The relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students

Andrew Lepp
Jacob E Barkley
Gabriel J Sanders
Michael Rebold
Peter Gates
The relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students
R E S E A R C H Open Access
Background: Today's cell phones increase opportunities for activities traditionally defined as sedentary behaviors (e.g., surfing the internet, playing video games). People who participate in large amounts of sedentary behaviors, relative to those who do not, tend to be less physically active, less physically fit, and at greater risk for health problems. However, cell phone use does not have to be a sedentary behavior as these devices are portable. It can occur while standing or during mild-to-moderate intensity physical activity. Thus, the relationship between cell phone use, physical and sedentary activity, and physical fitness is unclear. The purpose of this study was to investigate these relationships among a sample of healthy college students.Methods: Participants were first interviewed about their physical activity behavior and cell phone use. Then body composition was assessed and the validated self-efficacy survey for exercise behaviors completed. This was followed by a progressive exercise test on a treadmill to exhaustion. Peak oxygen consumption (VO 2 peak) during exercise was used to measure cardiorespiratory fitness. Hierarchical regression was used to assess the relationship between cell phone use and cardiorespiratory fitness after controlling for sex, self-efficacy, and percent body fat. Interview data was transcribed, coded, and Chi-square analysis was used to compare the responses of low and high frequency cell phone users. Results: Cell phone use was significantly (p = 0.047) and negatively (β = −0.25) related to cardio respiratory fitness independent of sex, self-efficacy, and percent fat which were also significant predictors (p < 0.05). Interview data offered several possible explanations for this relationship. First, high frequency users were more likely than low frequency users to report forgoing opportunities for physical activity in order to use their cell phones for sedentary behaviors. Second, low frequency users were more likely to report being connected to active peer groups through their cell phones and to cite this as a motivation for physical activity. Third, high levels of cell phone use indicated a broader pattern of sedentary behaviors apart from cell phone use, such as watching television.Conclusion: Cell phone use, like traditional sedentary behaviors, may disrupt physical activity and reduce cardiorespiratory fitness.

Introduction

Recent advances in digital technology have transformed the modern cellular/mobile telephone (cell phone) from a device once singular in function into a multi-function device with capabilities similar to an internet-connected computer. At almost anytime and anyplace, today's cell phones allow users to call, send and receive text messages, update social networking sites (e.g., Facebook), stream videos and live events, play video games, and search the internet. Historically, these types of activities have been defined as sedentary behaviors [1]. Participation in large amounts of sedentary, or sitting, behaviors is associated with multiple health problems such as impaired lipid profiles and glucose uptake, greater energy intake and waist circumferences, and greater mortality risk [2][3][4][5][6]. Participation in large amounts of sedentary behavior is problematic even for individuals who meet weekly physical activity guidelines (i.e., "active couch potatoes"). Relative to active adults who do not participate in large amounts of sedentary behavior, these "active couch potatoes" exhibit impaired glucose metabolism and increased blood pressure despite their regular exercise habits [6,7]. While there are certainly "active couch potatoes", multiple studies have suggested that individuals who participate in large amounts of sedentary behavior do so at the expense of physical activity and exhibit lower cardio-respiratory fitness than individuals who do not participate in large amounts of sedentary behaviors [8][9][10][11][12][13]. Taken together, these studies suggest that participation in large amounts of sedentary behavior has negative health consequences, may interfere with physical activity behavior, and could contribute to suppressed cardiorespiratory fitness.

Of course, cell phone use does not have to be a sedentary behavior. The very premise of its design is to allow use while being mobile. Thus, because of its inherent portability, it is possible to utilize a variety of cell phone functions during physical inactivity (i.e., standing), light intensity physical activity (< 3 multiples of the metabolic rate or METs) such as slow walking, and moderate intensity physical activity (3-6 METs) such as fast walking. In addition, numerous software applications have been developed for cell phones which are designed to promote physical activity and therefore could have a positive effect on physical fitness. Lastly, there is evidence that cell phones can play a supportive role in therapeutic interventions designed to increase physical activity when coupled with more traditional strategies such as face-to-face counseling [14]. However, available research has not been able to discern the unique contribution of the cell phone to changing physical activity behavior relative to the other important components of these interventions. In light of this, the relationship between cell phone use, physical and sedentary activity, and physical fitness is not as clear as the previously established relationship between participation in traditional sedentary behaviors (e.g., watching television, using a computer), physical activity, and physical fitness. Considering the ubiquity of cell phones, cell phone use appears to be a variable worth exploring in an effort to better understand physical and sedentary activity behavior and physical fitness in the digital age.

Today's college students are the vanguard of the first cohort of young people raised entirely in the digital age (i.e. "digital natives"). Among this cohort, the integration of digital technology within daily life is the cultural norm [15]. Cell phones, by keeping college students constantly connected with an array of digital media, are the devices which make the integration of technology and life possible. A 2011 report by the Pew Internet and American Life Project stated that 96% of undergraduate college students and 89% of non-students of the same age own a cell phone [16]. Furthermore, the same report stated that 63% of undergraduates and 61% of nonstudents of the same age access the internet through their cell phones. The high rate of cell phone ownership and use among today's college students and their generational peers has led to the development of the term "hyper-connected" to describe this population [17]. Because the cell phone is so pervasive among college students, this population makes a logical starting point for investigating the potential relationship between cell phone use, physical and sedentary activity, and physical fitness.

No research that we are aware of has examined the cell phone as a potential sedentary device. Likewise, no research has investigated the relationship between cell phone use and an objective measure of physical fitness. However, emerging research has explored the relationship between a range of health related variables and what has been called "problematic" cell phone use. Problematic cell phone use has been described as an addiction-like behavior leading individuals to use the cell phone compulsively [18][19][20]. A large study of Spanish adolescents aged 13 to 20 years estimated that up to 20% may be problematic cell phone users [21]. Problematic cell phone use has been linked to depression, anxiety, low self-esteem, and unhealthy lifestyle practices such as skipping meals, multiple sexual partners, poor sleep habits, alcohol consumption, smoking, and illegal drug use [18][19][20][21][22][23][24][25][26]. It is important to expand our understanding of the potential health impact these devices may have on users. This current study looks beyond problematic cell phone use by considering a range of cell phone users from low to high frequency. As such, the purpose of this study was to assess the relationship between cell phone use, physical and sedentary activity, and an objective measure of physical fitness (i.e., cardiorespiratory fitness) among a sample of typical college students in the United States. The following hypotheses were developed: cell phone use would be positively associated with sedentary activity and inversely associated with physical activity; additionally, cell phone use would be inversely associated with cardiorespiratory fitness independent of known correlates (sex, body fat percentage, and self-efficacy for physical activity -an individual's belief in their ability to participate in physical activity). Thus, we posit that cell phone use may influence both physical and sedentary activity, as well as cardiorespiratory fitness, in a manner similar to what has been described for traditional sedentary behaviors (e.g., television watching).

Methods

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][33][34][35][36]. Because of these previously established associations, percent fat, like selfefficacy, 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 sexrelated differences for VO 2 peak in that males typically present with greater VO 2 peak than females [37]. Selfefficacy 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][33][34][35][36]. Because sex, selfefficacy 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. Table 1 presents descriptive statistics for the phase one measures of daily cell phone use categorized by sex.

Results

Independent samples t-tests showed no significant difference between males and females in any of the measures (p ≥ 0.337). As a whole, students in this sample averaged just over 300 minutes (5 hours) of cell phone use per day. In addition, 88.2% of participants reported using the cell phone primarily for leisure. Table 2 illustrates the mean and standard deviation for the phase two study variables categorized by sex. Males had a significantly greater (p ≤ 0.03) VO 2 peak and lower percent fat than females. There were no other sex differences (p ≥ 0.13) for the remaining variables. Table 3 illustrates the results of the hierarchical regression analysis. Overall, the model was significant (R 2 = 0.389, F = 7.004, p < 0.001). For the prediction of VO 2 peak, each block (sex, self-efficacy for physical activity, percent fat and total daily cell phone use) significantly added to the prediction of the criterion variable. Males had a significantly greater VO 2 peak than females (β = −0.42, p = 0.003). There was a significant, positive relationship between VO 2 peak and selfefficacy for physical activity (β = 0.26, p = 0.049). There was a significant, negative relationship between percent fat and VO 2 peak (β = −0.36, p = 0.02). Finally, there was a significant, negative relationship between total daily cell phone use and VO 2 peak (β = −0.25, p = 0.047). For VO 2 peak, two additional regression models were found to be significant by substituting the number of text messages sent per day (R 2 = 0.399, F = 7.296, p < 0.001) and then the number of calls made per day (R 2 = 0.420, F = 7.966, p < 0.001) for total cell phone use as the fourth block of the model. For these additional models, both texting (ΔR 2 =0.068) and calls made (ΔR 2 = 0.09) significantly (p ≤ 0.03) added to the prediction of VO 2 peak after controlling for sex (block 1), self-efficacy (block 2) and percent fat (block 3). Similar to total cell phone use, both texting (β = −0.26, p = 0.03) and calling (β = −0.31, p = 0.01) were negatively associated with VO 2 peak.

