{"paper_id":"0ef62bcc-a6ef-4815-9341-3d19339f19cd","body_text":"The use of generic health-related quality of life instruments among children and adolescents in low- and middle-income countries: a scoping review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The use of generic health-related quality of life instruments among children and adolescents in low- and middle-income countries: a scoping review Goitom Molalign Takele, Trudy Sullivan, Ari Samaranayaka, Mimmi Åström, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7617141/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2026 Read the published version in Systematic Reviews → Version 1 posted 4 You are reading this latest preprint version Abstract Background Health-related quality of life (HRQoL) measures are widely applied in research and clinical practice; however, their use among children and adolescents in low- and middle-income countries (LMICs) has not been explored in depth to date. This is particularly important given that nearly two-thirds of the world’s children and adolescents reside in LMICs. This scoping review aims to identify generic HRQoL instruments used in studies of children and adolescents in LMICs, and to describe their use and reported psychometric properties. Method Guided by Arksey and O’Malley’s framework, a search for peer-reviewed papers published between 1 January 2000 and 31 December 2024 was conducted in six databases (Medline, Embase, PubMed, Scopus, CINAHL, and Web of Science). The review included papers reporting the use of generic HRQoL instruments among children and adolescents aged 0–19 years in LMICs. Results A total of 152 papers originating from 22 (of 75) LMICs met the inclusion criteria. Most papers were from two countries: India 50 (32.9%) and Egypt 25 (16.5%). Eight generic HRQoL instruments (PedsQL 4.0 GCS, KIDSCREEN-10/27/52, HUI-2/3, EQ-5D-Y-3L/5L, PROMIS-25, PedsQL Short Form (15-items), EQ-5D-3L/5L, and SF-36) were identified. Of these, PedsQL 4.0 GCS was reported in 78% of papers reviewed. Only eleven studies (7.2%) reported use of the instruments in the general population, and only one generated population norm data. Very few studies 2 (1.3%) were conducted in the younger age group (≤ 4 years). Three-quarters of the studies (34.9%) included both self- and proxy-reported HRQoL data. Instruments identified were most often used for general health assessment (77.6%); only 9.9% of studies evaluated treatment or intervention outcomes, and 12.5% reported on psychometric testing of the instruments. Nineteen studies (12.5%) reported psychometric properties, but none evaluated all nine properties recommended by COSMIN. Conclusion The use of generic HRQoL instruments in studies of children and adolescents in LMICs, mainly with PedsQL, has increased in recent years, though almost half of the studies identified were carried out in only two countries. Despite their growing use, gaps remain in population coverage, generation of evidence on their psychometric performance in LMICs, and the geographic distribution of research in LMICs using generic HRQoL instruments. Scoping review registration: The protocol was submitted to Open Science Framework on 24 January 2025. Registration DOI: https://doi.org/10.17605/OSF.IO/MVG62 Health-related quality of life (HRQoL) Children Adolescents Generic instrument Low- and middle-income countries (LMICs) Figures Figure 1 Figure 2 Figure 3 Background In recent years, the population of children and adolescents in low- and middle-income countries (LMICs) has increased significantly ( 1 , 2 ). Almost two-thirds (65%) of the global children and adolescent populations live in LMICs, and this is projected to rise through to 2050 ( 2 , 3 ). However, this demographic shift is accompanied by a disproportionate burden of both communicable and non-communicable diseases, maternal and reproductive health problems, and injuries borne in LMICs compared to high-income countries ( 4 – 6 ). These burdens can restrict, or even prevent, children and adolescents from attaining their full potential ( 7 ). In light of this, major global organizations such as the United Nations, through Sustainable Development Goal 3 (SDG-3) and the global strategy for Women’s, Children’s, and Adolescent health, have emphasized the well-being of LMIC populations ( 8 , 9 ). Similarly, the Africa Research Implementation Science and Education (ARISE) Network has highlighted the importance of assessing adolescents’ health and wellbeing in LMICs ( 1 ). While measures of mortality and morbidity are important, comprehensive assessment of general health, as experienced by children and adolescents, is also necessary to understand the health needs of children and adolescents from their perspectives. Consequently, measures of health-related quality of life (HRQoL), which assess the physical, psychological, and social aspects of overall health, are increasingly used as patient-reported outcome measures (PROMs) ( 10 ) in general population research as well as in clinical settings (11). Generic HRQoL instruments cover broad health domains and can be completed by both healthy and ill individuals, allowing comparisons across populations and within groups (e.g. pre- and post-treatment) (12). In contrast, disease-specific HRQoL instruments focus on health aspects related to particular disease conditions and are less appropriate for general population comparisons (10, 13). Generic HRQoL instruments have been used as outcome measures monitoring population health changes over time, in clinical settings, and in economic evaluations (14–21). For LMICs with limited healthcare budgets, such instruments could provide substantial benefits by enabling the monitoring of health changes, evaluating medical interventions, identifying high risk groups, and prioritizing healthcare interventions. Although various HRQoL instruments have been developed and validated for children and adolescents, no comprehensive review has examined the use of generic HRQoL instruments in LMICs. Existing reviews have focused on specific diseases (22–24), geographic regions (25) or populations (26), leaving a gap in understanding of their broader use in LMICs. Given the broad applicability of generic HRQoL instruments, this review focuses on their use among children and adolescents in LMICs. This scoping review aims to systematically identify the generic HRQoL instruments used among children and adolescents in LMICs, with particular attention to their stated purposes and reported psychometric properties. The evidence generated is expected to provide researchers, clinicians, and policymakers with a comprehensive understanding of the current state of HRQoL measurement in LMICs and highlight opportunities for improving their use in future studies. Methods The scoping review was registered in Open Science Framework (OSF) (27). The review follows the framework proposed by Arksey and O’Malley (28), incorporating the enhancement of Levac et al.’s guidelines for conducting scoping reviews (29). The Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Review (PRISMA-ScR) (30) was used to ensure complete and transparent reporting of the scoping review (Supplementary Data 1). Search strategy A systematic search of Medline (via Ovid), Embase (via Ovid), PubMed, Scopus, CINAHL, and Web of Science databases was conducted to identify peer-reviewed papers published in English from 2000 to 2024. The search strategy was developed in consultation with the Health Sciences librarian at the University of Otago and the wider research team. It combined Medical Subject Headings (MeSH), keywords, abbreviations, and synonyms using Boolean operators (‘AND’ and ‘OR’). Search terms included: (i) “Child,” “Adolescents,” “Paediatrics,” “Teenagers,” and “school children”; (ii) “Quality of Life,” “Health-related quality of life,” “PROM,” and “HRQoL”; (iii) “PedsQL 4.0 GCS,” “HUI-2/3,” “KIDSCREEN-52/27/10,” “EQ-5D-Y-3L/5L,” “PROMIS-25,” “AQoL-6D,” “CHU-9D,” ”AHUM,” “16D-HRQoL,” and “EQ-TIPS,”; and (iv) a list of 75 LMIC names (31). Study selection and eligibility criteria Identified papers were independently screened by two reviewers (GM and a second reviewer). The first 100 abstracts were jointly reviewed to ensure consistency. Subsequently, the reviewers independently conducted title and abstract screening, followed by a full-text screening of all eligible papers, with regular meetings to resolve any discrepancies at the end of each stage. Papers fulfilling the following criteria were included: Peer-reviewed research papers presenting empirical quantitative findings derived from the use of generic HRQoL instruments in children and adolescents aged ≤ 19 years in LMICs (based on the World Bank Classification (31)). Papers that included both LMICs and non-LMICs data were eligible if the LMICs findings were reported separately. Peer-reviewed papers published between 1 January 2000 and 31 December 2024. Papers published in the English language. Papers were excluded if they: Reported only on the use of non-generic HRQoL instruments; Were conference abstracts, editorials, discussion papers, or papers that were unable to be retrieved; Did not specify the target population as being within an LMIC or in the relevant age group; Reported findings from