1.0% 4.4% 46.9% 26.7% 15.9% 5.1%

Note: time = % of all SOFIT observation periods (N = 7,829)

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Association between lesson context and physical activity level Percentage activity time and average activity score from SOFIT data by lesson context are presented in Table 3. Highest mean activity levels were associated with lesson contexts of fitness activity (3.90), skill activity (3.61), and game play (3.36), while the lowest mean activity score was during free play (1.95). Similar patterns were evident when the data were expressed as percentage time spent in MVPA; more activity time was spent in MVPA during fitness activity (69.2 percent time spent in MVPA), skill activity (44.9 percent MVPA), and game play (41.7 percent MVPA), while MVPA comprised minimal time during general knowledge (2.4 percent MVPA), free play (3.1 percent MVPA), and fitness knowledge (6.3 percent MVPA) lesson contexts. The same question (association between lesson contexts and activity level) was also investigated using class-level data (i.e. BEST data and average class pedometer scores). This was a different method of analysis than the SOFIT data, in which only one child is observed during each 20-s period (i.e. class summary data for physical activity are not possible). Using the lesson as the unit of analysis, percentage of time spent in each of the BEST lesson categories (activity, instruction, management, and wait time) was correlated with average pedometer step rate (steps/minute) during each lesson. A significant positive, yet relatively low correlation (r = .27, d.f. = 68, p < .05) between percentage activity time and step rate indicated that, as expected, when activity lesson contexts take up more lesson time, objectively measured physical activity is higher. Similarly, a significant negative correlation (r = –.67, d.f. = 68, p < .05) between percent management time and step rate indicated that, as more class time is spent on management, activity levels decrease. When lesson contexts were expressed as absolute time and pedometer data were expressed as absolute number of steps, a similar pattern was evident for activity time (r = .39, d.f. = 68, p < .05) and management time (r = –.44, d.f. = 68, p < .05). No significant correlations were found between percentage wait time and pedometer-measured activity, though this

Table 3

Frequency (%) and average SOFIT activity level, by lesson context

Lesson Context

(1)

(2)

(3)

(4)

(5)

SOFIT average

Management (M) General knowledge (K) Fitness knowledge (P) Fitness activity (F) Skill activity (S) Game play (G) Other (free play) (O)

0.2 0.1 0.8 1.8 0.0 0.4 17.3

48.0 75.2 48.0 12.4 2.5 19.8 76.5

26.0 22.2 44.9 16.6 52.8 38.2 3.1

23.1 1.8 5.5 32.2 26.1 26.4 0.0

2.6 0.6 0.8 37.0 18.8 15.3 3.1

2.80 2.28 2.57 3.90 3.61 3.36 1.95

Note: 1 = lying down; 2 = sitting; 3 = standing; 4 = walking; 5 = very active; physical activity levels 4 and 5 are classified as MVPA.


may have been due to the lack of variability in the wait time data. The results were less clear for instruction time. A significant, negative correlation (r = –.36, d.f. = 68, p < .05) indicated that lessons where more time was spent in instruction were associated with lower total activity (total steps). However, this pattern was not replicated when instruction time was expressed as percentage of total class time and physical activity was expressed as step rate (r = –.02, d.f. = 68, p > .05). Association between teacher behaviors and physical activity level Table 4 shows the percentage activity time and average activity score from SOFIT data associated with different teacher behaviors. Overall, higher mean SOFIT scores were associated with times when the teacher of influence was promoting fitness (4.77), demonstrating fitness (3.83), or observing (3.55), and lower mean activity levels were associated with teacher behaviors of general instruction (2.83) and management (2.89). Percentage observed time spent in MVPA also supported this pattern, i.e. highest when the teacher was promoting fitness (95.1 percent observations spent in MVPA), demonstrating fitness (73.6 percent MVPA), or observing (47.4 percent MVPA), and lowest during general instruction (23.2 percent MVPA) and management behaviors (29.0 percent MVPA). Discussion In the current study, a rich body of quantitative data was gathered on physical activity and classroom variables over an entire academic year of middle school PE classes in one school. To our knowledge, this is the first time an exploratory case study of this design and duration has been conducted. Additionally, data were gathered using two separate measurement processes (SOFIT and BEST/pedometer data). This allowed us to examine patterns of physical activity across various lesson contexts and teacher behaviors via individual-level and class-level data, and to investigate total physical activity (steps) as well as activity patterns (SOFIT intensity categories).