Considering statistical power, the addition of total cell phone use to the fourth block of the hierarchical  All data are mean±SD. *Indicates a significant difference between male and females (p ≤ 0.03).

regression model predicting VO 2 peak yielded a power of 0.95 and an effect size (f 2 ) of 0.30. Given this effect size, a minimum sample size of only N = 32 is required to achieve adequate power (0.80) at an α ≤ 0.05. Therefore, our current sample (N = 49) was adequate. The significant negative relationship between cell phone use and cardiorespiratory fitness suggests differences in physical and sedentary activity behaviors may exist between high and low frequency cell phone users. The interview data, which is summarized in Table 4, allowed for the investigation of this hypothesis. Low and high frequency cell phone users identified a similar amount of leisure activities in which they normally participate; however, low frequency users identified significantly more physical activities and significantly fewer sedentary (or physically inactive) activities compared to high frequency users (χ 2 = 6.791, df = 1, p = 0.009). When examining the specific types of leisure activities the two groups utilized their cell phones for, low users more frequently engaged in calling, both low and high users engaged in texting, and high frequency users more frequently used their phone for gaming, surfing the internet, checking social networking sites like Facebook and Twitter, and utilizing a number of other cell phone applications ("apps"). These differences were significant (χ 2 = 19.214, df = 8, p = 0.014) and suggest that for high frequency users, relative to low users, the cell phone more often is a medium for participation in traditionally defined sedentary activities such as playing video games and surfing the internet.

Lastly, low and high frequency users had significantly different perceptions (χ 2 = 9.600, df = 2, p = 0.008) of the relationship between their cell phone use and their physical activity. Although both groups had an equal number of participants who perceived no relationship between their cell phone use and their physical activity, six low users and zero high users felt that the cell phone increased their physical activity. Explanations for this relationship suggested that the cell phone encouraged physical activity by connecting the user to a network of active people. In addition, low frequency users recognized that the cell phone has the potential to interfere with physical activity behavior and therefore explained that they set the device aside or turn it off when engaging in physical activity. Both ideas are illustrated in the following quote from a low frequency user interviewed for this study:

"When I have my phone people are texting me and asking me to come out and do something. If my cell  • # of participants indicating using cell phone for text messaging 12 16

• # of participants indicating using cell phone for e-mail 2 1

• # of participants indicating using cell phone for video games 1 8

• # of participants indicating using cell phone for web browsing 1 6

• # of participants indicating using cell phone for taking photographs 0 1

• # of participants indicating using cell phone for Twitter 3 10

• # of participants indicating using cell phone for Facebook 2 12

• # of participants indicating using cell phone for other "apps" 2 9

Perceived relationship between cell phone use and physical activity* 9.6 2 0.008

• # of participants indicating cell phone use is not related to physical activity 8 8

• # of participants indicating cell phone uses increases physical activity 6 0

• # of participants indicating cell phone uses decreases physical activity 2 8

*Indicates (p ≤ 0.05) a significant difference between groups.

phone is turned off or if I'm not using it then I'm probably alone. And then there is the other side of the coin, which is that once I get out and start playing a sport I set my cell phone aside. I have my cell phone on me all the time except for when I'm playing an intense sport. So when I'm playing Lacrosse it's not on me or when I'm playing hacky sack I put it down. So that's like the only time I don't actually have it on but I wouldn't be outside playing unless I had my cell phone I guess."

In contrast, eight high frequency users and two low frequency users believed that cell phone use decreased their physical activity. As illustrated by the two quotes below from the interview data, explanations of this relationship suggested that the cell phone encourages sedentary behavior and also interferes with physical activity behavior:

"Now that I have switched to the iPhone I would say it definitely decreases my physical activity because before I just had a Blackberry so I didn't have much stuff on it but now if I'm bored I can just download whatever I want and just sit there and play. I really cannot get bored using it. Before I would always get bored and I would have to find something else to do and that would involve like going somewhere or playing sports or doing something."

Or, as another high frequency user explained:

"It decreases physical activity because, for instance, the other day, one of my friends called me during my work out, and like, I haven't talked to her in a while and I had to tell her a lot of stuff. So it kind of distracted me from my work out."

As illustrated in the quotes above, the perceived relationship between cell phone use and physical activity is complex and may vary by frequency of cell phone use. Nevertheless, the possibility that cell phone use may encourage physical activity among some low frequency users while disrupting physical activity and encouraging sedentary activity among high frequency users helps explain the significant negative relationship between cell phone use and cardiorespiratory fitness identified in this study.

Discussion

Previous research into cell phone use and health-related behaviors has tended to focus on problematic cell phone users [18][19][20][21][22][23][24][25][26]. The current study builds upon this previous research by evaluating the relationship between cell phone use, physical and sedentary activity, and fitness among a range of low to high frequency cell phone users. Our findings demonstrate that, among this sample of college students, cell phone use is negatively associated with cardiorespiratory fitness (i.e., VO 2 peak). That is to say, high frequency users tended to be less physically fit than low frequency users. This relationship is significant irrespective of the contributions of sex, selfefficacy for physical activity, and percent body fat to the model.

As an explanation of this relationship, our data suggests that the leisure repertoire of low frequency users included more physical activity than high frequency users. In contrast, the leisure repertoire of high frequency users included more sedentary activities than low frequency users. Thus, cell phone use among this cohort of young people may be a marker for a broader pattern of sedentary behavior. In other words, the most intensive cell phone users may be similarly attracted to other forms of digital media such as computers, video games, movies and televisionall of which are considered traditional sedentary behavior and are inversely related to fitness [2][3][4][5][6][7][8][9][10][11][12][13] with the possible exception of physically-interactive video games such as the Nintendo Wii [38]. An important complementary explanation of the negative relationship between cell phone use and physical fitness emerged from the data as well. Namely, our data suggests a complex relationship between cell phone use and physical activity. In the interviews, some low frequency users described how the device increased physical activity by connecting them with a physically active peer group. On the other hand, many high frequency users (and some low frequency users) described how the ever-present cell phone disrupted their physical activity behavior and consumed their time with cellphone facilitated sedentary behaviors such as playing video games, surfing the internet, texting, checking social networking sites, and playing with new "applications". Thus, cell phone use appears to have the ability to both facilitate and disrupt physical activity. In this sample, cell phone use tended to be a facilitator among low frequency users and a disrupter among high frequency users.

By and large, this supports our original hypothesis that cell phone use is associated with physical activity and fitness in a manner that is similar to traditionally defined sedentary behaviors such as watching television and using a computer. While cell phones provide many of the same temptations as television and internet connected computers, the difference is that cell phones fit in our pockets and purses and are with us wherever we go. Thus, they provide an ever-present invitation to "sit and play". Consequently, we recommend that cell phone use be included in future systematic reviews examining sedentary behavior and fitness.

Although the cell phone has the potential to disrupt physical activity, it may also serve to motivate physical activity. Indeed, six low frequency users in this study reported such an effect. This may have implications for mHealth, the practice of supporting public health interventions with mobile devices. In particular, some mHealth practitioners have begun incorporating the cell phone into interventions aimed at increasing physical activity [14]. These interventions have shown promise, yet all use the cell phone in conjunction with face-to-face support as well as other strategies. As a result, the unique contribution of the cell phone at changing behavior has been difficult to discern. Nevertheless, the cell phone's ability to connect individuals who share similar physical activity goals may be important for maintaining participation in extended interventions [39,40]. This sentiment was echoed by the low frequency users in this study who felt that cell phone use increased their physical activity by connecting them with a physically active peer group. Thus, future mHealth interventions aimed at increasing physical activity may do well to emphasize this aspect of the mobile platform; more specifically, to connect participants to a nearby network of people who share similar activity and fitness goals.

