Assessing Sleep Concerns in Individuals With Acquired Brain Injury: The Feasibility of a Smartpad Sleep Tool

Sleep disorders, particularly insomnia and sleep apnea, are prominent (30%−70%) among individuals with acquired brain injury (ABI), often persisting several years after injury (1). Growing evidence shows that some sleep disorders (e.g., insomnia and sleep apnea) cause significant morbidity, affect rehabilitation outcomes, decrease quality of life, and serve as risk factors for other neurologic and psychiatric disorders (1, 2).

Technological advances leading to mobile health diagnostic and therapeutic applications suggest a potentially highly effective method with which to assess sleep disturbance in post-ABI individuals. Electronic applications have been shown to be effective adjunctive tools within the field of medicine (3–5). No studies to our knowledge have investigated the use of an electronic tool to improve sleep care in the ABI clinic population.

Here, we assess the feasibility of using MySleepScript, an electronic customizable sleep behavioral software application, to improve the detection and assessment of sleep disturbances in patients with ABI. The primary aims were to determine the utility of MySleepScript in patient populations with ABI, to investigate the presence of disrupted sleep in patients with ABI, and to investigate the association between various types of ABI and sleep disturbance.

Methods

Study Participants

In this study, we utilized a cross-sectional study design comprising 68 participants. Participants were recruited at the Acquired Brain Injury Psychiatry Clinic at Johns Hopkins Bayview Medical Center. Inclusion criteria included proficiency in English, age ≥18, and history of ABI. Adults unable to consent were enrolled with the written consent of a legally authorized representative. Study visits were completed either before or at the conclusion of the clinic visit. All study staff completed basic sleep training in MySleep101 (an educational program), which covered the diagnostic criteria for sleep disorders, clinical sleep presentations, and treatment of common sleep disorders (6). Trained study staff then obtained written consent, completed the MySleepScript application with participants, and shared the results with their clinician. This study was approved by the Johns Hopkins Institutional Review Board. All included participants gave written informed consent for the collection of clinical data.

Customizable Electronic Questionnaire

MySleepScript is an electronic, customizable application (on iPhone/iPad and licensed by Hopkins) developed by a team of interdisciplinary sleep experts at Johns Hopkins University. Details of MySleepScript and the algorithms used are described elsewhere (6). In brief, the application uses a tree algorithm to trigger the addition of relevant questionnaires based on patient responses and excludes redundant questions based on negative responses. Questionnaires administered to all participants included demographic surveys, the Pittsburgh Sleep Quality Index (PSQI; with a global score >5 indicating poor sleep quality) (7), and an internally developed Johns Hopkins Sleep Environment Inventory. Based on patient responses in MySleepScript, the following additional questionnaires were generated: the Insomnia Severity Index (ISI) to assess for insomnia (no clinical significance=0–7, subthreshold insomnia=8–14, clinically significant moderate insomnia=15–21, clinically significant severe insomnia=22–28) (8), the Patient Health Questionnaire–9 (PHQ-9) to assess for depression (mild=5–9, moderate=10–14, moderately severe=15–19, severe=>20) (9), the Berlin Questionnaire to assess for sleep apnea (positive score defined by at least two categories) (10), a single-question restless legs syndrome (RLS) screen (11), and the Primary Care (posttraumatic stress disorder) PTSD Screen for DSM-5 (PC-PTSD-5; cutoff score, ≥3) (12). The program then used a preset algorithm to make the following referrals: sleep clinic, sleep behavioral clinic, urology clinic, and headache clinic. MySleepScript also generated customized patient educational materials that study participants could elect to receive electronically. Educational topics included insomnia, circadian rhythm sleep disorder, RLS, sleep apnea, and PTSD, in addition to a generic sleep environment education hand-out. These educational materials gave patients general information about the symptoms that raised concerns and behavioral interventions that could be implemented at home to potentially improve sleep.

Statistics

Descriptive statistics were performed for demographic and clinical characteristics. Continuous variables are presented as means and standard deviations, and categorical variables are presented as frequencies and percentages. Independent samples t test was used to compare mean questionnaire scores, and one-way analysis of variance was done to compare differences across several groups. Categorical variables were compared using the chi-square test and Fisher’s exact test. All tests were two-sided with a significance level of p-value <0.05. Analyses were performed using SPSS version 20 (IBM, Armonk, N.Y.) and STATA version 12 (StataCorp, College Station, Tex.); data did not violate the assumptions of the parametric tests used in analyses.

Results

Demographic Characteristics

Demographic characteristics of study participants are displayed in Table 1. The mean age of the cohort was 46.3 years (SD=14.8, range: 19–73 years); the majority of participants were Caucasian (82.4%) and male (57.4%).

