Trends in concussion mechanism of injury during the COVID-19 pandemic

Schaffer, Olivia; Xie, Frank; Cheng, Debby; Grossman, Sebastian P.; Galetta, Steven; Balcer, Laura J.

doi:10.1016/j.jns.2022.120538

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Concussion is a significant public health problem that affects approximately 20% of adolescents in the United States each year [ 1 2 ]. The prevalence of lifetime concussions has been estimated to be between 6.5% and 18.3% among adolescents aged 13-17 years [ 3 ], with approximately 13.7% experiencing persistent symptoms for at least 3 months [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Response Rate Patterns in Adolescents With Concussion Using Mobile Health and Remote Patient Monitoring: Observational Study

Ren,

McDonald,

Corwin

et al. 2024

JMIR Pediatr Parent

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Background A concussion is a common adolescent injury that can result in a constellation of symptoms, negatively affecting academic performance, neurobiological development, and quality of life. Mobile health (mHealth) technologies, such as apps for patients to report symptoms or wearables to measure physiological metrics like heart rate, have been shown to be promising in health maintenance. However, there is limited evidence about mHealth engagement in adolescents with a concussion during their recovery course. Objective This study aims to determine the response rate and response rate patterns in concussed adolescents reporting their daily symptoms through mHealth technology. It will also examine the effect of time-, demographic-, and injury-related characteristics on response rate patterns. Methods Participants aged between 11-18 years (median days since injury at enrollment: 11 days) were recruited from the concussion program of a tertiary care academic medical center and a suburban school’s athletic teams. They were asked to report their daily symptoms using a mobile app. Participants were prompted to complete the Post-Concussion Symptom Inventory (PCSI) 3 times (ie, morning, afternoon, and evening) per day for 4 weeks following enrollment. The primary outcome was the response rate pattern over time (by day since initial app use and the day since injury). Time-, demographic-, and injury-related differences in reporting behaviors were compared using Mann Whitney U tests. Results A total of 56 participants were enrolled (mean age 15.3, SD 1.9 years; n=32, 57% female). The median response rate across all days of app use in the evening was 37.0% (IQR 27.2%-46.4%), which was significantly higher than the morning (21.2%, IQR 15.6%-30.5%) or afternoon (26.4%, IQR 21.1%-31.5%; P<.001). The median daily response was significantly different by sex (female: 53.8%, IQR 46.2%-64.2% vs male: 42.0%, IQR 28.6%-51.1%; P=.003), days since injury to app use (participants starting to use the app >7 days since injury: 54.1%, IQR 47.4%-62.2% vs starting to use the app ≤7 days since injury: 38.0%, IQR 26.0%-53.3%; P=.002), and concussion history (participants with a history of at least one prior concussion: 57.4%, IQR 44.5%-70.5% vs participants without concussion history: 42.3%, IQR 36.8%-53.5%; P=.03). There were no significant differences by age. Differences by injury mechanism (sports- and recreation-related injury: 39.6%, IQR 36.1%-50.4% vs non–sports- or recreation-related injury: 30.6%, IQR 20.0%-42.9%; P=.04) and initial symptom burden (PCSI scores greater than the median score of 47: 40.9%, IQR 35.2%-53.8% vs PCSI scores less than or equal to the median score: 31.9%, IQR 24.6%-40.6%; P=.04) were evident in the evening response rates; however, daily rates were not statistically different. Conclusions Evening may be the optimal time to prompt for daily concussion symptom assessment among concussed adolescents compared with morning or afternoon. Multiple demographic- and injury-related characteristics were associated with higher daily response rates, including for female participants, those with more than 1 week from injury to beginning mHealth monitoring, and those with a history of at least one previous concussion. Future studies may consider incentive strategies or adaptive digital concussion assessments to increase response rates in populations with low engagement.

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Section: Introductionmentioning

confidence: 99%

Response Rate Patterns in Adolescents With Concussion Using Mobile Health and Remote Patient Monitoring: Observational Study

Ren,

McDonald,

Corwin

et al. 2024

JMIR Pediatr Parent

View full text Add to dashboard Cite

show abstract

Response rate patterns in adolescents with concussion using mobile health and remote patient monitoring (Preprint)

Ren,

McDonald,

Corwin

et al. 2023

Preprint

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BACKGROUND Concussion is a common adolescence injury that can result in a constellation of symptoms, negatively affecting their academic performance, neurobiological development, and quality of life. Mobile health (mHealth) technology has been shown its promising role in managing real-time symptoms. However, there is limited evidence about mHealth engagement in adolescents with concussion during recovery course. OBJECTIVE To determine the response rate and response rate patterns in concussed adolescents reporting their daily symptoms through mHealth technology; and examine the effect of time-, demographic- and injury-related characteristics on response rate patterns. METHODS Participants aged between 11 – 18 years were recruited from the concussion program of a tertiary care academic medical center and a suburban school’s athletic teams. They were asked to report their daily symptoms using a mobile app. Participants were prompted to complete the Post-Concussion Symptom Inventory (PCSI) 3 times (i.e., morning, afternoon, and evening) per day for 4 weeks following enrollment. The primary outcome was response rate pattern over time since enrollment. Time-of-day-, demographic-, and injury-related differences in reporting behaviors were compared using Wilcoxon rank sum tests. RESULTS Fifty-six participants were enrolled (age 15.3 ± 1.9 years, 57.1% female). Median response rate across all days of app usage in the evening was 37.0% which was significantly higher than morning (21.2%) or afternoon (26.4%) (P < .001). Median daily response was significantly different by sex (53.8% in females vs 42.0% in males, P < .01), days since injury to app usage (54.1% for participants starting to use app > 7 days since injury vs 38.0% for those ≤ 7 days, P < .01), and concussion history (57.4% for participants with history of at least one prior concussion vs 42.3% for those without concussion history, P = .03). There were no significant differences in median daily response by age (47.5% for 11 — 14 years vs 50.9% for 15 — 18 years, P = .34). Differences by injury mechanism (39.6% for sports- and recreation-related injury vs 30.6% for non-sports-and-recreation-related injury, P = .04) and initial symptom burden (40.9% for PCSI scores > the median score of 47 vs 31.9% for PCSI score ≤ median score, P = .04) were evident in the evening response rates. CONCLUSIONS Evening may be the optimal time to prompt for daily concussion symptom assessment among concussed adolescents compared with morning or afternoon. Multiple demographic- and injury-related characteristics were associated with higher response rates, including females, more than one week from injury to beginning of mHealth monitoring, history of at least one previous concussion, relatively higher initial symptom burden, and injury caused by sports and recreation activities. Future studies may consider incentive strategies or adaptive digital concussion assessments to increase response rates in the population with low engagement.

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Trends in concussion mechanism of injury during the COVID-19 pandemic

Cited by 2 publications

References 20 publications

Response Rate Patterns in Adolescents With Concussion Using Mobile Health and Remote Patient Monitoring: Observational Study

Response Rate Patterns in Adolescents With Concussion Using Mobile Health and Remote Patient Monitoring: Observational Study

Response rate patterns in adolescents with concussion using mobile health and remote patient monitoring (Preprint)

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