While these findings are novel, there are some limitations to the present study. First, while the study used some objective measures (i.e., cardiorespiratory fitness, percent body fat), other measures were subjective (i.e., self-efficacy, cell phone use, physical and sedentary activity). While subjective assessments of sedentary behavior (e.g., television watching) are often performed in a manner similar to that which was used to assess cell phone use presently [41] and self-report methods are regularly used to assess physical and sedentary activity behavior [42], objective or validated selfreport measures would strengthen future studies. Second, the sample consisted of only college students enrolled at a single, large, public university in the Midwestern United States. While the practice of being hyper-connected to an array of digital media and peers through the cell phone is the cultural norm for this cohort of young people, our ability to generalize these results to other populations is limited. Therefore, future research should include not only a larger sample of college students from different types of universities and in different geographic regions, but also non-students and other age groups ranging from early adolescents to older adults. Likewise, diverse ethnicities and socioeconomic groups should be studied as well.

Conclusion

In conclusion, this research identified a relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness. The negative relationship between cell phone use and fitness may be explained in two ways. First, cell phone use can disrupt leisure time physical activity and promote sedentary behaviors among high frequency users. Our data suggests that in comparison to low frequency users, high frequency users are more likely to forgo opportunities for physically active pursuits in order to use their cell phones for moresedentary activities such as using Facebook, Twitter, video games, apps, and surfing the internet. Second, relatively high levels of cell phone use may serve as a marker for a broader pattern of leisure time sedentary behaviors which are independent of cell phone use, such as watching television, playing video games and using the computer. This is the first study that we are aware of to assess these relationships in any population. Given that cell phones are ever-present on college campuses and their most common uses such as texting, updating social networking sites, and browsing the internet are standard practices [16,17], the negative association between cell phone use and fitness illustrated herein deserves further attention as cardiorespiratory fitness (i.e., VO 2 peak) is an excellent indicator of an individual's risk for a number of health concerns [43,44].

Table 1
1Phase one self-reported cell phone useVariable
Males
(N = 134)

Females
(N = 168)

Total use per
day (minutes)

Mean±SD
298.9 ± 301.1
313.04 ± 252.1

Median
202.5
240.0

Range
1440
1200

Texts messages
sent per day

Mean ± SD
214.5 ± 1297.6
157.6 ± 427.5

Median
50.0
60.0

Range
15000
5000

Calls made
per day

Mean±SD
6.7 ± 21.9
5.04 ± 4.9

Median
3.0
4.0

Range
250
30

Table 2
2Phase two study variables categorized by sexMales
(N = 22)

Females
(N = 27)

Table 3
3Hierarchical regression analysesSex
Self-efficacy Percent fat
Total cell
phone use

Block 1
Block 2
Block 3
Block 4

(ΔR 2 , ΔF, p) (ΔR 2 , ΔF, p) (ΔR 2 , ΔF, p) (ΔR 2 , ΔF, p)

VO 2 peak
(ml kg·min -1 )

0.18, 10.2,
0.003

0.07, 4.1,
0.049

0.09, 5.8,
0.02

0.06, 4.2,
0.047

Table 4
4Summary of interview responses for low and high frequency cell phone usersLow frequency cell
phone users

High frequency cell
phone users

X 2
df
p

http://www.ijbnpa.org/content/10/1/79
AcknowledgementsThe authors of this manuscript would like to acknowledge Drs. John Gunstad and Angela Ridgel for their assistance with the preparation of this manuscript.Funding sourceFunding for this research was provided by Kent State University's Division of Research and Sponsored Programs.Competing interestsThe authors declare that they have no competing interests.Authors' contributions AL and JEB were co-principal investigators on this study. They designed the study, obtained university funding and IRB approval, assisted with data collection, performed the data analysis, and drafted the manuscript. GJS and MR assisted with all phases of the implementation of the study including: pilot testing, participant scheduling and data collection. Additionally GJS and MR critically commented on the manuscript. PG collected the qualitative data (i.e., interviews) and critically commented on the qualitative analysis. All authors read and approved the final manuscript.Submit your next manuscript to BioMed
Reliability and validity of the Sedentary Behavior Questionnaire (SBQ) for adults. D E Rosenberg, G J Norman, N Wagner, J Phys Act Health. 7Rosenberg DE, Norman GJ, Wagner N, et al: Reliability and validity of the Sedentary Behavior Questionnaire (SBQ) for adults. J Phys Act Health 2010, 7:697-705.

A review of TV viewing and its association with health outcomes in adults. D M Williams, H A Raynor, J T Ciccolo, Am J Lifestyle Med. 23Williams DM, Raynor HA, Ciccolo JT: A review of TV viewing and its association with health outcomes in adults. Am J Lifestyle Med 2008, 2(3):250-259.

Correlates of prolonged television viewing time in U.S. adults to inform program development. A C King, J H Goldberg, J Salmon, Am J Prev Med. 381King AC, Goldberg JH, Salmon J, et al: Correlates of prolonged television viewing time in U.S. adults to inform program development. Am J Prev Med 2010, 38(1):17-26.

Television viewing time and mortality: The Australian diabetes, obesity and lifestyle study. D W Dunstan, Elm Barr, G N Healy, Circulation. 121Dunstan DW, Barr ELM, Healy GN, et al: Television viewing time and mortality: The Australian diabetes, obesity and lifestyle study. Circulation 2010, 121:384-391.

Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. M T Hamilton, D G Hamilton, T W Zderic, Diabetes. 5611Hamilton MT, Hamilton DG, Zderic TW: Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes 2007, 56(11):2655-67.

Too much sitting: the population health science of sedentary behavior. N Owen, G N Healy, C E Matthews, D W Dunstan, Exerc Sport Sci Rev. 383Owen N, Healy GN, Matthews CE, Dunstan DW: Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev 2010, 38(3):105-113.

Television time and continuous metabolic risk in physically active adults. G N Healy, D W Dunstan, J Salmon, Med Sci Sports Exerc. 404Healy GN, Dunstan DW, Salmon J, et al: Television time and continuous metabolic risk in physically active adults. Med Sci Sports Exerc 2008, 40(4):639-45.

Physical activity and sedentary behavior: a review of longitudinal studies of weight and adiposity in youth. A Must, D J Tybor, Int J Obes Relat Metab Disord. 29S2Must A, Tybor DJ: Physical activity and sedentary behavior: a review of longitudinal studies of weight and adiposity in youth. Int J Obes Relat Metab Disord 2005, 29(S2):S84-S96.

Sedentary behavior, physical activity, and the metabolic syndrome among U.S. adults. E Ford, H Kohl, Iii, A Mokdad, U Ajani, Obes Res. 13Ford E, Kohl H III, Mokdad A, Ajani U: Sedentary behavior, physical activity, and the metabolic syndrome among U.S. adults. Obes Res 2005, 13:608-614.

Physical activity and television watching in relation to risk for Type 2 Diabetes Mellitus in men. F B Hu, M F Leitzmann, M J Stampfer, Arch Intern Med. 161Hu FB, Leitzmann MF, Stampfer MJ, et al: Physical activity and television watching in relation to risk for Type 2 Diabetes Mellitus in men. Arch Intern Med 2001, 161:1542-1548.

Television watching and other sedentary behaviors in relation to risk of obesity and Type 2 Diabetes Mellitus in women. F B Hu, T Li, G Colditz, JAMA. 289Hu FB, Li T, Colditz G, et al: Television watching and other sedentary behaviors in relation to risk of obesity and Type 2 Diabetes Mellitus in women. JAMA 2003, 289:1785-1791.

Systematic review of sedentary behaviour and health indicators in school-aged children and youth. M S Tremblay, A G Leblanc, M E Kho, Int J Behav Nut Phys Act. 898Tremblay MS, LeBlanc AG, Kho ME, et al: Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nut Phys Act 2011, 8:98.

The independent associations of sedentary behaviour and physical activity on cardiorespiratory fitness. R Santos, J Mota, A D Okely, Br J Sports Med. Epub ahead of printSantos R, Mota J, Okely AD, et al: The independent associations of sedentary behaviour and physical activity on cardiorespiratory fitness. Br J Sports Med 2013. Epub ahead of print.

Increasing Physical Activity with mobile devices: A Meta-Analysis. J Fanning, S P Mullen, E Mcauley, J Med Internet Res. 20126161Fanning J, Mullen SP, McAuley E: Increasing Physical Activity with mobile devices: A Meta-Analysis. J Med Internet Res 2012, 14(6):e161.

Born Digital: Understanding the First Generation of Digital Natives. J Palfrey, U Gasser, New York (NYBasic BooksPalfrey J, Gasser U: Born Digital: Understanding the First Generation of Digital Natives. New York (NY): Basic Books; 2008.