Table 4

Frequency (%) and average SOFIT activity level, by teacher behavior

Teacher Behavior

(1)

(2)

(3)

(4)

(5)

SOFIT average

Promotes fitness (P) Demonstrates fitness (D) Instructs generally (I) Manages (M) Observes (O) Other (T)

0.0 0.0 0.5 0.8 1.3 2.3

2.4 5.2 48.3 43.5 16.9 30.5

2.4 21.3 28.0 26.7 34.4 25.8

11.0 59.2 14.4 24.0 20.4 17.0

84.1 14.4 8.8 5.0 27.0 24.5

4.77 3.83 2.83 2.89 3.55 3.31

Note: 1 = lying down; 2 = sitting; 3 = standing; 4 = walking; 5 = very active; physical activity levels 4 and 5 are classified as MVPA.

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Step rate and MVPA Step rate average in the present study (49 steps/min) was found to be considerably lower than the 82–8 steps/min suggested as an optimal rate for meeting MVPA guidelines in middle school PE lessons (Scruggs, 2007). Across all classes observed, students engaged in moderate to vigorous physical activity 32.5 percent of lesson time. Only during 12.5 percent of that time were students considered ‘very active’ (SOFIT level of 5). This percentage was less than was documented in a study of California middle schools that reported 48.4 percent MVPA time (McKenzie et al., 2000), and a similar study of 3rd grade PE classes in four states that documented 46.8 percent MVPA time (McKenzie et al., 1995). Although lower than both the optimal step rate suggested by Scruggs (2007) and the percentage of MVPA reported by McKenzie et al. (2000), several sources have reported physical activity levels similar to the present study. For example, Martin and Kulinna (2005) also found students to be quite inactive during elementary, middle, and high school PE classes, as indicated by SOFIT levels of 3 or less (standing, sitting, and lying down) for approximately 61 percent of class time. A comprehensive review of 40 studies on physical activity levels in middle and high school PE classes found that students engaged in MVPA for 27 to 47 percent of lesson time, dependent on the measurement instrument used (Fairclough and Stratton, 2005). Among these studies, an average of 26.6 ± 15.2 percent was spent in MVPA time in descriptive studies where middle and high school PE classes were conducted under nonintervention conditions (i.e. studies of a similar design to the current study). Physical activity participation levels in all of these studies falls short of the Healthy People 2010 recommendations (US Dept of Health and Human Services, 2000). These recommendations encourage PE teachers to meet the minimal 50 percent MVPA guideline in order to have a positive impact on efforts to address the childhood obesity epidemic. Although the results of the current study indicate that PA in PE were considerably below the optimal goal of achieving 50 percent MVPA time, recent intervention studies report positive efforts along these lines. These studies reported significant increases in MVPA time during PE classes (Fairclough and Stratton, 2005), using various types of interventions. The interventions ranged from fitness-oriented to an assortment of pedagogical strategies relative to instructional, contextual, and managerial lesson components. BEST, SOFIT, and pedometer comparisons SOFIT data indicated that 48 percent of lesson time during middle school PE classes was spent in contexts of motor activity (fitness, skill work, game play, and free play), while 52 percent of lesson time was spent in non-activity contexts (management, general knowledge, and fitness knowledge). BEST data supported SOFIT results relative to activity versus non-activity lesson contexts with a reported 47 percent time spent in activity contexts and 53 percent time spent in non-activity-related contexts


