Wellness Survey Responses and Smartphone App Response Efficiency: Associations With Remote History of Sport-Related Concussion

Abstract

Recent research findings have strongly suggested that sport-related concussion (SRC) increases risk for subsequent injury of any type, as well as a potential for long-term adverse effects on neurological and psychological well-being. The primary purpose of this study was to explore the reliability and discriminatory power of clinical testing procedures for detecting persisting effects of SRC. We used a cross-sectional study design to assess both self-reported symptoms commonly associated with post-concussion syndrome, and the effects of mental or physical activity on metrics derived from a smartphone app designed to test perceptual-motor responses. Among 30 physically active college students, 15 participants reported a SRC occurrence prior to testing ( M time-since-injury = 4.0 years, SD = 3.1, range = 5 months to 11 years). We found good test-retest reliability for key metrics derived from the smartphone app (ICC ≥ .70); and the internal consistency for the Overall Wellness Index (OWI) for 10 categories of 82 post-concussion symptoms was ideal (Cronbach’s α ≥ .80). Moderate intensity treadmill running demonstrated the strongest differential effect on perceptual-motor responses between participants with a history of SRC (HxSRC) and those with no such history (No SRC), which was best represented by the speed-accuracy trade-off quantified by the inverse efficiency index (IEI: group X trial interaction p = .055). Self-reported OWI symptoms ≥4 and post-physical activity IEI ≥ 568 ms provided the strongest discrimination between HxSRC and NoSRC participants (≥1 versus 0: OR = 9.75). Our findings suggest that persisting effects from a remote SRC occurrence can be detected by easily administered screening procedures that have the potential to identify individual athletes who might derive benefit from interventions to restore their optimal function and well-being.

A Novel Approach to Assessment of Perceptual-Motor Efficiency and Training-Induced Improvement in the Performance Capabilities of Elite Athletes

“…The Institutional Review Board of the University of Tennessee at Chattanooga approved all study procedures. Surveys administered prior to initiation of the first training session included the Sports Fitness Index (Wilkerson et al, 2016), the Overall Wellness Index (Wilkerson et al, 2021b), and the Depression, Anxiety, and Stress Scale (Edmed and Sullivan, 2012), which were used to confirm greater frequency of selfreported problems among the athletes who affirmed a history of at least one SRC (HxSRC).…”

Perceptual-Motor Efficiency and Concussion History Are Prospectively Associated With Injury Occurrences Among High School and Collegiate American Football Players

“…The surveys included a Sport Fitness Index (SFI), which documents self-ratings of persisting effects of prior musculoskeletal injuries, 55 and an Overall Wellness Index (OWI), which is designed to document the temporal proximity and the frequency of physical, cognitive, behavioral, sleep-related, and mood disorders associated with postconcussion syndrome ( Appendix Figure A1 ). 54 Responses to 10 items on both the SFI and the OWI were used to generate 0 to 100 scores for each, with low values indicating suboptimal status. Previous research has demonstrated good internal consistency for both the SFI score 55 (Cronbach α = .89) and the OWI score 54 (Cronbach α = .82).…”

“… 54 Responses to 10 items on both the SFI and the OWI were used to generate 0 to 100 scores for each, with low values indicating suboptimal status. Previous research has demonstrated good internal consistency for both the SFI score 55 (Cronbach α = .89) and the OWI score 54 (Cronbach α = .82). The OWI does not make any reference to concussion, but its 10 categories of problems include 82 postconcussion symptoms.…”

“… 32 , 52 The number of reported postconcussion symptoms derived from OWI responses was evaluated as another potential predictor variable, which has previously demonstrated strong predictive validity for identification of former athletes with a self-reported history of SRC. 54 …”

Machine-Learning Predictive Models For Dependency On Smartphones Based On Risk Factors

“…Smartphones have a repertoire of mobile tools, which altered the consumption dynamics and the way users interact [7]. In particular, consumer and information applications as well as social networks have high demand, and have high in uence in individual communication and lifestyle habits [8].…”