Development of a phone application for assessing fatigue levels in rare disorders: a feasibility and validity study

Barth syndrome (BTHS) is a rare genetic disorder characterized by skeletal myopathy, cardiomyopathy, and exercise intolerance due to early fatigue. The purpose of this study was to test the feasibility and validity of a new phone application designed to capture multi-dimensional aspects of fatigue across the lifespan. The specific study aims were to (1) assess the feasibility of using the app to record perceived fatigue levels in real-time, (2) evaluate discriminant validity by assessing if the app can differentiate between those with and without BTHS, and (3) content validity by assessing the relationship between perceived energy levels and actual energy expenditure. Eighteen participants with BTHS and 18 age-matched control participants completed the study. The participants wore an activity tracker for 14 days and were prompted to respond to an Android app to report their fatigue levels 6 × /day. Statistical analysis was completed to examine perceived fatigue and the relationship between reported fatigue and actual energy expenditure. Feasibility was supported by the majority of participants responding to at least 50% of the application prompts and scores indicative of good internal consistency between responses (92–95%) and reliability of the battery scale (p < .001). Discriminant validity of the app was only partially supported, with the number of “crashes” being significantly different between those with and without BTHS (p = 0.042). Other measures of perceived fatigue were not found to be significantly different between groups, even though individuals with BTHS showed significantly lower energy expenditure than control participants during the day as measured by actigraphy (p < 0.001). Content validity of the app was supported, with perceived energy levels significantly correlating with actual energy expenditure collected with the activity tracker (p < 0.001). In summary, the phone app developed by our team allowed researchers to capture the lived experience of individuals with BTHS while also capturing objective data. We verified that the app was able to consistently and accurately capture participant-reported fatigue. The battery scale tested as part of our feasibility aim was successful in capturing perceived levels of energy and can be used as a valid measure of fatigue in future studies. It was interesting to note that “crashes” appear to be the main differentiating factor in fatigue between the BTHS and control participants, where other measures of perceived fatigue were not found to be significantly different. These results highlight the complex nature of measuring fatigue as a subjective construct. This study provides foundational information on methods for quantifying fatigue in adults, adolescents, and children with BTHS and can provide possible targets for future therapeutic trials.


