Measuring the Prevalence of Fatigue in children with cancer: Evidence from Egypt.

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Nourhan Abdalkader, Alaa Mahmoud Zawara, Shaimaa Lashien, Ahmad Mohamed Yehia Osman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4757144/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cancer related fatigue (CRF) is a common side effect of cancer and cancer treatment that impacts quality of life. To our knowledge, the statistics for prevalence in pediatrics are lacking in Egypt. The aim of this study is to record the prevalence of fatigue and its significant predicting factors in pediatric oncology patients. Methods: we interviewed children aged 8-18 years with cancer, prescribed chemotherapy and not in severe distress. The children personally filled 2 fatigue-related questionnaires (PROMIS Pediatric Short Forms of Fatigue (PROMIS fatigue), pedsQL multidimensional fatigue (PedsQL fatigue)) and 3 symptoms related questionnaires. Results: 42 children (47.6% female) (mean age 12.1 years (SD 3.3 years)) participated. Reported moderate to severe fatigue in children is between half to third of the children depending on the measurement tool used. The mean T-score for PROMIS fatigue was 53.76 (SD 12.5), and for PedsQL fatigue was 74.27 (SD 21.79). Stepwise standardized multivariant linear regression showed that fatigue following PROMIS fatigue could be predicted by depressive symptoms (𝜷= 0.47, p <0.001) and mobility (𝜷= -0.39, p =0.002) while following PedsQL fatigue, it could be predicted by upper extremity function (𝜷= 0.34, p= 0.005), depressive symptoms (𝜷=-0.49, p <0.001) and treatment status (𝜷=-0.25, p= 0.013). Conclusion: CRF is multifactorial and prevalent among children and adolescents with cancer. Moreover, predicting factors differed between different tools as they measure different dimensions of fatigue. There is a need to include fatigue screening for pediatric oncology patients and incorporate its management in the medical care plan. PedsQL Quality of Life PROMIS Pediatric Short Form-Fatigue Arabic oncology Introduction The National Comprehensive Cancer Network (NCCN) defined cancer-related fatigue (CRF) as “a distressing persistent, subjective sense of physical, emotional, and/or cognitive tiredness or exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning”( 1 ). The official definition of CRF is resilient to practice and research as it is rare to find significant fatigue indications over consecutive check points. To overcome this issue in the absence of a universal tool measuring fatigue, researchers divided fatigue into mild, moderate and severe depending on cutoff scores focusing on moderate to severe fatigue in their work ( 2 ). Based on a recent review, the prevalence in pediatrics ranges from 36–93% of the cases with a greater level in cases receiving chemotherapy ranging from 70–100% of the cases ( 3 , 4 ). A report from Jordan estimated fatigue prevalence among cancer patients to be around 82% ( 5 ).To our knowledge, these statistics for pediatrics are lacking in Egypt. Multiple risk factors have been linked with CRF. they are classified into modulating (gender and age), maintaining (lifestyle factors, demographic, and current health), triggering (disease and treatment-related factors), and pre-disposing (genetic disposition) ( 2 ). Under this general classification there are multiple subclasses, to illustrate; Cancer and its treatment can lead to complications such as anemia and gastrointestinal symptoms, which contribute to fatigue during each treatment cycle ( 4 ). Furthermore, diseases such as depression are often correlated with fatigue in cancer patients ( 4 ). In addition, lifestyle, as physical activity could be linked to fatigue ( 4 , 6 ). Other factors related to severe fatigue are cancer stage and elevated body mass index (BMI) ( 2 ). Consequently, assessing CRF requires considering a variety of factors, Multiple instruments have been developed ranging from single-item questions to multidimensional assessment tools. The former assesses multiple fatigue dimensions, allowing for the most accurate measurement and precise results. One of the largest Quality of life (QoL) questionnaires database is the patient reported outcomes measurement information system (PROMIS), It evaluates the social, physical, and psychological health of patients and can be used in clinical practice and research ( 7 ). the Pediatric Short Form v2.0 - Fatigue 10a was developed by The National institution of health (NIH) ( 8 , 9 ) and then used in multiple studies, including studies about pediatric chronic pain ( 10 ), cancer ( 4 , 10 – 12 ) and sickle cell disease ( 13 ). The questionnaire has been proven valid and sensitive to change ( 14 – 16 ). Another promising measure ( 17 ) is the Pediatric Quality of Life Inventory (PedsQL) measurement model, it measures health-related quality of life (HRQoL). It is a multidimensional fatigue measurement tool that has been used and verified in studies regarding pediatric rheumatology ( 18 ), pediatric obesity ( 19 ), multiple sclerosis ( 20 ) and cancer patients ( 21 – 23 ). It also has an Arabic translation that was validated in native Arabic-speaking cancer patients ( 24 ). In this research we opted for the use of both questionnaires as they measure distinct aspects of fatigue. Severe fatigue has a significant effect on children with cancer as it interferes with the disease, treatment, function, every aspect of quality of life (QoL) ( 4 ). In some cases, it might even lead to discontinuation of treatment ( 25 ). Early recognition of fatigue in children with cancer is expected to improve their prognosis and quality of life. Despite the burden of CRF in children, it is yet to be explored in Egypt. Our current aim is 1) Record the prevalence of fatigue in the oncology pediatric patient population. 2) Explore the relationship between fatigue and some risk factors, including demographic data, type of cancer and treatment, patient-reported symptoms (depression), functionality (upper extremity, mobility), and selected related medical data (white blood cell counts and hemoglobin). Shedding light on children’s CRF and their risk factors will provide grounds for addressing this chronic symptom and support the efforts to find a channel that predicts and manages cancer-related fatigue. Methodology Study design: This is a cross-sectional study to assess fatigue prevalence and its associated risk factors in children and adolescents suffering from cancer in Egypt. Convenience sampling was used to recruit participants from Dar El Salam oncology hospital (Harmil) between October and December 2022. Sample size calculations were done according to the minimal sample size needed to run a regression analysis with an acceptable error level that being six participants per variable in the regression model + 1 ( 26 ), 7 variables would be included into the regression making the minimum targeted sample around 43 participants. Participants: Inclusion criteria were: 1) Age 8- <18 years at the time of the study, 2) Diagnosed with any type of cancer, 3) Ability to speak, read (alone or with supervision) and understand Arabic, 4) Not in severe distress, 5) prescribed chemotherapy. Exclusion criteria were children suffering from sensory or cognitive deficits that prevent understanding or answering the questionnaire. Measurements: Five questionnaires: PROMIS pediatric short form v2.0 Fatigue 10a (PROMIS fatigue), PedsQL multidimensional fatigue score acute version 3.0 (PedsQL fatigue), PROMIS Pediatric Item Bank v2.0 – Depressive Symptoms – Short Form 8a, PROMIS Pediatric Item Bank v2.0 – Mobility– Short Form 8a and PROMIS Pediatric Item Bank v2.0 – Upper Extremity – Short Form 8a. They are self-filled questionnaires by the child. They are listed in the appendix ( 1 – 5 ). PROMIS pediatric short form v2.0 Fatigue 10a (PROMIS fatigue): It has 10 items with a 7-day recall period that measures the degree of fatigue generally from mild to severe. Each item is measured by a 5-point Likert response scale ranging from “never” to “almost always”. PROMIS scores are reported as T-scores with a mean of 50 and a standard deviation (SD) of 10. A higher T-score means a higher degree of fatigue. The questionnaire has been proven valid and sensitive to change ( 14 – 16 ) and has been used and validated in oncology patients ( 27 ). PedsQL multidimensional fatigue score acute version 3.0 (PedsQL fatigue): It has eighteen items with 7-day recall period that measures 3 dimensions of fatigue; sleep/rest fatigue (6 items), general fatigue (6 items), and cognitive fatigue (6 items). A 5-point