College students and technology, The Pew Research Center's Internet and American Life Project. A Smith, L Raine, K Zickuhr, Smith A, Raine L, Zickuhr K: College students and technology, The Pew Research Center's Internet and American Life Project. 2011 [http://pewinternet.org/Reports/2011/College-students-and-technology.aspx]

Millennials will benefit and suffer due to their hyperconnected lives, The Pew Research Center's Internet and American Life Project. J Anderson, L Rainie, Anderson J, Rainie L: Millennials will benefit and suffer due to their hyperconnected lives, The Pew Research Center's Internet and American Life Project. 2012 [http://www.pewinternet.org/Reports/2012/Hyperconnected- lives/Overview.aspx]

Problematic internet and mobile phone use and clinical symptoms in college students: The role of emotional intelligence. M Beranuy, U Oberst, X Carbonell, A Chamarro, Comput Hum Behav. 25Beranuy M, Oberst U, Carbonell X, Chamarro A: Problematic internet and mobile phone use and clinical symptoms in college students: The role of emotional intelligence. Comput Hum Behav 2009, 25:1182-1187.

Addictive personality and problematic mobile phone use. M Takao, S Takahashi, M Kitamura, CyberPsych & Beh. 12Takao M, Takahashi S, Kitamura M: Addictive personality and problematic mobile phone use. CyberPsych & Beh 2009, 12:501-507.

Psychological predictors of problem mobile phone use. A Bianchi, J G Phillips, CyberPsych & Beh. 8Bianchi A, Phillips JG: Psychological predictors of problem mobile phone use. CyberPsych & Beh 2005, 8:39-51.

Factors associated with cell phone use in adolescents in the community of Madrid (Spain). M Sánchez-Martínez, A Otero, CyberPsych Behav. 12Sánchez-Martínez M, Otero A: Factors associated with cell phone use in adolescents in the community of Madrid (Spain). CyberPsych Behav 2009, 12:131-137.

Problematic internet and cellphone use: Psychological, behavioral, and health correlates. C Jenaro, N Flores, M Gómez-Vela, Addic Res & Theory. 15Jenaro C, Flores N, Gómez-Vela M, et al: Problematic internet and cell- phone use: Psychological, behavioral, and health correlates. Addic Res & Theory 2007, 15:309-320.

S Frank, L Dahler, L E Santurri, K Knight, Hyper-texting and hyper-networking: a new health risk for teens? Conference presentation from the American Public Health Association's Annual Meeting and Expo. Frank S, Dahler L, Santurri LE, Knight K: Hyper-texting and hyper-networking: a new health risk for teens? Conference presentation from the American Public Health Association's Annual Meeting and Expo. 2010 [http://case.edu/ medicus/breakingnews/scottfrankhypertextingandteenrisks.html]

The association between problematic cellular phone use and risky behaviors and low self-esteem among Taiwanese adolescents. Y Yuan-Sheng, Y Ju-Yu, K Chih-Hung, BMC Pub Health. 10Yuan-Sheng Y, Ju-Yu Y, Chih-Hung K, et al: The association between problematic cellular phone use and risky behaviors and low self-esteem among Taiwanese adolescents. BMC Pub Health 2010, 10:217-224.

Mobile phone dependence and health-related lifestyle of university students. M Toda, K Monden, K Kubo, K Morimoto, Soc Behav Pers Int J. 34Toda M, Monden K, Kubo K, Morimoto K: Mobile phone dependence and health-related lifestyle of university students. Soc Behav Pers Int J 2006, 34:1277-1284.

Relationships of personality and lifestyle with mobile phone dependence among female nursing students. S Ezoe, M Toda, K Yoshimura, Soc Behav Pers Int J. 37Ezoe S, Toda M, Yoshimura K, et al: Relationships of personality and lifestyle with mobile phone dependence among female nursing students. Soc Behav Pers Int J 2009, 37:231-238.

Practical body composition assessment for children, adults, and older adults. V Heyward, Int J Sport Nutr. 8Heyward V: Practical body composition assessment for children, adults, and older adults. Int J Sport Nutr 1998, 8:285-307.

The development of self-efficacy scales for health-related diet and exercise behaviors. J F Sallis, R B Pinski, R M Grossman, Health Educ Res. 3Sallis JF, Pinski RB, Grossman RM, et al: The development of self-efficacy scales for health-related diet and exercise behaviors. Health Educ Res 1988, 3:283-292.

Energetics of marathon running. D L Costill, E L Fox, Med Sci Sports Exer. 1Costill DL, Fox EL: Energetics of marathon running. Med Sci Sports Exer 1969, 1:81-86.

Long-term maintenance of exercise, selfefficacy, and physiological change in older adults. E Mcauley, C Lox, T E Duncan, J Gerontol. 48McAuley E, Lox C, Duncan TE: Long-term maintenance of exercise, self- efficacy, and physiological change in older adults. J Gerontol 1993, 48:218-224.

Social cognitive determinants of physical activity in young adults: A prospective structural equation analysis. L Rovniak, E S Anderson, R A Winett, R S Stephens, Ann Behav Med. 24Rovniak L, Anderson ES, Winett RA, Stephens RS: Social cognitive determinants of physical activity in young adults: A prospective structural equation analysis. Ann Behav Med 2002, 24:149-156.

Relationship of VO2 Peak, Body Fat Percentage, and Power Output Measured During Repeated Bouts of a Wingate Protocol. J R Mclester, J M Green, P J Wickwire, T R Crews, Int J Exerc Sci. 12Mclester JR, Green JM, Wickwire PJ, Crews TR: Relationship of VO2 Peak, Body Fat Percentage, and Power Output Measured During Repeated Bouts of a Wingate Protocol. Int J Exerc Sci 2008, 1(2):79-90.

Assessment of methods for assigning treadmill exercise workloads for lean and obese women. P A Farrell, A B Gustafson, R K Kalkhoff, Int J Obes. 91Farrell PA, Gustafson AB, Kalkhoff RK: Assessment of methods for assigning treadmill exercise workloads for lean and obese women. Int J Obes 1985, 9(1):49-58.

BMI, fat percentage and VO2max in college female staff. B Nakhostin-Roohi, Z Niknam, J Sports Med Phys Fitness. 482Nakhostin-Roohi B, Niknam Z: BMI, fat percentage and VO2max in college female staff. J Sports Med Phys Fitness 2008, 48(2):211-6.

Objectively measured physical activity and fat mass in a large cohort of children. A R Ness, S D Leary, C Mattocks, S N Blair, J J Reilly, J Wells, S Ingle, K Tilling, G D Smith, C Riddoch, PLoS Med. 4397Ness AR, Leary SD, Mattocks C, Blair SN, Reilly JJ, Wells J, Ingle S, Tilling K, Smith GD, Riddoch C: Objectively measured physical activity and fat mass in a large cohort of children. PLoS Med 2007, 4(3):e97.

Physical activity and fat-free and fat mass by bioelectrical impedance in 3853 adults. U G Kyle, G Gremion, L Genton, D O Slosman, A Golay, C Pichard, Med Sci Sports Exerc. 334Kyle UG, Gremion G, Genton L, Slosman DO, Golay A, Pichard C: Physical activity and fat-free and fat mass by bioelectrical impedance in 3853 adults. Med Sci Sports Exerc 2001, 33(4):576-84.

Effects of aging, sex, and physical training on cardiovascular responses to exercise. T Ogawa, R J Spina, W H Martin, Circulation. 86Ogawa T, Spina RJ, Martin WH, et al: Effects of aging, sex, and physical training on cardiovascular responses to exercise. Circulation 1992, 86:494-503.

Physiologic Responses and Motivation to Play a Physically Interactive Video Game Relative to a Sedentary Alternative in Children. A Penko, J E Barkley, Ann Behav Med. 392Penko A, Barkley JE: Physiologic Responses and Motivation to Play a Physically Interactive Video Game Relative to a Sedentary Alternative in Children. Ann Behav Med 2010, 39(2):162-169.

Iterative development of MobileMums: a physical activity intervention for women with young children. B S Fjeldsoe, Y D Miller, J L O'brian, A L Marshall, Int J Behav Nutr Phys Act. 2012151Fjeldsoe BS, Miller YD, O'Brian JL, Marshall AL: Iterative development of MobileMums: a physical activity intervention for women with young children. Int J Behav Nutr Phys Act 2012, 9:151.

Do interventions to promote walking in groups increase physical activity? A meta-analysis. A Kassavou, A Turner, D P French, Int J Behav Nutr Phys Act. 1018Kassavou A, Turner A, French DP: Do interventions to promote walking in groups increase physical activity? A meta-analysis. Int J Behav Nutr Phys Act 2013, 10:18.