qualitative studies, meta-analyses, systematic reviews, or scoping reviews. Data extraction Papers identified for inclusion were read in full, and study data were extracted by the first reviewer (GM). Data were collected on the study characteristics (Table 1 , Table 3 , Fig. 1 , Fig. 2 , and Supplementary Table S1 ) using a Microsoft Excel spreadsheet. Data analyses The description of included papers was guided by the review’s research questions. Psychometric properties were categorized and reported according to the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) categorization (32). Reported disease conditions were categorized based on the International Classification of Diseases version (ICD-11) (33). For the application (i.e. purpose of the HRQoL instrument), first, the objectives of the extracted studies were examined, and three categories were identified: general HRQoL assessment, evaluation of treatment or care outcomes/assessing change for intervention (trials), and cross-cultural validation and/or psychometric studies. Then, within each paper’s reported purpose category, data were summarized descriptively with frequencies (percentages). A narrative summary of the findings and their relationship to the review’s aims and research questions, along with supporting data, was also produced. Results The search of six databases identified a total of 2154 potential papers. After removing duplicate papers, the number was reduced to 1,484. Following title and abstract screening, 1104 papers were excluded, leaving 380 eligible for full-text review. Of these, 228 were removed after full-text review. Consequently, 152 papers reporting findings from 145 distinct studies were included in the review's synthesis (Fig. 1 ). Fifteen papers reported findings from seven of the 145 distinct studies. The studies with multiple publications were conducted in India (34–39), Vietnam (40–42), Nigeria (43, 44), Sri Lanka (45, 46), and Malawi (47, 48). Characteristics of the included papers The search sought to identify papers published from 1 January 2000 to 31 December 2024; however, all retained eligible papers were published from 2009 to 2024. Of the 152 papers, 126 (83%) were published in the last 10 years (Table 1 ); 26 (19%) being published in 2024 alone. Most of the papers reported on studies using cross-sectional designs (n = 128) (34–161), followed by randomized controlled trials (n = 11) (162–172), case-control studies (n = 8) (173–180), cohort studies (n = 4) (181–184), and there was one quasi-experimental study (n = 1) (185). Only one study employed a mixed-methods approach, combining both quantitative and qualitative methods (only the quantitative results were included in this review) (108) (Table 1 ). Participants were mostly recruited from outpatient settings (n = 70), inpatient settings (n = 16), schools (n = 46), and other community sites (e.g., homes, public parks) (n = 20). A smaller number of papers reported recruitment from both inpatient and outpatient settings (n = 6), care homes (n = 3), and specialized schools (n = 3, including two for Deaf children and one for children with intellectual disabilities). The recruitment setting was not clearly stated in 38 papers (Supplementary Data 2 Table S1 ). Table 1 Characteristics of the included papers Characteristics Category Papers n (%) Mutually exclusive age groups 1 month – 4 years 2 (1.3) 1–12 years 8 (5.3) 2–18 years 26 (17.0) 5–18 years 41 (27.0) 8–18 years 65 (42.8) < 18 years 10 (6.6) Years of publication 2005–2009 1 (0.7) 2010–2014 25 (16.4) 2015–2019 48 (31.6) 2020–2024 78 (51.3) Study designs Randomized controlled trials 11 (7.3) Cross-sectional 128 (84.3) Case-control 8 (5.2) Cohort study 4 (2.6) Quasi experimental 1 (0.6) Study settings* Outpatient clinics 70 (46.0) Inpatient clinics 16 (10.5) Inpatient and outpatient clinics 6 (4.0) Community 20 (13.2) Schools 46 (30.3) Care homes 3 (2.0) Specialized schools 3 (2.0) Not stated 38 (25.0) *Participant recruitment (since participants were recruited in more than one setting in several papers the total is more than the number of papers included). The papers covered a wide range of health conditions as well as studies focusing on healthy child and adolescent populations (Table S1 ). Among the 152 papers, the most frequently reported clinical conditions were cancer (n = 22), blood disorders (n = 21), and infectious diseases (n = 18) (Fig. 2 ). Eleven (7.2%) studies focused on general child and adolescent populations; one study generated population norm data. Countries of origin The 152 papers identified are from 22 of the 75 LMICs (31). The majority, 129 (84.9%), were conducted in lower-middle-income countries, while the remaining 23 (15.1%) papers came from low-income countries (Table S1 ). Fifty papers (32.9%) came from studies undertaken in India, 25 (16.5%) from Egypt, and the remaining coming from the 20 other countries (Table S1 ). One cross-country study reported HRQoL findings from two countries (Uganda and Kenya) (123). Characteristics of participants in the included papers The participant sample size reported in the included papers ranged from 18 (133) to 3227 (105) children and/or adolescents. Most papers reported HRQoL findings for children and adolescents aged 8–18 years (n = 65), followed by those aged 5–18 years (n = 41); 10 included children and adolescents of all age groups. Notably, only two papers specifically focused on young children; one on those aged 1 month to 2 years (141) and another on toddlers 2–4 years (41)(Table S1 ). HRQoL instruments Eight different generic HRQoL instruments were used in the 152 papers (Supplementary Data 3 Table S2 ; Fig. 3 ). The most frequently used instrument was the Paediatrics Quality of Life Inventory 4.0 Generic Core Scale (PedsQL 4.0 GCS) (n = 118; 77.6%) (186), followed by the KIDSCREEN (n = 14; 9.2%: KIDSCREEN-10: n = 6, KIDSCREEN-27: n = 8, and KIDSCREEN-52: n = 2) (187, 188), the EQ-5D (n = 8; 5.3%; EQ-5D-Y-3L: n = 6, EQ-5D-Y-5L: n = 3, EQ-5D-3L: n = 1, EQ-5D-5L: n = 2) (189–191), the Health Utility Index (n = 5; 3.3%; HUI-2: n = 2, and HUI-3: n = 4) (192, 193), Paediatrics Quality of Life Inventory Short Form 15-items (PedsQL SF 15-items) (n = 3; 2.0%) (194), Patient-Reported Outcomes Measurement Information System-25 (PROMIS-25) (n = 2; 1.3%) (195), and Short Form Health Survey 36-items (SF-36) (n = 1; <1%) (196). The two instruments developed for use in adult populations, the EQ-5D-3L/5L (159, 160) and SF-36 (50), were used in studies among adolescents aged 13–18 years and 15–18 years, respectively. The use of PedsQL instruments has increased through the years (Fig. 3 ). Among the n = 118 papers reporting use of PedsQL 4.0 GCS, 40 were from India and 23 from Egypt. Use of the EQ-5D-Y-3L/5L and EQ-5D-3L/5L instruments in LMICs has emerged more recently, with eligible papers using these instruments all being published after 2020. Participants completing the HRQoL instruments Details regarding the methods of HRQoL data collection, including the respondent (child/adolescent or proxy), response mode, and administration format were obtained or derived from the papers (Table S1 ). Almost all the identified generic HRQoL instruments are available for self- and proxy-report. Most papers (n = 53; 34.9%) reported both self- and proxy-reports (by parents or caregivers), followed by self-report only (n = 48; 31.6%), and proxy-report only (n = 17; 11.2%). Additionally, 21 papers (13.8%) reported the use of proxies only in certain situations, such as younger children under 7 years. Twelve papers (7.9%) did not clearly state the reporting method. Among proxy-reported HRQoL, only one paper used physicians (rather than parents/caregivers) as proxies (75). Most papers used self-completed questionnaires (n = 67; 44.1%), followed by interviewer-administered format (n = 52; 34.2%) and mixed methods (self-completed and interviewer-administered) (n = 10; 6.6%). The mode of administration was unclear or not reported in 23 papers (15.1%). Methods of instrument completion The HRQoL instruments were administered in several ways, mainly via pen-and-paper questionnaires (n = 95; 62.5%), digital questionnaires on devices such as tablets, PCs, and smartphones (n = 9; 5.9%), telephone interviews (n = 4; 2.6%), and mixed methods (n = 4; 2.6%). The method of instrument completion was not clearly stated in 40 papers (26.3%). Purpose of using the HRQoL instruments Eligible retained papers were categorized into three main groups based on the primary purpose of HRQoL instrument application: general HRQoL assessment (n = 118; 77.6%), evaluation of treatment or intervention outcomes (n = 15; 9.9%), and cross-cultural validation and/or psychometric studies (n = 19; 12.5%). Within the general HRQoL assessment category, various purposes were reported. Papers focused on: describing HRQoL among specific clinical populations (72, 75, 76, 83, 88, 91, 101, 102, 104, 107, 119, 124, 132, 137, 156, 180, 182, 184), identifying factors associated with disease and/or comparing HRQoL across clinical and healthy populations (35, 39, 44–46, 49–53, 55, 56, 61, 63–65, 67, 68, 70, 77, 80–82, 84, 86, 87, 93, 94, 96, 97, 99, 106, 108–113, 115, 116, 118, 120, 121, 128–131, 134, 135, 142–144, 146, 147, 149, 151–154, 157, 173, 175–179) assessing