Likert scale from 0 “Never” to 4 “Almost always” is used to scale each of the items, Then the scores are reversed and linearly transformed to a 0-100 scale. It has been proven to have strong internal consistency, reliability, and validity in cancer patients ( 28 ) and has an Arabic-validated translation( 29 ). Co-occurring symptoms (depressive symptoms) and function (mobility and upper extremity) were measured using the Arabic versions of the PROMIS Pediatric Short Forms of depressive symptoms, mobility, and upper extremity measures. These instruments follow the same measurement technique as PROMIS Fatigue except having eight items. Except for the depressive symptoms form as a higher score means a higher experience of the symptoms. Other data recorded were demographics (age, gender, height, weight, caregiver relationship, nationality (ethnicity), province and education level), disease and treatment information (type of cancer, time since diagnosis, treatment protocol and other chronic health conditions) and relative clinical data (most recent test results of hemoglobin and white blood cell count). Data collection: The guardian was approached by the investigator who introduced herself and explained the concept of the survey. If they accepted participation in the study or wanted further information, the couple were directed to a quiet location. Then the investigator explained the research aims to the guardian and the child and asked for their consent. Once written consent was gained, the investigator asked the guardian not to interrupt their child while filling out the questionnaires and any inquiries from the child were directed to the investigator. The questionnaires were printed, and the child read the questions and filled in the questionnaire without interference. The questionnaires were expected to take about 15 minutes. The other data were gathered from the guardian and patient files. Pilot study: The questionnaires were the first run in a pilot study to standardize interviewing, ensure clarity of translation and confirm average completion duration. The first stage was run on healthy children, they commented on some expressions that they were unfamiliar such as (shirt – in Arabic) in item 5 of PROMIS fatigue, which was explained to be a (T-Shirt). The average completion time was 12 minutes. Cancer children were recruited in the following stage, confirming the viability of the cultural adapted explanation examples from the first stage with delicate refinements in the approach and interview. The average duration for this stage was 14 minutes. Full description of the sample in both stages is in appendix 6. Ethical consideration: Ethical approval was obtained from the Ethics Research Committee at School of pharmacy Newgiza University and the IRB committees at the participating hospital. Written consent was obtained from the child’s guardian. A verbal assent was obtained from the child. They were assured that participation was voluntary. All personal information was kept with the principal investigator. Statistical analysis: For the descriptive data, means (SD) were calculated for continuous variables and frequencies (%) for categorical and ordinal variables. Subgroups’ characteristics and measures were compared using Mann Whitney U test, Fischer exact test and chi-square test to reveal possible significant differences. Correlation analysis between fatigue from both fatigue measurement tools and patient characteristics was run using spearman test, then the characteristics that showed a correlation probability value of < 0.1 were used in a multivariant regression analysis to further quantify this relationship for both fatigue measurement tools. The regression was refined through stepwise regressions which is the iterative repetitive regressions where there is a contentious selection of significant independent variables and exclusion of insignificant variables to reach a final optimized model. The coefficients were further standardized to unify the scales and ease the interpretation of the results. Results A total of 46 patients were approached between October and December 2022, 42 (91.3%) of them agreed to participate while 4 opted out of participating. After the consent process, all 42 children completed the questionnaires taking an average time of 14.61 minutes (SD: 4.18). Characteristics of participants: About half of the children were female (20, 47.6%) with a mean age of 12.1 years (SD 3.3). According to their body mass index (BMI) a substantial number of children were normal weight (31, 73.8%) - only 7 (16.7%) were obese or overweight- and had a mean body surface area (BSA) of 1.27 m 2 (SD: 0.31). most of the participants had a hematological tumor while only 5 (11.9%) had a solid tumor. All children received chemotherapy but were at different stages: most were actively receiving chemotherapy treatment; others were between cycles of chemotherapy or had finished chemotherapy treatment and one hadn’t started chemotherapy yet. The average blood hemoglobin level was 11.03 g/dl (SD 1.86) and the average total leukocytic count was 4.17 x10 3 cell/ml (SD 3.89). The vast majority did not have any other chronic health conditions (39, 92.8%) except for two (4.8%) with a heart condition and one (2.4%) with diabetes. Further sample characteristics are summarized in table 1. The sample could be stratified into 2 distinct subgroups, in-patients, and out-patients. The in-patient group (13, 30.9%) were patients that received care and stayed in admission unit, while the out-patient group (29, 69.1%) were patients treated in day-care setting. The two groups were significantly different in terms of: type of cancer (p value =0.005), time since diagnosis (p value =.0038), type of treatment (p value =0.004), current treatment status (p value =0.007) and hemoglobin (p value= 0.009) as well as showing a significant difference in the mean score of the PROMIS mobility questionnaire (p value 0.026). [Table 1] Fatigue questionnaires’ scores: The mean fatigue score measured by PROMIS fatigue of all participants was 53.76 (SD: 12.5) which interprets to mild fatigue, while for PedsQL fatigue it was 74.27 (SD: 21.79). The participants had variant answers in the two questionnaires, only 33.3% of the participants had matching scores in the two questionnaires (appendix 7). For PROMIS fatigue almost half showed no fatigue (20, 47.6%) and for pedsQL fatigue about half scored mild fatigue (17, 40.5%). Further details are presented in Table 2. In the in-patient group the two questionnaires were the same in describing the absence of fatigue or having a mild fatigue but showed differences in moderate and severe fatigue. As for the out-patient group the score distribution was completely different between the two tools. For instance, PedsQL fatigue valued about half (15, 51.7%) with mild fatigue while only one (3.4%) was mild in PROMIS fatigue. Correlations between fatigue score and characteristics: There was a significant correlation between mobility and depressive symptoms with both PROMIS fatigue and PedsQL fatigue that was consistent even after subgrouping. Subgrouping impacted the significant correlation between fatigue scores and BSA as it did not show any significant correlation in the in-patient group with both questionnaires, it also impacted upper extremity as it didn’t show significant correlation in PROMIS fatigue in the in-patient group. In relation to PROMIS fatigue, all participants’ scores moderately correlated with PROMIS mobility ( r = -0.702, p value <0.001), PROMIS depressive symptoms ( r= 0.669, p value <0.001) and PROMIS upper extremity ( r= -0.545, p value <0.001). As for pedsQL fatigue, the characteristics that were significantly correlated were mobility ( r = 0.667, p value <0.001), depressive symptoms ( r = -0.597, p value <0.001) and upper extremity ( r = 0.524, p value <0.001). The correlations were stratified by the two subgroups and reported in Table 3. [Table 2] [Table 3] Regression (factors): Table 4 contains the results for the stepwise regression of PROMIS fatigue where two variables were significant: PROMIS mobility (𝜷 -0.39, p value= 0.002) and PROMIS depressive symptoms (𝜷 = 0.47, p value <0.001). Regarding PedsQL fatigue, stepwise regression results showed three significant variables: PROMIS upper extremity (𝜷 = 0.34, p value= 0.005), PROMIS depressive symptoms (𝜷 = -0.49, p value <0.001), treatment status (𝜷 = -0.25, p value= 0.013). [Table 4] [Table 5] Discussion This study evaluated the prevalence, degree of and factors predicting fatigue in children with cancer prescribed chemotherapy at various stages of treatment. Results