The Journal of School Nursing2024, Vol. 40(6) 662–674© The Author(s) 2022Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/10598405221132207journals.sagepub.com/home/jsn
Abstract
This study aims to demonstrate the effect of a transtheoretical model (TTM)-based physical activity program on the exercise behavior of adolescents using WhatsApp. The study was conducted with high school students (N = 185) in a pre-/posttest, quasi-experimental design. TTM-based text messages were sent to the intervention group (n = 95) over WhatsApp every day for 8 weeks. The intervention group demonstrated more statistically significant progression in the exercising stages of change compared to the control group (n = 90) (χ2 = 20.10; p = .00). It was also found that this group had a higher exercising self-efficacy score (t = 2.647; p = .009) and a higher physical activity total metabolic equivalent of task score (Z = −3.521; p = .000). There was no significant difference in BMI and perception of pros and cons (p > .05). The program was found to be effective in terms of recording progress in the exercising stages of change, increasing exercising self-efficacy, and maintaining a physical activity program.
Keywordsphysical activity, adolescent, transtheoretical model, WhatsApp, exercising
A rapid decrease in physical activity is currently being observed among adolescents, and this tendency continues to prevail in adulthood (Ghiami et al., 2017). The World Health Organization (WHO) recommendation for adolescents is a daily minimum of 60 min of moderate to vigorous physical activity and additionally at least 3 days of muscle and bone strengthening activities. It has been observed that only one out of five adolescents complies with these recommendations (World Health Organization, 2020). Guthold et al. (2020) conducted a study with 1.6 million adolescents aged 11–17 years from 148 countries, which showed 81.0% of all participants and 81.3% of Turkish adolescents, in particular, did not engage in physical activity adequately. Physical activity behavior is of critical significance to public health in preventing obesity and building a foundation for healthy habits, and it should be developed during adolescence.
Various theories and model-based interventions are recommended to help adolescents acquire positive health behavior (Gur et al., 2019). The transtheoretical model (TTM), also known as the stages of change model, was developed by the psychologist Prochaska and DiClemente (1982) to promote behavioral change (Prochaska & DiClemente, 1982). The stages of change that comprise the fundamental structure of the TTM refer to the stages of precontemplation, contemplation, preparation, action and maintenance (shown in Table 1) (Prochaska et al., 2015). The process of change encompasses the behaviors individuals exhibit as they progress through the stages of change (Prochaska et al., 2015). The model comprises five cognitive and five behavioral processes, a total of 10 processes of change. Another component of the model is decisional balance, the process in which the decision to change is made on the basis of considering the pros and cons of making a behavioral change (Prochaska & DiClemente, 1982; Prochaska et al., 2015). Self-efficacy defines the self-confidence an individual has in their ability to act to achieve a behavioral change and maintain that action (Prochaska et al., 2015). The model, with all of its components, offers a framework by which to plan, implement, assess and follow up on interventions suitable for each stage of change (Erol & Erdoğan,, 2007). Studies have indicated that different intervention programs based on the TTM have helped adolescents achieve progress in the stages of change in adopting exercising behavior (Ham et al., 2016) and increasing their exercise self-efficacy (Ghiami et al., 2017; Ham et al., 2016), also increasing participation in moderate and vigorous physical activity (Pirzadeh et al., 2020).
During the COVID-19 pandemic, schools were shut down for a long period, and the level of inactivity increased. Under these circumstances, there has emerged a need for interventions that can make use of mobile technologies as a means of encouraging individuals to increase their levels of physical activity.
Mobile health (m-Health) is defined as applications that include the use of mobile or wireless devices to support, protect and improve people’s health (Domin et al., 2022), which has emerged in recent years. It offers a wide range of content such as m-Health applications, text messages, smartphone applications, and social media applications (Facebook, Instagram, etc.) WhatsApp is one of the most frequently used mobile-based messaging applications which provides free messaging and is also a frequently preferred application among adolescents (Newman et al., 2021). The prevalence of its use among adolescents offers new m-Health intervention opportunities for health professionals interested in maintaining and improving adolescent health. There have been several studies carried out with successful results obtained in developing behavior and attitude using various WhatsApp-based interventions with adolescents, such as safe food intake (Pandya, 2020), adherence to treatment and supporting mental health (Chory et al., 2021), smoking behavior (Yusriani and Acob, 2020), and diabetes management (Döğer et al., 2019). WhatsApp-based physical activity studies with adolescents are not available in the literature.