HRQoL among healthy populations (54, 57, 93, 100, 103, 105, 117, 127, 136, 139, 149), comparing self- and proxy-reported outcomes (34, 36, 38, 40, 43, 58, 65, 66, 70, 71, 74, 76, 79, 85, 89, 92, 96, 98–100, 114, 125, 174, 197), and generating population norms (139). Studies evaluating treatments or interventions used HRQoL instruments to assess physical therapies such as sensory-perceptual motor training (162), progressive resistance exercises (163), Pilates exercises (165), a structured physiotherapy programme (169), muscle stretching and isometric exercises (170), respiratory muscle exercises (171), and vestibular-specific neuromuscular training (172). Other studies evaluated medical or educational interventions, including surgery (167), omega-3 fatty acid supplement (183), the impact of subcutaneous administration of emicizumab (181), and the effectiveness of specific educational programmes (166, 185). Cross-cultural validation and psychometric studies were often reported as being conducted for the first time in their respective countries (42, 47, 48, 59, 62, 78, 133, 145, 160, 161). Some were nested within larger studies that required instrument translation and cross-cultural validation (59, 74, 138), while some explicitly aimed to undertake psychometric testing (159). Comparison between self-reported and Proxy-reported HRQoL One third (n = 52, 35%) of the papers reported using a combined approach of both self- and proxy-reports to gather HRQoL data, and comparison of these approaches was reported in 24 papers. In 12 (50%) of the papers investigating the relationships between self-report and proxy-report, there was moderate to excellent correlation (40, 58, 65, 74, 89, 125, 174, 197) or no difference between self- and proxy-reported data (66, 71, 76, 92). Nevertheless, in nine (37%) of the papers, proxies (parents or caregivers) reported poorer HRQoL compared to child or adolescent self-reported HRQoL (36, 38, 43, 79, 85, 96, 97, 99). Only in two papers did the proxy-parents or caregivers report a better HRQoL than self-reported by the children or adolescents (100, 114). Reported psychometric properties of included instruments Of the included studies, 19 reported on the cross-cultural validation and psychometric testing of generic HRQoL instruments. These included PedsQL 4.0 GCS (42, 47, 59, 60, 62, 74, 78, 90, 95, 99, 123) KIDSCREEN-10/27/52 (123, 138), EQ-5D-Y-3L, EQ-5D-Y-5L, EQ-5D-3L and EQ-5D-5L (47, 133, 159, 160), and PROMIS-25 (161). Among these, more than half (63.2%) focused on the PedsQL 4.0 GCS. Table 3 shows the psychometric properties reported by these studies categorized using COSMIN guidelines. Across all papers, 103 (68.0%) stated that they used a cross-culturally validated instrument, eight (5.0%) reported the use of instruments which had not been cross-culturally validated, and in 41 (27.0%) papers this information was not clearly stated. Across the included papers, Cronbach’s alpha was the most frequently reported psychometric statistic in 42 (27.6%) papers, primarily for the PedsQL, KIDSCREEN, and PROMIS-25 instruments (40–42, 47, 50, 52, 57–60, 62–66, 78, 90, 92, 94, 95, 100, 105, 113, 117, 123, 126, 128, 129, 136, 138, 140, 146, 149, 153, 154, 161, 180). None of the included studies reported Cronbach’s alpha for the SF-36 or EQ-5D instruments. Of the 19 cross-cultural and/or psychometrics testing studies, reliability was reported in various forms: internal consistency using Cronbach’s alpha was reported in 14 studies (42, 47, 59, 60, 62, 78, 90, 95, 99, 123, 126, 138, 145, 161), test-retest reliability measured with the intraclass correlation coefficient (ICC) was reported in five studies (42, 59, 90, 126, 160), and inter-rater reliability was reported in five studies (42, 59, 78, 138, 145). Some studies assessed convergent validity by examining correlation with other instruments (47, 48, 60, 62, 126) or examined correlation between domains within the same instrument (78, 90, 95, 161). Most of the studies evaluated known-groups validity (42, 47, 48, 59, 60, 78, 90, 95, 138, 145, 159–161). Structural validity was assessed using confirmatory factor analysis, exploratory factor analysis, or Rasch analysis in some studies (42, 60, 95, 123, 126, 138). Two studies used quantitative content analysis with item-level content validity (I-CVI) and scale-level content validity (S-CVI), and face validity was reported (62, 78, 126). Although linguistic validation procedures (translation and cognitive debriefing) were performed for all the instruments, none of the studies evaluated cross-cultural validity. Responsiveness (sensitivity to change) and measurement error of the instruments were not assessed in any of the studies. Table 3 Psychometric properties of included instruments reported according to COSMIN Reliability (Referenced to study evaluated in) Validity (Referenced to study evaluated in) Test-retest Internal consistency Interrater reliability Structural validity Hypothesis testing Content validity PedsQL 4 GCS (42, 59, 90, 126) (42, 47, 59, 60, 62, 78, 90, 95, 126, 145) (42, 59, 78, 145) (42, 60, 95, 123, 126) (59, 60, 62, 78, 90, 95, 145) (62, 126) KIDSCREEN-10 (123) (123) KIDSCREEN-27 (123, 138) (138) (123, 138) (138) KIDSCREEN-52 (123) (123) EQ-5D-Y-3L (48) EQ-5D-Y-5L (48) EQ-5D-5L (160) (160) PROMIS-25 (161) (161) Reported strengths and limitations of included papers Although we did not formally assess the quality of the included studies, we have summarized their reported strengths and limitations. Some papers (n = 17) reported the use of validated instruments (34, 35, 45, 46, 49, 62, 76, 82, 89, 91, 99, 110, 140, 141, 143, 144, 179), supporting the use of HRQoL instruments in their research contexts. The inclusion of large sample sizes (37, 45, 46, 69, 76, 83, 110, 117, 140, 141, 143–145, 153, 197) and achieving a balanced sample across urban-rural areas, socioeconomic status, health facilities (at different levels of the health system), and public and private schools (81, 105, 117, 139, 157, 182, 197) were also strengths reported in some papers. Some studies were reported for being the first of their kind within a given country or population group (48, 57, 71, 101, 113, 117, 127, 133, 157, 161, 164), which is also arguably a strength in terms of providing new information about the health of children and adolescents from LMICs. Additional strengths commonly reported included the use of child self-reported HRQoL (39, 49, 105, 117), including proxy report by parents or caregivers (34, 37, 76, 117, 157), the inclusion of control groups (37, 57, 141), and achieving a high response rate (71, 76, 139–141, 151, 182). Several limitations were also reported across the included papers, including small sample sizes (49, 51, 53, 58, 59, 66, 74, 88, 99, 104, 107, 116, 135, 162, 163), limited generalizability due to sampling challenges (e.g. urban vs rural areas, and single rather than multi-centre recruitment strategies) or narrow inclusion criteria (e.g. restricted age ranges such as 10–12 years) (52, 60, 62, 66, 70, 74, 78, 86, 93, 100, 105, 117, 122, 123, 127, 140, 141, 162–164, 166, 198). Other methodological issues included follow-up times that were considered by the researchers to be too short (51, 69, 162, 164), limited consideration of relevant sociodemographic and clinical covariates (e.g. socioeconomic status, family size, family psychological relations, educational status, length of hospital stay, and disease stages) (53, 55, 69–71, 76, 108, 109, 143–145, 165). Potential response bias was another limitation, particularly where proxy-reports only were used or where researchers did not account for the characteristics of the reporting proxy (40, 41, 66, 89, 91, 103, 104, 106, 111, 117, 127, 130, 174, 175, 182, 198). Additionally, sampling-related concerns such as using convenience sampling which may introduce selection bias (34, 37, 38, 56, 59, 61, 71, 74, 79, 85, 106, 108, 110, 135, 151, 163, 167, 171), recall bias (45, 46, 56, 57, 59, 82, 88, 89, 94, 111, 117, 128, 136), and low response rates attrition (112, 122, 166, 175) were also reported. Importantly, psychometric limitations were mentioned in many papers, including the lack of evidence on test-retest reliability, linguistic validation, and responsiveness (34, 35, 47, 48, 58–60, 72, 78, 99, 106, 108, 116, 123, 126, 129, 138, 139, 160, 161, 179). The absence of population norms (66, 70, 86, 87, 101, 103, 106, 151) to allow comparison with their samples, and not including proxy reports where appropriate (72, 101), were also noted as potential limitations. Discussion To the best of our knowledge, this scoping review is the first to systematically examine the use of generic HRQoL instruments among children and adolescents in LMICs. We identified a total of 152 papers derived from 145 distinct studies across 22 countries. Interestingly, 82% of these papers were published in the last 10 years, reflecting a growing interest in evaluating HRQoL in this population. This increase reflects the recent focus on assessing the well-being of children and adolescents in LMICs ( 1 ), which is particularly important given the increasing global proportion of children and adolescents living in LMICs ( 2 , 3 ). It is well established that the HRQoL of children and adolescents should be assessed