Experience of mTBI-like symptoms in a sample without brain injury in Aotearoa/New Zealand
Jason Chua
A * and Alice Theadom A
A
Abstract
Post-mild traumatic brain injury (mTBI) symptoms are not specific to mTBI and are experienced in populations without brain injury. Understanding how people without brain injury experience mTBI-like symptoms and factors influencing symptom reporting is important to determine how symptom experience differs following an mTBI.
To understand how people without a history of brain injury experience mTBI-like symptoms, we conducted a cross-sectional survey comprising sociodemographic characteristics, the Brain Injury Screening Tool symptom scale, general health rating, Illness Attitude Scale, Positive and Negative Affect Scale and Perceived Stress Scale. The mean total symptom score and proportion of people experiencing moderate or severe symptoms (≥4) were reported. Associations between sociodemographic variables, stress, negative affect, illness attitudes, health status and symptoms were examined using regression models.
One-hundred and seventy-three people completed the survey with a mean age of 40 years (s.d. = 15.8; n = 82, 47.4% male). The mean total symptom score was 34.5( ± 26.6). Commonly experienced symptoms were tiredness (n = 73, 42.2%), poor sleep (n = 64, 37.0%) and headaches (n = 56, 32.4%). Regression analysis revealed that on average higher levels of worry about illness and negative affect were associated with higher symptoms (β = 0.5, P = 0.027 and β = 0.9, P = 0.020 respectively) but there were no significant associations with other variables.
Cognitive and vestibular-ocular symptoms occur much less frequently than physical symptoms in the general population and may be more specific to mTBI. However, there is a need to consider vestibular-ocular symptoms alongside illness attitudes due to greater concerns about these symptoms by patients.
Keywords: illness attitudes, injury, mood, mild traumatic brain injury, negative affect, post-concussion symptoms, stress, symptom experience.
Introduction
Mild traumatic brain injury (mTBI) is the most common form of TBI (Dewan et al. 2018) and can have a significant impact on a person’s quality of life (Theadom et al. 2016). The burden extends to society through increased lifetime medical costs and productivity losses (Maas et al. 2017; Theadom et al. 2017, 2018). Symptoms commonly reported following mTBI include headaches, fatigue, nausea and feelings of frustration, which may last days, weeks, months or years (Theadom et al. 2018). The need for symptom evaluation features consistently in mTBI guideline recommendations as part of a multimodal assessment and for monitoring recovery (Silverberg et al. 2020). A number of tools have been developed to evaluate post-injury symptoms (Alla et al. 2009), such as the Rivermead Post-Concussion Symptoms Questionnaire (RPQ) (King et al. 1995), Sport Concussion Assessment Tool 5th Edition (SCAT-5) (Echemendia et al. 2017) and Brain Injury Screening Tool (BIST) (Theadom et al. 2021; Shaikh et al. 2022).
However, post-mTBI symptoms have poor diagnostic utility because of their low specificity (Mulhern and McMillan 2006; Silverberg et al. 2020). A number of studies have shown that mTBI-like symptoms are commonly experienced in people without a history of brain injury (Iverson and Lange 2003; Wang et al. 2006; Garden et al. 2010; Zakzanis and Yeung 2011; Suzanne et al. 2018; Voormolen et al. 2019). For example, in the New Zealand (NZ) population, back pain, fatigue and headaches are commonly experienced (Petrie et al. 2014). Indeed, Iverson and Lange (2003) report that in a community sample without a history of brain injury, up to 15.5% of participants experienced moderate–severe mTBI-like symptoms.
A number of factors have been found to influence symptom reporting, including psychological traits (e.g. coping, illness perceptions (Hou et al. 2012) and low mood (Suhr and Gunstad 2002; Iverson and Lange 2003; Garden et al. 2010). It is thought that dysregulation of the body that involves stress, inflammation and emotional attention may also give rise to increased symptom experience in the absence of injury (Viktoriya 2019). Consequently, because symptoms can be experienced as a normal part of life and due to comorbid health conditions, it is important for practitioners to understand patterns of mTBI-like symptoms that may or may not be associated with mTBI pathology. Indeed, people who have an mTBI may sometimes misattribute symptoms to an mTBI that may be due to other causes (Mittenberg et al. 1992).