There is a limited number of studies carried out with adolescents in the international literature in which the TTM and mobile technologies are used in coordination, and there is no such study in the Turkish literature. The aim of this study is, therefore, to demonstrate the effect of a Transtheoretical Model-based physical activity program on the exercise behavior of adolescents through WhatsApp.
Hypotheses:After TTM-based physical activity program,
H1: The experimental group will make more progress in terms of the exercise behavior change stages in comparison to the control group.
H2: The experimental group will score higher on the exercise self-efficacy scale in comparison to the control group.
H3: The experimental group will score higher on the pros scale of exercise and lower on the cons perception scale in comparison to the control group.
H4: The experimental group will have a higher physical activity total MET (metabolic equivalent of task-minute) score in comparison to the control group.
H5: More adolescents in the experimental group fall between the <5th and <85th percentile body mass index (BMI) category in comparison to the control group.
This study was conducted using a quasi-experimental research design with a pretest/posttest design for experimental and control groups.
The study was conducted at two public high schools in Istanbul from January to June 2020. The names of the schools were designated as School 1 and School 2. School 1 had a total of 1193 students; School 2 had 893 students. The schools were selected from the same region where middle-income families live with a similar socioeconomic structure and similar cultural characteristics. Neither of the schools had sports facilities or a school nurse. Both schools are public high schools with the same curriculum and neither had extra programs for physical activity. Both schools had a required 2-h physical education class per week.
The sample size was calculated using the G*Power 3.1.9 software (Erdfelder, 2007). The effect size was moderate at d = 0.05 at a power of 95% with α = .05. Sample sizes were computed to be 88 for the intervention group and 88 for the control group, for a total of 176.
Considering the possibility of some adolescents withdrawing from the study, all of the students in the randomly drawn classes would be recruited and therefore 99 students in the interventional group and 90 in the control group — a total of 189 adolescents — were included. Ultimately, after two students from the intervention group expressed their wish to withdraw from the study and another two did not complete the posttest, the study was concluded with 185 students (Figure 1).
To prevent the intervention and control groups from influencing each other, one of the schools was selected as the intervention group and the other one was assigned as the control group. To make the random selection, the names of the schools were first written down and placed in closed envelopes. Then, the school written in the first envelope drawn by a researcher with no affiliationtothestudybecametheintervention group. The school in the second envelope was assigned as the control group. The names of 9th-, 10th-, and 11th-grade classes were placed into closed envelopes, and again an unaffiliated researcher randomly selected a class from each of the grades in the same manner. The 12th-grade students were not included in the study because they were unwilling to participate due to their heavy exam schedules.
Volunteering adolescents with no barriers to exercise or communicate who owned smartphones and whose parents permitted them to participate were recruited into the study. Two 9th-grade students in the experimental group did not have a smartphone and were not included in the study because their parents did not allow them to use a smartphone. All students in the control group had a smartphone, and all who met the inclusion criteria were included in the study. The mean age of the intervention group was 15.60 ± 0.89 years; that of the control group was 15.41 ± 0.91 years. In the intervention group, 50.5% were females; 49.5% were males. The control group comprised 56.7% of females and 43.3% of males. Among the students in the intervention group, 35.8% were in the 9th grade, 37.9% were in the 10th grade, and 26.3% were in the 11th grade. In the control group, 31.1% were in the 9th grade, 33.3% were in the 10th grade, and 35.6% were in the 11th grade. No statistically significant difference was found between the groups in terms of age, gender, or class distribution (p > .05).
The program was planned as a 12-week activity through the WhatsApp application. WhatsApp is a messaging application that can be downloaded free of charge to smartphones and allows sending video, audio, and text messages when an internet connection is provided. The application also allows individual or group messaging as well as video and voice calls (refer to: https://www.whatsapp.com/).
