Impact of Cell Phone Texting on the Amount of Time Spent Exercising at Different Intensities

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The Impact of Cell Phone Texting During Aerobic Exercise on Measures of Cognition

International Journal of Exercise Science 12(5): 646-656, 2019. This study assessed the effect of cell phone texting during a 30-minute bout of cycle ergometer exercise on measures of cognition (i.e., reaction time and accuracy). Twenty-eight college students participated in two conditions (cell phone and no cell phone). Reaction time and accuracy were assessed pre- and post-exercise with the use of the Stroop test. Reaction time was significantly worse (p \u3c 0.001) in the cell phone condition from pre- (1003.75 ± 178.04 ms) to post-exercise (1124.46 ± 238.55 ms). Reaction time was significantly better (p \u3c 0.001) in the no cell phone condition from pre- (1107.71 ± 229.54 ms) to post-exercise (953.86 ± 177.42 ms). Accuracy was significantly worse (p = 0.01) in the cell phone condition from pre- (97.61 ± 2.32) to post-exercise (94.04 ± 7.88). Accuracy was significantly better (p \u3c 0.001) in the no cell phone condition from pre- (94.82 ± 4.42) to post-exercise (97.39 ± 2.42). In conclusion, using your cell phone for texting can interfere with the cognitive benefits associated with reaction time and accuracy that are developed from participating in aerobic exercise

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

ECOLOGICAL MOMENTARY ASSESSMENT AND TIME-VARYING FACTORS ASSOCIATED WITH EATING AND PHYSICAL ACTIVITY

The obesity epidemic is a significant problem in the United States. It is well established that lifestyle factors, such as unhealthy eating and physical inactivity, are key contributors. These causes are generally voluntary activities and it is important to examine health decision-making with respect to these behaviors. The current study examined time-varying factors of stress and mood and their relationship with healthy eating and physical activity in a sample of undergrads (N = 26). Ecological momentary assessment via one\u27s cell phone was used to collect multiple measurements over six days. Positive mood was found to follow physical activity episodes for up to five hours, and preceded physical activity for up until five before the activity occurred. These results are consistent with those from previous literature, and suggest a clear association between positive mood and physical activity. Future research should incorporate more objective measures of physical activity and eating

National Time Accounting: The Currency of Life

This monograph proposes a new approach for measuring features of society’s subjective well-being, based on time allocation and affective experience. We call this approach National Time Accounting (NTA). National Time Accounting is a set of methods for measuring, comparing and analyzing how people spend and experience their time -- across countries, over historical time, or between groups of people within a country at a given time. The approach is based on evaluated time use, or the flow of emotional experience during daily activities. After reviewing evidence on the validity of subjective well-being measures, we present and evaluate diary-based survey techniques designed to measure individuals’ emotional experiences and time use. We illustrate NTA with: (1) a new cross-sectional survey on time use and emotional experience for a representative sample of 4,000 Americans; (2) historical data on the amount of time devoted to various activities in the United States since 1965; and (3) a comparison of time use and wellbeing in the United States and France. In our applications, we focus mainly on the Uindex, a measure of the percentage of time that people spend in an unpleasant state, defined as an instance in which the most intense emotion is a negative one. The U-index helps to overcome some of the limitations of interpersonal comparisons of subjective well-being. National Time Accounting strikes us as a fertile area for future research because of advances in subjective measurement and because time use data are now regularly collected in many countries.

Testing Fitness-Related Phone Application Technology in Physical Activity Classes

Many young adults are inactive (Centers for Disease Control and Prevention, 2010) and time spent on smartphones and applications (“apps”) is high (Pew Research Center, 2014; The Nielsen Company, 2014). Technology is often viewed as a barrier to health behavior, so seeking ways of using technology to facilitate physical activity (PA) and other health-related behaviors could be beneficial. The Social Cognitive Theory (SCT) framework was used to determine if the NexTrack smartphone app could increase PA behaviors and SCT-related constructs among university students in PA courses. Participants in the NexTrack app intervention group were hypothesized to report increased psychosocial and behavioral PA outcomes compared to students in the control condition. Using quasi-experimental design, university students (N=181) were randomly assigned to one of two groups during an eight-week intervention. The intervention group was introduced to NexTrack and asked to log PA while control participants used paper and pencil logs. All received an instructional presentation on goal setting and were emailed weekly reminders to log their activity. Each participant completed previously established surveys on self-reported PA behavior, self-efficacy (SE), and self-regulation (SR) at baseline and post-intervention. Descriptive statistics, bivariate correlation estimates, and internal consistency estimates were calculated. Main analyses included a series of 2 (gender: male; female) x 2 (group: intervention; control) x 2 (time: baseline; 8-weeks) repeated measures analysis of variance (RM-ANOVA) tests and follow-up mean comparisons to examine group differences. Findings revealed no significant differences in PA, SE, or SR as a result of the intervention. However, participants in the control group logged significantly more events than those in the intervention. Results can help guide technology use in PA courses. Findings revealed that incorporating the NexTrack smartphone app did not facilitate students’ PA or psychosocial related behavior. Although increases in SCT related constructs were not seen by the control group, it may be beneficial to incorporate paper and pencil logging for a comprehensive understanding of PA habits. Based on the findings, use of NexTrack did not facilitate SE, SR, or increases in PA. More research is needed to determine how to best use app technologies as facilitators of PA