Brain Injury

Characterization of brain injuries is summarized in Table 1. The mean time from the first incident of brain injury was 13.1 years (SD=12.9); 26% of the cohort had more than a single ABI. Traumatic brain injury (TBI) was the most prevalent (69.2%) type of ABI. A total of 57.5% of participants had a period of confirmed loss of consciousness.

Sleep Disturbance

A total of 48.3% of participants self-reported dissatisfaction with sleep, as indicated by a response of “dissatisfied” or “very dissatisfied” on the ISI (Table 2). On the basis of the PSQI score (score >5), 83.9% of individuals had poor sleep quality. The mean PSQI score was 9.2 (SD=4.7), and the mean ISI score was 11.8 (SD=7.8; absence of insomnia 0–7). The ISI scores indicated that 44.9% of study participants had moderate to severe clinical insomnia. PSQI and ISI scores were significantly correlated (r=0.82, p<0.001). Higher ISI scores were significantly associated with more minutes taken to fall asleep (r=0.47, p<0.001), greater frequency of difficulty falling asleep within 30 minutes (r=0.54, p<0.001), and greater frequency of waking up in the middle of the night or early in the morning (r=0.44, p<0.01). The Berlin Questionnaire for sleep apnea was positive for 36.5% of participants, and the RLS screen was positive for 39.3% of participants. On the basis of referral algorithms, 65% (N=42) of participants were referred to a sleep clinic and 34% (N=22) were referred to a behavioral sleep medicine clinic.

Feasibility of MySleepScript in ABI

The majority of participants elected to complete the questionnaires with study staff assistance (69.1%). Assistance consisted of a staff member reading the questions on the application and inputting the patient’s response on the iPad. The mean time of application completion was 16.6 minutes (SD=5.4); mean time was not significantly correlated with whether the questionnaires were completed alone (17.8 minutes [SD=7.6]), with study staff assistance (15.9 minutes [SD=4.4]), or with caregiver assistance (19.8 minutes [SD=7.0], p=0.19). Longer time to completion was significantly associated with older age (r=0.43, p<0.01). Time to completion was also significantly different between groups on the basis of education (F=3.28, df=3, 51, p=0.028, η²=0.16). Specifically, Bonferroni-adjusted pairwise comparisons between the education groups indicated that patients with a high school education/GED or less (16.9 minutes [SD=3.6]) had significantly longer times to completion than patients who completed some college (12.7 minutes [SD=4.8], p=0.048). There were no significant group differences for time taken to complete the application for income levels (F=1.24, df=3, 50, p=0.305, η²=0.07) and type of ABI (F=1.85, df=4, 54, p=0.132, η²=0.12).

Less than half (44.6%) of participants elected to receive the generated information sheets on sleep education and hygiene. Individuals electing to receive information significantly differed on the basis of education levels (χ²=8.19, df=3, p=0.04); a higher proportion of patients with a high school education/GED or less (53.1%) were less likely to elect receiving sleep materials than patients with higher levels of education (some college: 21.9%; college graduate: 18.8%; professional degree: 6.2%). There were no age (t=0.59, df= 63, p=0.56), sex (χ²=0.58, df=1, p=0.45), or income (χ²=3.20, df=3, p=0.36) differences in electing to receive sleep materials.

Association Between ABI and Sleep Disturbance

Individuals with TBI had significantly higher PSQI scores than individuals with other types of ABI (mean=10.1 [SD=4.5] versus 7.4 [SD=4.7]; t=2.08, df=57, p=0.04, respectively). There were no significant associations between ISI scores (t=0.47, df=25, p=0.64) or Berlin scores (t=48=0.60, 48, p=0.55) and type of ABI. PSQI scores differed significantly across the different levels of consciousness following brain injury (p=0.05). However, this was not significant after Bonferroni correction (Table 2). There were significant differences in ISI scores and level of consciousness following brain injury. Insomnia severity scores were significantly different for participants based on the level of consciousness following injury, with higher scores in the group with loss of consciousness ranging from 0 to 30 minutes and in the group with unknown loss of consciousness compared with individuals in the medically induced coma group (15.7 [SD=6.6] versus 12.9 [SD=7.5] versus 2.0 [SD=1.6], respectively, p=0.003). Results from the Berlin Questionnaire were not associated with level of consciousness (p=0.260). The Berlin Questionnaire was positive for 77.6% of participants with a loss of consciousness ranging from 0 to 30 minutes and for 13.3% of participants who lost consciousness for >30 minutes and negative for participants placed in a medically induced coma.