Both the students in the intervention and control groups and their parents were informed about the nature of the research and invited to participate. The informed consent forms of the families and adolescents willing to participate were collected. Participants gave consent before knowing what group they would be in. The researcher gathered the study data in the students’ classrooms in a self-reporting questionnaire that took 30 min to fill out. Baseline data were collected took place before assigning a school to the intervention or control group. The adolescents’ height and weight were measured and recorded. After the application of the 4-week intervention to the intervention group, the intervention and control groups were administered a face-to-face interim test. In the interim test, only the “Exercise, Stages of Change Questionnaire” was applied (Figure 1). Following the interim test, another 4-week intervention was applied to the intervention group, after which the intervention with a total duration of 8 weeks was completed (Figure 1). No action was taken with the control group in this period. Posttest data were collected 4 weeks after the end of the intervention on an online basis due to the circumstances of the COVID-19 pandemic. The adolescents were sent the data collection forms via WhatsApp. The flow chart of the study is shown in Figure 1.
Each class in the intervention group was divided into five groups according to the TTM exercising stage of change they were in (precontemplation, contemplation, preparation, action, and maintenance), amounting to a total of 15 WhatsApp groups. The researcher (F.C.), who was the group supervisor, sent out the WhatsApp texts. Each group received a message once a day every day at the same time. After the interim test, the WhatsApp groups were reorganized according to the stage of change the adolescents in the intervention group had reached. The groups were sent messages specific to their stages of change for 4 more weeks. At the end of the intervention and until the posttest data were collected, no other intervention was applied to the intervention group for a period of 4 weeks.
The authors drew up the content of the program and the messages in line with the literature on the basis of the TTM stages, processes, and levels of change (Corbin et al., 2014; Piercy et al., 2018; Powell et al., 2018; Demirel et al., 2014; World Health Organization, 2010, 2019). To ensure the suitability of the content of the messages, two nurse researchers familiar with the TTM, a pediatrician and a physiotherapist were asked for their views. The texts contained basic information on physical activity, the benefits of physical activity, types of physical activity, encouraging messages on increasing self-efficacy and diminishing barriers, as well as videos and photographs. The intervention plan and text samples are shown in Table 1.
Following the interim test, the first case of the COVID-19 pandemic broke out in Turkey (March 10, 2020), and since the schools were closed (March 16, 2020), the text content had to be revised. In the last 8 weeks of our study, schools remained closed; sports facilities and parks closed down in the last 7 weeks; and in the last 6 weeks, a complete lockdown was applied to those under the age of 20 years.
As a result, messages were sent out to the groups encouraging the students to engage in physical activity at home.
An example of such a message was: “One of the activities you can do at home is of course yoga.”
“Maybe you’ve always thought about doing yoga, but you never got the chance.”
“Now is just the time. You don’t even need any equipment for this.”
Query messages were sent out to the adolescents so that they could provide feedback.
Sample message: “You’ve been informed so far of the benefits of many physical activities. Now think—what is the physical activity that appeals to you most? I’ll be expecting your return message ☺.”
Statistics were not kept on those who provided feedback.
The researchers only kept track of the “blue checks” on the WhatsApp application to follow up on whether the addressee had seen the message. It was noted that blue checks appeared on all of the students’ messages (100%), indicating that the messages had been read.
Sociodemographic Questionnaire: This is a form containing a total of 14 closed-ended questions on the sociodemographic features of adolescents (e.g., age, gender, education) and factors affecting their engagement in exercise.
Exercise—Stages of Change: This tool was developed by Nigg and Courneya (1998) to assess the TTM exercising stages of change in adolescents (Nigg & Courneya 1998). It was adapted to the Turkish language by Kafalı and Ergün (Kafalı & Ergün 2009). The instrument was used to determine which TTM-exercising stage of behavioral change the adolescent was in (precontemplation, contemplation, preparation, action, and maintenance).