supported that CRF in children undergoing chemotherapy treatment was prevalent and affected the children’s day-to-day functions. Findings also confirmed the convoluted nature of CRF and added to previous research on the significant roles of depressive symptoms, mobility, upper extremity function and treatment status on fatigue in children (8–18 years) with cancer specially in Egypt. PROMIS fatigue showed that over half of the children had a fatigue score higher than the public (T score = 50). The mean T score for all children was 53.76 which is less than 1 point above their U.S counterparts (T score 52.9) ( 30 ) and five points above their Chinese counterparts (T score 48.52) ( 4 ). The minimal significant difference is 3 points ( 31 ), making our sample equivalent to their U.S counterparts and different from their Chinese counterparts. Suggested reasons could be cultural or sample differences. Culturally, Chinese might view “enduring” fatigue as “tolerating hardship” and thus must bear it and refuse reporting. Looking further into the study sample, half of them were suffering from solid and brain tumors and those diagnosed with solid tumors were on average approaching the end of their active treatment( 4 ) which is expected to have less fatigue overall. As for the PedsQL fatigue, around half (47.6%) of children reported more fatigue than the mean score of healthy children from a sample in the United States (score = 80.49) ( 32 ). The mean score of the sample (mean score 70.98) was around 3.5 points higher than their U.S counterparts (mean score 70.98) ( 32 ), even after stratification of the sample the trend for both strata remained the same ( 33 ). An explanation behind our participants showing less fatigue than their U.S counterparts could be traced back to sample differences. Their sample was more diverse including brain tumors, recent remissions, and long-term off-treatment which could influence the results as long-term off-treatment patients are more fatigued than cancer patients on active treatment ( 34 ). Our sample showed different results for measured level of fatigue in the two questionnaires. That could be attributed to differences between the dimensions measured by the two questionnaires. PROMIS fatigue was focused on general fatigue a child might experience in their day-to-day life while PedsQL fatigue had one section for general fatigue and two other sections for sleep/rest fatigue and cognitive fatigue making it more sensitive to detecting fatigue and determining the source of it. That is reflected in our sample by PedsQL fatigue detecting fatigue in more participants than PROMIS fatigue in our overall sample and the outpatients group. Subgroup comparison between in-patients and out-patients revealed interesting observations. At first, although the perceived reason for admission is low TLC and hemoglobin, the two subgroups were equivalent regarding these two clinical indicators. This might be due to other admission reasons that were not properly recorded like the start of treatment or complaining of fever. It was anticipated that the inpatient group would show more fatigue than the outpatient group as observed by previous research ( 33 ). Our sample followed that observation. However, the difference between the fatigue scores in both questionnaires was not significant, which could be due to the lack of statistical power in our sample. It is worth noting that the subgroups were significantly different in terms of mobility and time since diagnosis, which could be used as an indicator to the patients that would display more fatigue. The predictors of PedsQL fatigue and PROMIS fatigue scores were not the same, For PROMIS fatigue they were PROMIS mobility and PROMIS depressive symptoms, while for PedsQL fatigue predictors were PROMIS upper extremity, PROMIS depressive symptoms and treatment status. Depressive symptoms have been recognized as a part of symptoms cluster made of fatigue and were found to be a significant predictor of fatigue in other studies ( 35 , 36 ). Looking at the standardized 𝜷 coefficients, PROMIS depressive symptoms is the highest predicting factor of fatigue in both PROMIS fatigue and PedsQL fatigue emphasizing the importance of mental health in fatigue management. PROMIS mobility and PROMIS upper extremity being significant predictors also complies with previous research as it was found that inactivity contributes to the development and persistence of fatigue ( 36 ). As for treatment status being a significant predictor, chemotherapy leads to an increase in the level of specific inflammatory markers and associated with increase in fatigue prevalence ( 36 ). Despite fatigue’s magnitude in children in Egypt, it is not being screened for on a regular basis, that could be for multiple reasons, for example there is no agreed upon measure to be used in screening( 2 ) as well as health care professionals having limited knowledge about fatigue importance and its management ( 37 ). Also, patients perceive fatigue as a normal thing to experience because of their diagnosis and not as something they should seek medical attention for( 38 ) and these factors collectively lead to overlooking fatigue and not including it in the care plan. A few interventions have been explored and developed to reduce fatigue in cancer patients. NCCN developed an algorithm for pediatrics made of screening, primary evaluation, intervention and re-evaluation, their intervention plan is based on eliminating the factors causing fatigue and if the fatigue is unresolved, that there is a spectrum of recommendations ranging between nonpharmacological approaches like exercise and psychological programs to pharmacological approaches like antidepressants ( 1 ). This study has limitations. The small sample size affects the power of differentiation and prediction of some factors as stated earlier. This study could be viewed as an opportunity to raise the problem of fatigue in cancer patients and open the door for future larger studies. Our data was only collected from one site. Despite that the location was a tertiary hospital and our sample coming from different provinces of Egypt, it does not cover the whole population. However, it can be the start for further studies in more diverse locations. Lastly, the guardians and children sat at the same location during the interviews Although we tried to eliminate the guardian’s influence on the child, the simple presence of the guardian could affect the independence of children’s answers to the questionnaires. In conclusion, children with cancer in Egypt experience fatigue during their treatment that is underreported and undermanaged. Factors predicting fatigue in the current sample were depressive symptoms, upper extremity, mobility, and treatment status meaning that patients during active chemotherapy treatment who show mobility issues and/or depressive symptoms are in most need for fatigue management. PedsQL Fatigue might represent more detailed information about fatigue which allows for a better care plan designing while PROMIS fatigue measures general fatigue which might fit more for being part of longer surveys or research activities. 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Quality of Life Research. 2016 Jan 1;25(1):13–23. Grant WB. The PedsQL TM in Pediatric CancerReliability and Validity of the Pediatric Quality of Life Inventory TM Generic CoreScales, Multidimensional Fatigue Scale, and Cancer Module. Cancer. 2002 Mar 15;94(6):1867–75. Mccabe M, Patricia B. Fatigue in the Acute Care and Ambulatory Setting. J Pediatr Nurs. 2014;29(4):344–7. Spathis A, Hatcher H, Booth S, Gibson F, Stone P, Abbas L, et al. Cancer-Related Fatigue in Adolescents and Young Adults After Cancer Treatment: Persistent and Poorly Managed. J Adolesc Young Adult Oncol [Internet]. 2017 Sep [cited 2023 Jan 3];6(3):489–93. Available from: http://www.liebertpub.com/doi/10.1089/jayao.2017.0037 Berger AM, Mooney K, Alvarez-Perez A, Breitbart WS, Carpenter KM, Cella D, et al. Cancer-Related Fatigue, Version 2.2015. Journal of the National Comprehensive Cancer Network [Internet]. 2015 Aug;13(8):1012–39. Available from: https://jnccn.org/doi/10.6004/jnccn.2015.0122 Bower JE. Cancer-related fatigue-mechanisms, risk factors, and treatments. Nature Publishing Group [Internet]. 2014;11:597–609. Available from: www.nature.com/nrclinonc Silva MCM da, Lopes Júnior LC, Nascimento LC, Lima RAG de. Fatigue in children and adolescents with cancer from the perspective of health professionals. Rev Lat Am Enfermagem [Internet]. 2016;24(0). Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-11692016000100405&lng=en&tlng=en Thong MSY, van Noorden CJF, Steindorf K, Arndt V. Cancer-Related Fatigue: Causes and Current Treatment Options. Curr Treat Options Oncol. 2020;21(2). Tables Tables 1 to 5 are available in the Supplementary Files section Additional Declarations The authors declare no competing interests. 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Faculty of Medicine, Cairo University","correspondingAuthor":false,"prefix":"","firstName":"Shaimaa","middleName":"","lastName":"Lashien","suffix":""},{"id":328296376,"identity":"51b74f40-8f76-4aa6-966f-68285fb8de22","order_by":3,"name":"Ahmad Mohamed Yehia Osman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACCSDmAbN4GA4wVABpZuYGUrScAWlhJEELA2MbiEFAC//s7sQHbxi2MRgc7z144Oe82mj+dqCWHxXbcFty5+xmwzkMtxkMzpxLONi77XjujMOMDYw9Z27jtuZG7jZpHpCWGzkGB3i3HcttAGphZmzDrUX+Ru7232At998YHPw751jufEJaDIC2MENs4TE4zNtQk7uBkBbDG7mbJecY3OaRPJNjcFjm2IHcjUAtB/H5Re5G7sYPbypuy/EdP2P88U1NXe6884cPPvhRgcf7EOcx8CgcALMOg8kDBNRDgHwDmKojSvEoGAWjYBSMLAAA7NhiqCYxBhcAAAAASUVORK5CYII=","orcid":"","institution":"Pharmacy Practice Department, Kulliyyah of Pharmacy, International Islamic University Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"Mohamed Yehia","lastName":"Osman","suffix":""}],"badges":[],"createdAt":"2024-07-17 15:07:31","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4757144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4757144/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60661757,"identity":"1b0bc5d3-f59b-42b9-9555-ff138db05173","added_by":"auto","created_at":"2024-07-19 08:42:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":362748,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4757144/v1/939cf697-139e-49cc-987f-b367234e92b9.pdf"},{"id":60661748,"identity":"f7f8e569-b020-4fba-ab0c-49be79b7225e","added_by":"auto","created_at":"2024-07-19 08:42:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":64436,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4757144/v1/46b49d4d8f24fa992cca7dfb.docx"},{"id":60661749,"identity":"6370c9bc-60c1-4027-a376-ff18ef6747ad","added_by":"auto","created_at":"2024-07-19 08:42:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":184881,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4757144/v1/dc8a56bd7c0ad9fa968cc1bc.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMeasuring the Prevalence of Fatigue in children with cancer: Evidence from Egypt.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe National Comprehensive Cancer Network (NCCN) defined cancer-related fatigue (CRF) as \u0026ldquo;a distressing persistent, subjective sense of physical, emotional, and/or cognitive tiredness or exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning\u0026rdquo;(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The official definition of CRF is resilient to practice and research as it is rare to find significant fatigue indications over consecutive check points. To overcome this issue in the absence of a universal tool measuring fatigue, researchers divided fatigue into mild, moderate and severe depending on cutoff scores focusing on moderate to severe fatigue in their work (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on a recent review, the prevalence in pediatrics ranges from 36\u0026ndash;93% of the cases with a greater level in cases receiving chemotherapy ranging from 70\u0026ndash;100% of the cases (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A report from Jordan estimated fatigue prevalence among cancer patients to be around 82% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).To our knowledge, these statistics for pediatrics are lacking in Egypt.\u003c/p\u003e \u003cp\u003eMultiple risk factors have been linked with CRF. they are classified into modulating (gender and age), maintaining (lifestyle factors, demographic, and current health), triggering (disease and treatment-related factors), and pre-disposing (genetic disposition) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Under this general classification there are multiple subclasses, to illustrate; Cancer and its treatment can lead to complications such as anemia and gastrointestinal symptoms, which contribute to fatigue during each treatment cycle (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Furthermore, diseases such as depression are often correlated with fatigue in cancer patients (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In addition, lifestyle, as physical activity could be linked to fatigue (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Other factors related to severe fatigue are cancer stage and elevated body mass index (BMI) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequently, assessing CRF requires considering a variety of factors, Multiple instruments have been developed ranging from single-item questions to multidimensional assessment tools. The former assesses multiple fatigue dimensions, allowing for the most accurate measurement and precise results.\u003c/p\u003e \u003cp\u003eOne of the largest Quality of life (QoL) questionnaires database is the patient reported outcomes measurement information system (PROMIS), It evaluates the social, physical, and psychological health of patients and can be used in clinical practice and research (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). the Pediatric Short Form v2.0 - Fatigue 10a was developed by The National institution of health (NIH) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and then used in multiple studies, including studies about pediatric chronic pain (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), cancer (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and sickle cell disease (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The questionnaire has been proven valid and sensitive to change (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Another promising measure (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) is the Pediatric Quality of Life Inventory (PedsQL) measurement model, it measures health-related quality of life (HRQoL). It is a multidimensional fatigue measurement tool that has been used and verified in studies regarding pediatric rheumatology (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), pediatric obesity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), multiple sclerosis (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and cancer patients (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). It also has an Arabic translation that was validated in native Arabic-speaking cancer patients (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In this research we opted for the use of both questionnaires as they measure distinct aspects of fatigue.\u003c/p\u003e \u003cp\u003eSevere fatigue has a significant effect on children with cancer as it interferes with the disease, treatment, function, every aspect of quality of life (QoL) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In some cases, it might even lead to discontinuation of treatment (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Early recognition of fatigue in children with cancer is expected to improve their prognosis and quality of life.\u003c/p\u003e \u003cp\u003eDespite the burden of CRF in children, it is yet to be explored in Egypt. Our current aim is 1) Record the prevalence of fatigue in the oncology pediatric patient population. 2) Explore the relationship between fatigue and some risk factors, including demographic data, type of cancer and treatment, patient-reported symptoms (depression), functionality (upper extremity, mobility), and selected related medical data (white blood cell counts and hemoglobin). Shedding light on children\u0026rsquo;s CRF and their risk factors will provide grounds for addressing this chronic symptom and support the efforts to find a channel that predicts and manages cancer-related fatigue.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design:\u003c/h2\u003e \u003cp\u003eThis is a cross-sectional study to assess fatigue prevalence and its associated risk factors in children and adolescents suffering from cancer in Egypt. Convenience sampling was used to recruit participants from Dar El Salam oncology hospital (Harmil) between October and December 2022. Sample size calculations were done according to the minimal sample size needed to run a regression analysis with an acceptable error level that being six participants per variable in the regression model\u0026thinsp;+\u0026thinsp;1 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), 7 variables would be included into the regression making the minimum targeted sample around 43 participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants:\u003c/h2\u003e \u003cp\u003eInclusion criteria were: 1) Age 8- \u0026lt;18 years at the time of the study, 2) Diagnosed with any type of cancer, 3) Ability to speak, read (alone or with supervision) and understand Arabic, 4) Not in severe distress, 5) prescribed chemotherapy.