Evidence suggests that the female sex is associated with worse symptom experience following mTBI; however, the evidence is drawn predominantly from sports-related injury studies (Koerte et al. 2020). In general population samples, the relationship between sex and symptom experience is unclear, with some studies reporting a relationship between sex and symptom experience (Kjeldsberg et al. 2013; Bardel et al. 2019) and others not (Chan 2001; Suhr and Gunstad 2002; Garden et al. 2010).
Premorbid mental health problems (e.g. depression, anxiety) are a risk factor for prolonged recovery following mTBI (Iverson et al. 2020) and are also associated with greater symptom experience in the general population (Suhr and Gunstad 2002; Iverson and Lange 2003; Zakzanis and Yeung 2011; Voormolen et al. 2019; Shaikh et al. 2022). There is also preliminary evidence that higher levels of stress may be linked to higher levels of physical symptom reporting, but this study was not specific to mTBI-like symptoms (Goldman et al. 1996). Perceptions of increased injury severity and emotional impact have also been found to be highly correlated with higher symptoms on the RPQ (Snell et al. 2011). Consequently, there is a need to understand how broader illness attitudes, such as engaging in healthy lifestyle behaviours (e.g. smoking and eating) and fear of developing a serious illness, influence symptom reporting in the general population.
An mTBI-like symptom experience may also be influenced by social determinants of health. For example, age can influence symptom type (e.g. somatic vs psychological) and severity of symptoms experienced over time (Kjeldsberg et al. 2013; Petrie et al. 2014; Bardel et al. 2019; Voormolen et al. 2019). Health disparities, such as income, education and vulnerability, can also influence health state (Wagstaff 2002; Johnson and Diaz 2023). Cultural interpretations of different symptoms may also influence reporting (Goodyear-Smith and Ashton 2019).
In 2018, the BIST (Theadom et al. 2021) was developed to support healthcare decision-making following a symptom assessment. Strengths of the BIST include a user-agnostic design (it can be used by any health professional), culturally responsive for the NZ Indigenous populations and use of simple language. The BIST has demonstrated sound internal consistency, factor structure, concurrent validity and test-retest reliability (Theadom et al. 2021; Shaikh et al. 2022). Following clinical testing, the BIST was revised in 2022 (BIST 2.0). There are currently no normative data available for the revised tool to enable comparison. Additionally, factors unrelated to mTBI that could affect symptom reporting on this measure, such as stress, and illness attitudes in addition to known symptom-related factors such as low mood and sex, need to be explored.
The aims of the current study are to (1) report the experience of mTBI-like symptoms using BIST 2.0 and (2) determine if factors such as negative affect, stress, sex, age and illness attitudes influence symptom reporting in adults without a history of brain injury. We hypothesised that higher levels of negative affect, stress, poorer health and negative illness attitudes, and older age would be associated with increased symptom reporting. Additionally, we hypothesised that symptom reporting would be significantly higher in females than males.
Methods
Design
A cross-sectional online survey of adults without a history of brain injury was designed. Institutional ethical approval was sought from the Auckland University of Technology Ethics Committee (Ref: 22/148). We reported the current study following guidance from the Strengthening the Reporting of Observational Studies in Epidemiology Statement for cross-sectional studies (Supplementary File S1).
Sampling and recruitment
A convenience sample of participants was recruited through dynata™ (https://www.dynata.com/) between 3 and 9 August 2022. An online recruitment strategy was used, consisting of a blend of proprietary survey panels and other recruitment approaches, such as ‘intercepts’, whereby traffic is redirected from a product or service to the study survey to minimise response bias. This approach enabled purposive sampling based on age, ethnicity and gender to reflect the age, sex and ethnicity profiles of people with mTBI in NZ (Feigin et al. 2013).
