A Digital Health Intervention for Concussion: Development and Clinical Feasibility Study

d'Offay, Christine; Ng, Xin Yi; Alexander, Laura; Grant, Alison; Grahamslaw, Julia; Pagliari, Claudia; Reed, Matthew J; Carson, Alan; Gillespie, David; Jamjoom, Aimun A B

doi:10.2196/43557

Cited by 6 publications

(4 citation statements)

References 63 publications

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“…A prior study using a similar version of this app explored different incentive strategies and demonstrated how features like incentives that are more engaging and motivating for youth can increase response rates [ 27 ]. The decreasing engagement may reflect concussion recovery, as symptom presence and severity decreased over time in the studies using mHealth technology to monitor patients with a concussion [ 17 21 28 29 ]. Regarding response patterns across the three daily time points used in this study, evening (ie, prompts were sent at 8 PM and remained open until midnight) had the highest response rate and may be the optimal time to prompt adolescents in assessing their concussion symptoms.…”

Section: Discussionmentioning

confidence: 99%

“…For example, evening and daily response rates in participants with higher symptom burden were greater; however, morning response rates showed the opposite trend. High symptom burden may affect self-reporting behaviors both positively and negatively [ 28 ]. Adolescents with a concussion and higher symptom burden may demonstrate a stronger motive to engage with medical care compared to those with a lower symptom burden, facilitating mHealth app use to report daily symptoms.…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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

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“…A system is an interconnected set of elements coherently organized in a way that achieves something [ 6 ]. The structure of a system is therefore comprised of a complex interplay of 3 types of things: elements, connections, and a function or purpose.…”

Section: Introductionmentioning

confidence: 99%

“…Often, logic models are linear in nature (eg, inputs leading to processes, leading to outputs, leading to immediate impacts, and leading to long-term outcomes), showing the intentions, assumptions, and rationale of an intervention and how it can influence a system to achieve an outcome. A dynamic model identifies and uses feedback loops and can show what things may lead to another and different factors that might lead to change, both those that can be controlled and others unrelated to the program and hence out of the organization’s control [ 6 ]. A systems approach that can incorporate feedback loops will create sustainability in a rapidly evolving, responsive program.…”

Section: Introductionmentioning

confidence: 99%

Building Culturally Centered System Dynamics Logic Models for the Brown Buttabean Motivation Organization: Protocol for a Systems Science Approach

Savila¹,

Harding²,

Bagg³

et al. 2023

JMIR Res Protoc

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Background Brown Buttabean Motivation (BBM) is an organization providing support for Pacific people and Indigenous Māori to manage their weight, mainly through community-based exercise sessions and social support. It was started by DL, a man of Samoan and Māori descent, following his personal weight loss journey from a peak weight of 210 kg to less than half that amount. DL is a charismatic leader with a high media profile who is successful in soliciting donations from corporations in money and kindness. Over time, BBM’s activities have evolved to include healthy eating, food parcel provision, and other components of healthy living. A co-design team of university researchers and BBM staff are evaluating various components of the program and organization. Objective The purpose of this study is to build culturally centered system dynamics logic models to serve as the agreed theories of change for BBM and provide a basis for its ongoing effectiveness, sustainability, and continuous quality improvements. Methods A systems science approach will clarify the purpose of BBM and identify the systemic processes needed to effectively and sustainably achieve the study’s purpose. Cognitive mapping interviews with key stakeholders will produce maps of their conceptions of BBM’s goals and related cause-and-effect processes. The themes arising from the analysis of these maps will provide the initial indicators of change to inform the questions for 2 series of group model building workshops. In these workshops, 2 groups (BBM staff and BBM members) will build qualitative systems models (casual loop diagrams), identifying feedback loops in the structures and processes of the BBM system that will enhance the program’s effectiveness, sustainability, and quality improvement. The Pacific and Māori team members will ensure that workshop content, processes, and outputs are grounded in cultural approaches appropriate for the BBM community, with several Pacific and Māori frameworks informing the methods. These include the Samoan fa’afaletui research framework, which requires different perspectives to be woven together to create new knowledge, and kaupapa Māori–aligned research approaches, which create a culturally safe space to conduct research by, with, and for Māori. The Pacific fonofale and Māori te whare tapa whā holistic frameworks for interpreting people’s dimensions of health and well-being will also inform this study. Results Systems logic models will inform BBM’s future developments as a sustainable organization and support its growth and development beyond its high dependence on DL’s charismatic leadership. Conclusions This study will adopt a novel and innovative approach to co-designing culturally centered system dynamics logic models for BBM by using systems science methods embedded within Pacific and Māori worldviews and weaving together a number of frameworks and methodologies. These will form the theories of change to enhance BBM’s effectiveness, sustainability, and continuous improvement. Trial Registration Australian New Zealand Clinical Trial Registry ACTRN 12621-00093-1875; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382320 International Registered Report Identifier (IRRID) PRR1-10.2196/44229

