Data collection occurred in two phases. Phase one served two purposes: to measure cell phone use; and to generate a random sample, representative of the larger student body, from which we could recruit subjects for phase two. For phase one, a random sample (N = 305) of the student population at a large, public, university in the mid-western United States completed a one-page survey. The survey consisted of a self-report questionnaire measuring cell phone use in three ways: 1) total cell phone use per day, 2) total number of text messages sent per day, and 3) total number of calls made per day. The following items were used for this purpose:

1. As accurately as possible, please estimate the total amount of time you spend using your mobile phone each day. Please consider all uses except listening to music. For example: consider calling, texting, sending photos, gaming, surfing, watching videos, Facebook, e-mail, and all other uses driven by “apps” and software. 2. As accurately as possible, please estimate the total number of text messages that you send and receive each day. 3. As accurately as possible, please estimate the total number of calls you make and receive each day.

The survey also collected basic demographic information and whether the primary use of the cell phone was for leisure or work/school purposes. Lastly, the questionnaire invited participants to provide their e-mail address if interested in participating in phase two of the study, described simply as a paid follow up. In all, 292 (95.4%) participants provided an e-mail address. Of those, 105 were randomly selected and e-mailed to determine if they were interested in participating in phase two of the study. Fifty six students agreed to participate. However, seven did not attend their scheduled appointments. Thus, 49 students (N = 27 females) participated in phase two of the study. The purpose of phase two was to assess the relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness.

Data collection for phase two of the study took place in the university’s exercise science laboratory. Once in the laboratory, each participant read and signed a consent form, completed a medical history questionnaire, and was measured for height and weight. Height and weight were measured three times each and the median values were recorded utilizing a balance beam scale (Health-O-Meter, Alsip, IL) and electronic stadiometer (Charder Electronic, Taipei, Taiwan), respectively. Bodyweight data were necessary for calculating relative oxygen consumption (VO 2 ml · kg-1 · min-1) during the cardiorespiratory fitness testing. Participants then underwent a three-site skinfold measurement protocol to estimate body fat percentage [27]. Participants then completed the validated Self-Efficacy Survey for Exercise Behaviors [28]. Next, participants were interviewed about their leisure time physical activity behavior and leisure time cell phone use. Interviews lasted about 20 minutes, were tape recorded, and later transcribed. Finally, participants underwent a progressive treadmill exercise test to exhaustion to assess cardiorespiratory fitness [29]. All procedures in both phases of the study were approved by the university’s institutional review board.

Measures

Self-efficacy for physical activity

Participants completed the validated Self-Efficacy Survey for Exercise Behaviors [28]. In the survey participants rated how confident they were that they could motivate themselves to do the listed items (e.g., running, brisk walking, bicycle riding, or aerobic exercise) consistently for at least six months. Each question was a five point Likert scale anchored by “I know I cannot” (one) to “I know I can” (five). Responses for all items were summed as the estimate of self efficacy. Self efficacy has been repeatedly demonstrated to be positively associated with physical activity behavior and, to a lesser extent, cardiorespiratory fitness [30, 31]. Because of these previously established associations, self efficacy was utilized as a co-variate in the present study when assessing the relationship between cell phone use and cardiorespiratory fitness.

Body composition

Participants underwent a three-site skinfold protocol during which the thickness of the skin and subcutaneous fat was measured to the nearest millimeter in three different sites (males: chest, abdomen, thigh; females: triceps, suprailiac, thigh) utilizing skinfold calipers (Slim Guide, Creative Health Products, Plymouth, MI). The sum of these three skinfolds were utilized to estimate body fat percentage (i.e., percent fat) using the previously established equations [27]. Percent fat has been repeatdly demonstrated to be negatively associated with physical activity behavior and maximal cardiorespiratory fitness (VO 2 peak) when expressed relative to total body weight (ml · kg-1 · min-1) [32–36]. Because of these previously established associations, percent fat, like self-efficacy, was also utilized as a co-variate in the present study.

Physical and sedentary behaviors

An in-depth interview framed around 12 open-ended questions was used to elicit information regarding each participant’s daily leisure activities (e.g., physical and sedentary behaviors). Questions targeted behavior, motivation, experience, and the role of the cell phone in leisure. For the purposes of this study, three questions were analyzed. These were:

1. “In your daily life, what are the leisure activities in which you most often participate?” All participants were asked to consider both weekdays and weekends. 2. “Please explain all the ways in which you use your cell phone for leisure?” 3. “Thinking about your daily life, would you say that your cell phone increases or decreases your physical activity?” Please explain your answer.

All participants were provided multiple opportunities to explain and elaborate upon their responses to each question. Likewise, the interviewer probed participants with follow-up questions until a sufficient depth of understanding was reached.