People who responded to survey invitations sent by dynata were redirected to an online survey created using the Qualtrics (Provo, UT, USA) survey platform. The survey took approximately 15 min to complete. Upon accessing the survey, respondents were presented with an information sheet about the study. Initial questions checked the eligibility of participants. Respondents were included in the study if they were aged ≥18 years, read and understood English, and were currently living in NZ. Individuals were excluded if they had ever experienced a TBI of any severity or had an unstable, severe or life-threatening condition (e.g. pancreatic cancer). mTBI was defined to participants as being an impact to the head or body causing loss of consciousness (being knocked out), feeling dazed or confused afterwards, not remembering what happened or seeing stars, including injuries such as concussion. To obtain a history of TBI, participants were then asked ‘Have you had a concussion or mild traumatic brain injury in the past 5 years?’ Individuals who did not consent or meet the inclusion criteria were branched out of the survey and thanked for their interest. Individuals who met the inclusion criteria and completed the survey received reward points by dynata. Informed consent was assumed if they chose to complete the survey questions, and as a result, the data were anonymous. Recruitment ceased after response quotas were achieved.
Survey instruments
Participants were asked to complete general demographic questions, including their age, sex, highest level of education and employment status. Participants were also asked to rate their general health from 0 (worst imaginable health state) to 100 (best imaginable health state).
Symptom experience
mTBI-related symptoms were assessed using the BIST 2.0 symptom scale (Theadom et al. 2021). The symptom scale involves rating the severity of 16 symptoms, such as ‘headache (my head hurts)’ and ‘my neck hurts’ on a scale of 0 (not at all) to 10 (severe). The 16 symptom ratings are then summed to produce a total score (range 0–160). The symptom scale has excellent internal consistency for the total symptom score (α = 0.94) and its three subscales (physical (α = 0.90), cognitive (α = 0.92) and vestibular-ocular (α = 0.80)). Test-retest reliability is moderate to good with intraclass correlation coefficients ranging between 0.51 and 0.83 (Shaikh et al. 2022). High concurrent validity has been established against the RPQ (r = 0.91) and SCAT-5 (r = 0.90) symptom scales (Theadom et al. 2021). A cut-off score of ≥66 has been proposed for clinical use to indicate a level of symptoms indicative of the need for treatment following a mTBI.
Mood
The Positive and Negative Affect Scale – Short Form (PANAS-SF (Watson et al. 1988)) comprises 20 items describing positive and negative feelings (e.g. ‘interested’, ‘enthusiastic’, ‘distressed’ and ‘upset’). Each item is rated on a scale of 1 (very slightly) to 5 (extreme) over the past week. The scale is based on a two-dimensional model of mood, which aligns with its two subscales (each with 10 items that are summed): (1) the positive affect scale (range 10–50), with higher scores representing higher positive affect and (2) the negative affect scale (range 10–50), with lower scores representing lower negative affect. The PANAS-SF has good test-retest reliability (Watson et al. 1988) and excellent internal reliability for the positive (α = 0.89) and negative affect scales (α = 0.85). Discriminant and convergent validity of the scales have been established with measures of depression and anxiety (Watson et al. 1988; Crawford and Henry 2004).
Stress
The Perceived Stress Scale (PSS-10 (Cohen et al. 1983; Cohen 1988)) consists of 10 items scored between 0 (never) and 4 (very often) and is used to measure psychological stress in general. For example, ‘In the last month, how often have you been upset because of something that happened unexpectedly?’. The PSS-10 has been used in both healthy and clinical populations to measure how different situations affect people’s perceived levels of stress in the past month. A total score is calculated by summing the individual item ratings, with higher scores representing higher levels of perceived stress (range 0–40). The original 14-item version (Cohen et al. 1983), later refined into a 10-item version used in the current study, had test-retest reliability coefficients of 0.85 after 2 days and 0.55 after 6 weeks in a college sample. The PSS possesses excellent internal consistency (α = 0.89), and its construct validity has been established against various other tools (Roberti et al. 2006; Lee 2012).