(instruction, management, and wait time). Although it was not the primary intent of this study to compare the instruments used, it appears from the results of this study that BEST and SOFIT methods provide similar estimates of percentage of lesson time spent in activity or non-activity contexts. Similarly, secondary correlation analysis of the class-level BEST and pedometer data provided convergent validity evidence for these two methods. For example, correlations between BEST activity time (expressed as absolute and percentage lesson time) was positively correlated with pedometer total steps and step rate, respectively (r = .27 and .39, p < .05), and BEST management time (expressed as absolute and percentage lesson time) was negatively correlated with pedometer total steps and step rate, respectively (r = –.67 and –.44, p < .05). Association between lesson contexts and physical activity A primary focus of the study was to examine students’ levels of physical activity specific to lesson contexts naturally occurring during middle school PE instruction. Highest student physical activity levels were associated with the activity-based lesson contexts of fitness activities, skill activity, and game play, in terms of average intensity and percentage time spent in MVPA. Results of the current study indicate that within certain contexts it is possible to achieve higher levels of physical activity during skill development and game play. This is an important finding, given that some intervention programs designed to promote MVPA in PE are focused primarily on addressing aspects of health-related fitness (e.g. implementing activities that maintain high levels of MVPA or specific health-related fitness aspects) and less on improving motor skill competency during PE instruction (Baquet et al., 2002; Fairclough and Stratton, 2005; Seliger et al., 1980). The design and organization of skill development activities may prove to be key elements that must be taken into consideration when attempting to maximize practice opportunities in order to keep students more physically active as they develop their motor and sport skills. Interestingly, the lowest level of physical activity was documented in the SOFIT other/free play lesson context. This may be due in part to the limited amount of time that the other/free play context occurred during the study. Alternatively, it might provide some evidence that middle school students would be more likely to achieve higher rates of physical activity if activity time during PE was structured as opposed to unstructured free play. Percentage of time spent in MVPA was lowest during instructional contexts of general knowledge and fitness knowledge. These results are consistent with those reported by McKenzie et al. (2000). The association between lesson context and physical activity levels was also examined using class level data (i.e. BEST data and class pedometer scores), where the lesson served as the unit of analysis. The positive association between percentage activity time and step rate supports the notion that more time spent during PE instruction in activity-based lesson contexts successfully results in higher rates of physical activity. In contrast, other results indicated a negative influence on physical activity levels when lesson contexts are instructional or managerial in nature.

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The data regarding levels of physical activity during middle school instruction under varying lesson contexts overall can be summarized as follows: higher levels of physical activity occurred during activity-based lesson contexts and lower levels occurred during non-activity-based contexts. Many other variables may influence physical activity levels, such as the unit of instruction, amount of PE equipment available, number of classes in the gym at a time, and other teacher-related variables. Certainly, further investigation in these areas is warranted. Regardless of the effects of various other constraints on MVPA during PE instruction, these results indicate that physical educators should consider carefully the amount of time spent in specific lesson contexts, in order to encourage MVPA during PE lesson time. Association between teacher behaviors and physical activity This study also focused on students’ levels of physical activity relative to teacher behaviors occurring during middle school PE instruction. Overall, higher mean SOFIT scores were associated with teacher behaviors of promoting fitness, demonstrating fitness, or observing; while lower mean activity levels were associated with teacher behaviors of general instruction and management. The percentage of time spent in MVPA also supported this pattern. These results are consistent with those of Martin and Kulinna (2005) who reported students were more likely to be engaged in higher levels of activity when the teacher demonstrated or promoted fitness, and at lower levels of physical activity during general instruction and management teacher behaviors. Likewise, Schuldheisz and van der Mars (2001) demonstrated that teachers’ efforts to promote fitness (in addition to active supervision) directly impacted middle school students’ MVPA during PE classes. In the present study, 73.6 percent of teacher behaviors involved general instruction and management, and only 5.4 percent related to promoting and demonstrating fitness. The results of the current study offer additional support for the need to consider the emphasis of specific teacher behaviors during Physical Education Teacher Education (PETE) pre-service instruction that facilitate and promote sufficient P-12 student physical activity levels during PE instruction. Similarly, PE teachers should be challenged to increase promoting and demonstrating fitness behaviors since it is within these teacher behaviors that we see the highest levels of student physical activity occur. Strengths and limitations The main strength of this study is the richness of quantitative data within a single case-study design. Physical activity was observed during PE lessons across the entire school year, occurring in a wide variety of instructional units (i.e. basketball, soccer, leisure games, etc.). The use of a familiarization period and the length of the data collection period both led researchers to be confident that the data were likely not influenced by reactivity (which is a concern in observational data, especially over short-term periods). Anecdotally, we observed that the students did not seem to notice