using HRQoL instruments specifically developed for the respective age groups of interest (199–201). In line with this, almost all studies included in this review used child-specific HRQoL instruments. A few studies (n = 3) used adult instruments (i.e. EQ-5D-3L/5L and SF-36) to assess the HRQoL of adolescents, though it has been considered appropriate for individuals aged > 14 years to use such adult HRQoL instruments (189). Among the eight generic HRQoL instruments identified, the PedsQL was by far the most frequently reported. This is consistent with previous reviews among Indigenous children or youth (26) and children with Down syndrome (202), where the PedsQL was also most widely used. The less frequent use of some instruments, particularly the EQ-5D-Y-3L/Y-5L, may be attributed to their relatively recent adoption in LMICs as papers reporting the use of this family of instruments were all published within the past three years (48, 61, 116, 122, 133, 159, 160, 181). Furthermore, the licensing fees associated with the use of some HRQoL instruments, such as the HUI instruments, may limit their use in LMICs (203). The concentration of nearly half of the eligible papers coming from only two countries may have also skewed instrument selection towards specific HRQoL instruments. Evaluating HRQoL in children and adolescents is essential for clinical evaluation, monitoring population health, identifying hidden morbidities, and informing health policy (17, 18, 204). With the growing burden of communicable and non-communicable diseases in LMICs, assessing HRQoL is vital for measuring health and prioritizing health interventions (205) (206). In this review, while the increase in HRQoL research in children and adolescent populations of LMICs is encouraging, this trend was not evenly distributed across all LMIC countries; in fact, papers were identified in only 22 (29%) of 75 LMICs. As mentioned, half the papers originated from only two countries (India and Egypt). Although this review was limited to generic HRQoL instruments, our findings suggest that more consideration should be given to including HRQoL assessments in future child and adolescent studies, particularly in underrepresented countries. It would be useful for future research to identify the barriers to assessing HRQoL in all LMICs, particularly given the health challenges facing children and adolescents globally – and inequitably in LMICs. Longitudinal studies that monitor changes in HRQoL over time and identify factors affecting HRQoL outcomes are important for understanding children's and adolescents' general health, as well as the impact of specific diseases and injuries (14–16). However, in this review, only four (2.6%) papers reported findings from longitudinal cohort studies and one quasi-experimental design. This suggests a paucity of research assessing changes in HRQoL over time or evaluating the long-term effects of interventions. An increase in the number of longitudinal studies in LMICs will enable the improved identification of factors affecting HRQoL, the identification of vulnerable or marginalized groups within the population, and the monitoring of population well-being. Such research would align well with the United Nations Sustainable Development Goal 3, which aims to ensure the well-being of all children (207, 208). Again, it would be important to identify and address the barriers to this important longitudinal research being undertaken. We hypothesise that in some countries, the funding to support research activities is very constrained relative to need. In the current review, although most of the included papers assessed HRQoL in children and adolescents with a range of conditions (e.g. cancer, blood disorders, and infectious diseases), very few focused on general populations, especially adolescents (57, 103, 105, 117, 127, 136, 149). Only one study reported PedsQL population norm data for children and adolescents in India (139). Despite the widespread use of generic HRQoL instruments in clinical settings, their application in the child and adolescent general population remains limited. Notably, in this review, the KIDSCREEN instrument was used in a few large population studies to assess HRQoL in general adolescent populations (57, 105, 117, 136). KIDSCREEN and EQ-5D-Y-3L/5L have been used to generate population norm data in developed countries (206, 209). Such data serve as essential benchmarks for monitoring health changes over time and comparing with clinical populations (205, 210). Therefore, future research should also focus on general children and adolescent populations in LMICs. Another important component to consider in assessing HRQoL is who completes the questionnaires. Where possible, HRQoL should be self-reported by children and adolescents (211, 212). In this review, most studies (n = 101) used self-reported measurements, with 53 of these including both self-reports and proxy reports. This could be attributed to the fact that the age distribution of the samples, i.e. 65 papers included children and adolescents aged 8–18 years and 41 papers focused on those aged 5–18 years, aligned with recommendations for self-reported data from the age of 8 years (213), and proxy-reports recommended for younger children or those with an intellectual disability (214). In 17 studies, proxy-parents or caregivers’ reports were used, with one study reporting proxy data from physicians (75), mainly due to the younger age of children or intellectual disability (36, 41, 52, 53, 69, 107, 118, 141, 146, 147, 169, 182, 184, 198). Previous studies have reported discrepancies between self- and proxy-reports. In nine of the included papers, proxy-parent or caregiver respondents reported poorer HRQoL than the children themselves (36, 38, 43, 79, 85, 96, 97, 99), better (100, 114), or no significant difference (66, 92, 166). These inconsistencies suggest that while proxy reports may be necessary in some cases, they should be interpreted with caution and ideally complement child self-reports (215). In terms of the administration method, a previous review recommended the use of electronic measures (216). A review of the use of PROMs/PREMs within routine care of children and young people found that most of the reports were collected through electronic devices (17). In contrast, our review found that most of the HRQoL data were collected in LMICs using pen-and-paper. Only nine papers employed electronic methods such as PCs, tablets or smartphones, mostly during the time of the COVID-19 pandemic. This finding aligns with recent evidence highlighting multiple barriers to implementation of digital endpoint in LMICs, including limited infrastructure, affordability challenges, and technological literacy (217). This highlights a gap in digital data collection in LMICs, despite the recent digitalized methods of health research data collection. Nevertheless, our review points the importance of pen-and-paper method of HRQoL data collection in LMICs, ensuring inclusivity and feasibility, and future research should consider retaining it alongside digital tools to maximize data collection in LMICs. All the instruments identified in this review were originally developed in English. Given that English is not the primary language in most LMICs, rigorous translation and cross-cultural adaptation are critical before their use in these settings. Such adaptation should follow standardized guidelines to ensure conceptual, semantic, and cultural equivalence of items and response formats. Following adaptation, psychometric evaluation should be used to establish the reliability and validity of these instruments in the target population (210). Our review identified 19 papers that describe the evaluation of five generic HRQoL instruments, yet none of the studies evaluated all nine psychometric properties recommended by the COSMIN guidelines (32). Across all the instruments, key psychometric properties such as test-retest reliability, content validity, and structural validity were only sporadically reported. Moreover, psychometric properties such as measurement error and responsiveness that are essential for evaluating sensitivity to change were not reported in any of the studies. The findings of our review indicate that there is limited evidence about the psychometric properties of instruments used to assess HRQoL of children and adolescents in LMICs. The lack of comprehensive psychometric testing raises concerns about the appropriateness of using these instruments to capture HRQoL across culturally diverse populations. Future research should focus on comprehensive psychometric validation of HRQoL instruments prior to their widespread use. Strengths and limitations of the review A strength of this review is the systematic search of relevant databases: Medline, Embase, PubMed, Scopus, CINAHL, and Web of Science using a rigorous dual-review screening process. The review adhered to predefined objectives and systematically mapped the use and evaluation of generic HRQoL instruments in LMICs. Additionally, the review identified the application of HRQoL instruments in the general children and adolescent populations. However, this scoping review had some limitations. As the aim of this review was to identify papers reporting