Validity and reliability of measures of television viewing time and other non-occupational sedentary behavior of adults: a review. B K Clark, T Sugiyama, G N Healy, Obes Rev. 101Clark BK, Sugiyama T, Healy GN, et al: Validity and reliability of measures of television viewing time and other non-occupational sedentary behavior of adults: a review. Obes Rev 2009, 10(1):7-16.

Assessment of physical activity: a critical appraisal. K R Westerterp, Eur J Appl Physiol. 1056Westerterp KR: Assessment of physical activity: a critical appraisal. Eur J Appl Physiol 2009, 105(6):823-8.

Exercise Capacity and Mortality among Men Referred for Exercise Testing. J Myers, M Prakash, V Froelicher, N Engl J Med. 346Myers J, Prakash M, Froelicher V, et al: Exercise Capacity and Mortality among Men Referred for Exercise Testing. N Engl J Med 2002, 346:793-801.

Cardiorespiratory fitness in young adulthood and the development of cardiovascular disease risk factors. M R Carnethon, S S Gidding, R Nehgme, 10.1186/1479-5868-10-79JAMA. 290Carnethon MR, Gidding SS, Nehgme R, et al: Cardiorespiratory fitness in young adulthood and the development of cardiovascular disease risk factors. JAMA 2003, 290:3092-3100. doi:10.1186/1479-5868-10-79

The relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students. Lepp, International Journal of Behavioral Nutrition and Physical Activity. 1079Cite this article asCite this article as: Lepp et al.: The relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students. International Journal of Behavioral Nutrition and Physical Activity 2013 10:79.


Digitization of the British Medical Journal and its forerunners (1840-1996) was completed by the
27 March 2010

Kenneth F Schulz
Source: BMJ: British
CONSORT Group
Oxford 3Ottawa Methods Centre, Clinical Epidemiology Program
Department of Epidemiology and Community Medicine
University of Oxford
Wolfson College
Ottawa Hospital Research Institute
University of Ottawa
OttawaCanada

Douglas G Altman
Source: BMJ: British
CONSORT Group
Oxford 3Ottawa Methods Centre, Clinical Epidemiology Program
Department of Epidemiology and Community Medicine
University of Oxford
Wolfson College
Ottawa Hospital Research Institute
University of Ottawa
OttawaCanada

David Moher
Source: BMJ: British
CONSORT Group
Oxford 3Ottawa Methods Centre, Clinical Epidemiology Program
Department of Epidemiology and Community Medicine
University of Oxford
Wolfson College
Ottawa Hospital Research Institute
University of Ottawa
OttawaCanada

Kenneth Schulz
Source: BMJ: British
CONSORT Group
Oxford 3Ottawa Methods Centre, Clinical Epidemiology Program
Department of Epidemiology and Community Medicine
University of Oxford
Wolfson College
Ottawa Hospital Research Institute
University of Ottawa
OttawaCanada

Kenneth F Schulz
Source: BMJ: British
CONSORT Group
Oxford 3Ottawa Methods Centre, Clinical Epidemiology Program
Department of Epidemiology and Community Medicine
University of Oxford
Wolfson College
Ottawa Hospital Research Institute
University of Ottawa
OttawaCanada

Douglas G Altman
Source: BMJ: British
CONSORT Group
Oxford 3Ottawa Methods Centre, Clinical Epidemiology Program
Department of Epidemiology and Community Medicine
University of Oxford
Wolfson College
Ottawa Hospital Research Institute
University of Ottawa
OttawaCanada

David Moher
Source: BMJ: British
CONSORT Group
Oxford 3Ottawa Methods Centre, Clinical Epidemiology Program
Department of Epidemiology and Community Medicine
University of Oxford
Wolfson College
Ottawa Hospital Research Institute
University of Ottawa
OttawaCanada

Digitization of the British Medical Journal and its forerunners (1840-1996) was completed by the

Medical Journal
340774827 March 201010.1136/bmj.c332Accessed: 22/06/2014 18:20 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . BMJ is collaborating with JSTOR to digitize, preserve and extend access to BMJ: British Medical Journal. This content downloaded from 188.8.131.52 on Sun, 22 Jun 2014 18:21:00 PM All use subject to JSTOR Terms and Conditions
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact email@example.com..

The CONSORT statement is used worldwide to improve the reporting of randomised controlled trials. Kenneth Schulz and colleagues describe the latest version, CONSORT 2010, which updates the reporting guideline based on new methodological evidence and accumulating experience Randomised controlled trials, when appropriately designed, conducted, and reported, represent the gold standard in evaluating healthcare interventions. However, randomised trials can yield biased results if they lack methodological rigour.1 To assess a trial accurately, readers of a published report need complete, clear, and transparent information on its methodology and findings. Unfortunately, attempted assessments frequently fail because authors of many trial reports neglect to provide lucid and complete descriptions of that critical information. 24 That lack of adequate reporting fuelled the development of the original CONSORT (Consolidated Standards of Reporting Trials) statement in 19965 and its revision five years later. 6 8 While those statements improved the reporting quality for some randomised controlled trials, 9 10 many trial reports still remain inadequate.2 Furthermore, new methodological evi-dence and additional experience has accumulated since the last revision in 2001. Consequently, we organised a CONSORT Group meeting to update the 2001 statement. 6 8 We introduce here the result ofthat process, CONSORT 2010.

Intent of CONSORT 2010

The CONSORT 2010 Statement is this paper including the 25 item checklist in the table and the flow diagram. It provides guidance for reporting all randomised controlled trials, but focuses on the most common design typeindividually randomised, two group, parallel trials. Other trial designs, such as cluster randomised trials and non-inferiority trials, require varying amounts of additional information. CONSORT extensions for these designs, 11 12 and other CONSORT products, can be found through the CONSORT website (www.consort-statement.org). Along with the CONSORT statement, we have updated the explanation and elaboration article,13 which explains the inclusion of each checklist item, provides methodological background, and gives published examples of transparent reporting. Diligent adherence by authors to the checklist items facilitates clarity, completeness, and transparency of reporting. Explicit descriptions, not ambiguity or omission, best serve the interests of all readers. Note that the CONSORT 2010 Statement does not include recommendations for designing, conducting, and analysing trials. It solely addresses the reporting of what was done and what was found.

Nevertheless, CONSORT does indirectly affect design and conduct. Transparent reporting reveals deficiencies in research if they exist. Thus, investigators who conduct inadequate trials, but who must transparently report, should not be able to pass through the publication process without revelation of their trial's inadequacies. That emerging reality should provide impetus to improved trial design and conduct in the future, a secondary indirect goal of our work. Moreover, CONSORT can help researchers in designing their trial.

Background to CONSORT Efforts to improve the reporting of randomised controlled trials accelerated in the mid-1990s, spurred partly by methodological research. Researchers had shown for many years that authors reported such trials poorly, and empirical evidence began to accumulate that some poorly conducted or poorly reported aspects of trials were associated with bias.1A Two initiatives aimed at developing reporting guidelines culminated in one of us (DM) and Drummond Rennie organising the first CONSORT statement in 1996.5

Flow diagram of the progress through the phases of a parallel randomised trial of two groups (that is, enrolment, intervention allocation, follow-up, and data analysis) Sources of funding and other support (such as supply of drugs), role of funders *We strongly recommend readingthis statement in conjunction with the CONSORT 2010 Explanation and Elaboration13 for important clarifications on all the items. If relevant, we also recommend reading CONSORT extensions for cluster randomised trials,11 non-inferiority and equivalence trials,12 non-pharmacological treatments,32 herbal interventions,33 and pragmatic trials.34 Additional extensions are forthcoming: for those and for up to date references relevant to this checklist, see www.consort-statement.org.

Further methodologi cal research on similar topics reinforced earlier findings15 and fed into the revision of 200 1.6"8 Subsequently, the expanding body of methodological research informed the refinement of CONSORT 2010. More than 700 studies comprise the CONSORT database (located on the CONSORT website), which provides the empirical evidence to underpin the CONSORT initiative.

Indeed, CONSORT Group members continually monitor the literature. Information gleaned from these efforts provides an evidence base on which to update the CONSORT statement. We add, drop, or modify items based on that evidence and the recommendations of the CONSORT Group, an interna-tional and eclectic group of clinical trialists, statisticians, epidemiologists, and biomedicai editors. The CONSORT Executive (KFS, DGA, DM) strives for a balance of established and emerging researchers. The membership of the group is dynamic. As our work expands in response to emerging projects and needed expertise, we invite new members to contribute. As such, CONSORT continually assimilates new ideas and perspectives. That process informs the continually evolving CONSORT statement.