Exercise—Self-efficacy Scale: Nigg and Courneya (1998) developed this tool to evaluate the self-efficacy of adolescents in terms of exercising (Nigg & Courneya 1998). The Turkish adaptation was tested for validity and reliability by Kafalı and Ergün (Cronbach’s alpha = .92, CVI = 93%, r = .81) (Kafalı & Ergün 2009). The instrument consists of 10 items and is an 11-point Likert-type scale. Each item scores between 0 and 10, where 0 = “I’m not at all sure,” 5 = “I’m reasonably sure,” and 10 = “I’m absolutely sure.” The highest possible score on the scale is 100; the lowest is 0. Higher scores indicate a higher level of self-efficacy in terms of performing exercises; lower scores indicate low selfefficacy. The higher the level of self-efficacy, the higher the chance that the individual will achieve the goal of behavioral change. Cronbach’s alpha was found to be 0.87 in this study.
Exercise—Decisional Balance Scale: The decisional balance scale for adolescents was developed by Marcus and Owen (1992). The Turkish validity and reliability study was conducted by Kafalı and Ergün (Cronbach’s alpha for pros = .93; r = 0.9 for cons = .76, r = .51, CVI = 93%). (Kafalı & Ergün 2009). The scale consists of a total of 16 items and is a 5-point Likert-type of instrument (1 = Not important; 2 = A little important; 3 = Important; 4 = Very important and 5 = Extremely important). The scale has two sub-dimensions that are defined as perceptions of pros and cons. The first 10 items on the scale pertain to pros and the next 6 items—to cons. The higher the score for the perception of pros, the higher the individual’s chance of changing and maintaining the behavioral change. The higher the perception of cons, the harder it will be for the individual to make a change. In this study, Cronbach’s alpha for the perception of pros for exercising was .88; Cronbach’s alpha for the perception of cons for exercising was .73.
International Physical Activity Questionnaire (Short form) (IPAQ): This was developed by an International Consensus Group for the purpose of evaluating physical activity in populations (Öztürk & Arıkan, 2005). The validity and reliability of the Turkish version were tested by Öztürk and Arıkan (2005). The questionnaire has a long and a short form; the short form was employed in this study. The instrument consists of four separate sections and seven questions that ask for information on the time spent in the last 7 days on walking, moderate and vigorous activity, and the time the individual spent sitting (Öztürk & Arıkan, 2005). The scoring is calculated on the basis of MET-minutes/week.
Values used in MET scores:
“Walking ═ 3.3 MET,
Moderate physical activity ═ 4.0 MET,
Vigorous physical activity ═ 8.0 MET.”
For example, if an individual takes a 20-min walk 4 days a week, the calculation will be 4 × 20 × 3.3 = 264 MET.
The results are interpreted as inactive (Category 1: <600 MET),
Minimally active (Category 2: 600–3000 MET),
Very active (Category 3: >3000 MET) (Öztürk & Arıkan, 2005)
The researchers measured the students’ height and weight before the intervention. Height measurements were taken with a stadiometer, with the individual standing against the wall with no shoes. Height and weight were each measured twice. If values were not the same, a third measurement was taken, and then, the two closest measurements were averaged.
A digitally calibrated bathroom scale was used in measuring weight with the individual taking off all outer clothing (e.g., coat, jacket, bags). The BMI was then calculated according to the measures taken (kg/m2 ).
The researchers were unable to measure the students’ height and weight at the end of the intervention due to the COVID-19 pandemic. Instead, an informative message was sent to the groups over WhatsApp about height and weight measuring standards (including standards applied by researchers at the first measurement), and the students were asked to take their own measurements as well as record these in the form.
The interpretation of BMI was based on the percentile curve developed by Neyzi et al. (2008) specifically for age and gender in the Turkish population (Neyzi et al., 2008).
The analysis was performed using the IBM SPSS Statistics 23 program. Descriptive data were indicated with numbers, percentages, or means and standard deviation. The normality hypothesis for the numerical variables by group was examined with the Kolmogorov–Smirnov normality test. The relationships between two independent categorical variables were explored with chi-square or Fisher’s exact analysis. The differences between the two independent groups were examined with the independent samples t-test for variables exhibiting normal distribution and with Mann–Whitney U analysis for variables that did not show normal distribution. The differences between the two dependent groups were examined with the dependent samples t-test for variables exhibiting normal distribution and with Wilcoxon analysis for variables that did not show normal distribution. The analysis of two dependent categorical variables was subjected to the McNemar test and Friedman’s analysis in the case of differences between the measures of more than two dependent variables. Significance was accepted as 0.05 in the statistical analyses.