Introduction
Barth syndrome (BTHS) is a rare genetic disorder affecting 200-250 living males worldwide [5,26].The X-linked condition is caused by mutation or deletion of TAZ; a gene which is the main determinant of the composition of the mitochondrial phospholipid, cardiolipin affecting predominantly males [5].With the TAZ gene mutated, the cardiolipin cannot be altered correctly and thus cannot perform its necessary functions, which impairs normal mitochondrial function to meet energy demands (Genetics Home Reference).Cardinal characteristics of BTHS include cardiomyopathy, neutropenia, skeletal myopathy, growth delay, and exercise intolerance due to early fatigue [5].Fatigue is a common characteristic in many mitochondrial disorders that have either a neuromuscular and/or a cardiovascular involvement [7,17], and is rated as the most burdensome symptom amongst individuals with mitochondrial disorders by patients and their parents [13].Individuals with BTHS are reported to have higher than typical rates of perceived fatigue, mental fatigue, emotional fatigue, sleep problems, and psychosocial concerns like anxiety and depression; all symptoms that have been linked to fatigue in other mitochondrial disorders [1,9,10,15,18,21,25].
Despite its ubiquitous presentation within the condition, limited research has been done to examine fatigue in persons with BTHS.Of the studies that have been done, the focus has primarily been on the physiological aspect of fatigue such as exercise tolerance or endurance [2,4,24].Collectively, this line of research has established that altered cardiac and skeletal muscle bioenergetics and impairments, secondary to mitochondrial defects, are directly correlated with decreased exercise capacity in adolescents and young adults with BTHS.While exercise intolerance is an essential feature of fatigue that needs to be understood in BTHS, fatigue is known to be a much broader construct that extends well beyond the exertion required for a 6-min walk test or multiple repetitions of sit-to-stand patterns.
Research in other medical conditions has explored fatigue as a multidimensional concept.Studies in primary mitochondrial disease have examined perceived fatigue and performance fatigability, which is also known as physiological fatigue [7,14,16,20].Perceived fatigue has been reported as a core symptom that patients with mitochondrial disorders would like to improve when participating in clinical trials [29].A systemic review of the literature also documented an increased rate of performance fatigability in individuals with mitochondrial disease [11].Research in pediatric cancer patients with chronic fatigue has further expanded on the concepts of fatigue to describe perceived fatigue, in areas of mental, physical, and emotional fatigue [27], which provides a more comprehensive framework for documenting perceived fatigue.
Exactly what "Barth Tired" really means remains elusive; this is, in part, due to the fact that perceived fatigue in BTHS has proved difficult to quantify.Given the prevalence of fatigue in the BTHS population, it is important to both understand and quantify perceived fatigue in a holistic way, in real-time, so that it can be evaluated as an outcome measure in future clinical trials.This study is the third part of a larger study that aims to answer the larger question of "What is Barth tired?"The first two parts of the study [1,21] used a qualitative approach to examine the perspective of individuals with BTHS and from close family members of individuals with BTHS.In both of these studies, individuals with BTHS and their families reported impacts of emotional and mental fatigue in addition to physical fatigue.The purpose of this phase of the project was to develop a method to quantify perceived fatigue across multiple dimensions (i.e., physical fatigue, mental fatigue, emotional fatigue).This study is focused on measuring perceived fatigue.We define perceived fatigue as the subjective sensation of tiredness and exhaustion, also sometimes known as experienced fatigue [3,12].In particular, we were interested in capturing (1) the subjective qualification of whether someone is tired (perceived fatigue), (2) quantification of the subjective level of energy someone feels they have (perceived energy level) [23], and (3) whether the individual is experiencing the different dimensions of perceived fatigue (physical, mental, emotional) [27].Furthermore, we were interested in examining the relationship between perceived fatigue and performance fatigability; actual energy expenditure throughout the day was used as a proxy for capturing performance fatigability [12].For this study, we defined perceived physical fatigue as the feeling of weakness or tiredness in the body, feeling drained or sleepy.Perceived mental fatigue refers to difficulty with attention, memory, and concentration.Finally, perceived emotional fatigue was defined as the feeling of loss of interest or pleasure in usual activities, having no spirits.Furthermore, we are also interested in examining the relationship between perceived fatigue and performance fatigability.
Traditionally, perceived fatigue is measured using patient-report questionnaires [8], but survey tools are limited to capturing a snapshot of the patient's perceived fatigue at a single point in time or as an aggregate for the period shortly before the survey is completed.The broad availability of smartphones has allowed phone apps to be used as research tools to collect real-time data from participants.Phone apps have been used to track fatigue in real-time in individuals with cancer and multiple sclerosis [19,23].Other phone apps have also been developed to test novel methods of detecting fatigue, such as using fine motor assessments to detect physical and mental fatigue [28].There is potential for developing other objective fatigue assessment tools using phone apps.Current phone apps that record and track fatigue over time have focused primarily on perceived physical fatigue levels.In this study, we expand the use of phone apps to record and track perceived physical fatigue, mental fatigue, and emotional fatigue over time.

Study design and objectives
This study used a non-experimental, cross-sectional, study design to objectively measure perceived fatigue and energy expenditure patterns across a two-week data collection period in individuals with BTHS and a matched control group.This project was part of a larger research study comprehensively examining the meaning of "Barth Tired" by affected individuals and families.The aims of this study were to (1) assess the feasibility of using an app to record perceived fatigue levels in real-time, (2) evaluate discriminant validity by assessing if the app can differentiate between those with and without BTHS, and (3) evaluate content validity by assessing the relationship between perceived energy levels and energy expenditure.The relationship between perceived energy levels and energy expenditure is of interest, as individuals likely expend their energy differently based on their perceived energy level.The following research questions (RQ) and hypothesis were established for this study: • RQ5 (Aim 3): Do perceived (self-reported) energy levels recorded on the phone app correlate to actual energy expenditure collected via an accelerometer?o H6: There will be a strong and significant correlation between perceived energy level and energy expenditure in the total sample.