Over time, CONSORT has garnered much support. More than 400 journals, published around the world and in many languages, have explicitly supported the CONSORT statement. Many other healthcare journals support it without our knowledge. Moreover, thousands more have implicitly supported it with the endorsement of the CONSORT statement by the International Committee of Medical Journal Editors (www.icmje.org). Other prominent editorial groups, the Council of Science Editors and the World Association of Medical Editors, officially support CONSORT. That support seems warranted: when used by authors and journals, CONSORT seems to improve reporting.9

Development of CONSORT 2010

Thirty one members of the CONSORT 2010 Group met in Montebello, Canada, in January 2007 to update the 2001 CONSORT statement. In addition to the accumulating evidence relating to existing checklist items, several new issues had come to prominence since 2001. Some participants were given primary responsibility for aggregating and synthesising the relevant evidence on a particular checklist item of interest. Based on that evidence, the group deliberated the value of each item. As in prior CONSORT versions, we kept only those items deemed absolutely fundamental to reporting a randomised controlled trial. Moreover, an item may be fundamental to a trial but not included, such as approval by an institutional ethical review board, because funding bodies strictly enforce ethical review and medical journals usually address reporting ethical review in their instructions for authors. Other items may seem desirable, such as reporting on whether on-site monitoring was done, but a lack of empirical evidence or any consensus on their value cautions against inclusion at this point. The CONSORT 2010 Statement thus addresses the minimum criteria, although that should not deter authors from including other information if they consider it important.

After the meeting, the CONSORT Executive convened teleconferences and meetings to revise the checklist. After seven major iterations, a revised checklist was distributed to the larger group for feedback. With that feedback, the executive met twice in person to consider all the comments and to produce a penultimate version. That served as the basis for writing the first draft of this paper, which was then distributed to the group for feedback. After consideration of their comments, the executive finalised the statement.

The CONSORT Executive then drafted an updated explanation and elaboration manuscript, with assistance from other members of the larger group. The substance of the 2007 CONSORT meeting provided the material for the update. The updated explanation and elaboration manuscript was distributed to the entire group for additions, deletions, and changes.

That final iterative process converged to the CONSORT 2010 Explanation and Elaboration.13

Changes in CONSORT 2010

The revision process resulted in evolutionary, not revolutionary, changes to the checklist (table), and the flow diagram was not modified except for one word (figure). Moreover, because other reporting guidelines augmenting the checklist refer to item numbers, we kept the existing items under their previous item numbers except for some renumbering of items 2 to 5. We added additional items either as a sub-item under an existing item, an entirely new item number at the end of the checklist, or (with item 3) an interjected item into a renumbered segment. We have summarised the noteworthy general changes in box 1 and specific changes in box 2. The CONSORT website contains a side by side comparison of the 2001 and 2010 versions.

Implications and limitations

We developed CONSORT 2010 to assist authors in writing reports of randomised controlled trials, editors and peer reviewers in reviewing manuscripts for publication, and readers in critically appraising published articles. The CONSORT 2010 Explanation and Elaboration provides elucidation and context to the checklist items. We strongly recommend using the explanation and elaboration in conjunction with the checklist to foster complete, clear, and transparent reporting and aid appraisal of published trial reports.

CONSORT 2010 focuses predominantly on the two group, parallel randomised controlled trial, which accounts for over half of trials in the literature.2 Most of the items from the CONSORT 2010 Statement, however, pertain to all types of randomised trials. Nevertheless, some types of trials or trial situations dictate the need for additional information in the trial report. When in doubt, authors, editors, and readers should consult the CONSORT website for any CONSORT extensions, expansions (amplifications), implementations, or other guidance that may be relevant.

The evidence based approach we have used for CONSORT also served as a model for development of other reporting guidelines, such as for reporting systematic reviews and meta-analyses of studies evaluating interventions,16 diagnostic studies,17 and observational studies. 18 The explicit goal of all these initiatives is to improve reporting. The Enhancing the Quality and Transparency of Health Research (EQUATOR) Network will facilitate development of reporting guidelines and help disseminate the guidelines: www. equator-network.org provides information on all reporting guidelines in health research.

With CONSORT 2010, we again intentionally declined to produce a rigid structure for the reporting of randomised trials. Indeed, SORT19 tried a rigid format, and it failed in a pilot run with an editor and authors.20 Consequently, the format of articles should abide by journal style, editorial directions, the traditions of the research field addressed, and, where possible, author preferences. We do not wish to standardise the structure of reporting. Authors should simply address checklist items somewhere in the article, with ample detail and lucidity. That stated, we think that manuscripts benefit from frequent subheadings within the major sections, especially the methods and results sections. • Item lb (title and abstract)-We added a sub-item on providing a structured summary of trial design, methods, results, and conclusions and referenced the CONSORT for abstracts article21 • Item 2b (introduction)-^!^ added a new sub-item (formerly item 5 in CONSORT 2001) on "Specific objectives or hypotheses"

• Item 3a (trial design)-'Ne added a new item including this sub-item to clarify the basic trial design (such as parallel group, crossover, cluster) and the allocation ratio • Item 3b (trial design)--We added a new sub-item that addresses any important changes to methods after trial commencement, with a discussion of reasons • Item 4 (participantsj-tormeúy item 3 in CONSORT 2001

• Item 5 (interventions)formerly item 4 in CONSORT 2001 . We encouraged greater specificity by stating that descriptions of interventions should include "sufficient details to allow replication"3 • Item 6 (outcomes)--We added a sub-item on identifying any changes to the primary and secondary outcome (endpoint) measures after the trial started. This followed from empirical evidence that authors frequently provide analyses of outcomes in their published papers that were not the prespecified primary and secondary outcomes in their protocols, while ignoring their prespecified outcomes (that is, selective outcome reporting).4 22 We eliminated text on any methods used to enhance the quality of measurements • Item 9 (allocation concealment mechanism)--We reworded this to include mechanism in both the report topic and the descriptorto reinforce that authors should report the actual steps taken to ensure allocation concealment rather than simply report imprecise, perhaps banal, assurances of concealment • Item 1 1 (blinding)--We added the specification of how blindingwas done and, if relevant, a description ofthe similarity of interventions and procedures. We also eliminated text on "how the success of blinding (masking) was assessed" because of a lack of empirical evidence supportingthe practice as well as theoretical concerns about the validity of any such assessment23 24

• Item 12a (statistical methods)-'Ne added that statistical methods should also be provided for analysis of secondary outcomes

• Sub-item 14b (recruitment)-Based on empirical research, we added a sub-item on "Why the trial ended or was stopped"25

• Item 15 (baseline data)-'Ne specified "Atable" to clarify that baseline and clinical characteristics of each group are most clearly expressed in a table

• Item 16 (numbers analysed)--We replaced mention of "intention to treat" analysis, a widely misused term, by a more explicit request for information about retaining participants in their original assigned groups26 • Sub-item 17b (outcomes and estimation)-for appropriate clinical interpretability, prevailing experience suggested the addition of "For binary outcomes, presentation of both relative and absolute effect sizes is recommended"27 • Item 19 (harms)-We included a reference to the CONSORT paper on harms28

• Item 20 (limitations)--We changed the topic from "Interpretation" and supplanted the prior text with a sentence focusing on the reporting of sources of potential bias and imprecision • Item 22 (interpretation)--We changed the topic from "Overall evidence." Indeed, we understand that authors should be allowed leeway for interpretation under this nebulous heading. However, the CONSORT Group expressed concerns that conclusions in papers frequently misrepresented the actual analytical results and that harms were ignored or marginalised. Therefore, we changed the checklist item to include the concepts of results matching interpretations and of benefits being balanced with harms • Item 23 (registration)-^ added a new item on trial registration. Empirical evidence supports the need for trial registration, and recent requirements by journal editors have fostered compliance29 • Item 24 (protocol)--We added a new item on availability ofthe trial protocol. Empirical evidence suggests that authors often ignore, in the conduct and reporting of their trial, what they stated in the protocol.422 Hence, availability ofthe protocol can instigate adherence to the protocol before publication and facilitate assessment of adherence after publication • Item 25 (funding)-'Ne added a new item on funding. Empirical evidence points toward funding source sometimes being associated with estimated treatment effects30 CONSORT urges completeness, clarity, and transparency of reporting, which simply reflects the actual trial design and conduct. However, as a potential drawback, a reporting guideline might encourage some authors to report fictitiously the information suggested by the guidance rather than what was actually done. Authors, peer reviewers, and editors should vigilantly guard against that potential drawback and refer, for example, to trial protocols, to information on trial registers, and to regulatory agency websites. Moreover, the CONSORT 2010 Statement does not include recommendations for designing and conducting randomised trials. The items should elicit clear pronouncements of how and what the authors did, but do not contain any judgments on how and what the authors should have done. Thus, CONSORT 2010 is not intended as an instrument to evaluate the quality of a trial. Nor is it appropriate to use the checklist to construct a "quality score."