Discussion

In this study, we investigated the feasibility of using an application (MySleepScript) to assess sleep disturbance in patients with ABI. Given the common motor, cognitive, and memory impairments seen post-ABI, we expected a longer average time to completion (13). The mean time to completion among patients with ABI was 16.6 minutes (SD=5.4), with a range of 9–40 minutes. This was longer than the range of time to completion for patients seen for urologic complaints in the men’s health clinic (range: 0–5 minutes), but similar to that for other patients with neurologic disease, such as multiple sclerosis (range: 15–20 minutes) (14, 15). In addition, the time to questionnaire completion was comparable whether or not the questionnaires were completed independently or with assistance, suggesting that despite a history of brain injury, the use of MySleepScript may be a time efficient way of assessing sleep disturbance in this population. These results are consistent with previous preliminary studies in which 94% of patients at outpatient clinics reported the application was easy to use (6). Studies on the use of applications by age groups suggest that older individuals are more passive users and may be more hesitant to use new phone or tablet applications (16, 17). In our study, older age was associated with longer time to questionnaire completion. In individuals with ABI, it is difficult to make usability predictions of this application based on the age of the target population, as the age of individuals with ABI varies widely (18).

The potential benefits of this electronic questionnaire pertain to time and resources. There are limited data on the time to complete each of these questionnaires using the traditional method (pencil and paper) in patients with ABI. However, the tree algorithm used by MySleepScript likely reduces the redundancy of questions, decreasing the amount of time required to complete the questionnaires. In addition, use of an electronic application allows for immediate analysis of results with auto-generation of referrals and patient education materials. Conversely, paper questionnaires would need to be manually reviewed and would require additional time by office personnel to make the adequate referrals and distribute the appropriate patient education materials. In terms of the resources needed to implement this mobile health app, any smartpad with access to the application could be used; in the long run, costs would likely be comparable to traditional survey methods. Long-term management of an electronic application would also require basic information technology services and skills for troubleshooting. Despite these potential benefits, the target population would likely need to meet the eligibility criteria used in this study. Our study population was limited to individuals proficient in English and able to understand and respond to questions about their sleep patterns. It is unclear whether caretaker responses in lieu of patient responses would provide sufficient and accurate information for the application to accurately detect the presence of sleep disorders.

By using validated questionnaires, we also investigated the presence of sleep disturbances and association with type of ABI. The results confirmed that there is a high prevalence of sleep problems in patients with ABI. We found that 48% of participants were dissatisfied with their sleep, consistent with previously published estimates of 30%−70% of individuals following ABI (1, 19). Previous studies have also suggested that sleep disturbances may improve with time, accounting for the slightly lower estimates of sleep dissatisfaction in our study population, where a longer amount of time had elapsed from the time of injury (1). In addition, ISI scores indicated that 44.9% of participants were at risk of moderate to severe insomnia. This is congruent with previous studies that have demonstrated estimates of 29.4% and 30% of an insomnia syndrome in patients following a TBI (1, 20). Given the growing evidence that sleep disturbance can impede recovery and rehabilitation, these results indicate an alarmingly high proportion of patients with unresolved sleep problems years after initial head injury (21). In fact, studies have shown that treatment of obstructive sleep apnea by continuous positive airway pressure following a stroke can improve functional and motor outcomes and that sleep disturbance is associated with longer hospitalization (22, 23). Our findings suggest there may be a lack of formal assessment and treatment of sleep disturbance in individuals with ABI (24, 25). These results also underscore the need for efficient methods of sleep assessment by non-sleep specialists in order to identify patients with ABI who need formal sleep evaluation on initial assessment, as well as serial reassessments on follow-up.

This is the first study, to our knowledge, to investigate the use of an application to evaluate sleep in patients with ABI. Strengths of this study include the use of questionnaires validated in patients with TBI (PSQI, ISI, Berlin, PC-PTSD, PHQ-9) and the inclusion of various types and causes of ABI (26). Limitations to this study include a small sample size, the large amount of time from brain injury, lack of Glasgow coma scores, the absence of assessment for hypersomnia, and the presence of multiple brain injuries, making it difficult to associate sleep disturbance with one specific injury. Another limitation is the use of caregiver-assisted responses in cases where patients could not provide responses. Future studies are needed to help determine whether these nuances may affect the overall utility and feasibility of the tool.

Conclusions

We found that MySleepScript, an electronic customizable software program, is an effective method of identifying patients with ABI at increased risk for sleep disorders. Given the high number of patients with unaddressed sleep disturbance and the deleterious effects of poor sleep, this study underscores the existing need for clinicians to address sleep disturbance in patients with ABI. This pilot study suggests the incorporation of MySleepScript in the clinic as a feasible method for identifying appropriate patients for sleep referral. Future studies with longitudinal follow-up should assess the functional impact of referring patients for further sleep evaluation, the accuracy of identifying patients needing formal sleep assessment, and the rate of referral follow-up. Further investigation is needed to assess for specific sleep disturbances, patient satisfaction with the electronic software, and validation of caregiver responses in lieu of patient responses.