App development
An Android phone app was created for this project so that participants could record their fatigue responses in real time throughout the day.Prompts to complete the reports were sent to participants at the beginning of each of the following time periods: early morning (EM) from 6 am to 8:59 am, late morning (LM) from 9 am to 11:59 am, early afternoon (EA) from 12 pm to 2:59 pm, late afternoon (LA) from 3 pm to 5:59 pm, early evening (EEVE) from 6 pm to 8:59 pm, and late evening (LEVE) from 9 pm to 11:59 pm.Each participant was given an app ID number so their answers could be collected and identified at the end of the 14 days of the study.There were four questions (Fig. 1) each participant answered as accurately as possible within these periods each day: (1) rate your energy level from 0 to 100% (on a visual battery scale); (2) How do you currently feel; (3) what feels tired; and (4) how long have you been tired.App users were asked to use the sliding bar to indicate how much energy they have on a scale of 0 to 100 (question 1: battery scale).On questions 2 to 4, users were asked to answer questions about how they feel, what feels tired, and how long have they been tired.The answers were transferred and stored on a secure database accessible only by the research team.The questions on the app were designed to capture the multi-dimensions of perceived fatigue: (1) the subjective qualification of whether one is tired or not (perceived fatigue, question 2), (2) a quantification of how much perceived energy one still has (perceived energy, question 1), and (3) type of perceived fatigue the individual is experiencing (question 3).Since the participants were only required to respond to the app during certain periods throughout the day, we also asked them to report how long since they felt fatigued in order to better estimate the timing of the onset of perceived fatigue (Question 4).The language and images used on the app were tailored to increase understanding in children, e.g."tired" is used instead of "fatigue", and "mind feels tired" was used instead of "mental fatigue".The timing of the fatigue report (question 4) was also designed to be easy for children to understand instead of asking the participants to report a specific time that they felt tired due to limited understanding of time concepts in children.In question 2, we further delineated between "tired" and "crashed", where we defined as needing to stop all activity due to overwhelming fatigue.This description of fatigue being related to needing to stop all activity was frequently mentioned in the interviews of individuals with Barth syndrome and their family members [1,21].Our measurement of perceived energy (question 1) also served as the comparison between participants' perceived fatigue (perceived energy) vs performance fatigability (actual energy expenditure levels).

BTHS study participants and recruitment
A convenience sample of 20 males and 1 female diagnosed with BTHS aged 5-56 years were enrolled in the study; however, data was unable to be collected with three of the participants due to international shipping challenges (lost package, package held in customs).Table 1 Fig. 1 Images showing the questions on the phone application screen Initial study recruitment occurred in June 2021 during a round table talk hosted by the Barth Syndrome Foundation (BSF).A presentation was given to provide families and individuals with an opportunity to learn about the study.Part of this presentation included a question/ answer portion between the director of research at BSF, the interested families, and the research team.Recruitment flyers were also posted to the BSF website where interested participants could follow a link to fill out an online eligibility survey via REDCap.Consent forms were sent to eligible participants via DocuSign; parents consented for participants below age 18 while children ages between 7 and 17 years provided written assent while children below age 7 years provided verbal assent during online orientation via Zoom.All study procedures were approved by the sponsoring university's institutional review board.

Study procedures
All participants were screened for inclusion criteria by the research team.Once consent was provided, demographic surveys were emailed to participants to collect contact information and metrics such as height and weight needed in order to calculate energy expenditure and initialize activity trackers.After materials were mailed and received by participants, virtual orientations were scheduled with each participant via Zoom.During these sessions, participants were introduced to the research procedures and materials.Participants were educated on different definitions of fatigue including "crash", mental, physical, and emotional fatigue as well as how to report these measures to the app.A short survey was used to confirm understanding of fatigue at the end of the orientation.The material used for the participant education is available along with our app on our lab website (link provided at the end of the manuscript).Since this study used actigraphy, training was also provided during this time for proper care and charging of the activity tracker (ActiGraph GTX9 Link).All participants were provided a brief ActiGraph care guide; an ActiGraph GTX9 Link, a charging cable, a charging dock, paper copies of the app questions in case technical errors occurred, and a prepaid shipping label to return their materials.For participants who did not have access to an Android phone, an Android phone and charger were included in the mailed package as well.
Participants were instructed to report their fatigue ratings to the app five times per day during their normal waking hours and to aim for the following time periods: early morning (EM) from 6 am to 8:59 am, late morning (LM) from 9 am to 11:59 am, early afternoon (EA) from 12 pm to 2:59 pm, late afternoon (LA) from 3 pm to 5:59 pm, early evening (EEVE) from 6 pm to 8:59 pm, and late evening (LEVE) from 9 pm to 11:59 pm.While six time frames were provided, the participants were only expected to complete five responses each day based on their awake times (e.g.participants will not have to respond if they are typically in bed by the LEVE time).The app also provided timed "push notification" reminders as an additional reinforcement in the middle of each period, between 7 am and 10 pm each day.Each participant was also instructed to wear the ActiGraph GTX9 Link on their non-dominant wrist for 14 consecutive days and mail all study materials back to investigators once completed.