Approval for the study was obtained from the Ethics Committee of Health Sciences Institute. The written informed consent forms of parents of all adolescents were obtained in this study. In addition, assent was obtained from all adolescents by means of a voluntary participation form.
The interim test (χ2 = 17.022; p = .002) and the posttest (χ2 = 20.102; p = .000) showed that the intervention group recorded more progress in the stages of change than the control group; the difference between the intervention and control groups was statistically significant. When the intervention group itself was examined in terms of progress in the stages of behavioral change, a statistically significant difference was found between the interim and posttest compared to before the intervention (Fr. = 25.636; p = .000). In the control group, on the other hand, no statistically significant difference was found between the interim and posttest compared to before the intervention (p > .05) (Table 2).
After the intervention, the intervention group had a higher exercising self-efficacy score than the control group; the difference between the groups was statistically significant (ta = 2.647; p = .009). The comparison of the self-efficacy mean scores within the intervention and control groups before and after the intervention showed that while the intervention group displayed a statistically significant difference (tb = −2.586; p = .011), no statistically significant difference was found in the control group (p > .05) (Table 3).
The scores of the intervention and control groups were comparedintermsoftheirmeanscoresinexercisingandtheirperception of the pros and cons following the intervention. It was seen that following the intervention, no statistically significant difference could be found between the intervention and control groups in terms of their mean scores on both scales (p >.05).In the comparison of the intervention and control groups in terms of their mean scores in exercising, the perceptions of pros and cons before and after the intervention within each group, a statistically significant difference was observed between the intervention group (tb = 5.409; p = .000) and the control group (tb = −3.784; p = .000). The scores of each group had increased compared to the pretest. In the comparison of the intervention and control groups in terms of their mean scores in exercising, the perceptions of pros and cons before and after the intervention within each group, it was seen that the cons score fell significantly in the intervention group (tb = 2.217; p = .029). In the control group, although the cons score fell, this was not found to be statistically significant (p > .05) (Table 3).
After the intervention, there was a statistically significant difference between the intervention and the control group in terms of their MET scores for performing vigorous physical activity (Za = −3.586; p = .000), walking (Za = −2.282; p = .023) and total MET scores (Za = −4.112; p = .000). There was no significant difference between the groups in terms of their MET scores for moderate activity (p > .05). No statistically significant difference was found in the intervention group between the pre- and posttest in terms of their MET scores for moderate physical activity or their total MET scores (p > .05), but there was a significant fall in their walking MET score (Zb = −2.253; p = .024). A statistically significant decrease was found in the pre- and posttests of the control group in terms of their physical activity (Zb = 2.825; p = .005), walking (Zb = −5.689; p = .000), and overall MET (Zb = −5.665; p = .000) scores; no difference was found in terms of moderate activity and MET scores (p > .05), (Table 3).
The MET scores of the intervention and control groups were compared by activity categories before and after the intervention. Accordingly, a statistically significant difference was found between the groups in the activity categories (χ2 = 17.135; p = .000); the intervention group was more active than the control group.
Within the intervention group, no significant difference was found between the pre- and posttest MET scores in terms of the category (p > .05). In the control group, there was a significant difference between the pre- and posttest MET scores in terms of the category (Mc = 27.471; p = 0.000), (Table 4). The reason for the significant difference in the control group was that the number of very active adolescents decreased and the number of inactive adolescents increased after the intervention.
After the intervention and in terms of the categories according to BMI, no significance was found between the intervention and control groups nor inside each group separately (p >.05)(Table 4).
Following the TTM-based physical activity program conducted on the WhatsApp application, when compared to the control group, the intervention group showed more progress in the TTM exercising stages of behavioral change, displayed an increase in their exercising self-efficacy scores, did not display any difference in their pros and cons scores but did show a significant difference in vigorous activity, walking and total MET scores while no difference was seen between BMI categories.