Cardiorespiratory fitness test

After the interviews, participants completed a 10 minute warm-up on a treadmill (Quinton MedTrack CR60, Bothell, WA) at a self-selected pace. After warming up, participants maintained their speed and the grade of the treadmill was increased by 2.5% every two minutes until volitional exhaustion. This protocol was modeled after that of Costill and Fox [29]. Oxygen consumption (VO 2 ml · kg-1 · min-1) was recorded throughout the test via indirect calorimetry using a calibrated metabolic cart (Parvo Medics, Truemax 2400 Metabolic System, Sandy, UT) and a facemask (Hans Rudolph, inc, Shawnee, KS). Peak achieved VO 2 was the measure of cardiorespiratory fitness.

Data analysis

All analyses were performed using SPSS for Windows (version 18.0, SPSS Inc, Evanston, IL). Independent samples t-tests were used to compare male and female responses to the phase one questionnaire. There were no additional statistical analyses performed on the data from phase one of the study. Therefore, the following is the analytic plan for phase two of the study. A preliminary analysis was performed on the phase two data to ensure no violation of the assumptions of normality, linearity, and homoscedasticity. Linearity was assessed using Lack of Fit tests and residual scatterplots. Normality was assessed using the Shapiro-Wilk test of normality and residual scatterplots. Homoscedasticity was assessed using residual scatterplots. These tests confirmed all assumptions were satisfied and the analysis proceeded as follows.

Fitness data

First, independent samples t-tests were performed to compare age, percent fat, VO 2 peak, cell phone use (total minutes per day, texts sent per day, calls made per day), and self-efficacy for physical activity in males and females. Second, a series of hierarchical regressions were performed. One of the primary goals of the present study was to determine if cell phone use significantly added to the prediction of cardiorespiratory fitness after accounting for previously established correlates: sex, self-efficacy and percent fat. Therefore, the following hierarchical regression model was tested, alternately substituting the three measures of cell phone use into block 4:

VO 2 peak = sex (block 1) + self-efficacy for physical activity (block 2) + percent fat (block 3) + cell phone use (total minutes, texting or calls made) (block 4)

Sex (dummy coded as: 1 = females, 0 = males) was included in the model as there are well-established sex-related differences for VO 2 peak in that males typically present with greater VO 2 peak than females [37]. Self-efficacy was included in the model as previous research has indicated that it is positively related to VO 2 peak [30, 31]. Percent fat was included in the model as it has been repeatedly shown to be negatively associated with cardiorespiratory fitness when expressed relative to total body weight (ml · kg-1 · min-1) [32–36]. Because sex, self-efficacy and percent fat are established contributors to VO 2 peak they were entered into the model before total cell phone use. Therefore, the model tested whether or not cell phone use (total minutes, texting, and calls made) uniquely predicted VO 2 peak after controlling for these other, previously established variables.

Interview data

Interview data was analyzed by first dividing participants into even tertiles based on frequency of cell phone use. Low frequency users averaged 101 min∙day-1 (n = 16, SD = 50), moderate users averaged 293 min∙day-1 (n = 17, SD = 78), and high frequency users averaged 840 min∙day-1 (n = 16, SD = 234). To best illustrate emerging trends, interview data for low and high frequency users was compared. Responses to interview question number 1 produced a list of the leisure time activities in which participant’s most often participate. All participants were able to provide at least three activities. All activities were entered into an SPSS spreadsheet and coded by the primary investigators as either a physical activity or a sedentary activity/physical inactivity. Physical activity included walking, running, swimming, working out at the student recreation and wellness center, basketball, soccer, flag football, Lacrosse, and racquetball. Sedentary activity/physical inactivity included arts and crafts, playing musical instruments, “hanging out”, cooking, eating and drinking, watching TV, using the computer, and playing video games. Crosstabs with Chi-Square analysis was used to compare the frequency of these activities among low and high frequency cell phone users. For interview question number 2, a list of the various ways participants use their cell phone for leisure was produced. The list, in its entirety, is: call friends and family, e-mail, text, Facebook, Twitter, play games, surf the internet, send photos, and use a variety of additional applications such as E-bay, Amazon, ESPN, Pintrest, and Instagram. Applications other than Facebook and Twitter were combined into a single category labeled “apps”. All activities were entered into an SPSS spreadsheet and a crosstabs with Chi-Square analysis was used to compare the frequency of responses among low and high frequency users. Lastly, interview question 3 produced one of three responses: there is no relationship between my cell phone use and physical activity, my cell phone use increases physical activity, or my cell phone decreases physical activity. Responses were entered into an SPSS spreadsheet and a crosstabs with Chi-Square analysis was used to compare the frequency of responses among low and high intensity cell phone users.