Materials and measures
The following methodology was adapted from a previous study [10].

ActiGraph GT9X Link
The activity tracker, ActiGraph GT9X Link (GT9X), was used to collect sleep and energy expenditure data for the duration of this study.The GT9X is a wrist-worn activity tracker that utilizes a 3-axis accelerometer to collect sleep and physical activity data.The GT9X was initialized using ActiLife software (Version 6, ActiGraph, LLC., Pensacola, FL) to collect data at a sample rate of 30 Hz, and data was uploaded to ActiLife at 60-s epochs.

Energy expenditure
The ActiLife software used the 3-axis accelerometer data from the ActiGraph GT9X Link to compute physical activity counts per minute (CPM) and vector magnitude (VM) for each epoch (60-s time-interval) during the entire 14 days of data collection.CPM is calculated from the summation axis 1, axis 2, and axis 3 whereas the formula for VM is the square root of all 3 axes squared and refers to the magnitude of the triaxial vector (ActiLife 6 User's Manual).We utilized the algorithm developed by Romero-Ugalde and colleagues to determine average energy expenditure (kcals/minute) using weight (kg) and CPM and VM values from the ActiLife software [22].
Average energy expenditure was calculated in Excel using the Romero-Ugalde algorithm for the following time frames: early morning (EM) from 6 am to 8:59 am, late morning (LM) from 9 am to 11:59 am, early afternoon (EA) from 12 pm to 2:59 pm, late afternoon (LA) from 3 pm to 5:59 pm, early evening (EEVE) from 6 pm to 8:59 pm, and late evening (LEVE) from 9 pm to 11:59 pm.These time frames correspond to the time frames for reporting perceived fatigue levels to the phone app.
The energy expenditure data obtained from Acti-Graph GT9X Link allowed us to examine the relationship between perceived energy level, as recorded on the phone app, with energy expenditure as recorded using actigraphy.We anticipated that individuals would expend less energy (i.e., move less) during periods of lower perceived energy level (e.g., when they felt tired).

Data analysis
Statistical analysis was conducted with IBM SPSS Statistic Version 28.Our first study aim was to assess the feasibility of using an app to record perceived fatigue levels in real time.To answer our first question related to this aim (RQ1) we calculated the average number of daily responses for each participant.To address our second question related to this aim (RQ2), we verified the internal consistencies of responses (Fig. 1, question 2 vs 3).We calculated the percentage of responses where their responses between questions 2 and 3 were consistent (e.g., if they reported they were fine in question 2, did they also report they were fine in question 3).To verify app-recorded perceived fatigue levels, we also employed an analysis of variance test using a one-way ANOVA, with the dependent variable being the perceived energy level (battery scale in Fig. 1, question 1), and the independent factor being the participant's subjective report of whether they were tired or not (Fig. 1, question 2).
Our second study's aim was to evaluate discriminant validity by assessing if the app can differentiate between those with and without BTHS.To answer questions related to this aim (RQ3) is used MANOVA to examine the difference in the frequency perceived fatigue levels (battery scale) (RQ3), of fatigue reports (both general fatigue and across the domains of physical, mental, and emotional fatigue) (RQ3), and frequency of crash report (RQ4), made by BTHS and control participants while controlling for age as a covariate.
Our third aim was to evaluate content validity by assessing the relationship between perceived energy levels and energy expenditure.To assess our only research question related to this aim (RQ5) we used Pearson's correlation to determine the relationship between perceived ratings of energy and objective energy expenditure data.

Feasibility of using the phone app to report perceived energy levels
The total average response rate for the BTHS group was 3.1 times per day, with adults having the highest daily average (child 2.3, adolescent 2.6, adult 4.4).For the control group, the total average response rate was 2.9 times per day, with adults having the highest daily average (child 2.7, adolescent 3.0, adult 3.1).Aside from the child BTHS participants, all participant groups responded to more than 50% of the data entry requests (average higher 2.5 times/day).
We checked for internal consistency of the phone app responses by calculating the percentage of responses where responses on questions 2-3 in the phone app were consistent.The internal consistency percentage for the BTHS group was 92% and 95% for the control group.
Each response was classified into a "fatigue response" (responded "tired" or "crash" in question 2, see Fig. 1) or "feel fine response" (responded "fine" in question 2).There was a statistically significant difference in the energy level reported on the battery scale (Fig. 1, question 1) for both BTHS and control participants in fatigue vs feel fine responses (fatigue mean = 46.68,feel fine mean = 81.49;F(1,1526) = 1337.181,p < 0.001).This demonstrated the feasibility of the energy level reported on the battery scale given that higher reported energy levels corresponded with feel-fine responses.