After a 4-week intervention, it was found that the intervention group showed more positive progress toward the preparation and action stages, while the control group experienced relapses, especially in the maintenance stage, creating a statistically significant difference between the groups (p < .05) (Table 2). Among the TTM-based interventional studies reported in the literature, a study addressing overweight and obese children featuring a TTM-based exercise counseling program and an activity consisting of jumping rope to music shows progress on at least one stage in 36.2% of the intervention group after the intervention as opposed to 17.4% in the control group. A significant difference was not found in the stages of change in either of the groups after the intervention (p > .05) (Ham et al., 2016). Lau et al. (2012) report in a TTM-based interventional study using SMS and the internet, conducted to increase physical activity in adolescents, that 39.5% of the interventional group progressed in the stages of change (p < .05), while no progress was recorded in the control group (p > .05). A significant difference was not found in the stages of change between the groups after the intervention (p > .05) (Lau et al., 2012). A review of our study results showed that our intervention program was more effective in achieving progress in the stages of change. The interim test and the posttest showed that there were relapses in the maintenance stage in the control group, but no relapses were recorded in the intervention group. Marcus and Lewis (2003) report that individuals in the preparation and maintenance stages may experience relapses due to situations such as vacations, stressful events, the boredom of physical activity, and withdrawal from physical activity. Lach et al. (2004) also assert that individuals may revert for different reasons while in the maintenance stage. For individuals to maintain or progress in their behavior, they are expected to have registered an increase in their self-efficacy scores (Nigg et al., 2011). We attributed the relapses in the control group to the COVID-19 pandemic. We believe that the reason the intervention group did not experience relapses was because of the encouraging reminders that were sent to their phones, helping to increase their self-efficacy. Our findings support Hypothesis 1.
After the intervention, a comparison of the exercising self-efficacy scores of the intervention and control groups revealed a statistically significant difference (p < .05); the intervention group had higher self-efficacy scores (Table 3). The studies indicate that physical activity selfefficacy does not easily change in children and adolescents. Pitman (2020) reported in an intervention study conducted with adolescents and based on the social cognitive theory that no significant difference could be found between the three intervention groups in the study in terms of physical activity self-efficacy scores. A look into a model-based study reveals that a TTM-based physical activity intervention carried out with overweight and obese adolescents brought about a statistically significant increase in the selfefficacy score of the intervention group following the intervention (p < .05); no difference was found in the control group (p > .05). Taymoori and Lubans (2008) report the results of a study in which the authors conducted a program of model-based health improvement education to increase physical activity in adolescent girls in one group and a combined TTM and model-based health improvement education program in another group. The results of the study showed that both groups recorded significant increases in their self-efficacy scores (p < .05). In a study with adolescent groups by Ghiami et al. (2017), it was found that the group to which appropriate physical activity education was given registered a significant increase in self-efficacy (p < .05), while there was no significant increase in the control group (p > .05) (Ghiami et al., 2017). Taymoori and Lubans (2008) and Dishman et al. (2005) reported in their studies with adolescent girls that the increase in the adolescents’ self-efficacy could be associated with the decrease in their perception of the cons of physical activity. In another TTM and web-based study, self-efficacy was found to be increased (Pirzadeh et al., 2020). Based on the results of the study and our own findings, it may be said that studies in which the TTM model was used had more of an effect on self-efficacy. Our findings support Hypothesis 2.
After the intervention, no statistically significant difference was found between the intervention and control groups in terms of perceptions of the pros of exercising (p >.05). Within the groups, there was a statistically significant increase in exercising pros perceptions as compared to before the intervention (p < .05). After the intervention, no statistically significant difference was found between the intervention and control groups in terms of perceptions of the cons of exercising (p > .05). While there was a statistically significant decrease in the cons perception pretest and posttest mean scores of the intervention group (p < .05), no difference was recorded in the control group (p > .05) (Table 3). In a study by Ham et al. (2016) that used a TTM-based rope-jumping program with overweight adolescents, the authors could not find a statistically significant difference between exercising pro and con perceptions after the intervention in either the intervention or control group (p > .05) (Ham et al., 2016). The balance of decision-making is important in physical activity behavior and this structure should be given importance in educational studies (Pirzadeh et al., 2020). We saw in our study that our intervention was partially effective in changing the perception of exercising pros and cons. Our findings do not verify Hypothesis 3.
