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Markus Schäfer and Catharina Vögele

Content analysis is a popular method in communication and media research. However, to what extent and in which contexts it is used in sport communication research has hardly been investigated. In order to provide empirically grounded insight, the authors conducted a quantitative content analysis of scholarly journal articles using content analysis as a research method, focusing on three major international sport communication journals during the 10 years between 2010 and 2019 (N = 267). Results indicate that qualitative and quantitative methods are used equally while combinations with other methods are comparatively rare. The studies cover a broad portfolio of different topics. Social media as communication channels becomes an increasingly central issue of scientific exploration. Although the studies deal with 31 different sports in total, most of them focus on popular team sports such as football, basketball, soccer, baseball, and ice hockey.

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Josh Matti

This paper explores how emotional cues from unexpected sports outcomes impact consumers’ perception of their experience at local businesses. Using nearly 1 million Yelp reviews from the Phoenix area, I empirically test for the presence of loss aversion and reference-dependent preferences in reviewer behavior. Consistent with loss aversion, unexpected losses lead to worse reviews while there is no effect for unexpected wins. The impact of unexpected losses is concentrated in home games, with no effect for away games. The results also reflect reference-dependent preferences since wins and losses in games predicted to be close do not impact reviewer behavior. Consumer services that cater to National Basketball Association fans (e.g., sports bars) experience pronounced effects.

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João R. Pereira, Dylan P. Cliff, Eduarda Sousa-Sá, Zhiguang Zhang, Jade McNeill, Sanne L.C. Veldman, and Rute Santos

Background: This study aimed to understand whether a higher number of sedentary bouts (SED bouts) and higher levels of sedentary time (SED time) occur according to different day types (childcare days, nonchildcare weekdays, and weekends) in Australian toddlers (1–2.99 y) and preschoolers (3–5.99 y). Methods: The SED time and bouts were assessed using ActiGraph GT3X+ accelerometers. The sample was composed of 264 toddlers and 343 preschoolers. The SED bouts and time differences were calculated using linear mixed models. Results: The toddlers’ percentage of SED time was higher on nonchildcare days compared with childcare days (mean difference [MD] = 2.3; 95% confidence interval, 0.7 to 3.9). The toddlers had a higher number of 1- to 4-minute SED bouts on nonchildcare days compared with childcare days. The preschoolers presented higher percentages of SED time during nonchildcare days (MD = 3.1; 95% confidence interval, 1.6 to 4.5) and weekends (MD = 1.9; 95% confidence interval, 0.4 to 3.4) compared with childcare days. The preschoolers presented a higher number of SED bouts (1–4, 5–9, 10–19, and 20–30 min) during nonchildcare days and weekends compared with childcare days. No SED times or bout differences were found between nonchildcare days and weekends, neither SED bouts >30 minutes on toddlers nor on preschoolers. Conclusion: The SED time and bouts seem to be lower during childcare periods, which means that interventions to reduce sedentary time should consider targeting nonchildcare days and weekends.

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Aashirwad Mahajan, Satish Mahajan, and Swanand Tilekar

The primary objective of this pilot randomized controlled trial was to study the feasibility (recruitment and retention rates) for interval training and sleep hygiene (SH) in adults aged above 60 years. Thirteen out of 46 screened individuals from a home for older adults in Shirdi (Maharashtra, India) were randomly assigned by permuted block randomization to either an interval training with SH group (n = 6) or an SH alone group (n = 7). The authors measured sleep with the S+ sleep monitor manufactured by ResMed (USA) Pittsburgh Sleep Quality Index and quality of life with Short Form-12 health survey version 2. Interval training consisted of 8 weeks of stationary cycling, whereas SH consisted of lecture and handouts. Recruitment was 38.2%, retention was >80% for both the interventions, and there was one loss to follow-up in SH. Interval training and SH were feasible for older adults and supported a full-scale randomized controlled trial.

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Lyndel Hewitt, Anthony D. Okely, Rebecca M. Stanley, Marjika Batterham, and Dylan P. Cliff

Background: Tummy time is recommended by the World Health Organization as part of its global movement guidelines for infant physical activity. To enable objective measurement of tummy time, accelerometer wear and nonwear time requires validation. The purpose of this study was to validate GENEActiv wear and nonwear time for use in infants. Methods: The analysis was conducted on accelerometer data from 32 healthy infants (4–25 wk) wearing a GENEActiv (right hip) while completing a positioning protocol (3 min each position). Direct observation (video) was compared with the accelerometer data. The accelerometer data were analyzed by receiver operating characteristic curves to identify optimal cut points for second-by-second wear and nonwear time. Cut points (accelerometer data) were tested against direct observation to determine performance. Statistical analysis was conducted using leave-one-out validation and Bland–Altman plots. Results: Mean temperature (0.941) and z-axis (0.889) had the greatest area under the receiver operating characteristic curve. Cut points were 25.6°C (temperature) and −0.812g (z-axis) and had high sensitivity (0.84, 95% confidence interval, 0.838–0.842) and specificity (0.948, 95% confidence interval, 0.944–0.948). Conclusions: Analyzing GENEActiv data using temperature (>25.6°C) and z-axis (greater than −0.812g) cut points can be used to determine wear time among infants for the purpose of measuring tummy time.

