The Relationship Between Cell Phone Use And Motivation To Exercise In College Students

Past research suggests that high cell phone usage is related to sedentary behavior, poor physical fitness, and poor mental health. College students cell phone usage has increased over previous years while physical activity levels have declined, but due to little research, this relationship is still unclear. The purpose of this study was to examine the relationship between college student\u27s smartphone usage, exercise motivation, and physical activity. College students completed an electronic survey (n=157; female = 135; age = 20.01±1.49; BMI = 24.39) that assessed exercise motivation, physical activity, smartphone usage, height and weight (to calculate BMI), depression, anxiety, stress and fear of missing out. Data was analyzed with Pearson correlation and independent t-tests using SPSS. Results showed that amotivation (p \u3c 0.01) was positively associated with cell phone usage, while intrinsic motivation (p \u3c 0.01) was negatively associated. High cell phone users (M = 0.75 ± 0.80) showed greater amotivation for exercise than low-users (M = 0.33 ± 0.52), while low-users showed higher levels of intrinsic motivation. This novel study suggests that cell phone usage may interfere with exercise motivation and could be a possible barrier for individuals trying to become more motivated to exercise. Future research should examine ways to limit cell phone use, increase motivation to exercise to improve overall quality of life

Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification

Mobile exergaming in adolescents’ everyday life—contextual design of where, when, with whom, and how: the SmartLife case

Exergames, more specifically console-based exergames, are generally enjoyed by adolescents and known to increase physical activity. Nevertheless, they have a reduced usage over time and demonstrate little effectiveness over the long term. In order to increase playing time, mobile exergames may increase potential playing time, but need to be engaging and integrated in everyday life. The goal of the present study was to examine the context of gameplay for mobile exergaming in adolescents’ everyday life to inform game design and the integration of gameplay into everyday life. Eight focus groups were conducted with 49 Flemish adolescents (11 to 17 years of age). The focus groups were audiotaped, transcribed, and analyzed by means of thematic analysis via Nvivo 11 software (QSR International Pty Ltd., Victoria, Australia). The adolescents indicated leisure time and travel time to and from school as suitable timeframes for playing a mobile exergame. Outdoor gameplay should be restricted to the personal living environment of adolescents. Besides outdoor locations, the game should also be adaptable to at-home activities. Activities could vary from running outside to fitness exercises inside. Furthermore, the social context of the game was important, e.g., playing in teams or meeting at (virtual) meeting points. Physical activity tracking via smart clothing was identified as a motivator for gameplay. By means of this study, game developers may be better equipped to develop mobile exergames that embed gameplay in adolescents’ everyday life

Associations of Objectively-Assessed Smartphone Use with Physical Activity, Sedentary Behavior, Mood, and Sleep Quality in Young Adults: A Cross-Sectional Study

This study assesses the associations of objectively-measured smartphone time with physical activity, sedentary behavior, mood, and sleep patterns among young adults by collecting real-time data of the smartphone screen-state. The sample consisted of 306 college-aged students (mean age ± SD: 20.7 ± 1.4 years; 60% males). Over seven days of time, the following variables were measured in the participants: objectively-measured smartphone use (Your Hour and Screen Time applications), objective and subjective physical activity (GoogleFit and Apple Health applications, and the International Physical Activity Questionnaire (IPAQ), respectively), the number of hours sitting (IPAQ), mood (The Profile of Mood State (POMS)), and sleep (The Pittsburgh Sleep Quality Index (PSQI)). Multiple regressions analyses showed that the number of hours sitting per day, physical activity, and the POMS Global Score significantly predicted smartphone use (adj.R2 = 0.15). Further, participants with low levels of physical activity were more likely to increase the use of smartphones (OR = 2.981). Moreover, mood state (β = 0.185; 95% CI = 0.05, 0.32) and sleep quality (β = 0.076; 95% CI = −0.06, 0.21) predicted smartphone use, with those reporting poor quality of sleep (PSQI index >5) being more likely to use the smartphone (OR = 2.679). In conclusion, there is an association between objectively-measured smartphone use and physical activity, sedentary behavior, mood, and sleep patterns. Those participants with low levels of physical activity, high levels of sedentary behavior, poor mood state, and poor sleep quality were more likely to spend more time using their smartphones

The Comparison of Using the Preferred or Non-Preferred Wrist When Measuring Physical Activity in College Students

Introduction. People who participate in regular physical activity have a decreased risk of chronic diseases and premature death. A dramatic decrease of physical activity occurs from adolescence to young adulthood. With important implications to health, physical activity is an important behavior to measure. However, inconsistencies exist on how to measure physical activity. When using accelerometers, differences between the preferred or non-preferred wrist may result in different estimates of physical activity. Purpose. The purpose of this study was to compare the preferred and non-preferred wrist accelerometry measured physical activity using commonly used research accelerometers during structured daily college activities (Actigraph GT3x-bt and GT9X Link) and free-living conditions of college students (Actigraph GT9X Link). Methods: 30 college students (15 females and 15 males) completed 7 laboratory tasks including shooting a basketball (BB), relaxing on a couch (Relax), hitting a racquetball (RB), walking up and down stairs (WUS), walking on an inclined surface (WUI), walking while using a smart phone (WSP), and using a laptop (COM). An Actigraph GT3x-bt and Actigraph Link on each wrist and the right hip. After the tasks, the students completed one week of free-living conditions wearing an Actigraph Link on each wrist. Accelerometer counts from the preferred and non-preferred wrists were compared using Wilcoxon signed-rank tests for the lab activities and a paired t tests for the free-living conditions with α at .05. Results: Preferred and non-preferred total counts per minute from the Actigraph Link were significantly different for BB (p= \u3c.001), COM (p=.004), RB (p= \u3c.001), Relax (p=.027), WSP (p=.001), and WUS (p=.043). The free-living conditions showed no significant differences between the preferred and non-preferred wrist. Conclusion. Researchers should be aware when measuring physical activity in structured activities that the preferred and non-preferred wrist can affect the measurement. Though for free living conditions, less concern should be placed on the preferred or preferred wrist