Illness attitudes
The Illness Attitude Scale (IAS (Kellner 1987)) was designed to assess people’s fears, attitudes and beliefs associated with hypochondriacal concerns and abnormal illness behaviour. It is considered a gold standard for dimensional evaluation of hypochondriacal symptoms (Sirri et al. 2008). The measure consists of 27 items, such as ‘Do you worry about your health?’ and ‘Are you worried that you may get a serious illness in the future?’, which are scored between 0 (no) and 4 (most of the time). A total score is calculated by summing the individual item score, with higher scores representing more worry about their health (range 0–108). The original IAS comprises nine subscales representing different areas of worry (e.g. worry about illness, concerns about pain and disease phobia); however, fewer factor solutions (e.g. 2–5) have been found to produce more favourable internal consistency (Ferguson and Daniel 1995; Sirri et al. 2008). The use of the IAS is supported by evidence for its test-retest reliability and internal, concurrent and discriminative validity (Sirri et al. 2008; Hedman et al. 2015).
Data analysis
Outliers were identified and removed from analysis using the outlier labelling rule defined by Hoaglin and Iglewicz (1987). Additionally, the time taken to complete the questionnaire was recorded, with responses of <2 min duration deemed to be ‘unreliable’ and removed before analysis. Descriptive statistics were used to summarise the sample characteristics and BIST 2.0 symptom scores by their mean and median (minimum, maximum) values. We also calculated the proportion of the sample reporting moderate symptoms defined as ≥4 on each of the 16 BIST symptoms.
Multivariable linear regression was used to identify associations between BIST 2.0 total symptom score (outcome variable) and the independent variables age, sex, ethnicity, education status, employment status, general health rating (GHR), illness attitudes, affect and stress. Three additional regressions were performed with each symptom cluster as the outcome variable to explore which symptom clusters were driving associations between significant associations between independent and total symptom scores. Associations with P values ≤0.05 were considered of interest. A Bonferroni correction was used to adjust the alpha for additional regressions with the symptom clusters as the outcome variable. We tested the following assumptions of our regression model: (1) normality of the dependent variable by visually inspecting a Q–Q plot, (2) homoskedasticity using the Breusch–Pagan test (P < 0.05 was considered problematic), (3) multicollinearity using the variance inflation factor (VIF; VIFs >5 were considered problematic (Meuleman et al. 2014). Inspection of these items revealed heteroscedasticity, which was addressed by calculating robust standard errors for all hypothesis tests. All analyses were conducted using IBM SPSS Statistics (v.29).
Results
Of the 290 people who clicked on the survey link, 117 (40.3%) were excluded due to not meeting the inclusion criteria (e.g. history of TBI) or not completing the questionnaire (2.8%) as outlined in Fig. 1. Of the 10 that started the survey but did not finish, five stopped after answering the inclusion/exclusion criteria questions, three stopped after answering the demographic questions, one stopped part-way through the IAS and one stopped after completing the BIST 2.0. One outlier was removed, as their total symptom score exceeded the upper limit calculated as the 99.6th percentile of the sample. Data were available for 173 participants with a mean age of 40 years (s.d. = 15.8; median (IQR) = 34(22)).