our presence in the classroom, and the process of wearing pedometers and scrimmage vests became a natural part of the classroom routine. Additionally, the use of two separate sets of measurement methods and data analyses allowed us to test whether our findings were independently confirmed. This study represents a picture of an academic year in a middle school PE program in NC; and therefore, as a descriptive study, generalizations cannot be inferred to other school settings given the case study design. Although the rigor of the design adds confidence to our findings in this setting, they may apply to greater or lesser degrees in other types of school environments (e.g. urban vs rural, large vs small school, different class size, public vs private school, etc.). Additionally, the use of observational methods of assessing physical activity may be subject to observer bias or observer drift. However, the rigorous training and establishment of inter-observer agreement at the outset of the study and the subsequent high agreement between one observer using SOFIT and another, expert observer using BEST indicates that observer drift likely did not occur. Finally, the percentage of 8th graders who chose to participate (49 percent) in the study was quite a bit lower than that of the 6th and 7th grade participants. While, one cannot infer why less 8th graders chose to participate, one plausible explanation may exist. Only one participant chose to participate from one of two fall semester 8th grade classes. Perhaps this teacher did not recruit or encourage her students as much to become participants as the other teachers did; thus, accounting in part for the difference in percentages. Conclusions In summary, the results of this in-depth exploratory case study confirm that teacher behaviors and lesson contexts are associated with students’ physical activity levels during PE instruction. Specifically, throughout the academic year, teacher behaviors of promoting and demonstrating fitness during instruction appeared to elicit higher levels of physical activity among students. In addition, lesson contexts that were active in nature (fitness, skill, and game play activities) rather than non-active contexts such as general knowledge and management promoted higher levels of physical activity, as one would expect. Considering that class management and general instruction were associated with lower levels of physical activity, it is disconcerting that so much of scheduled lesson time was comprised of these lesson contexts. The findings inform PE teachers and PETE program faculty as they endeavor to positively influence current adolescent physical activity behavior. Several variables are within teachers’ control that can positively impact physical activity levels during PE instruction. Some examples include: (a) effective classroom management (development of appropriate rules, establishment and consistent use of routines/protocols, etc.), (b) task design and structure that provides for maximum participation and increases level of PA, (c) appropriate game modifications (smallsided games), and (d) effective task demonstrations (full demonstration of the task),

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to name a few. Attention to and implementation of these variables alone may enable better quality physically active PE instruction for P-12 students. Suggested future research includes investigating whether specific pedagogical and instructional planning strategies (such as those just outlined) can reduce time spent in lesson contexts and teacher behaviors that do not promote physical activity; and whether this, in turn, will increase adolescent physical activity levels during PE instruction. Intervention studies that address these strategies and their implementation during PE instruction on the pre-service and/or in-service teacher levels may provide great insight as to how PETE faculty should train pre-service teachers for physically active PE instruction. Likewise, knowledge gained may also provide targeted areas of focus for in-service teacher development and mentoring as well. One must also consider that the goals of PE programs are numerous (National Association for Sport and Physical Education, 2004), including the goal of maintaining high PA levels during PE instruction. Consequently, examining whether focusing on increasing adolescents’ PA levels during PE instruction diminishes time spent on other important educational objectives also merits investigation. Finally, it is imperative that physical educators embrace the following challenge: the development of motor skill competency for a variety of physical activities (particularly lifetime activities), while maintaining an acceptable level of MVPA during PE instruction. This balance will enable our youth to develop the skills necessary to competently participate in lifelong physical activity, resulting in both enjoyment and regular active participation. This is one challenge that all PE programs should strive to achieve.

Acknowledgement: This study was funded by a grant from the East Carolina University Brody School of Medicine Pediatric Healthy Weight Research and Treatment Center.