\u003c/p\u003e \u003cp\u003eExclusion criteria were children suffering from sensory or cognitive deficits that prevent understanding or answering the questionnaire.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements:\u003c/h2\u003e \u003cp\u003eFive questionnaires: \u003cem\u003ePROMIS pediatric short form v2.0 Fatigue 10a\u003c/em\u003e (PROMIS fatigue), \u003cem\u003ePedsQL multidimensional fatigue score acute version 3.0\u003c/em\u003e (PedsQL fatigue), \u003cem\u003ePROMIS Pediatric Item Bank v2.0 \u0026ndash; Depressive Symptoms \u0026ndash; Short Form 8a, PROMIS Pediatric Item Bank v2.0 \u0026ndash; Mobility\u0026ndash; Short Form 8a and PROMIS Pediatric Item Bank v2.0 \u0026ndash; Upper Extremity \u0026ndash; Short Form 8a.\u003c/em\u003e They are self-filled questionnaires by the child. They are listed in the appendix (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePROMIS pediatric short form v2.0 Fatigue 10a\u003c/em\u003e (PROMIS fatigue): It has 10 items with a 7-day recall period that measures the degree of fatigue generally from mild to severe. Each item is measured by a 5-point Likert response scale ranging from \u0026ldquo;never\u0026rdquo; to \u0026ldquo;almost always\u0026rdquo;. PROMIS scores are reported as T-scores with a mean of 50 and a standard deviation (SD) of 10. A higher T-score means a higher degree of fatigue. The questionnaire has been proven valid and sensitive to change (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and has been used and validated in oncology patients (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePedsQL multidimensional fatigue score acute version 3.0\u003c/em\u003e (PedsQL fatigue): It has eighteen items with 7-day recall period that measures 3 dimensions of fatigue; sleep/rest fatigue (6 items), general fatigue (6 items), and cognitive fatigue (6 items). A 5-point Likert scale from 0 \u0026ldquo;Never\u0026rdquo; to 4 \u0026ldquo;Almost always\u0026rdquo; is used to scale each of the items, Then the scores are reversed and linearly transformed to a 0-100 scale. It has been proven to have strong internal consistency, reliability, and validity in cancer patients (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and has an Arabic-validated translation(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCo-occurring symptoms (depressive symptoms) and function (mobility and upper extremity) were measured using the Arabic versions of the PROMIS Pediatric Short Forms of depressive symptoms, mobility, and upper extremity measures. These instruments follow the same measurement technique as PROMIS Fatigue except having eight items. Except for the depressive symptoms form as a higher score means a higher experience of the symptoms.\u003c/p\u003e \u003cp\u003eOther data recorded were demographics (age, gender, height, weight, caregiver relationship, nationality (ethnicity), province and education level), disease and treatment information (type of cancer, time since diagnosis, treatment protocol and other chronic health conditions) and relative clinical data (most recent test results of hemoglobin and white blood cell count).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection:\u003c/h2\u003e \u003cp\u003eThe guardian was approached by the investigator who introduced herself and explained the concept of the survey. If they accepted participation in the study or wanted further information, the couple were directed to a quiet location. Then the investigator explained the research aims to the guardian and the child and asked for their consent. Once written consent was gained, the investigator asked the guardian not to interrupt their child while filling out the questionnaires and any inquiries from the child were directed to the investigator. The questionnaires were printed, and the child read the questions and filled in the questionnaire without interference. The questionnaires were expected to take about 15 minutes.\u003c/p\u003e \u003cp\u003eThe other data were gathered from the guardian and patient files.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePilot study:\u003c/h2\u003e \u003cp\u003eThe questionnaires were the first run in a pilot study to standardize interviewing, ensure clarity of translation and confirm average completion duration. The first stage was run on healthy children, they commented on some expressions that they were unfamiliar such as (shirt \u0026ndash; in Arabic) in item 5 of PROMIS fatigue, which was explained to be a (T-Shirt). The average completion time was 12 minutes. Cancer children were recruited in the following stage, confirming the viability of the cultural adapted explanation examples from the first stage with delicate refinements in the approach and interview. The average duration for this stage was 14 minutes. Full description of the sample in both stages is in appendix 6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Ethics Research Committee at School of pharmacy Newgiza University and the IRB committees at the participating hospital. Written consent was obtained from the child\u0026rsquo;s guardian. A verbal assent was obtained from the child. They were assured that participation was voluntary. All personal information was kept with the principal investigator.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eFor the descriptive data, means (SD) were calculated for continuous variables and frequencies (%) for categorical and ordinal variables. Subgroups\u0026rsquo; characteristics and measures were compared using Mann Whitney U test, Fischer exact test and chi-square test to reveal possible significant differences. Correlation analysis between fatigue from both fatigue measurement tools and patient characteristics was run using spearman test, then the characteristics that showed a correlation probability value of \u0026lt;\u0026thinsp;0.1 were used in a multivariant regression analysis to further quantify this relationship for both fatigue measurement tools. The regression was refined through stepwise regressions which is the iterative repetitive regressions where there is a contentious selection of significant independent variables and exclusion of insignificant variables to reach a final optimized model. The coefficients were further standardized to unify the scales and ease the interpretation of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u0026nbsp;A total of 46 patients were approached between October and December 2022, 42 (91.3%) of them agreed to participate while 4 opted out of participating. After the consent process, all 42 children completed the questionnaires taking an average time of 14.61 minutes (SD: 4.18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of participants:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout half of the children were female (20, 47.6%) with a mean age of 12.1 years (SD 3.3). According to their body mass index (BMI) a substantial number of children were normal weight (31, 73.8%) - only 7 (16.7%) were obese or overweight- and had a mean body surface area (BSA) of 1.27 m\u003csup\u003e2\u003c/sup\u003e (SD: 0.31). most of the participants had a hematological tumor while only 5 (11.9%) had a solid tumor. All children received chemotherapy but were at different stages: most were actively receiving chemotherapy treatment; others were between cycles of chemotherapy or had finished chemotherapy treatment and one hadn’t started chemotherapy yet. The average blood hemoglobin level was 11.03 g/dl (SD 1.86) and the average total leukocytic count was 4.17 x10\u003csup\u003e3\u003c/sup\u003e cell/ml (SD 3.89). The vast majority did not have any other chronic health conditions (39, 92.8%) except for two (4.8%) with a heart condition and one (2.4%) with diabetes. Further sample characteristics are summarized in table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sample could be stratified into 2 distinct subgroups, in-patients, and out-patients. The in-patient group (13, 30.9%) were patients that received care and stayed in admission unit, while the out-patient group (29, 69.1%) were patients treated in day-care setting. The two groups were significantly different in terms of: type of cancer (p value =0.005), time since diagnosis (p value =.0038), type of treatment (p value =0.004), current treatment status (p value =0.007) and hemoglobin (p value= 0.009) as well as showing a significant difference in the mean score of the PROMIS mobility questionnaire (p value 0.026). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 1]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFatigue questionnaires’ scores:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean fatigue score measured by PROMIS fatigue of all participants was 53.76 (SD: 12.5) which interprets to mild fatigue, while for PedsQL fatigue it was 74.27 (SD: 21.79). The participants had variant answers in the two questionnaires, only 33.3% of the participants had matching scores in the two questionnaires (appendix 7). For PROMIS fatigue almost half showed no fatigue (20, 47.6%) and for pedsQL fatigue about half scored mild fatigue (17, 40.5%). Further details are presented in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the in-patient group the two questionnaires were the same in describing the absence of fatigue or having a mild fatigue but showed differences in moderate and severe fatigue. As for the out-patient group the score distribution was completely different between the two tools. For instance, PedsQL fatigue valued about half (15, 51.7%) with mild fatigue while only one (3.4%) was mild in PROMIS fatigue.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations between fatigue score and characteristics:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was a significant correlation between mobility and depressive symptoms with both PROMIS fatigue and PedsQL fatigue that was consistent even after subgrouping. Subgrouping impacted the significant correlation between fatigue scores and BSA as it did not show any significant correlation in the in-patient group with both questionnaires, it also impacted upper extremity as it didn’t show significant correlation in PROMIS fatigue in the in-patient group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn relation to PROMIS fatigue, all participants’ scores moderately correlated with PROMIS mobility (\u003cem\u003er\u003c/em\u003e = -0.702, \u003cem\u003ep value\u003c/em\u003e \u0026lt;0.001), PROMIS depressive symptoms (\u003cem\u003er=\u003c/em\u003e0.669, \u003cem\u003ep value\u003c/em\u003e \u0026lt;0.001) and PROMIS upper extremity (\u003cem\u003er=\u003c/em\u003e -0.545, \u003cem\u003ep value\u003c/em\u003e \u0026lt;0.001). As for pedsQL fatigue, the characteristics that were significantly correlated were mobility (\u003cem\u003er\u003c/em\u003e= 0.667, \u003cem\u003ep value\u003c/em\u003e \u0026lt;0.001), depressive symptoms (\u003cem\u003er\u003c/em\u003e= -0.597, \u003cem\u003ep value\u003c/em\u003e \u0026lt;0.001) and upper extremity (\u003cem\u003er\u003c/em\u003e= 0.524, \u003cem\u003ep value\u003c/em\u003e \u0026lt;0.001). The correlations were stratified by the two subgroups and reported in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 2]\u003c/p\u003e\n\u003cp\u003e[Table 3]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression (factors):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 contains the results for the stepwise regression of PROMIS fatigue where two variables were significant: PROMIS mobility (𝜷\u0026nbsp;-0.39, \u003cem\u003ep\u003c/em\u003e value= 0.002) and PROMIS depressive symptoms (𝜷\u0026nbsp;= 0.47, \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.001). Regarding PedsQL fatigue, stepwise regression results showed three significant variables: PROMIS upper extremity (𝜷\u0026nbsp;= 0.34, \u003cem\u003ep\u003c/em\u003e value= 0.005), PROMIS depressive symptoms (𝜷\u0026nbsp;= -0.49, \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.001), treatment status (𝜷\u0026nbsp;= -0.25, \u003cem\u003ep\u003c/em\u003e value= 0.013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 4]\u003c/p\u003e\n\u003cp\u003e[Table 5]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the prevalence, degree of and factors predicting fatigue in children with cancer prescribed chemotherapy at various stages of treatment. Results supported that CRF in children undergoing chemotherapy treatment was prevalent and affected the children\u0026rsquo;s day-to-day functions. Findings also confirmed the convoluted nature of CRF and added to previous research on the significant roles of depressive symptoms, mobility, upper extremity function and treatment status on fatigue in children (8\u0026ndash;18 years) with cancer specially in Egypt.\u003c/p\u003e \u003cp\u003ePROMIS fatigue showed that over half of the children had a fatigue score higher than the public (T score\u0026thinsp;=\u0026thinsp;50). The mean T score for all children was 53.76 which is less than 1 point above their U.S counterparts (T score 52.9) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and five points above their Chinese counterparts (T score 48.52) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The minimal significant difference is 3 points (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), making our sample equivalent to their U.S counterparts and different from their Chinese counterparts. Suggested reasons could be cultural or sample differences. Culturally, Chinese might view \u0026ldquo;enduring\u0026rdquo; fatigue as \u0026ldquo;tolerating hardship\u0026rdquo; and thus must bear it and refuse reporting. Looking further into the study sample, half of them were suffering from solid and brain tumors and those diagnosed with solid tumors were on average approaching the end of their active treatment(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) which is expected to have less fatigue overall.\u003c/p\u003e \u003cp\u003eAs for the PedsQL fatigue, around half (47.6%) of children reported more fatigue than the mean score of healthy children from a sample in the United States (score\u0026thinsp;=\u0026thinsp;80.49) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The mean score of the sample (mean score 70.98) was around 3.5 points higher than their U.S counterparts (mean score 70.98) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), even after stratification of the sample the trend for both strata remained the same (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). An explanation behind our participants showing less fatigue than their U.S counterparts could be traced back to sample differences. Their sample was more diverse including brain tumors, recent remissions, and long-term off-treatment which could influence the results as long-term off-treatment patients are more fatigued than cancer patients on active treatment (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur sample showed different results for measured level of fatigue in the two questionnaires. That could be attributed to differences between the dimensions measured by the two questionnaires. PROMIS fatigue was focused on general fatigue a child might experience in their day-to-day life while PedsQL fatigue had one section for general fatigue and two other sections for sleep/rest fatigue and cognitive fatigue making it more sensitive to detecting fatigue and determining the source of it. That is reflected in our sample by PedsQL fatigue detecting fatigue in more participants than PROMIS fatigue in our overall sample and the outpatients group.\u003c/p\u003e \u003cp\u003eSubgroup comparison between in-patients and out-patients revealed interesting observations. At first, although the perceived reason for admission is low TLC and hemoglobin, the two subgroups were equivalent regarding these two clinical indicators. This might be due to other admission reasons that were not properly recorded like the start of treatment or complaining of fever. It was anticipated that the inpatient group would show more fatigue than the outpatient group as observed by previous research (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Our sample followed that observation. However, the difference between the fatigue scores in both questionnaires was not significant, which could be due to the lack of statistical power in our sample. It is worth noting that the subgroups were significantly different in terms of mobility and time since diagnosis, which could be used as an indicator to the patients that would display more fatigue.