Nevertheless, we suggest that researchers begin trials with their end publication in mind. Poor reporting allows authors, intentionally or inadvertently, to escape scrutiny of any weak aspects of their trials. However, with wide adoption of CON-SORT by journals and editorial groups, most authors should have to report transparently all important aspects of their trial. The ensuing scrutiny rewards well conducted trials and penalises poorly conducted trials. Thus, investigators should understand the CONSORT 2010 reporting guidelines before starting a trial as a further incentive to design and conduct their trials according to rigorous standards.

CONSORT 2010 supplants the prior version published in 2001. Any support for the earlier version accumulated from journals or editorial groups will automatically extend to this newer version, unless specifically requested otherwise. Journals that do not currently support CONSORT may do so by registering on the CONSORT website. If a journal supports or endorses CONSORT 2010, it should cite one ofthe original versions of CONSORT 2010, the CONSORT 2010 Explanation and Elaboration, and the CONSORT website in their "Instructions to authors." We suggest that authors who wish to cite CONSORT should cite this or another of the original journal versions of CONSORT 2010 Statement, and, if appropriate, the CONSORT 2010 Explanation and Elaboration.13 All CON-SORT material can be accessed through the original publishing journals or the CONSORT website. Groups or individuals who desire to translate the CONSORT 2010 Statement into other languages should first consult the CONSORT policy statement on the website.

We emphasise that CONSORT 2010 represents an evolving guideline. It requires perpetual reappraisal and, if necessary, modifications. In the future we will further revise the CON-SORT material considering comments, criticisms, experiences, and accumulating new evidence. We invite readers to submit recommendations via the CONSORT website.

Box 1 1
1Noteworthy general changes ¡n CONSORT 2010 Statement • We simplified and clarified the wording, such as in items 1,8,10,13,15,16,18,19, and 21 • We improved consistency of style across the items by removing the imperative verbs that were in the 2001 version • We enhanced specificity of appraisal by breaking some items into sub-items. Many journals expect authors to complete a CONSORT checklist indicating where in the manuscript the items have been addressed. Experience with the checklist noted pragmatic difficulties when an item comprised multiple elements. For example, item 4 addresses eligibility of participants and the settings and locations of data collection. With the 2001 version, an author could provide a page number forthat item on the checklist, but might have reported only eligibility in the paper, for example, and not reported the settings and locations. CONSORT 2010 relieves obfuscations and forces authors to provide page numbers in the checklist for both eligibility and settings Box 2 1 Noteworthy specific changes in CONSORT 2010 Statement

Assessed for eligibility (n=...) I£
Excluded (n-...):
|
^ *~ Not meeting inclusion criteria (n=...)
"g

^ *~
Declined to participate (n=...)
¿■j
Other reasons (n=...)

Randomised (n=...)
I
Introduction

Background and
2a
Scientific background and explanation of rationale
objectives
2b
Specific objectives or hypotheses
Methods
Trial design
3a
Description of trial design (such as parallel, factorial) including allocation ratio
3b
Important changes to methods aftertrial commencement (such as eligibility criteria), with reasons

Participants
4a
Eligibility criteria for participants
4b
Settings and locations where the data were collected
Interventions
5
The interventions for each group with sufficient details to allow replication, including how and when they were actually administered

Outcomes
6a
Completely defined pre-specified primary and secondary outcome measures, including how and when they were assessed
6b
Any changes to trial outcomes after the trial commenced, with reasons

Sample size
7a
How sample size was determined

7b
When applicable, explanation of any interim analyses and stopping guidelines

Randomisation:

Sequence
8a
Method used to generate the random allocation sequence
generation
gb
jype of randomisation; details of any restriction (such as blocking and block size)
Allocation
9
Mechanism used to implement the random allocation sequence (such as sequentially numbered containers), describing any steps taken to conceal the
concealment
sequence until interventions were assigned
mechanism

Implementation 10
Who generated the random allocation sequence, who enrolled participants, and who assigned participants to interventions

Blinding
Ila
If done, who was blinded after assignment to interventions (for example, participants, care providers, those assessing outcomes) and how
lib
If relevant, description of the similarity of interventions

Statistical methods 1 2a
Statistical methods used to compare groups for primary and secondary outcomes

12b
Methods for additional analyses, such as subgroup analyses and adjusted analyses

Results

Participant flow (a
4 13a
For each group, the numbers of participants who were randomly assigned, received intended treatment, and were analysed for the primary outcome
diagram is strongly 13b
For each group, losses and exclusions after randomisation, together with reasons
recommended)
Recruitment
14a
Dates definingthe periods of recruitment and follow-up

14b
Why the trial ended or was stopped
Baseline data
15
Atable showing baseline demographic and clinical characteristics for each group

Numbers analysed 16
For each group, number of participants (denominator) included in each analysis and whether the analysis was by original assigned groups

Outcomes and
1 7a
For each primary and secondary outcome, results for each group, and the estimated effect size and its precision (such as 95% confidence interval)
estimation
^ 7b
For binary outcomes, presentation of both absolute and relative effect sizes is recommended

Ancillary analyses 18
Results of any other analyses performed, including subgroup analyses and adjusted analyses, distinguishing pre-specified from exploratory

Harms
19
All important harms or unintended effects in each group (for specific guidance see CONSORT for harms28)

Discussion

Limitations
20
Trial limitations, addressing sources of potential bias, imprecision, and, if relevant, multiplicity of analyses

Generalisability 21
Generalisability (external validity, applicability) of the trial findings

Interpretation 22
Interpretation consistent with results, balancing benefits and harms, and considering other relevant evidence
Other information

Registration
23
Registration number and name of trial registry
Protocol
24
Where the full trial protocol can be accessed, if available

Funding
25

This content downloaded from 126.96.36.199 on Sun, 22 Jun 2014 18:21:00 PM All use subject to JSTOR Terms and Conditions

Systematic reviews in health care: assessing the quality of controlled clinical trials. P Juni, D G Altman, M Egger, BMJ. 323Juni P, Altman DG, Egger M. Systematic reviews in health care: assessing the quality of controlled clinical trials. BMJ 2001 ;323:42-6.

Epidemiology and reporting of randomised trials published in PubMed journals. A W Chan, D G Altman, Lancet. 365Chan AW, Altman DG. Epidemiology and reporting of randomised trials published in PubMed journals. Lancet 2005:365:1159-62.

What is missing from descriptions of treatment in trials and reviews. P Glasziou, E Meats, C Heneghan, S Shepperd, BMJ. 336Glasziou P, Meats E, Heneghan C, Shepperd S. What is missing from descriptions of treatment in trials and reviews? BMJ 2008;336:1472-4.

Systematic review of the empirical evidence of study publication bias and outcome reporting bias. K Dwan, D G Altman, J A Arnaiz, J Bloom, A W Chan, E Cronin, PLoS O/V£2008:3:e3081Dwan K, Altman DG, Arnaiz JA, Bloom J, Chan AW, Cronin E, et al. Systematic review of the empirical evidence of study publication bias and outcome reporting bias. PLoS O/V£2008:3:e3081.

Improving the quality of reporting of randomized controlled trials. The CONSORT statement. C Begg, M Cho, S Eastwood, R Horton, D Moher, I Olkin, JAMA. 276Begg C, Cho M, Eastwood S, Horton R, Moher D, Olkin I, et al. Improving the quality of reporting of randomized controlled trials. The CONSORT statement. JAMA 1996:276:637-9.

The CONSORT statement: revised recommendations for improvingthe quality of reports of parallel-group randomised trials. D Moher, K F Schulz, D G Altman, Lancet. 357Moher D, Schulz KF, Altman DG. The CONSORT statement: revised recommendations for improvingthe quality of reports of parallel-group randomised trials. Lancet 2001;357:1191-4.

The CONSORT statement: revised recommendationsforimprovingthe quality of reports of parallel-group randomized {rials. D Moher, K F Schulz, D G Altman, Ann Intern Med. 134Moher D, Schulz KF, Altman DG. The CONSORT statement: revised recommendationsforimprovingthe quality of reports of parallel-group randomized {rials. Ann Intern Med 2001:134:657-62.