References American Academy of Pediatrics (2000) ‘Physical Fitness and Activity in Schools’, Pediatrics 105: 1156–7. Baquet, G., Berthoin, S. and Van Praagh, E. (2002) ‘Are Intensified Physical Education Sessions Able to Elicit Heart Rate at a Sufficient Level to Promote Aerobic Fitness in Adolescents?’, Research Quarterly for Exercise and Sport 73: 282–8. Barfield, J. P., Rowe, D. A. and Michael, T. (2004) ‘Interinstrument Reliability of the Yamax Digi-Walker in Elementary School Children’, Measurement in Physical Education and Exercise Science 8: 109–16. Barnett, L. M., van Beurden, E., Zask, A., Brooks, L. O. and Dietrich, U. C. (2002) ‘How Active are Rural Children in Australian Physical Education?’, Journal of Science and Medicine in Sport 5: 253–65. Bassett, D. R., Jr., Ainsworth, B. E., Leggett, S. R., Mathien, C. A., Main, J. A., Hunter, D. C. et al. (1996) ‘Accuracy of Five Electronic Pedometers for Measuring Distance Walked’, Medicine and Science in Sports and Exercise 28: 1071–7.


Burgeson, C. R., Wechsler, H., Brener, N. D., Young, J. C. and Spain, C. G. (2001) ‘Physical Education and Activity: Results from the School Health Policies and Programs Study 2000’, Journal of School Health 71: 279–93. Cardon, G., Verstraete, S., De Clercq, D. and De Bourdeaudhuij, I. (2004) ‘Physical Activity Levels in Elementary School Physical Education: A Comparison of Swimming and Nonswimming Classes’, Journal of Teaching in Physical Education 23: 252–63. Centers for Disease Control and Prevention (1997) ‘Guidelines for School and Community Programs to Promote Lifelong Physical Activity among Young People’, Morbidity and Mortality Weekly Report 46: 1–36. Dwyer, J. J., Allison, K. R., Barrera, M., Hansen, B., Goldenberg, E. and Boutilier, M. (2003) ‘Teachers’ Perspective on Barriers to Implementing Physical Activity Curriculum Guidelines for School Children in Toronto’, Canadian Journal of Health 94: 448–56. Fairclough, S. J. (2003) ‘Girls Physical Activity During High School Physical Education: Influences of Body Composition and Cardiorespiratory Fitness’, Journal of Teaching in Physical Education 22: 382–95. Fairclough, S. J. and Stratton, G. (2005) ‘Physical Activity Levels in Middle and High School Physical Education: A Review’, Pediatric Exercise Science 17: 217–36. Heath, E. M., Coleman, K. J., Lensegrav, T. L. and Fallon, J. A. (2002) ‘Comparison of the SOFIT and the BEST to Determine Activity Level during Elementary Physical Education’, Medicine and Science in Sports and Exercise 34(5, suppl.): S278. Lee, A. (2002) ‘Promoting Quality School Physical Education: Exploring the Root of the Problem’, Research Quarterly for Exercise and Sport 73: 118–24. Lowry, R., Wechsler, H., Kann, I. and Collins, J. L. (2001) ‘Recent Trends in Participation in Physical Education among US High School Students’, Journal of School Health 71(4): 145–52. MacFarlane, D. and Kwong, W. T. (2003) ‘Children’s Heart Rates and Enjoyment Levels during PE Classes in Hong Kong Primary Schools’, Pediatric Exercise Science 15: 179–90. McKenzie, T. L. (1999) SOFIT: System for Observing Fitness Instruction Time. San Diego, CA: San Diego State University. McKenzie, T. L., Feldman, H., Woods, S. E., Romero, K. A., Dahlstrom, V., Stone, E. J. et al. (1995) ‘Children’s Activity Levels and Lesson Context during Third-Grade Physical Education’, Research Quarterly for Exercise and Sport 66: 184–93. McKenzie, T. L., Nader, P. R., Strikmiller, P., K., Yang, M., Stone, E. J., Perry, C. L. et al. (1996) ‘School Physical Education: Effect of the Child and Adolescent Trial for Cardiovascular Health’, Preventive Medicine 25: 423–31. McKenzie, T. L., Marshall, S. J., Sallis, J. F. and Conway, T. L. (2000) ‘Student Activity Levels, Lesson Context, and Teacher Behavior during Middle School Physical Education’, Research Quarterly for Exercise and Sport 71: 249–59. Martin, J. J. and Kulinna, P. H. (2005) ‘A Social Cognitive Perspective of Physical-ActivityRelated Behavior in Physical Education’, Journal of Teaching in Physical Education 24: 265–81. Morgan, D. F., Pangrazi, R. P. and Beighle, A. (2003) ‘Using Pedometers to Promote Physical Activity in Physical Education’, Journal of Physical Education, Recreation and Dance 74(7): 33–8. Nader, P. R. (2003) ‘Frequency and Intensity of Activity of Third-Grade Children in Physical Education’, Archives of Pediatrics and Adolescent Medicine 157: 185–90. National Association for Sport and Physical Education (2004) Moving into the Future: National Standards for Physical Education (2nd edition). Reston, VA: Author. Rowe, D. A., Mahar, M. T., Raedeke, T. D. and Lore, J. (2004) ‘Measuring Physical Activity in Children with Pedometers: Reliability, Reactivity, and Replacing Missing Data’, Pediatric Exercise Science 16: 343–54.