Discriminant validity of the phone app for reporting perceived energy levels
On the battery scale of 0-100, the average perceived energy level for the BTHS group was 66.93, with the control group reporting a slightly higher average perceived energy at 70.17.A MANOVA was used to examine these differences between the participant group (BTHS vs control) while controlling for age.No statistically significant difference was found between BTHS and control participants (F(1,32) = 0.374, p = 0.545).
Overall, "I feel tired" (perceived fatigue) was reported in 42.6% of the responses in BTHS participants and 34.5% in control participants.This difference was not statistically significant (F(1,32) = 0.986, p = 0.328).Among the different dimensions of perceived fatigue responses, physical fatigue (question 3) was most reported by both BTHS and control groups (Barth 79%, control 74% of fatigue responses respectively), while mental fatigue was the next most reported by both groups (BTHS 64% and control 53% of fatigue responses), and emotional fatigue was only occasionally reported in all groups (BTHS 9% and control 24% of fatigue responses); differences between groups were not statistically significant (p > 0.05, Table 2).

Content validity: the relationship between reported energy levels and energy expenditure measured by ActiGraph watches
There was a significant positive correlation between perceived energy levels and actual energy expenditure (recorded on the ActiGraph GT9X) during the same analysis time periods (EM, LM, EA, LA, EE, LE) (Pearson's r = 0.374, p < 0.001).While this was not part of the research questions, we observed that the BTHS participants showed a significantly lower actual energy expenditure than the control participants (Barth mean = 384.81kcal/min, Control mean = 561.72 kcal/ min, F(1, 180) = 22.064, p < 0.001).

Discussion
In this study, we provide evidence for the feasibility of using a phone application to record perceived fatigue levels in real time.We showed that participants recorded their fatigue levels anywhere from 2.3 to 4.4 times per day, with children having a lower number of daily reports on average compared to adults.Our participant response rate (100% of our participants used the app) is comparable to another study that used a phone app to capture fatigue responses in patients with multiple sclerosis (response rate of 88%) (Palotai et al. 2021).While our study is not the first to use an app to capture perceived fatigue, it is the first to do so in children, as other studies restricted participation to individuals 18 years or older [23], Palotai et al. 2021).The lower response rate in children and adolescents is mostly due to the lack of access to a mobile phone among school-age children during school hours.Although some of the children and adolescent participants owned a personal mobile device, they reported restricted use of their mobile devices when they were at school, which limited their ability to respond to the app.If future studies seek to record perceived fatigue in children, further design considerations need to be made to increase the response rate in this age group.
Our results further support the use of the phone app to reliably collect information about participant fatigue levels.Respondents were consistent within themselves 92% of the time when reporting "I feel fine", and 95% of the time when reporting "I feel tired" between questions 2 and 3 on the app.We also showed the feasibility of using a battery scale to represent varying levels of energy within our participants.This was evidenced by a significant difference in battery scale scores when participants reported feeling fine compared to when they felt tired.Collectively, these initial results suggest that our mobile phone application is able to be used in participants aged 5 years to adults with normal cognitive skills in order to reliably record perceived fatigue throughout the day.
The second aim of our study was to evaluate the ability of the phone application to differentiate between a diagnostic and non-diagnostic (control) group.We anticipated that participants with BTHS would report lower energy scores on the battery scale, and select a greater number of fatigue responses across all domains (physical, mental, and emotional), compared to controls since BTHS is a condition known for having poor endurance and chronic fatigue as primary symptoms [5].This hypothesis was not supported as evidenced by no significant differences between groups on our MANCOVA analyses.This finding was particularly surprising since our actigraphy data indicated the BTHS group objectively expended less energy throughout the day compared to controls, suggesting they were more fatigued.As an explanation, we suggest that the app captures a subjective perception of energy/fatigue that is scaled to the individual's own (adapted) experience.Individuals with chronic conditions (like BTHS) may have different ways of interpreting and describing their fatigue, making it challenging to capture their true levels of fatigue through self-reporting.People with chronic fatigue might also adapt to their condition over time and perceive their fatigue as a more normalized state.This may lead Interestingly, our phone application did identify a significant difference between diagnostic and non-diagnostic groups on the number of reported "crashes", which were defined in our training as needing to stop all activity due to overwhelming fatigue.Our ability to identify differences between groups for this important outcome measure, suggests that participants understood the distinction between feeling tired and "crashing", and that this was a phenomenon experienced almost exclusively in the group with the mitochondrial disorder.While further validation testing of the application is necessary, episodes of "crashing" may be an important patient-centered outcome measure to be considered in future clinical trials.
Our final aim was to evaluate the content validity of the application by comparing perceived fatigue scores from the battery scale and objective energy expenditure recorded on the ActiGraph GT9X watches.The perceived energy levels were found to be significantly correlated with their energy expended during the same time periods throughout the day, confirming that the perceived energy level is closely related to their actual energy expenditure.That is, participants were more active when they felt that they had more energy.This correlation is likely due to the perception of energy influencing energy expenditure decisions.While we saw a correlation between perceived energy level and energy expenditure, it is important to note that the two concepts are separate constructs.However, findings did confirm the usability of a numerical battery scale for participants to report their energy levels, allowing the quantification of the subjective perception of fatigue.