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Hua Gong, Nicholas M. Watanabe, Brian P. Soebbing, Matthew T. Brown, and Mark S. Nagel

The use of big data in sport and sport management research is increasing in popularity. Prior research generally includes one of the many characteristics of big data, such as volume or velocity. The present study presents big data in a multidimensional lens by considering the use of sentiment analysis. Specifically focusing on the phenomenon of tanking, the purposeful underperformance in sport competitions, the present study considers the impact that consumers’ sentiment regarding tanking has on game attendance in the National Basketball Association. Collecting social media posts for each National Basketball Association team, the authors create an algorithm to measure the volume and sentiment of consumer discussions related to tanking. These measures are included in a predictive model for National Basketball Association home game attendance between the 2013–2014 and 2017–2018 seasons. Our results find that the volume of discussions for the home team and sentiment toward tanking by the away team impact game attendance.

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Victor E. Ezeugwu, Piush J. Mandhane, Nevin Hammam, Jeffrey R. Brook, Sukhpreet K. Tamana, Stephen Hunter, Joyce Chikuma, Diana L. Lefebvre, Meghan B. Azad, Theo J. Moraes, Padmaja Subbarao, Allan B. Becker, Stuart E. Turvey, Andrei Rosu, Malcolm R. Sears, and Valerie Carson

Background: Movement behaviors (physical activity, sedentary time, and sleep) established in early childhood track into adulthood and interact to influence health outcomes. This study examined the associations between neighborhood characteristics and weather with movement behaviors in preschoolers. Methods: A subset of Canadian Healthy Infant Longitudinal Development birth cohort (n = 385, 50.6% boys) with valid movement behaviors data were enrolled at age 3 years and followed through to age 5 years. Objective measures of neighborhood characteristics were derived by ArcGIS software, and weather variables were derived from the Government of Canada weather website. Random forest and linear mixed models were used to examine predictors of movement behaviors. Cross-sectional analyses were stratified by age and season (winter and nonwinter). Results: Neighborhood safety, temperature, green space, and roads were important neighborhood characteristics for movement behaviors in 3- and 5-year-olds. An increase in temperature was associated with greater light physical activity longitudinally from age 3 to 5 years and also in the winter at age 5 years in stratified analysis. A higher percentage of expressways was associated with less nonwinter moderate to vigorous physical activity at age 3 years. Conclusions: Future initiatives to promote healthy movement behaviors in the early years should consider age differences, neighborhood characteristics, and season.

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David H. Perrin

In this essay, I reflect on my life and academic career, detailing my childhood, family background, education, and those who influenced me to study physical education and athletic training. My higher education started with a small college experience that had a transformative impact on my intellectual curiosity, leading to graduate degrees and, ultimately, a career in higher education. I chronicle my academic career trajectory as a non-tenure-track faculty member and clinician, tenured faculty member, department chair, dean, and provost. My personal and professional lives have been undergirded by a commitment to equity, diversity, and inclusion, with examples provided in this essay.

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Nathalie Berninger, Gill ten Hoor, Guy Plasqui, and Rik Crutzen

Purpose: Physical activity (PA) is crucial for health, but there is insufficient evidence about PA patterns and their operationalization. The authors developed two algorithms (SPORTconstant and SPORTlinear) to quantify PA patterns and check whether pattern information yields additional explained variance (compared with a compositional data approach [CoDA]). Methods: To measure PA, 397 (218 females) adolescents with a mean age of 12.4 (SD = 0.6) years wore an ActiGraph on their lower back for 1 week. The SPORT algorithms are based on a running value, each day starting with 0 and minutely adapting depending on the behavior being performed. The authors used linear regression models with a behavior-dependent constant (SPORTconstant) and a function of time-in-bout (SPORTlinear) as predictors and body mass index z scores (BMIz) and fat mass percentages (%FM) as exemplary outcomes. For generalizability, the models were validated using five-fold cross-validation where data were split up in five groups, and each of them was a test data set in one of five iterations. Results: The CoDA and the SPORTconstant models explained low variance in BMIz (2% and 1%) and low to moderate variance in %FM (both 5%). The variance being explained by the SPORTlinear models was 6% (BMIz) and 9% (%FM), which was significantly more than the CoDA models (p < .001) according to likelihood ratio tests. Conclusion: Among this group of adolescents, SPORTlinear explained more variance of BMIz and %FM than CoDA. These results suggest a way to enable research about PA patterns. Future research should apply the SPORTlinear algorithm in other target groups and with other health outcomes.

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Jillian J. Haszard, Tessa Scott, Claire Smith, and Meredith C. Peddie

Short sleep duration is associated with poorer outcomes for adolescents; however, sleep duration is often assessed (either by questionnaire or device) using self-reported bedtime (i.e., the time a person goes to bed). With sedentary activities, such as screen time, being common presleep in-bed behaviors, the use of “bedtime” may introduce error to the estimates of sleep duration. It has been proposed that self-reported “shuteye time” (i.e., the time a person starts trying to go to sleep) is used instead of bedtime. This study aimed to compare the bedtimes and shuteye times of a sample of 15- to 18-year-old female adolescents recruited from 13 high schools across New Zealand. The influence on sleep duration estimates and associations with healthy lifestyle habits was also examined. Sleep data were collected from 136 participants using actigraphy and self-report. On average, 52 min (95% confidence interval [43, 60] min) of sedentary time was misclassified as sleep when bedtime was used instead of shuteye time with actigraph data. Mean bedtimes on weekdays and weekends were 9:56 p.m. (SD = 58 min) and 10:40 p.m. (SD = 77 min), respectively. The relationship between bedtime and shuteye time was not linear—indicating that bedtime cannot be used as a proxy for shuteye time. Earlier shuteye times were more strongly associated with meeting fruit and vegetable intake and sleep and physical activity guidelines than earlier bedtimes. Using bedtime instead of shuteye time to estimate sleep duration may introduce substantial error to estimates of both sleep and sedentary time.