generic HRQoL instruments, there will be a range of condition-specific HRQoL measures that were not within the scope of this review. Although the search was focused on major databases, literature from regional databases and grey literature may potentially have been overlooked. Restricting eligible papers to those published in English potentially excluded papers published in other languages. Implications of the review Almost all studies used HRQoL instruments that were specifically developed for or applicable to child and adolescent populations. Research was identified from only 29% of LMICS, and there is a notable lack of studies conducted in low-income countries, highlighting a geographical research gap. Very few studies have included very young children (≤ 4 years) suggesting the need to include younger age groups in future research. Only a small number of studies focused on the general child or adolescent populations, and only one study reported population norm data. Evidence on the psychometric properties of the identified instruments is limited, highlighting the need for further validation studies. Conclusion The use of generic HRQoL instruments as a health outcome measure for children and adolescents in LMICs, has increased in recent years. These instruments, predominantly the PedsQL, have been applied across a range of health conditions and to a lesser extent in the general child and adolescent populations. However, despite their growing use, gaps remain in terms of population coverage, psychometric evidence, and the geographic distribution of countries reporting HRQoL using generic instruments. Abbreviations LMICs: Low- and middle-income countries, HRQoL: Health-related quality of life, Prisma-ScR: Preferred Reporting Items for Systematic Reviews and Scoping Reviews, RCTs: Randomized controlled trials Declarations Acknowledgement We acknowledge Christy Ballard, Health Sciences subject librarian at the University of Otago for her assistance in the development of search terms and strategies for this scoping review. We thank Gebretsadkan Gebremedhin for his counter-review of the papers through each of the screening stages. Authors’ contributions GM prepared the first draft of the scoping review manuscript and led the research within his programme of PhD research (supervised by SD, TS and AS). All authors (SD, TS, AS, MÅ, MH, and GA) provided input and comments on multiple iterations of this article. All authors read and approved the final manuscript. Funding This protocol has been prepared as part of the first author’s (GM) PhD research at the University of Otago. The EuroQol Research Foundation provided GM with funding for a PhD stipend. Ethical approval and consent to participate Ethical approval and consent to participate are not required for this scoping review as no human participants were involved in this study. Consent for publication Not applicable. Competing interest The authors declare they have no competing interests. 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14:03:30\",\"extension\":\"docx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":189264,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryData2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7617141/v1/d668f816b7f1b4f1a7e04a48.docx\"},{\"id\":93942193,\"identity\":\"9d4b94b9-52e4-4ecc-8609-96bdfc37d650\",\"added_by\":\"auto\",\"created_at\":\"2025-10-20 13:47:30\",\"extension\":\"docx\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":25685,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryData3.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7617141/v1/152c74a3dc2a350a7d87edd6.docx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"The use of generic health-related quality of life instruments among children and adolescents in low- and middle-income countries: a scoping review\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eIn recent years, the population of children and adolescents in low- and middle-income countries (LMICs) has increased significantly (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). Almost two-thirds (65%) of the global children and adolescent populations live in LMICs, and this is projected to rise through to 2050 (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). However, this demographic shift is accompanied by a disproportionate burden of both communicable and non-communicable diseases, maternal and reproductive health problems, and injuries borne in LMICs compared to high-income countries (\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). These burdens can restrict, or even prevent, children and adolescents from attaining their full potential (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). In light of this, major global organizations such as the United Nations, through Sustainable Development Goal 3 (SDG-3) and the global strategy for Women\\u0026rsquo;s, Children\\u0026rsquo;s, and Adolescent health, have emphasized the well-being of LMIC populations (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). Similarly, the Africa Research Implementation Science and Education (ARISE) Network has highlighted the importance of assessing adolescents\\u0026rsquo; health and wellbeing in LMICs (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eWhile measures of mortality and morbidity are important, comprehensive assessment of general health, as experienced by children and adolescents, is also necessary to understand the health needs of children and adolescents from their perspectives. Consequently, measures of health-related quality of life (HRQoL), which assess the physical, psychological, and social aspects of overall health, are increasingly used as patient-reported outcome measures (PROMs) (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e) in general population research as well as in clinical settings (11).\\u003c/p\\u003e\\u003cp\\u003eGeneric HRQoL instruments cover broad health domains and can be completed by both healthy and ill individuals, allowing comparisons across populations and within groups (e.g. pre- and post-treatment) (12). In contrast, disease-specific HRQoL instruments focus on health aspects related to particular disease conditions and are less appropriate for general population comparisons (10, 13).\\u003c/p\\u003e\\u003cp\\u003eGeneric HRQoL instruments have been used as outcome measures monitoring population health changes over time, in clinical settings, and in economic evaluations (14\\u0026ndash;21). For LMICs with limited healthcare budgets, such instruments could provide substantial benefits by enabling the monitoring of health changes, evaluating medical interventions, identifying high risk groups, and prioritizing healthcare interventions.\\u003c/p\\u003e\\u003cp\\u003eAlthough various HRQoL instruments have been developed and validated for children and adolescents, no comprehensive review has examined the use of generic HRQoL instruments in LMICs. Existing reviews have focused on specific diseases (22\\u0026ndash;24), geographic regions (25) or populations (26), leaving a gap in understanding of their broader use in LMICs. Given the broad applicability of generic HRQoL instruments, this review focuses on their use among children and adolescents in LMICs.\\u003c/p\\u003e\\u003cp\\u003eThis scoping review aims to systematically identify the generic HRQoL instruments used among children and adolescents in LMICs, with particular attention to their stated purposes and reported psychometric properties. The evidence generated is expected to provide researchers, clinicians, and policymakers with a comprehensive understanding of the current state of HRQoL measurement in LMICs and highlight opportunities for improving their use in future studies.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e The scoping review was registered in Open Science Framework (OSF) (27). The review follows the framework proposed by Arksey and O\\u0026rsquo;Malley (28), incorporating the enhancement of Levac et al.\\u0026rsquo;s guidelines for conducting scoping reviews (29). The Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Review (PRISMA-ScR) (30) was used to ensure complete and transparent reporting of the scoping review (Supplementary Data 1).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eSearch strategy\\u003c/h2\\u003e\\u003cp\\u003eA systematic search of Medline (via Ovid), Embase (via Ovid), PubMed, Scopus, CINAHL, and Web of Science databases was conducted to identify peer-reviewed papers published in English from 2000 to 2024. The search strategy was developed in consultation with the Health Sciences librarian at the University of Otago and the wider research team. It combined Medical Subject Headings (MeSH), keywords, abbreviations, and synonyms using Boolean operators (\\u0026lsquo;AND\\u0026rsquo; and \\u0026lsquo;OR\\u0026rsquo;). Search terms included: (i) \\u0026ldquo;Child,\\u0026rdquo; \\u0026ldquo;Adolescents,\\u0026rdquo; \\u0026ldquo;Paediatrics,\\u0026rdquo; \\u0026ldquo;Teenagers,\\u0026rdquo; and \\u0026ldquo;school children\\u0026rdquo;; (ii) \\u0026ldquo;Quality of Life,\\u0026rdquo; \\u0026ldquo;Health-related quality of life,\\u0026rdquo; \\u0026ldquo;PROM,\\u0026rdquo; and \\u0026ldquo;HRQoL\\u0026rdquo;; (iii) \\u0026ldquo;PedsQL 4.0 GCS,\\u0026rdquo; \\u0026ldquo;HUI-2/3,\\u0026rdquo; \\u0026ldquo;KIDSCREEN-52/27/10,\\u0026rdquo; \\u0026ldquo;EQ-5D-Y-3L/5L,\\u0026rdquo; \\u0026ldquo;PROMIS-25,\\u0026rdquo; \\u0026ldquo;AQoL-6D,\\u0026rdquo; \\u0026ldquo;CHU-9D,\\u0026rdquo; \\u0026rdquo;AHUM,\\u0026rdquo; \\u0026ldquo;16D-HRQoL,\\u0026rdquo; and \\u0026ldquo;EQ-TIPS,\\u0026rdquo;; and (iv) a list of 75 LMIC names (31).