The sample characteristics are summarised in Table 1. Participants ranged in age between 18 and 84 years. Just over half of the participants identified as European, and just over a third identified as Māori and/or Pasifika (NZ’s indigenous populations). Living with a physical or mental health condition was reported by 72 (41.6%) of participants. Forty-eight out of 72 (66.7%) participants specified their health condition; the most common conditions were high blood pressure (n = 22), diabetes (n = 12), anxiety and/or depression (n = 7), and musculoskeletal disorders (e.g. arthritis; n = 11).
| Demographic characteristic | N (%) | |
|---|---|---|
| Sex, male | 82 (47.4) | |
| Experiencing a physical or mental health condition, yes | 72 (41.6) | |
| Ethnicity | ||
| NZ European | 101 (58.4) | |
| Not NZ European | 72 (41.6) | |
| Māori | 48 (27.7) | |
| Pacific peoples | 20 (11.6) | |
| Other | 4 (2.3) | |
| Education | ||
| College, professional education or higher | 113 (65.3) | |
| Secondary education or lower | 60 (34.7) | |
| Current employment | ||
| Employed | 136 (78.6) | |
| Unemployed | 37 (21.4) | |
| Age (mean years ± s.d.) | 40.1 (±15.8) | |
| GHR (0–100)A | 72.5 (±17.2) | |
| IAS, mean ± s.d. (0–108)B,J | 34.5 (±14.9) | |
| PANAS-SF positive affect (10–50)C,K | 28.1 (±7.6) | |
| PANAS-SF negative affect (10–50)D,K | 18.4 (±7.4) | |
| PSS total score (0–40)E,K | 17.5 (±6.8) | |
| BIST physical score (0–40)F | 9.5 (±7.7) | |
| BIST vestibular-ocular score (0–40)G | 6.5 (±6.5) | |
| BIST cognitive score (0–40)H | 8.0 (±8.1) | |
| BIST total symptom score (0–160)I | 34.5 (±26.6) | |
Participants’ responses to the GHR, IAS, PANAS-SF, PSS and BIST symptom scales were skewed towards more positive health/attitudes as shown in Table 1. Table 2 shows the mean total symptom score and the proportion of participants experiencing each symptom at the moderate level of above (≥4), which ranged from 11.0 (nausea) to 42.2% (tiredness).
| Symptom | N (%) | |
|---|---|---|
| Total symptom score mean ± s.d., median (minimum, maximum) | 34.5 ± 26.6, 30 (0, 130) | |
| Physical symptom cluster | ||
| Headache (my head hurts) | 56 (32.4) | |
| My neck hurts | 46 (26.6) | |
| I don’t like bright lights | 41 (23.7) | |
| I don’t like loud noises | 47 (27.2) | |
| Vestibular-ocular symptom cluster | ||
| I feel dizzy or like I could be sick | 29 (16.8) | |
| If I close my eyes, I feel like I am at sea | 19 (11.0) | |
| I have trouble with my eyesight (vision) | 55 (31.8) | |
| I feel clumsy (bumping into things or dropping things more than usual) | 32 (18.5) | |
| Cognitive symptom cluster | ||
| It takes me longer to think | 35 (20.2) | |
| I forget things | 43 (24.9) | |
| I get confused easily | 28 (16.2) | |
| I have trouble concentrating | 40 (23.1) | |
| Other | ||
| I get angry or irritated easily | 51 (29.5) | |
| I just don’t feel right | 37 (21.4) | |
| I feel tired during the day | 73 (42.2) | |
| I need to sleep a lot more or find it hard to sleep at night | 64 (37.0) | |
Results of the regression analysis
Results of the regression model (Table 3) show that illness attitudes and negative affect were significantly associated with BIST total symptom score but that sociodemographic variables, such as sex and age, were not. Specifically, a 1-point increase in illness attitudes (more worry) was associated with, on average, 0.5 more symptoms (P < 0.001), and a 1-point increase in negative affect was associated with, on average, 0.9 more symptoms (P = 0.004). Regressing each symptom cluster as the outcome variable revealed that a 1-point increase in illness attitudes was associated with, on average, 0.2 more symptoms (P < 0.001) in the vestibular-ocular symptom cluster. No other symptom clusters remained of interest (Supplementary File S1). This suggests that the vestibular-ocular symptom cluster may be driving the association between total symptom score and illness attitudes.