\u003c/p\u003e \u003cp\u003eThe predictors of PedsQL fatigue and PROMIS fatigue scores were not the same, For PROMIS fatigue they were PROMIS mobility and PROMIS depressive symptoms, while for PedsQL fatigue predictors were PROMIS upper extremity, PROMIS depressive symptoms and treatment status. Depressive symptoms have been recognized as a part of symptoms cluster made of fatigue and were found to be a significant predictor of fatigue in other studies (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Looking at the standardized \u0026#120631; coefficients, PROMIS depressive symptoms is the highest predicting factor of fatigue in both PROMIS fatigue and PedsQL fatigue emphasizing the importance of mental health in fatigue management. PROMIS mobility and PROMIS upper extremity being significant predictors also complies with previous research as it was found that inactivity contributes to the development and persistence of fatigue (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). As for treatment status being a significant predictor, chemotherapy leads to an increase in the level of specific inflammatory markers and associated with increase in fatigue prevalence (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite fatigue\u0026rsquo;s magnitude in children in Egypt, it is not being screened for on a regular basis, that could be for multiple reasons, for example there is no agreed upon measure to be used in screening(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) as well as health care professionals having limited knowledge about fatigue importance and its management (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Also, patients perceive fatigue as a normal thing to experience because of their diagnosis and not as something they should seek medical attention for(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) and these factors collectively lead to overlooking fatigue and not including it in the care plan. A few interventions have been explored and developed to reduce fatigue in cancer patients. NCCN developed an algorithm for pediatrics made of screening, primary evaluation, intervention and re-evaluation, their intervention plan is based on eliminating the factors causing fatigue and if the fatigue is unresolved, that there is a spectrum of recommendations ranging between nonpharmacological approaches like exercise and psychological programs to pharmacological approaches like antidepressants (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has limitations. The small sample size affects the power of differentiation and prediction of some factors as stated earlier. This study could be viewed as an opportunity to raise the problem of fatigue in cancer patients and open the door for future larger studies. Our data was only collected from one site. Despite that the location was a tertiary hospital and our sample coming from different provinces of Egypt, it does not cover the whole population. However, it can be the start for further studies in more diverse locations. Lastly, the guardians and children sat at the same location during the interviews Although we tried to eliminate the guardian\u0026rsquo;s influence on the child, the simple presence of the guardian could affect the independence of children\u0026rsquo;s answers to the questionnaires.\u003c/p\u003e \u003cp\u003eIn conclusion, children with cancer in Egypt experience fatigue during their treatment that is underreported and undermanaged. Factors predicting fatigue in the current sample were depressive symptoms, upper extremity, mobility, and treatment status meaning that patients during active chemotherapy treatment who show mobility issues and/or depressive symptoms are in most need for fatigue management. PedsQL Fatigue might represent more detailed information about fatigue which allows for a better care plan designing while PROMIS fatigue measures general fatigue which might fit more for being part of longer surveys or research activities. Future studies are warranted on a wider spectrum of children with cancer post/pre-treatment to quantify the extent of fatigue and start implementing its prevention in the care plan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBerger AM, Abernethy AP, Atkinson A, Barsevick AM, Breitbart WS, Cella D, et al. Cancer-Related Fatigue Clinical Practice Guidelines in Oncology NCCN Clinical Practice Guidelines in Oncology on Cancer-Related Fatigue [Internet]. Vol. 8, Journal of the National Comprehensive Cancer Network |. 2010. Available from: www.NCCN.org.\u003c/li\u003e\n \u003cli\u003evan Deuren S, Boonstra A, van Dulmen-den Broeder E, Blijlevens N, Knoop H, Loonen J. Severe fatigue after treatment for childhood cancer. Vol. 2020, Cochrane Database of Systematic Reviews. John Wiley and Sons Ltd; 2020 Mar.\u003c/li\u003e\n \u003cli\u003eda Silva MCM, Lopes J\u0026uacute;nior LC, Nascimento LC, de Lima RAG. Fadiga em crian\u0026ccedil;as e adolescentes com c\u0026acirc;ncer sob a perspectiva dos profissionais de sa\u0026uacute;de. Rev Lat Am Enfermagem. 2016;24.\u003c/li\u003e\n \u003cli\u003eCheng L, Wang Y, Duan M, Wang J, Wang Y, Huang H, et al. Self-Reported Fatigue in Chinese Children and Adolescents During Cancer Treatment. Journal of Pediatric Oncology Nursing. 2021 Jul 1;38(4):262\u0026ndash;70.\u003c/li\u003e\n \u003cli\u003eAbu-Taha OM, Al Qadire MI, Maharmeh M, Alyami MS. Assessment of cancer-related fatigue among Jordanian patients: a cross-sectional survey. British Journal of Nursing [Internet]. 2020 Jan 23 [cited 2022 Sep 5];29(2):111\u0026ndash;7. Available from: http://www.magonlinelibrary.com/doi/10.12968/bjon.2020.29.2.111\u003c/li\u003e\n \u003cli\u003eBower JE. Cancer-related fatigue-mechanisms, risk factors, and treatments. Nature Publishing Group [Internet]. 2014;11:597\u0026ndash;609. Available from: www.nature.com/nrclinonc\u003c/li\u003e\n \u003cli\u003ePatient-Reported Outcomes Measurement Information System (PROMIS) | National Institute on Aging [Internet]. [cited 2022 Oct 2]. Available from: https://www.nia.nih.gov/research/resource/patient-reported-outcomes-measurement-information-system-promis\u003c/li\u003e\n \u003cli\u003eLai JS, Stucky BD, Thissen D, Varni JW, DeWitt EM, Irwin DE, et al. Development and psychometric properties of the PROMIS\u0026reg; pediatric fatigue item banks. Quality of Life Research. 2013 Nov;22(9):2417\u0026ndash;27.\u003c/li\u003e\n \u003cli\u003eQuinn H, Thissen D, Liu Y, Magnus B, Lai JS, Amtmann D, et al. Using item response theory to enrich and expand the PROMIS\u0026reg; pediatric self report banks. Health Qual Life Outcomes. 2014;12(1).\u003c/li\u003e\n \u003cli\u003eSikorskii A, Victorson D, O\u0026rsquo;Connor P, Hankin V, Safikhani A, Crane T, et al. PROMIS and legacy measures compared in a supportive care intervention for breast cancer patients and caregivers: Experience from a randomized trial. Psychooncology. 2018 Sep 1;27(9):2265\u0026ndash;73.\u003c/li\u003e\n \u003cli\u003eLeung YW, Brown C, Cosio AP, Dobriyal A, Malik N, Pat V, et al. Feasibility and diagnostic accuracy of the Patient-Reported Outcomes Measurement Information System (PROMIS) item banks for routine surveillance of sleep and fatigue problems in ambulatory cancer care. Cancer [Internet]. 2016 Sep 15 [cited 2022 Aug 31];122(18):2906\u0026ndash;17. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/cncr.30134\u003c/li\u003e\n \u003cli\u003eDobrozsi S, Yan K, Hoffmann R, Panepinto J. Patient-reported health status during pediatric cancer treatment. Pediatr Blood Cancer [Internet]. 2017 Apr 1 [cited 2022 Oct 12];64(4). Available from: https://pubmed.ncbi.nlm.nih.gov/27808460/\u003c/li\u003e\n \u003cli\u003eAmeringer S, Elswick JK, Smith W. Fatigue in Adolescents and Young Adults with Sickle Cell Disease: Biological and Behavioral Correlates and Health-Related Quality of Life. J Pediatr Oncol Nurs [Internet]. 2014 Jan [cited 2022 Oct 2];31(1):6. Available from: /pmc/articles/PMC3982311/\u003c/li\u003e\n \u003cli\u003eYoon IA, Sturgeon JA, Feinstein AB, Bhandari RP. The role of fatigue in functional outcomes for youth with chronic pain. European Journal of Pain [Internet]. 2019 Sep 1 [cited 2022 Oct 2];23(8):1548\u0026ndash;62. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/ejp.1431\u003c/li\u003e\n \u003cli\u003eHinds PS, Wang J, Cheng YI, Stern E, Waldron M, Gross H, et al. PROMIS pediatric measures validated in a longitudinal study design in pediatric oncology. Pediatr Blood Cancer [Internet]. 2019 May 1 [cited 2022 Sep 29];66(5):e27606. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/pbc.27606\u003c/li\u003e\n \u003cli\u003eKashikar-Zuck S, Carle A, Barnett K, Goldschneider KR, Sherry DD, Mara CA, et al. Longitudinal evaluation of Patient Reported Outcomes Measurement Information Systems (PROMIS) measures in pediatric chronic pain. Pain [Internet]. 2016 Feb 1 [cited 2022 Oct 2];157(2):339. Available from: /pmc/articles/PMC4724302/\u003c/li\u003e\n \u003cli\u003eUpton P, Eiser C, Cheung I, Hutchings HA, Jenney M, Maddocks A, et al. Measurement properties of the UK-English version of the Pediatric Quality of Life Inventory\u003csup\u003eTM\u003c/sup\u003e 4.0 (PedsQL\u003csup\u003eTM\u003c/sup\u003e) generic core scales. Health Qual Life Outcomes [Internet]. 