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eStudy selection and eligibility criteria\\u003c/h3\\u003e\\n\\u003cp\\u003eIdentified papers were independently screened by two reviewers (GM and a second reviewer). The first 100 abstracts were jointly reviewed to ensure consistency. Subsequently, the reviewers independently conducted title and abstract screening, followed by a full-text screening of all eligible papers, with regular meetings to resolve any discrepancies at the end of each stage.\\u003c/p\\u003e\\u003cp\\u003ePapers fulfilling the following criteria were included:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003ePeer-reviewed research papers presenting empirical quantitative findings derived from the use of generic HRQoL instruments in children and adolescents aged\\u0026thinsp;\\u0026le;\\u0026thinsp;19 years in LMICs (based on the World Bank Classification (31)). Papers that included both LMICs and non-LMICs data were eligible if the LMICs findings were reported separately.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003ePeer-reviewed papers published between 1 January 2000 and 31 December 2024.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003ePapers published in the English language.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003ePapers were excluded if they:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eReported only on the use of non-generic HRQoL instruments;\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eWere conference abstracts, editorials, discussion papers, or papers that were unable to be retrieved;\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eDid not specify the target population as being within an LMIC or in the relevant age group;\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eReported findings from qualitative studies, meta-analyses, systematic reviews, or scoping reviews.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eData extraction\\u003c/h3\\u003e\\n\\u003cp\\u003e Papers identified for inclusion were read in full, and study data were extracted by the first reviewer (GM). Data were collected on the study characteristics (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, and Supplementary Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e) using a Microsoft Excel spreadsheet.\\u003c/p\\u003e\\n\\u003ch3\\u003eData analyses\\u003c/h3\\u003e\\n\\u003cp\\u003eThe description of included papers was guided by the review\\u0026rsquo;s research questions. Psychometric properties were categorized and reported according to the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) categorization (32). Reported disease conditions were categorized based on the International Classification of Diseases version (ICD-11) (33). For the application (i.e. purpose of the HRQoL instrument), first, the objectives of the extracted studies were examined, and three categories were identified: general HRQoL assessment, evaluation of treatment or care outcomes/assessing change for intervention (trials), and cross-cultural validation and/or psychometric studies. Then, within each paper\\u0026rsquo;s reported purpose category, data were summarized descriptively with frequencies (percentages). A narrative summary of the findings and their relationship to the review\\u0026rsquo;s aims and research questions, along with supporting data, was also produced.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe search of six databases identified a total of 2154 potential papers. After removing duplicate papers, the number was reduced to 1,484. Following title and abstract screening, 1104 papers were excluded, leaving 380 eligible for full-text review. Of these, 228 were removed after full-text review. Consequently, 152 papers reporting findings from 145 distinct studies were included in the review's synthesis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eFifteen papers reported findings from seven of the 145 distinct studies. The studies with multiple publications were conducted in India (34\\u0026ndash;39), Vietnam (40\\u0026ndash;42), Nigeria (43, 44), Sri Lanka (45, 46), and Malawi (47, 48).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCharacteristics of the included papers\\u003c/h2\\u003e\\u003cp\\u003eThe search sought to identify papers published from 1 January 2000 to 31 December 2024; however, all retained eligible papers were published from 2009 to 2024. Of the 152 papers, 126 (83%) were published in the last 10 years (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e); 26 (19%) being published in 2024 alone.\\u003c/p\\u003e\\u003cp\\u003eMost of the papers reported on studies using cross-sectional designs (n\\u0026thinsp;=\\u0026thinsp;128) (34\\u0026ndash;161), followed by randomized controlled trials (n\\u0026thinsp;=\\u0026thinsp;11) (162\\u0026ndash;172), case-control studies (n\\u0026thinsp;=\\u0026thinsp;8) (173\\u0026ndash;180), cohort studies (n\\u0026thinsp;=\\u0026thinsp;4) (181\\u0026ndash;184), and there was one quasi-experimental study (n\\u0026thinsp;=\\u0026thinsp;1) (185). Only one study employed a mixed-methods approach, combining both quantitative and qualitative methods (only the quantitative results were included in this review) (108) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Participants were mostly recruited from outpatient settings (n\\u0026thinsp;=\\u0026thinsp;70), inpatient settings (n\\u0026thinsp;=\\u0026thinsp;16), schools (n\\u0026thinsp;=\\u0026thinsp;46), and other community sites (e.g., homes, public parks) (n\\u0026thinsp;=\\u0026thinsp;20). A smaller number of papers reported recruitment from both inpatient and outpatient settings (n\\u0026thinsp;=\\u0026thinsp;6), care homes (n\\u0026thinsp;=\\u0026thinsp;3), and specialized schools (n\\u0026thinsp;=\\u0026thinsp;3, including two for Deaf children and one for children with intellectual disabilities). The recruitment setting was not clearly stated in 38 papers (Supplementary Data 2 Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eCharacteristics of the included papers\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCategory\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePapers n (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMutually exclusive age groups\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 month \\u0026ndash; 4 years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2 (1.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u0026ndash;12 years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8 (5.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2\\u0026ndash;18 years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e26 (17.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u0026ndash;18 years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e41 (27.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8\\u0026ndash;18 years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e65 (42.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;18 years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10 (6.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYears of publication\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2005\\u0026ndash;2009\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1 (0.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2010\\u0026ndash;2014\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e25 (16.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2015\\u0026ndash;2019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e48 (31.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2020\\u0026ndash;2024\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e78 (51.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStudy designs\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRandomized controlled trials\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11 (7.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCross-sectional\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e128 (84.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCase-control\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8 (5.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCohort study\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (2.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eQuasi experimental\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1 (0.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStudy settings*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOutpatient clinics\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e70 (46.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eInpatient clinics\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e16 (10.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eInpatient and outpatient clinics\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6 (4.