Identification of Participation-Related Activities to Be Used As Part of the Development of a Self-Efficacy Questionnaire for Adolescents with Hearing Loss

Investigating self-efficacy beliefs among adolescents with hearing loss is imperative as these perceptions affect a broad range of age-related functioning. Validated self-efficacy questionnaires for use with persons with hearing loss are currently limited to four adult measures. Development of an adolescent-relevant questionnaire aims to quantify self-efficacy for participation in daily activities and to individualize treatment interventions for adolescents with hearing loss. Developing the self-efficacy questionnaire was based on a scoping literature review to develop a list of activities performed by typically developing adolescents. The questionnaire was piloted on a sample of youth with hearing loss. The Adolescent Self-Efficacy Questionnaire for Hearing Loss (ASEQ-HL) is a 37-item questionnaire based on the inventory of youth-related activities. The activities were linked to the International Classification of Functioning, Disability and Health - Child and Youth (ICF-CY) framework. The questionnaire was structured according to self-efficacy questionnaire development guidelines proposed by Bandura (2006b)

Psychometric properties of the smartphone addiction proneness scale in a sample of Malaysian adolescents

The aim of this study was to examine the psychometric properties of the 15-item smartphone addiction proneness scale (SAPS) among a sample of Malaysian adolescents. The gathered data were subjected to exploratory and confirmatory factor analysis. There were 922 secondary school students involved in this study. The exploratory factor analysis extracted a three-factor solution for SAPS. These factors were named disturbance of adaptive functions, withdrawal and tolerance. Results from confirmatory factor analysis also indicated that the three-factor structure fits well with the data. The internal consistency of the scale was found to be good. The positive and moderately strong correlation between SAPS and three widely adopted criterion variables (depression, loneliness and boredom proneness) supported the concurrent validity of SAPS. The results of this study showed that the SAPS is a reliable and valid instrument for identifying problematic smartphone use among Malaysian adolescents

Investigating Person-Specific Profiles of Readiness-To-Exercise: Exploring Associations with Hypothetical Experiential Outcomes and Perceived Relevance

Autoregulation is a person-adaptive strategy wherein exercise workloads are adjusted to match one’s readiness (e.g., acute mental, physical, perceptual state). Prior work demonstrated that structural features of readiness profiles (i.e., which factor(s) are most important) differ across individuals. As this work relied on mathematical modeling, research is needed to understand the informational utility of person-specific profiles (PSPs) of readiness. Purpose: Model heterogeneity in PSPs of readiness (Aim 1), explore associations between PSP factor scores and forecasted experiences to hypothetical muscle-strengthening exercise (Aim 2), and explore participants’ perceptions of relevance and utility regarding their PSP (Aim 3). Methods: For Aim 1, a reference structure was created by applying R-technique factor analysis to cross-sectional readiness data from surveys taken by adults (N=326) preparing to engage in muscle-strengthening exercise. This reference was compared to PSPs created by applying P-technique factor analyses to time-series readiness data from resistance-trained adults (N=11; up to 84 time points per person) using ecological momentary assessment (EMA) procedures. For Aim 2, scatter plots were created using EMA data to visualize PSP first factor (i.e., most mathematically important) scores against ratings of affective valence forecasted in response to hypothetical exercise. For Aim 3, following EMA, individuals were interviewed to view and discuss their PSP; qualitative data underwent thematic analyses to explore participants’ shared perceptions. Results: The reference readiness structure differed from PSPs in factor number (10 vs. mean=12), interpretation of the first factor (‘activation’, vs. ‘mood/emotions’ or ‘physical states’), and the amount of variance in the dataset it explained (26.2% vs. 18.1 to 45.3%). No consistent pattern was observed regarding factor one scores and forecasted ratings of affect. Thematic analyses revealed that atypical circumstances during the EMA period and incongruency between personal perceptions and data feedback fueled a general skepticism and reservations toward mathematically modeled PSPs. Conclusion: Results replicated previous observations of heterogeneity between nomothetic and idiographic models of readiness. However, mathematically modeling PSPs based on a single period of observation appears to hold insufficient informational utility for resistance-trained adults. Further research is needed to optimize conceptualizations of readiness to refine practical implementation of autoregulatory strategies for muscle-strengthening exercise