2005 Apr 1 [cited 2022 Oct 5];3:22. Available from: /pmc/articles/PMC1079918/\u003c/li\u003e\n \u003cli\u003eVarni JW, Burwinkle TM, Szer IS, Varni JW. The PedsQL\u003csup\u003eTM\u003c/sup\u003eMultidimensional Fatigue Scale inPediatric Rheumatology: Reliability and Validity [Internet]. Vol. 31, The Journal of Rheumatology. 2494. Available from: www.jrheum.org\u003c/li\u003e\n \u003cli\u003eVarni JW, Limbers CA, Bryant WP, Wilson DP. The PedsQL\u003csup\u003eTM\u003c/sup\u003e Multidimensional Fatigue Scale in pediatric obesity: Feasibility, reliability and validity. International Journal of Pediatric Obesity. 2010;5(1):34\u0026ndash;42.\u003c/li\u003e\n \u003cli\u003eGrover SA, Aubert-Broche B, Fetco D, Louis Collins D, Arnold DL, Finlayson M, et al. Lower physical activity is associated with higher disease burden in pediatric multiple sclerosis. Neurilogy. 2015;1663\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eMeeske K, Katz ER, Palmer SN, Burwinkle T, Varni JW. Parent proxy-reported health-related quality of life and fatigue in pediatric patients diagnosed with brain tumors and acute lymphoblastic leukemia. Cancer. 2004 Nov 1;101(9):2116\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eNunes MDR, Jacob E, Bomfim EO, Lopes-Junior LC, de Lima RAG, Floria-Santos M, et al. Fatigue and health related quality of life in children and adolescents with cancer. European Journal of Oncology Nursing. 2017 Aug 1;29:39\u0026ndash;46.\u003c/li\u003e\n \u003cli\u003eGrant WB. The PedsQL\u003csup\u003eTM\u003c/sup\u003e in Pediatric CancerReliability and Validity of the Pediatric Quality of Life Inventory\u003csup\u003eTM\u003c/sup\u003e Generic CoreScales, Multidimensional Fatigue Scale, and Cancer Module. Cancer. 2002 Mar 15;94(6):1867\u0026ndash;75.\u003c/li\u003e\n \u003cli\u003eAl-Gamal E, Long T. The Psychometric Properties of an Arabic version of the PedsQL Multidimensional Fatigue Scale Tested for Children with Cancer. Compr Child Adolesc Nurs. 2017 Jul 3;40(3):188\u0026ndash;99.\u003c/li\u003e\n \u003cli\u003eCheng KKF, Lee DTF. Effects of pain, fatigue, insomnia, and mood disturbance on functional status and quality of life of elderly patients with cancer. Crit Rev Oncol Hematol. 2011 May 1;78(2):127\u0026ndash;37.\u003c/li\u003e\n \u003cli\u003eJenkins DG, Quintana-Ascencio PF. A solution to minimum sample size for regressions. PLoS One. 2020 Feb 1;15(2).\u003c/li\u003e\n \u003cli\u003eDobrozsi S, Yan K, Hoffmann R, Panepinto J. Patient-reported health status during pediatric cancer treatment. Pediatr Blood Cancer. 2017 Apr 1;64(4).\u003c/li\u003e\n \u003cli\u003eMeeske K, Katz ER, Palmer SN, Burwinkle T, Varni JW. Parent proxy-reported health-related quality of life and fatigue in pediatric patients diagnosed with brain tumors and acute lymphoblastic leukemia. Cancer. 2004 Nov 1;101(9):2116\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eAl-Gamal E, Long T. The Psychometric Properties of an Arabic version of the PedsQL Multidimensional Fatigue Scale Tested for Children with Cancer. Compr Child Adolesc Nurs. 2017 Jul 3;40(3):188\u0026ndash;99.\u003c/li\u003e\n \u003cli\u003eHinds PS, Nuss SL, Ruccione KS, Withycombe JS, Jacobs S, Deluca H, et al. PROMIS pediatric measures in pediatric oncology: Valid and clinically feasible indicators of patient-reported outcomes. Pediatr Blood Cancer [Internet]. 2013 Mar 1 [cited 2022 Dec 26];60(3):402\u0026ndash;8. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/pbc.24233\u003c/li\u003e\n \u003cli\u003eThissen D, Liu Y, Magnus B, Quinn H, Gipson DS, Dampier C, et al. Estimating minimally important difference (MID) in PROMIS pediatric measures using the scale-judgment method. Quality of Life Research. 2016 Jan 1;25(1):13\u0026ndash;23.\u003c/li\u003e\n \u003cli\u003eGrant WB. The PedsQL\u003csup\u003eTM\u003c/sup\u003e in Pediatric CancerReliability and Validity of the Pediatric Quality of Life Inventory\u003csup\u003eTM\u003c/sup\u003e Generic CoreScales, Multidimensional Fatigue Scale, and Cancer Module. Cancer. 2002 Mar 15;94(6):1867\u0026ndash;75.\u003c/li\u003e\n \u003cli\u003eMccabe M, Patricia B. Fatigue in the Acute Care and Ambulatory Setting. J Pediatr Nurs. 2014;29(4):344\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eSpathis A, Hatcher H, Booth S, Gibson F, Stone P, Abbas L, et al. Cancer-Related Fatigue in Adolescents and Young Adults After Cancer Treatment: Persistent and Poorly Managed. J Adolesc Young Adult Oncol [Internet]. 2017 Sep [cited 2023 Jan 3];6(3):489\u0026ndash;93. Available from: http://www.liebertpub.com/doi/10.1089/jayao.2017.0037\u003c/li\u003e\n \u003cli\u003eBerger AM, Mooney K, Alvarez-Perez A, Breitbart WS, Carpenter KM, Cella D, et al. Cancer-Related Fatigue, Version 2.2015. Journal of the National Comprehensive Cancer Network [Internet]. 2015 Aug;13(8):1012\u0026ndash;39. Available from: https://jnccn.org/doi/10.6004/jnccn.2015.0122\u003c/li\u003e\n \u003cli\u003eBower JE. Cancer-related fatigue-mechanisms, risk factors, and treatments. Nature Publishing Group [Internet]. 2014;11:597\u0026ndash;609. Available from: www.nature.com/nrclinonc\u003c/li\u003e\n \u003cli\u003eSilva MCM da, Lopes J\u0026uacute;nior LC, Nascimento LC, Lima RAG de. Fatigue in children and adolescents with cancer from the perspective of health professionals. Rev Lat Am Enfermagem [Internet]. 2016;24(0). Available from: http://www.scielo.br/scielo.php?script=sci_arttext\u0026amp;pid=S0104-11692016000100405\u0026amp;lng=en\u0026amp;tlng=en\u003c/li\u003e\n \u003cli\u003eThong MSY, van Noorden CJF, Steindorf K, Arndt V. Cancer-Related Fatigue: Causes and Current Treatment Options. Curr Treat Options Oncol. 2020;21(2).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Newgiza University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PedsQL, Quality of Life, PROMIS, Pediatric Short Form-Fatigue, Arabic, oncology","lastPublishedDoi":"10.21203/rs.3.rs-4757144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4757144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cancer related fatigue (CRF) is a common side effect of cancer and cancer treatment that impacts quality of life. To our knowledge, the statistics for prevalence in pediatrics are lacking in Egypt. The aim of this study is to record the prevalence of fatigue and its significant predicting factors in pediatric oncology patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ewe interviewed children aged 8-18 years with cancer, prescribed chemotherapy and not in severe distress. The children personally filled 2 fatigue-related questionnaires (PROMIS Pediatric Short Forms of Fatigue (PROMIS fatigue), pedsQL multidimensional fatigue (PedsQL fatigue)) and 3 symptoms related questionnaires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e42 children (47.6% female) (mean age 12.1 years (SD 3.3 years)) participated. Reported moderate to severe fatigue in children is between half to third of the children depending on the measurement tool used. The mean T-score for PROMIS fatigue was 53.76 (SD 12.5), and for PedsQL fatigue was 74.27 (SD 21.79). Stepwise standardized multivariant linear regression showed that fatigue following PROMIS fatigue could be predicted by depressive symptoms (𝜷= 0.47, p \u0026lt;0.001) and mobility (𝜷= -0.39, p =0.002) while following PedsQL fatigue, it could be predicted by upper extremity function (𝜷= 0.34, p= 0.005), depressive symptoms (𝜷=-0.49, p \u0026lt;0.001) and treatment status (𝜷=-0.25, p= 0.013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eCRF is multifactorial and prevalent among children and adolescents with cancer. Moreover, predicting factors differed between different tools as they measure different dimensions of fatigue. There is a need to include fatigue screening for pediatric oncology patients and incorporate its management in the medical care plan.\u003c/p\u003e","manuscriptTitle":"Measuring the Prevalence of Fatigue in children with cancer: Evidence from Egypt.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 08:42:27","doi":"10.21203/rs.3.rs-4757144/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96d423aa-b4a9-41a3-b22a-93614583bbf7","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T06:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 08:42:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4757144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4757144","identity":"rs-4757144","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}