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCommunity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20 (13.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSchools\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e46 (30.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCare homes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3 (2.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSpecialized schools\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3 (2.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNot stated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e38 (25.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e*Participant recruitment (since participants were recruited in more than one setting in several papers the total is more than the number of papers included).\\u003c/p\\u003e\\u003cp\\u003eThe papers covered a wide range of health conditions as well as studies focusing on healthy child and adolescent populations (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Among the 152 papers, the most frequently reported clinical conditions were cancer (n\\u0026thinsp;=\\u0026thinsp;22), blood disorders (n\\u0026thinsp;=\\u0026thinsp;21), and infectious diseases (n\\u0026thinsp;=\\u0026thinsp;18) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Eleven (7.2%) studies focused on general child and adolescent populations; one study generated population norm data.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eCountries of origin\\u003c/h3\\u003e\\n\\u003cp\\u003eThe 152 papers identified are from 22 of the 75 LMICs (31). The majority, 129 (84.9%), were conducted in lower-middle-income countries, while the remaining 23 (15.1%) papers came from low-income countries (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Fifty papers (32.9%) came from studies undertaken in India, 25 (16.5%) from Egypt, and the remaining coming from the 20 other countries (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). One cross-country study reported HRQoL findings from two countries (Uganda and Kenya) (123).\\u003c/p\\u003e\\n\\u003ch3\\u003eCharacteristics of participants in the included papers\\u003c/h3\\u003e\\n\\u003cp\\u003eThe participant sample size reported in the included papers ranged from 18 (133) to 3227 (105) children and/or adolescents. Most papers reported HRQoL findings for children and adolescents aged 8\\u0026ndash;18 years (n\\u0026thinsp;=\\u0026thinsp;65), followed by those aged 5\\u0026ndash;18 years (n\\u0026thinsp;=\\u0026thinsp;41); 10 included children and adolescents of all age groups. Notably, only two papers specifically focused on young children; one on those aged 1 month to 2 years (141) and another on toddlers 2\\u0026ndash;4 years (41)(Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eHRQoL instruments\\u003c/h2\\u003e\\u003cp\\u003eEight different generic HRQoL instruments were used in the 152 papers (Supplementary Data 3 Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). The most frequently used instrument was the Paediatrics Quality of Life Inventory 4.0 Generic Core Scale (PedsQL 4.0 GCS) (n\\u0026thinsp;=\\u0026thinsp;118; 77.6%) (186), followed by the KIDSCREEN (n\\u0026thinsp;=\\u0026thinsp;14; 9.2%: KIDSCREEN-10: n\\u0026thinsp;=\\u0026thinsp;6, KIDSCREEN-27: n\\u0026thinsp;=\\u0026thinsp;8, and KIDSCREEN-52: n\\u0026thinsp;=\\u0026thinsp;2) (187, 188), the EQ-5D (n\\u0026thinsp;=\\u0026thinsp;8; 5.3%; EQ-5D-Y-3L: n\\u0026thinsp;=\\u0026thinsp;6, EQ-5D-Y-5L: n\\u0026thinsp;=\\u0026thinsp;3, EQ-5D-3L: n\\u0026thinsp;=\\u0026thinsp;1, EQ-5D-5L: n\\u0026thinsp;=\\u0026thinsp;2) (189\\u0026ndash;191), the Health Utility Index (n\\u0026thinsp;=\\u0026thinsp;5; 3.3%; HUI-2: n\\u0026thinsp;=\\u0026thinsp;2, and HUI-3: n\\u0026thinsp;=\\u0026thinsp;4) (192, 193), Paediatrics Quality of Life Inventory Short Form 15-items (PedsQL SF 15-items) (n\\u0026thinsp;=\\u0026thinsp;3; 2.0%) (194), Patient-Reported Outcomes Measurement Information System-25 (PROMIS-25) (n\\u0026thinsp;=\\u0026thinsp;2; 1.3%) (195), and Short Form Health Survey 36-items (SF-36) (n\\u0026thinsp;=\\u0026thinsp;1; \\u0026lt;1%) (196). The two instruments developed for use in adult populations, the EQ-5D-3L/5L (159, 160) and SF-36 (50), were used in studies among adolescents aged 13\\u0026ndash;18 years and 15\\u0026ndash;18 years, respectively. The use of PedsQL instruments has increased through the years (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Among the n\\u0026thinsp;=\\u0026thinsp;118 papers reporting use of PedsQL 4.0 GCS, 40 were from India and 23 from Egypt. Use of the EQ-5D-Y-3L/5L and EQ-5D-3L/5L instruments in LMICs has emerged more recently, with eligible papers using these instruments all being published after 2020.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eParticipants completing the HRQoL instruments\\u003c/h2\\u003e\\u003cp\\u003eDetails regarding the methods of HRQoL data collection, including the respondent (child/adolescent or proxy), response mode, and administration format were obtained or derived from the papers (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Almost all the identified generic HRQoL instruments are available for self- and proxy-report. Most papers (n\\u0026thinsp;=\\u0026thinsp;53; 34.9%) reported both self- and proxy-reports (by parents or caregivers), followed by self-report only (n\\u0026thinsp;=\\u0026thinsp;48; 31.6%), and proxy-report only (n\\u0026thinsp;=\\u0026thinsp;17; 11.2%). Additionally, 21 papers (13.8%) reported the use of proxies only in certain situations, such as younger children under 7 years. Twelve papers (7.9%) did not clearly state the reporting method. Among proxy-reported HRQoL, only one paper used physicians (rather than parents/caregivers) as proxies (75). Most papers used self-completed questionnaires (n\\u0026thinsp;=\\u0026thinsp;67; 44.1%), followed by interviewer-administered format (n\\u0026thinsp;=\\u0026thinsp;52; 34.2%) and mixed methods (self-completed and interviewer-administered) (n\\u0026thinsp;=\\u0026thinsp;10; 6.6%). The mode of administration was unclear or not reported in 23 papers (15.1%).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eMethods of instrument completion\\u003c/h2\\u003e\\u003cp\\u003eThe HRQoL instruments were administered in several ways, mainly via pen-and-paper questionnaires (n\\u0026thinsp;=\\u0026thinsp;95; 62.5%), digital questionnaires on devices such as tablets, PCs, and smartphones (n\\u0026thinsp;=\\u0026thinsp;9; 5.9%), telephone interviews (n\\u0026thinsp;=\\u0026thinsp;4; 2.6%), and mixed methods (n\\u0026thinsp;=\\u0026thinsp;4; 2.6%). The method of instrument completion was not clearly stated in 40 papers (26.3%).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePurpose of using the HRQoL instruments\\u003c/h2\\u003e\\u003cp\\u003eEligible retained papers were categorized into three main groups based on the primary purpose of HRQoL instrument application: general HRQoL assessment (n\\u0026thinsp;=\\u0026thinsp;118; 77.6%), evaluation of treatment or intervention outcomes (n\\u0026thinsp;=\\u0026thinsp;15; 9.9%), and cross-cultural validation and/or psychometric studies (n\\u0026thinsp;=\\u0026thinsp;19; 12.5%).\\u003c/p\\u003e\\u003cp\\u003eWithin the general HRQoL assessment category, various purposes were reported. Papers focused on: describing HRQoL among specific clinical populations (72, 75, 76, 83, 88, 91, 101, 102, 104, 107, 119, 124, 132, 137, 156, 180, 182, 184), identifying factors associated with disease and/or comparing HRQoL across clinical and healthy populations (35, 39, 44\\u0026ndash;46, 49\\u0026ndash;53, 55, 56, 61, 63\\u0026ndash;65, 67, 68, 70, 77, 80\\u0026ndash;82, 84, 86, 87, 93, 94, 96, 97, 99, 106, 108\\u0026ndash;113, 115, 116, 118, 120, 121, 128\\u0026ndash;131, 134, 135, 142\\u0026ndash;144, 146, 147, 149, 151\\u0026ndash;154, 157, 173, 175\\u0026ndash;179) assessing HRQoL among healthy populations (54, 57, 93, 100, 103, 105, 117, 127, 136, 139, 149), comparing self- and proxy-reported outcomes (34, 36, 38, 40, 43, 58, 65, 66, 70, 71, 74, 76, 79, 85, 89, 92, 96, 98\\u0026ndash;100, 114, 125, 174, 197), and generating population norms (139).\\u003c/p\\u003e\\u003cp\\u003eStudies evaluating treatments or interventions used HRQoL instruments to assess physical therapies such as sensory-perceptual motor training (162), progressive resistance exercises (163), Pilates exercises (165), a structured physiotherapy programme (169), muscle stretching and isometric exercises (170), respiratory muscle exercises (171), and vestibular-specific neuromuscular training (172). Other studies evaluated medical or educational interventions, including surgery (167), omega-3 fatty acid supplement (183), the impact of subcutaneous administration of emicizumab (181), and the effectiveness of specific educational programmes (166, 185).\\u003c/p\\u003e\\u003cp\\u003eCross-cultural validation and psychometric studies were often reported as being conducted for the first time in their respective countries (42, 47, 48, 59, 62, 78, 133, 145, 160, 161). Some were nested within larger studies that required instrument translation and cross-cultural validation (59, 74, 138), while some explicitly aimed to undertake psychometric testing (159).