Limitations of the study
A primary limitation of this study was limited data reported in the app for children and adolescents due to life events such as being in school and participating in extracurricular activities or forgetting to complete the app multiple times throughout the day.An additional limitation included the battery life of the ActiGraph watches in regards to shipping time, especially with international participants.Each ActiGraph watch when fully charged lasts about 5-7 days.ActiGraph watches had to be initialized with participant settings before shipping to the participant.If any shipping difficulties occurred, and the watch died en route, the individualized settings would be lost and the watch would not collect any new data.This resulted in several enrolled participants being unable to participate.
The Android app was designed for newer versions of these phones (OS Version 8 or later) which was also a limitation as it limited participation from individuals with older Android phones, as they had to wait for an available loaner phone from the study team.While our sample size was small (BTHS n = 18, control n = 18), our sample represented approximately 7-9% of those currently living with BTHS suggesting good external validity, although it would be ideal to continue this research study with more children and adolescents for more accurate results.

Future directions
Future research should further examine the relationship between perceived energy and objective energy expenditure in order to develop models to predict crashes to allow individuals to anticipate and take action before crashing.Our current analysis has grouped reported energy level and energy expenditure data into six specific time periods during the day.More sophisticated models that account for changes in energy expenditure on the minute level may be needed to fully capture the relationship between energy expenditure and fatigue.Further studies will also examine the criterion validity of the app, comparing the results captured from the app in real time to validated fatigue measures.We suggest that future studies may also consider elements of nutrition and cardiac function as part of a more comprehensive analysis of fatigue in BTHS.
An iOS version of the app has since been developed, which will allow participants of future studies to use both Android and iOS devices to report their fatigue.Links to both apps can be found on our website: https:// rampa ges.us/ spre/ barth-app/.Lastly, a longitudinal study on individuals within the BTHS population to track how their energy expenditure and sleep in relation to their perceived energy changes throughout childhood, adolescence, and adulthood will provide us with better insights into how fatigue changes with age.

Conclusion
There has been limited research in looking at the perceived physiological, mental, and emotional aspects of fatigue and energy levels in individuals with BTHS.The phone app developed allowed researchers to capture the lived experience of individuals with BTHS while also capturing objective data.The phone app was able to consistently and accurately capture participant-reported fatigue.The battery scale threshold established in the feasibility study was successful in capturing perceived levels of energy and can be used as a valid measure of fatigue in future studies.
Our study also found that "crashes" were the main factor differentiating between the BTHS and control participants, where other measures of perceived fatigue were not found to be significantly different.This highlights the complex nature of measuring fatigue as a subjective construct, and the need for further investigating "crashes" as a potentially defining fatigue metric for the BTHS population.
This study provides foundational information on methods for quantifying fatigue in adults, adolescents, and children with BTHS and can provide possible targets for future therapeutic trials.

Table 1
Participant demographics N (# of males) Mean age in years (range) N (# of males) Mean age in years (range) N (# of males) Mean age in years (range) N (# of males) Mean age in years (range)

Table 2
Descriptive statistics on the frequency of fatigue reports and perceived energy levels on the battery scale