| Predictor | B | Robust s.e. | P | 95% Confidence Interval | ||
|---|---|---|---|---|---|---|
| LL | UL | |||||
| (Constant) | 18.516 | 17.116 | 0.281 | −15.289 | 52.320 | |
| Age | −0.086 | 0.130 | 0.510 | −0.344 | 0.172 | |
| Male | −4.022 | 3.662 | 0.274 | −11.253 | 3.210 | |
| European | −2.457 | 3.886 | 0.528 | −10.133 | 5.219 | |
| Physical or MH condition | 2.735 | 4.518 | 0.546 | −6.187 | 11.658 | |
| Employed | 0.292 | 5.213 | 0.955 | −10.004 | 10.588 | |
| College or higher education | 3.256 | 3.364 | 0.335 | −3.389 | 9.901 | |
| GHR | −0.257 | 0.144 | 0.076 | −0.540 | 0.027 | |
| IAS total score | 0.492 | 0.220 | 0.027A | 0.058 | 0.926 | |
| PANAS-SF positive affect score | −0.239 | 0.281 | 0.396 | −0.793 | 0.315 | |
| PANAS-SF negative affect score | 0.854 | 0.362 | 0.020A | 0.139 | 1.570 | |
| PSS total score | 0.496 | 0.371 | 0.183 | −0.237 | 1.229 | |
Notes: total N = 173. R2 = 0.436 (R2 adjusted = 0.397), F(170) = 11.170, P < 0.001. B = unstandardised beta. s.e., standard error. LL and UL indicate the lower and upper limits of the 95% confidence interval, respectively. GHR, general health rating; MH, mental health; IAS, Illness Attitude Scale; PANAS-SF, Positive and Negative Affect Scale – Short Form; PSS, Perceived Stress Scale.
Discussion
This study aimed to determine the experience of mTBI-related symptoms on the BIST among a sample of adults without a history of brain injury and the factors influencing symptom reporting in this population. At least 1 in 10 participants reported experiencing each of the symptoms at a moderate level or above. The study also revealed that negative mood and illness attitudes have a significant effect on symptom reporting using the BIST but that stress, general health, age and gender did not. These findings are consistent with prior research, suggesting that post-mTBI symptoms are not specific to mTBI but are experienced at a less severe level.
Our results align with previous studies reporting the experience of post-mTBI-like symptoms in the general population (Iverson and Lange 2003; Cassidy et al. 2014; Petrie et al. 2014; Zeldovich et al. 2022). For example, the top three reported moderate symptoms in Table 2 align with a survey of the NZ general population, which found that fatigue and headaches were two of the most commonly experienced symptoms (Petrie et al. 2014). Similarly, Garden and Sullivan (2010) reported that headaches (28.1%), poor sleep (27.1%) and fatigue (24%) were the most commonly experienced moderate–severe symptoms in a sample of Australian adults with no history of brain injury or neurological disorders. Interestingly, a study of the general population in Europe (Zeldovich et al. 2022) reported a higher prevalence of moderately experienced symptoms on the RPQ (defined as ≥2/5 severity score) compared to the current study: fatigue (49.9% vs 42.2%), sleep (42.4% vs 37.0%), irritability (39.4% vs 29.5%) and headaches (38.6% vs 32.4%). The higher symptom burden reported by Zeldovich et al. (2022) may be due to our study excluding individuals with a history of brain injury, differences between symptoms included in the BIST 2.0 and RPQ measurement tools, or cross-cultural differences, which may be important in generating population-specific data. For example, Zakzanis and Yeung (2011) suggested that linguistic and cultural background may moderate individual symptom endorsement.
The current study also adds to the evidence base by revealing that there was no link between sex and age and symptom reporting. Previous findings have been heterogenous, with some studies reporting no relationship between sex (Chan 2001; Suhr and Gunstad 2002; Garden et al. 2010) or age (Garden et al. 2010) and others revealing that female sex and older age are linked to increased symptom reporting (Kjeldsberg et al. 2013; Bardel et al. 2019). Reasons for this finding may be because the symptom items included in the BIST 2.0 are less influenced by sex and age than other tools. It may also be that mTBI symptoms following mTBI persist beyond the 5-year exclusion TBI history criterion adopted in this study. Alternatively, the differences between the findings may reflect the complex interplay between symptom reporting and population characteristics.