After the intervention, the intervention group had higher scores in terms of vigorous activity, walking, and overall MET scores compared to the control group, and the difference between the two groups was significant (Table 3). Within the intervention group, no statistically significant difference was found between the MET scores in terms of vigorous, moderate, and overall activity either before or after the intervention (p > .05), but there was a significant decrease in the walking MET score, which was statistically significant (p < .05). This outcome suggests that this might have been caused by the lockdown restrictions placed on individuals under the age of twenty due to the COVID-19 pandemic and their inability to find safe places in which to walk. In our review of the posttest MET scores of the control group, we observed that the vigorous activity, walking, and overall activity MET scores showed a significant decrease compared to the pretest (p < .05) (Table 3). Patridge et al. found in a TTM-based SMS intervention carried out with young adults that the intervention group achieved a strong mean increase of 563.1 in their overall MET scores in 12 weeks (Partridge et al., 2015). In our study, we found a 117.89 MET increase in only the moderate activity mean scores in our intervention group after the intervention, but this difference was not statistically significant (p > .05). In a TTM-based SMS and internet-supported program for increasing physical activity among adolescents, Lau et al. (2012) found a statistically significant increase in the intervention group’s level of physical activity (p < .05). No significant increase was seen in the control group (Lau et al., 2012). Another study conducted in China, which described an intervention that divided students into three groups according to whether they were in the stages of precontemplation, contemplation, or action, revealed an increase in physical activity (Schwarzer et al., 2010). We can say that interventions based on the stages of change are effective in providing individuals the opportunity to increase or maintain the level of their physical activity. When we looked at the categories according to MET scores, we saw that there was a statistically significant difference between the intervention and control groups after the intervention (p < .05). No statistically significant difference was found in the intervention group after the intervention (p > .05). In the control group, however, the number of inactive students increased significantly, and the number of very active students showed a significant decrease (p < .05). Pirzadeh et al. (2020) reported that in a web-based physical activity study using TTM, the percentage of adolescents with low, moderate, and vigorous physical activity at the end of the intervention increased significantly after the intervention. In our study, physical activity levels may have been affected by home quarantines and restrictions brought on by COVID-19. Our findings verify Hypothesis 4.
No significant difference was found between the intervention and control groups nor inside each group separately after the intervention in terms of the BMI categories (p > .05) (Table 4). The fact that there was no significant change in our study in terms of BMI, we believe, was due to the restrictions placed on physical activity as a result of COVID-19 and also because of the lack of interventions to regulate eating habits during that period. As for BMI interventional studies in the literature, we found that Ham et al. (2016) reported no significant difference between the pre- and posttest of the intervention group in terms of BMI (Ham et al., 2016). It was noted in another 1-year interventional study conducted using mobile texting for the purpose of increasing the healthy eating habits, physical activity, and emotional wellbeing of obese children that there seemed to be no effect of the intervention on BMI (de Niet et al., 2012). Boff et al. (2018) carried out TTM-based motivational discussions with obese adolescents for 1.5-h sessions for 12 weeks. The study showed a decrease in BMI in the intervention groups (p < .05), but no statistically significant difference between the groups (p > .05) (Boff et al., 2018). Pittman (2020) reported in an article about an intervention conducted to increase physical activity that no significant change was seen in BMI (Pittman 2020). It was revealed in a systematic review that the effect of a school-based physical activity program on BMI among adolescents and children was slight (Dobbins et al., 2013). It is reported in various studies that the reasons no change is generally seen in the level of BMI can be traced to the rate of growth and development in adolescent age groups, the short duration of the interventions, and the need for multi-factor interventions (de Niet et al., 2012; Ham et al., 2016; Pittman, 2020). It is also said that the positive effects of interventions on BMI can be seen not only through school-based interventions but also through other interventions on a community, home, and public policy scale (Dobbins et al., 2013). Our findings do not support Hypothesis 5.