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Smartphone Pupillometry and Machine Learning for Detection of Acute Mild Traumatic Brain Injury: Cohort Study

Maxin,

Lim,

Kush

et al. 2024

JMIR Neurotech

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Background Quantitative pupillometry is used in mild traumatic brain injury (mTBI) with changes in pupil reactivity noted after blast injury, chronic mTBI, and sports-related concussion. Objective We evaluated the diagnostic capabilities of a smartphone-based digital pupillometer to differentiate patients with mTBI in the emergency department from controls. Methods Adult patients diagnosed with acute mTBI with normal neuroimaging were evaluated in an emergency department within 36 hours of injury (control group: healthy adults). The PupilScreen smartphone pupillometer was used to measure the pupillary light reflex (PLR), and quantitative curve morphological parameters of the PLR were compared between mTBI and healthy controls. To address the class imbalance in our sample, a synthetic minority oversampling technique was applied. All possible combinations of PLR parameters produced by the smartphone pupillometer were then applied as features to 4 binary classification machine learning algorithms: random forest, k-nearest neighbors, support vector machine, and logistic regression. A 10-fold cross-validation technique stratified by cohort was used to produce accuracy, sensitivity, specificity, area under the curve, and F1-score metrics for the classification of mTBI versus healthy participants. Results Of 12 patients with acute mTBI, 33% (4/12) were female (mean age 54.1, SD 22.2 years), and 58% (7/12) were White with a median Glasgow Coma Scale (GCS) of 15. Of the 132 healthy patients, 67% (88/132) were female, with a mean age of 36 (SD 10.2) years and 64% (84/132) were White with a median GCS of 15. Significant differences were observed in PLR recordings between healthy controls and patients with acute mTBI in the PLR parameters, that are (1) percent change (mean 34%, SD 8.3% vs mean 26%, SD 7.9%; P<.001), (2) minimum pupillary diameter (mean 34.8, SD 6.1 pixels vs mean 29.7, SD 6.1 pixels; P=.004), (3) maximum pupillary diameter (mean 53.6, SD 12.4 pixels vs mean 40.9, SD 11.9 pixels; P<.001), and (4) mean constriction velocity (mean 11.5, SD 5.0 pixels/second vs mean 6.8, SD 3.0 pixels/second; P<.001) between cohorts. After the synthetic minority oversampling technique, both cohorts had a sample size of 132 recordings. The best-performing binary classification model was a random forest model using the PLR parameters of latency, percent change, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity as features. This model produced an overall accuracy of 93.5%, sensitivity of 96.2%, specificity of 90.9%, area under the curve of 0.936, and F1-score of 93.7% for differentiating between pupillary changes in mTBI and healthy participants. The absolute values are unable to be provided for the performance percentages reported here due to the mechanism of 10-fold cross validation that was used to obtain them. Conclusions In this pilot study, quantitative smartphone pupillometry demonstrates the potential to be a useful tool in the future diagnosis of acute mTBI.

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A Digital Health Intervention for Concussion: Development and Clinical Feasibility Study

Cited by 6 publications

References 63 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

Building Culturally Centered System Dynamics Logic Models for the Brown Buttabean Motivation Organization: Protocol for a Systems Science Approach

Smartphone Pupillometry and Machine Learning for Detection of Acute Mild Traumatic Brain Injury: Cohort Study

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