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Rowe, P. J., Schuldheisz, J. M. and van der Mars, H. (1997) ‘Validation of SOFIT for Measuring Physical Activity of First- to Eighth-Grade Students’, Pediatric Exercise Science 9: 136–49. Sallis, J. F., McKenzie, T. L., Alearaz, J. E., Kolody, B., Faucette, N. and Hovell, M. F. (1997) ‘The Effects of a 2-Year Physical Education Program (SPARK) on Physical Activity and Fitness in Elementary School Students’, American Journal of Public Health 87: 1328–34. Schneider, P. L., Crouter, S. E., Lukajic, O. and Bassett, D. R., Jr. (2003) ‘Accuracy and Reliability of 10 Pedometers for Measuring Steps over a 400-m Walk’, Medicine and Science in Sports and Exercise 35: 1779–84. Schuldheisz, J. M. and van der Mars, H. (2001) ‘Active Supervision and Students’ Physical Activity in Middle School Physical Education’, Journal of Teaching in Physical Education 21: 75–90. Scruggs, P. W. (2005) ‘Quantification of Physical Activity Time via Pedometry in Fifth- through Eighth-Grade Physical Education’, Research Quarterly for Exercise and Sport 76: A33. Scruggs, P. W. (2007) ‘Middle School Physical Education Physical Activity Quantification: A Pedometer Steps/Min Guideline’, Research Quarterly for Exercise and Sport 78(4): 284–92. Scruggs, P. W., Beveridge, S. K., Eisenman, P. A., Watson, D. L., Schultz, B. B. and Ransdell, L. B. (2003) ‘Quantifying Physical Activity via Pedometry in Elementary Physical Education’, Medicine and Science in Sports and Exercise 35: 1065–71. Scruggs, P. W., Beveridge, S. K., Watson, D. L. and Clocksin, B. D. (2005) ‘Quantifying Physical Activity in First-through Fourth-Grade Physical Education via Pedometry’, Research Quarterly for Exercise and Sport 76: 166–75. Seliger, V., Heller, J., Zelenka, V., Sobolova, V., Pauer, M., Bartunek, Z. et al. (1980) ‘Functional Demands of Physical Education Lessons’, in K. Berg and B. O. Eriksson (eds) Children and Exercise IX, pp. 175–82. Baltimore, MD: University Park Press. Sharpe, T. and Koperwas, J. (1999) Behavior Evaluation Strategies and Taxonomies (BEST) Software. Thousand Oaks, CA: SAGE. United States Department of Health and Human Service (2000) Healthy People 2010. Washington, DC: US Government Printing Office. Vincent, S. D and Pangrazi, R. P. (2002) ‘Does Reactivity Exist in Children When Measuring Activity Levels with Pedometers?’, Pediatric Exercise Science 14: 56–63.