Among the strengths of the study was that this is the first physical activity program to be conducted in Turkey with adolescents based on the TTM and using the WhatsApp application. Additionally, the evidence provided in the context of using a model and mobile technologies in health education answers the need to fill a gap in the literature and creates a resource for future interventional studies. A further strength of the study, it is believed, is that we were able to successfully conduct the program even under the conditions of the COVID-19 pandemic.
Due to the constraints of the COVID-19 pandemic, the researchers were not able to measure the height and weight of the adolescents themselves. Height and weight measurement standards were explained to the adolescents via message, but it is not known whether they measured them correctly. The fact that the measurement was not made under the same measuring instruments and the same conditions was an important limitation of this study.
In this study, the self-reported IPAQ-S scale was used instead of the accelerometer, which is an objective data tool in physical activity measurement. Adolescents are likely to report more physical activity or its higher intensity. Therefore, it is recommended that future studies use accelerometers to collect objective physical activity data from adolescents. In addition, the study lasted for 3 months and the online collection of posttest data due to the COVID-19 pandemic is another limitation of the study.
School nurses are responsible for protecting and improving the physical, mental, and social health of students. School nurses are key to promoting positive health behavior such as physical activity in the school setting. It is very important to plan interventions to prevent obesity and provide other health benefits of physical activity in adolescents whose physical activity levels do not meet WHO standards (Gulthold et al., 2020).
This study was carried out with the WhatsApp application. WhatsApp application is very suitable for the intervention of school nurses because it can be used easily wherever there is internet access (whether via a data plan or Wi-Fi). Moreover, it is free, provides instant and fast communication, and allows working with crowded groups. Delivering information to many people at the same time with the WhatsApp application is time- and cost-effective for school nurses working with largegroups.Thewidespreaduseandeaseofuseoftheapplication offer a practical solution for school nurses. WhatsApp-based physical activity programs are considered a promising method for adolescents. WhatsApp-based programs are time-saving programs that provide continuity, the capability of reaching a wide audience, as well as appealing audio-visual educational content.
This study contributes to interventional studies in school nursing. The program is recommended to school nurses for use in their efforts to improve the physical activity behaviors of adolescents.
The study showed that a TTM-based physical activity program conducted for adolescents using the WhatsApp application was effective in achieving progress in exercising stages of change, attaining exercise self-efficacy, and maintaining physical activity levels. It was also seen that the program could be carried out even under the conditions of a pandemic.
It might also be suggested that rather than dwelling on the health benefits of exercise, increasing the perception of adolescents about the pros of physical activity may be achieved by stressing the fun side of engaging in exercise with friends, taking part in competitive games, and participating in programs that emphasize the advantages of looking fit.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
For the study, permission was obtained from the Ethics Committee of Marmara University Institute of Health Sciences (with protocol number 225), from the Istanbul Directorate of National Education for the schools where the research will be conducted, and from the scale owners for the scales used. The adolescents who took part in the study provided verbal and written information, and their parents provided written informed consent.
The authors received no financial support for the research, authorship, and/or publication of this article: Research expenses were covered by the researchers.
Fatma Ceylan, MSc, RN, https://orcid.org/0000-0001-8871-4611
Saime Erol, PhD, RN, https://orcid.org/0000-0001-7752-605X
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Fatma Ceylan, MSc, RN, is a research assistant in the Department of Public Health Nursing at Hacettepe University Faculty of Nursing, Turkey.
Saime Erol, PhD, RN, is a professor in the Department of Public Health Nursing at Marmara University Faculty of Nursing, Turkey.
1 Department of Public Health Nursing, Faculty of Nursing, Hacettepe University, Ankara, Turkey
2 Department of Public Health Nursing, Faculty of Health Sciences, Marmara University, Istanbul, Turkey
Corresponding Author:Fatma Ceylan, MSc, RN, Research Assistant, Department of Public Health Nursing, Faculty of Nursing, Hacettepe University, 06230 Altındağ/Ankara, Turkey.Email: ceylanfatma26@gmail.com