The Utility of a Protection Motivation Theory Framework for Understanding Sedentary Behaviour

This study aimed to 1) examine the factor structure and composition of sedentary-derived Protection Motivation Theory (PMT) constructs and 2) determine the utility of these constructs in predicting general and leisure sedentary goal intention (GI), implementation intention (II), and sedentary behaviour (SB). PMT, GI, II constructs, and a modified SB questionnaire were completed by undergraduate students. After completing socio-demographics and the PMT items (n = 787), participants were randomized to complete general or leisure intention and SB items. Irrespective of model, principal axis factor analysis revealed that the PMT items grouped into eight coherent and interpretable factors. Using linear regression, general and leisure models predicted 5% and 6% of the variance in GI, 12% and 18% of the variance in II, and 6% and 7% of the variance in SB, respectively. Support now exists for the tenability of an eight-factor PMT sedentary model with modest predictability for intentions and behaviour

Physical Activity, Wellness and Health: Challenges, Benefits and Strategies

Regular physical activity (PA) is both a preventive measure and a cure for non-communicable diseases. Moreover, PA improves mental health, quality of life, and well-being. Conversely, physical inactivity and sedentary lifestyles have negative impacts on individuals, families, and society, as evidenced in particular by the spread of the obesity epidemic. PA has proven to be a low-cost alternative for the treatment and prevention of disease. Therefore, interventions to prevent avoidable diseases by increasing the proportion of physically active people are fundamental. The Special Issue “Physical Activity, Wellness and Health: Challenges, Benefits and Strategies” was collected research articles on anthropometric determinants of health and performance, PA and healthy habits, exercise and diet, exercise and body composition, interventions to promote PA for people of all ages, strategies for the implementation of an active life, and the beneficial effects of exercise on metabolic syndrome. A total of 20 articles were published, falling mainly into the following three areas: anthropometry, health, and sport; health benefits of exercise; population studies and strategies for an active life. All of the studies support strategies to promote PA and reduce sedentary behavior among adolescents, adults and the elderly. There is no doubt that regular exercise is beneficial to health, but the general population should be encouraged to engage in more of it

The Use of Mobile Apps to Increase Physical Activity Level: A Systematic Review

Background: About 82% of the U.S. adult population owns a smartphone. More than half of that population downloaded a fitness or health app to increase the physical activity level. The current review included studies that have utilized mobile apps in conjunction with other intervention strategies to increase physical activity levels. Methods: The search was conducted in five electronic databases. Studies were included if they were randomized controlled trials, utilized mobile apps, physical activity was the primary outcome, written in English, and conducted between the years of 2007 and 2019. Results: Thirteen studies were included in the final review. Results indicated that multi-component interventions reported significant improvements in physical activity across all age groups. The most substantial behavior change effects were observed in interventions that combined apps with health coaching, individualized text messages, and self-monitoring component. The overall results indicated that 8 out of 13 included studies reported statistically significant improvement in physical activity level with mobile app utilization in multi-component interventions. Conclusion: This review suggests that mobile apps have the potential to effectively deliver physical activity interventions, by providing tailored-based approach, unlimited accessibility, and monitoring. Therefore, future studies must focus on the effective delivery of evidence-based physical activity interventions through mobile apps in various populations

Association of the Use of the Mobile Phone with Physical Fitness and Academic Performance: A Cross-Sectional Study

The aim of this study was to analyse the association of the use of the mobile phone with physical fitness (PF) and academic performance in secondary school students and its gender related differences. A total of 501 high school students participated in the study (236 girls and 265 boys; 12–18 years). Use of the mobile phone and sample distributions were done with the Mobile Related Experience Questionnaire (CERM): low use of mobile phone (LMP = 10–15 points), medium use of mobile phone (MMP = 16–23 points) and high use of mobile phone (HMP = 24–40 points). PF via Eurofit test battery and academic performance were recorded, and gender was used as a differentiating factor. The HMP group registered lower values than the LMP group for academic performance (Spanish: 4.78 ± 2.26 vs. 3.90 ± 1.96 points; p = 0.007, Mathematics: 4.91 ± 2.23 vs. 4.00 ± 1.84 points; p = 0.007) and PF (Abdominals: 6.83 ± 2.40 vs. 5.41 ± 2.46 points; p < 0.001, Broad jump: 6.24 ± 3.02 vs. 4.94 ± 2.28 points; p = 0.013). The boy students showed greater values than girl students for PF in the LMP (medicine-ball-throw: 6.34 ± 2.24 vs. 5.28 ± 1.86 points, p = 0.007) and MMP (medicine-ball-throw: 6.49 ± 2.52 vs. 5.02 ± 1.68 points; p < 0.001) groups, but no gender related differences were found in the HMP group. In conclusion, high use of the mobile phone was related to worse results in the PF tests and academic performance. Gender-related differences were found for academic performance regardless of the use of the mobile, but for physical fitness no gender differences were found in HMP group