It was hypothesised that general health, higher stress, negative affect and negative illness attitudes would be linked to higher levels of symptom reporting. However, general health and stress were not found to impact symptom reporting in this sample. These results on mood generally align with an earlier study using the original version of the BIST, which was found in a bivariate analysis to be linked with depression and anxiety (Shaikh et al. 2022). Differences in findings between the two studies on stress are likely due to the use of different measures of stress. The finding that illness attitudes influence symptom reporting in this sample highlights the need to consider other non-TBI-related factors (Suzanne et al. 2018), such as illness attitudes and negative affect, alongside symptoms. Further research is needed to see if the links between these non-injury-related factors are also evident in an mTBI population. If similar trends are identified, this would indicate the need to consider clinical symptom reporting in the context of these other variables. After performing regressions on each symptom cluster, only the vestibular-ocular symptom cluster and illness attitudes were significantly related. This suggests that the vestibular-ocular symptom cluster and illness attitudes may be driving the relationship between total symptom score and IAS. We postulate that this may be because the nature of the symptoms within this cluster (e.g. ‘I feel dizzy’ or like ‘I could be sick’, ‘I have trouble with my eyesight’) are weighted as more problematic by participants with higher levels of worry. It would be prudent to conduct a replication study to verify these findings.
Although the recruitment strategy included multiple approaches to minimise potential sampling bias (e.g. online panels and ‘intercepts’), the symptom scale was only completed online. This assessment modality, alongside the use of a recruitment service, may have introduced bias into the sample, such as bias towards the inclusion of people with higher levels of education. Although the use of a recruitment service facilitated recruitment to ensure the ethnic profile of this non-injured sample was similar to the ethnic profile of people sustaining mTBI (as identified in a national incidence study (Feigin et al. 2013)), the sample was older in age (mean age 40.1 vs 27.5 years) and underrepresented males (63% vs 47%). Other factors associated with prolonged mTBI, such as psychiatric illness and learning difficulties, were also not collected in the current study, which limits our ability to compare our findings against these risk factors for prolonged recovery (Mayer et al. 2017). Our findings may also be influenced by non-response bias; we do not know if the characteristics of the individuals who participated in the study differ from those who did not participate in the survey. The sample size was also modest, which may have undermined our ability to detect between-group differences. Respondents may have also misreported if they had a history of brain injury (e.g. due to recall bias), which means that we cannot rule out that our sample does not include individuals with a history of TBI. These factors may have led to an over- or under-estimation of the point estimates reported. A large proportion of people also reported a current health condition (41.6%) in our sample that is higher than the estimated multimorbidity (27.9% (Stanley et al. 2018)), prevalence of mental distress (depression (diagnosed), mood disorder 19.5%) or chronic physical conditions in NZ (e.g. the prevalence of chronic pain is 22.6% (Ministry of Health 2022).
Overall, the findings of this study highlight the importance of an assessment of symptoms to be conducted within the context of personal medical history, physiological tests and exploration of evidence of other factors that may be contributing to symptom experience (e.g. cervical neck pain). The data from this study assist in determining the clinical significance of symptom burden by providing normative reference values on the BIST 2.0 for a non-brain-injured population. This is particularly beneficial in the general population context where it is not possible to conduct baseline symptom assessments – unlike the context of professional sport participation.
Conclusion
mTBI-related symptoms are commonly experienced among adults without a history of brain injury. The reporting of post-mTBI symptoms among adults measured using the BIST symptom scale was influenced by worry about illness and negative affect. These psychosocial factors may be important to consider when exploring the symptom experience of the mTBI population.
Data availability
The data that support this study are available in the article and accompanying online supplementary material.
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