The CONSORT statement: revised recommendations for improvingthe quality of reports of parallel-group randomized trials. D Moher, K F Schulz, D Altman, jWAM. 285Moher D, Schulz KF, Altman D. The CONSORT statement: revised recommendations for improvingthe quality of reports of parallel-group randomized trials. jWAM 2001 ;285:1987-91.

Does the CONSORT checklist improve the quality of reports of randomised controlled trials? Asystematic review. A C Plint, D Moher, A Morrison, K Schulz, D G Altman, C Hill, MedJAust. 185Plint AC, Moher D, Morrison A, Schulz K, Altman DG, Hill C, et al. Does the CONSORT checklist improve the quality of reports of randomised controlled trials? Asystematic review. MedJAust 2006;185:263-7.

S Hopewell, S Dutton, L-M Yu, Chan A-W Altman, D G , The quality of reports of randomised trials in 2000 and 2006: a comparative study of articles indexed bv PubMed. 2010723Hopewell S, Dutton S, Yu L-M, Chan A-W, Altman DG. The quality of reports of randomised trials in 2000 and 2006: a comparative study of articles indexed bv PubMed. BMJ 2010:340:c723.

CONSORT statement: extension to cluster randomised trials. M K Campbell, D R Elbourne, D G Altman, BMI. 328Campbell MK, Elbourne DR, Altman DG. CONSORT statement: extension to cluster randomised trials. BMI 2004:328:702-8.

Reporting of noninferiority and equivalence randomized trials: an extension of the CONSORT statement. y/MM2006. G Piaggio, D R Elbourne, D G Altman, Pococksj, S J Evans, 295Piaggio G, Elbourne DR, Altman DG, PocockSJ, Evans SJ. Reporting of noninferiority and equivalence randomized trials: an extension of the CONSORT statement. y/MM2006;295:l 152-60.

Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. D Moher, S Hopewell, K F Schulz, V Montori, P C G0tzsche, P J Devereaux, BMJ. 340869Moher D, Hopewell S, Schulz KF, Montori V, G0tzsche PC, Devereaux PJ, et al. CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. BMJ 2010;340:c869.

Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. K F Schulz, I Chalmers, R J Hayes, D G Altman, JAMA. 273Schulz KF, Chalmers I, Hayes RJ, Altman DG. Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 1995;273:408-l 2.

Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses?. D Moher, B Pham, A Jones, D J Cook, A R Jadad, M Moher, /.ancef. 352Moher D, Pham B, Jones A, Cook DJ, Jadad AR, Moher M, et al. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? /.ancef 1998;352:609-13.

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. ß/W/2OO9. D Moher, A Liberati, J Tetzlaff, D G Altman, Group, 3392535Moher D, Liberati A, Tetzlaff J, Altman DG, forthe PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. ß/W/2OO9;339:b2535.

Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. P M Bossuyt, J B Reitsma, D E Bruns, C A Gatsonis, P P Glasziou, L M Irwig, BMJ. 326Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ 2003;326:41-4.

Strengthening the reportintkof observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. E Von Elm, D G Altman, Eggerm, Pococksj, P C G0tzsche, J P Vandenbroucke, Initiative, 335von Elm E, Altman DG, EggerM, PocockSJ, G0tzsche PC, Vandenbroucke JP, forthe STROBE Initiative. Strengthening the reportintkof observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. ß/W/2007;335:806-8.

A proposal for structured reporting of randomized controlled trials. Standardsof Reportingtrials Group, 272Standardsof ReportingTrials Group. A proposal for structured reporting of randomized controlled trials./A/W/' 1994;272:1926-31.

Reporting randomized controlled trials. An experiment and a call for responses from readers. D Rennie, JAMA. 273Rennie D. Reporting randomized controlled trials. An experiment and a call for responses from readers. JAMA 1995;273:1054-5.

CONSORT for reporting randomised trials in journal and conference abstracts. S Hopewell, M Clarke, D Moher, E Wager, P Middleton, D G Altman, /.ancef. 371Hopewell S, Clarke M, Moher D, Wager E, Middleton P, Altman DG, et al. CONSORT for reporting randomised trials in journal and conference abstracts. /.ancef 2008;371:281-3.

Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published artides.y/WM2004. A W Chan, A Hróbjartsson, Haahrmt, P C G0tzsche, D G Altman, 291Chan AW, Hróbjartsson A, HaahrMT, G0tzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published artides.y/WM2004;291:2457-65.

Commentary: Measuringthe success of blinding in RCTs: don't, must. Sackettdl, 36SackettDL. Commentary: Measuringthe success of blinding in RCTs: don't, must, can't or needn't? /nfyfp/dem/o/2007;36:664-5.

Blinding in randomised trials: hiding who got what. K F Schulz, D A Grimes, 359Schulz KF, Grimes DA. Blinding in randomised trials: hiding who got what, /.ancef 2002;359:696-700.

Randomized trials stopped early for benefit: a systematic review. V M Montori, P J Devereaux, N K Adhikari, K E Burns, C H Eggert, M Briel, JAMA. 294Montori VM, Devereaux PJ, Adhikari NK, Burns KE, Eggert CH, Briel M, et al. Randomized trials stopped early for benefit: a systematic review. JAMA 2005;294:2203-9.

What is meant by intention to treat analysis? Survey of published randomised controlled trials. S Hollis, F Campbell, BMJ. 31Hollis S, Campbell F. What is meant by intention to treat analysis? Survey of published randomised controlled trials. BMJ 1999;31 9:670-4.

Reporting number needed to treat and absolute risk reduction in randomized controlled trials. J Nuovo, J Melnikow, D Chang, JAMA. 287Nuovo J, Melnikow J, Chang D. Reporting number needed to treat and absolute risk reduction in randomized controlled trials. JAMA 2002:287:2813-4.

Better reporting of harms in randomized trials: an extension of the CONSORT statement. J P Loannidis, S J Evans, P C G0tzsche, R T O'neill, D G Altman, K Schulz, Ann Intern Med. 141loannidis JP, Evans SJ, G0tzsche PC, O'Neill RT, Altman DG, Schulz K, et al. Better reporting of harms in randomized trials: an extension of the CONSORT statement. Ann Intern Med 2004; 141: 781 -8.

Clinical trial registration: a statement from the International Committee of. C De Angelis, J M Drazen, F A Frizelle, C Haug, J Hoey, R Horton, Medical Journal Editors. /.ancef. 364De Angelis C, Drazen JM, Frizelle FA, Haug C, Hoey J, Horton R, et al. Clinical trial registration: a statement from the International Committee of Medical Journal Editors. /.ancef 2004;364:911-2.

Pharmaceutical industry sponsorship and research outcome and quality: systematic review. J Lexchin, L A Bero, B Djulbegovic, Clark 0 , BMJ. 326Lexchin J, Bero LA, Djulbegovic B, Clark 0. Pharmaceutical industry sponsorship and research outcome and quality: systematic review. BMJ 2003:326:1167-70.

CONSORT for reporting randomized controlled trials in journal and conference abstracts: explanation and elaboration. S Hopewell, M Clarke, D Moher, E Wager, P Middleton, D G Altman, PLoS Med. 520Hopewell S, Clarke M, Moher D, Wager E, Middleton P, Altman DG, et al. CONSORT for reporting randomized controlled trials in journal and conference abstracts: explanation and elaboration. PLoS Med 2008;5:e20.

Extendingthe CONSORT statementto randomized trials of nonpharmacologic treatment: explanation and elaboration. I Boutron, D Moher, D G Altman, K F Schulz, P Ravaud, /Ann /nfern Med. 148Boutron I, Moher D, Altman DG, Schulz KF, Ravaud P. Extendingthe CONSORT statementto randomized trials of nonpharmacologic treatment: explanation and elaboration. /Ann /nfern Med 2008;148:295-309.

Reporting randomized, controlled trials of herbal interventions: an elaborated CONSORT statement. Gagnierjj, H Boon, P Rochon, D Moher, J Barnes, Bombardiere, ¿nn/nfern Med. 144GagnierJJ, Boon H, Rochon P, Moher D, Barnes J, Bombardiere. Reporting randomized, controlled trials of herbal interventions: an elaborated CONSORT statement, ¿nn/nfern Med 2006;144:364-7.

Improvingthe reporting of pragmatic trials: an extension of the CONSORT statement. M Zwarenstein, Treweeks, Gagnierjj, D G Altman, S Tunis, B Haynes, BMJ. 3372390Zwarenstein M, TreweekS, GagnierJJ, Altman DG, Tunis S, Haynes B, et al. Improvingthe reporting of pragmatic trials: an extension of the CONSORT statement. BMJ 2008;337:a2390.