Résumé Facteurs associés l’activité physique chez les adolescents pendant les cours d’éducation physique au collège. L’objet de ce versant descriptif issu d’une large étude de cas exploratoire était d’étudier les relations entre les contextes d’enseignement, les comportements de l’enseignant, et l’activité physique des adolescents au cours d’une année d’éducation physique dans un collège. Une cohorte de collégiens (N = 206) et leurs enseignants ont été (N = 4) ont été observés deux fois par semaine pendant une année scolaire. Les données ont été recueillies en utilisant le système d’observation du temps d’enseignement en fitness (System for Observing Fitness Instruction Time ; SOFIT), le logiciel taxonomie et stratégie d’évaluation comportementale (Behavioral Evaluation Strategy and Taxonomy ; BEST), et des podomètres Yamax SW-200. Les adolescents ont passé 32,5% du temps de la séance en activité d’une intensité au moins modérée, et ont effectué en moyenne 1542 pas en moyenne. Les plus hauts niveaux d’activité physique étaient associés à des séances utilisant les activités de fitness, les activités d’habileté, le jeu comme supports ; alors que les plus faibles niveaux d’activité sont apparus lors de jeux libres. Les plus hauts niveaux d’activité étaient liés aux comportements de l’enseignant de


promotion, de démonstration des activités de fitness, et d’observation ; les plus faibles niveaux d’activité étaient associés aux comportements généraux de présentation des tâches et de gestion de groupe par l’enseignant.

Resumen Factores asociados con la actividad física adolescente durante la Educación Física en secundaria: Un estudio de caso anual El propósito de la parte descriptiva de un gran estudio exploratorio era examinar asociaciones entre el contexto de las lecciones, los comportamientos del professor, y la actividad física del adolescente durante un año de Educación Física en una escuela. Estudiantes de secundaria (N = 206) y sus profesores de educación física (N = 4) fueron observados dos veces por semana durante un año académico. Los datos fueron obtenidos usando el SOFIT, el software BEST y pedometros Yamax SW-200. Los estudiantes invirtieron un 32.5% del tiempo de clase al menos en una actividad de intensidad moderada, promediando 1542 pasos por clase. Niveles más altos de actividad estaban asociados a clases con actividades de fitness, de aprendizaje, y de juego. Niveles mas altos de actividad estaban asociados con comportamientos del profesor orientados a promoción y demostraciones de fitness , y a la observación; niveles más bajos de actividad se asociaban con comportamientos de los profesores relacionados con la instrucción y organización.

Zussamenfassung Faktoren sportlicher Aktivität von Jugendlichen während des Mittelstufen-Sportunterrichts: eine einjährige Fallstudie Ziel des vorliegenden deskriptiven Teils einer größeren explorativen Fallstudie war die Untersuchung von Zusammenhängen zwischen Unterrichtskontexten, Lehrer/innenVerhalten und sportlicher Aktivität von Jugendlichen innerhalb eines Jahres an einer Schule. Mittelstufenschüler/innen (N = 206) und ihre Sportlehrer/innen (N = 4) wurden während eines Schuljahres zweimal wöchentlich beobachtet. Die Daten wurden durch Nutzung der Messverfahren Observing Fitness Instruction Time (SOFIT), Verhaltensevaluations-Strategien (Behavioral Evaluation Strategy) und Taxonomie (BEST)-Software sowie Yamax SW-200Schrittmesser erhoben. Schülerinnen und Schüler haben 32,5% der Unterrichtszeit mit moderater sportlicher Aktivität verbracht, im Mittelwert wurden 1542 Schritte pro Unterricht gemessen. Höhere Aktivitätsstufen hingen zusammen mit Unterrichtskontexten, die Fitness-Aktivitäten, Bewegungsfertigkeiten und Spiele umfassten; niedrigere Aktivitätsstufen wurden während freier sportlicher Aktivität gemessen. Höhere Aktivitätsstufen standen im Zusammenhang mit einem Lehrer/innen-Verhalten, das die Bedeutung von Fitness hervorgehoben hat, bei dem Fitness selber demonstriert und konkret beobachtet wurde; niedrigere Aktivitätsstufen wurden gemessen im Zusammenhang mit einem Lehrer/innen-Verhalten, das sich stärker an generellen Instruktionen und am Unterrichtsmanagement orientiert hat.

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Terry Senne is an Associate Professor in the Department of Kinesiology at Texas Woman’s University. David Rowe is a Reader in the Department of Sport, Culture and the Arts at University of Strathclyde. Boni Boswell and James Decker are Associate Professors in the Department of Exercise and Sport Science at East Carolina University. Shaun Douglas is a physical education teacher at Githens Middle School in North Carolina. Address for correspondence: Terry Senne, PO Box 425647 Denton, TX 76204 USA. [email: [email protected]]