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eComparison between self-reported and Proxy-reported HRQoL\\u003c/h2\\u003e\\u003cp\\u003eOne third (n\\u0026thinsp;=\\u0026thinsp;52, 35%) of the papers reported using a combined approach of both self- and proxy-reports to gather HRQoL data, and comparison of these approaches was reported in 24 papers. In 12 (50%) of the papers investigating the relationships between self-report and proxy-report, there was moderate to excellent correlation (40, 58, 65, 74, 89, 125, 174, 197) or no difference between self- and proxy-reported data (66, 71, 76, 92). Nevertheless, in nine (37%) of the papers, proxies (parents or caregivers) reported poorer HRQoL compared to child or adolescent self-reported HRQoL (36, 38, 43, 79, 85, 96, 97, 99). Only in two papers did the proxy-parents or caregivers report a better HRQoL than self-reported by the children or adolescents (100, 114).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eReported psychometric properties of included instruments\\u003c/h2\\u003e\\u003cp\\u003eOf the included studies, 19 reported on the cross-cultural validation and psychometric testing of generic HRQoL instruments. These included PedsQL 4.0 GCS (42, 47, 59, 60, 62, 74, 78, 90, 95, 99, 123) KIDSCREEN-10/27/52 (123, 138), EQ-5D-Y-3L, EQ-5D-Y-5L, EQ-5D-3L and EQ-5D-5L (47, 133, 159, 160), and PROMIS-25 (161). Among these, more than half (63.2%) focused on the PedsQL 4.0 GCS. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e shows the psychometric properties reported by these studies categorized using COSMIN guidelines.\\u003c/p\\u003e\\u003cp\\u003eAcross all papers, 103 (68.0%) stated that they used a cross-culturally validated instrument, eight (5.0%) reported the use of instruments which had not been cross-culturally validated, and in 41 (27.0%) papers this information was not clearly stated.\\u003c/p\\u003e\\u003cp\\u003eAcross the included papers, Cronbach\\u0026rsquo;s alpha was the most frequently reported psychometric statistic in 42 (27.6%) papers, primarily for the PedsQL, KIDSCREEN, and PROMIS-25 instruments (40\\u0026ndash;42, 47, 50, 52, 57\\u0026ndash;60, 62\\u0026ndash;66, 78, 90, 92, 94, 95, 100, 105, 113, 117, 123, 126, 128, 129, 136, 138, 140, 146, 149, 153, 154, 161, 180). None of the included studies reported Cronbach\\u0026rsquo;s alpha for the SF-36 or EQ-5D instruments.\\u003c/p\\u003e\\u003cp\\u003eOf the 19 cross-cultural and/or psychometrics testing studies, reliability was reported in various forms: internal consistency using Cronbach\\u0026rsquo;s alpha was reported in 14 studies (42, 47, 59, 60, 62, 78, 90, 95, 99, 123, 126, 138, 145, 161), test-retest reliability measured with the intraclass correlation coefficient (ICC) was reported in five studies (42, 59, 90, 126, 160), and inter-rater reliability was reported in five studies (42, 59, 78, 138, 145).\\u003c/p\\u003e\\u003cp\\u003eSome studies assessed convergent validity by examining correlation with other instruments (47, 48, 60, 62, 126) or examined correlation between domains within the same instrument (78, 90, 95, 161). Most of the studies evaluated known-groups validity (42, 47, 48, 59, 60, 78, 90, 95, 138, 145, 159\\u0026ndash;161). Structural validity was assessed using confirmatory factor analysis, exploratory factor analysis, or Rasch analysis in some studies (42, 60, 95, 123, 126, 138). Two studies used quantitative content analysis with item-level content validity (I-CVI) and scale-level content validity (S-CVI), and face validity was reported (62, 78, 126). Although linguistic validation procedures (translation and cognitive debriefing) were performed for all the instruments, none of the studies evaluated cross-cultural validity. Responsiveness (sensitivity to change) and measurement error of the instruments were not assessed in any of the studies.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003ePsychometric properties of included instruments reported according to COSMIN\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eReliability\\u003c/p\\u003e\\u003cp\\u003e(Referenced to study evaluated in)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c7\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003eValidity\\u003c/p\\u003e\\u003cp\\u003e(Referenced to study evaluated in)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTest-retest\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eInternal consistency\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eInterrater reliability\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eStructural validity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eHypothesis testing\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eContent validity\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePedsQL 4 GCS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(42, 59, 90, 126)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(42, 47, 59, 60, 62, 78, 90, 95, 126, 145)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e(42, 59, 78, 145)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e(42, 60, 95, 123, 126)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e(59, 60, 62, 78, 90, 95, 145)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e(62, 126)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eKIDSCREEN-10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(123)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e(123)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eKIDSCREEN-27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(123, 138)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e(138)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e(123, 138)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e(138)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eKIDSCREEN-52\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(123)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e(123)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEQ-5D-Y-3L\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e(48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEQ-5D-Y-5L\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e(48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEQ-5D-5L\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(160)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e(160)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePROMIS-25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(161)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e(161)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eReported strengths and limitations of included papers\\u003c/h2\\u003e\\u003cp\\u003eAlthough we did not formally assess the quality of the included studies, we have summarized their reported strengths and limitations. Some papers (n\\u0026thinsp;=\\u0026thinsp;17) reported the use of validated instruments (34, 35, 45, 46, 49, 62, 76, 82, 89, 91, 99, 110, 140, 141, 143, 144, 179), supporting the use of HRQoL instruments in their research contexts. The inclusion of large sample sizes (37, 45, 46, 69, 76, 83, 110, 117, 140, 141, 143\\u0026ndash;145, 153, 197) and achieving a balanced sample across urban-rural areas, socioeconomic status, health facilities (at different levels of the health system), and public and private schools (81, 105, 117, 139, 157, 182, 197) were also strengths reported in some papers. Some studies were reported for being the first of their kind within a given country or population group (48, 57, 71, 101, 113, 117, 127, 133, 157, 161, 164), which is also arguably a strength in terms of providing new information about the health of children and adolescents from LMICs. Additional strengths commonly reported included the use of child self-reported HRQoL (39, 49, 105, 117), including proxy report by parents or caregivers (34, 37, 76, 117, 157), the inclusion of control groups (37, 57, 141), and achieving a high response rate (71, 76, 139\\u0026ndash;141, 151, 182).\\u003c/p\\u003e\\u003cp\\u003eSeveral limitations were also reported across the included papers, including small sample sizes (49, 51, 53, 58, 59, 66, 74, 88, 99, 104, 107, 116, 135, 162, 163), limited generalizability due to sampling challenges (e.g. urban vs rural areas, and single rather than multi-centre recruitment strategies) or narrow inclusion criteria (e.g. restricted age ranges such as 10\\u0026ndash;12 years) (52, 60, 62, 66, 70, 74, 78, 86, 93, 100, 105, 117, 122, 123, 127, 140, 141, 162\\u0026ndash;164, 166, 198).\\u003c/p\\u003e\\u003cp\\u003eOther methodological issues included follow-up times that were considered by the researchers to be too short (51, 69, 162, 164), limited consideration of relevant sociodemographic and clinical covariates (e.g. socioeconomic status, family size, family psychological relations, educational status, length of hospital stay, and disease stages) (53, 55, 69\\u0026ndash;71, 76, 108, 109, 143\\u0026ndash;145, 165). Potential response bias was another limitation, particularly where proxy-reports only were used or where researchers did not account for the characteristics of the reporting proxy (40, 41, 66, 89, 91, 103, 104, 106, 111, 117, 127, 130, 174, 175, 182, 198). Additionally, sampling-related concerns such as using convenience sampling which may introduce selection bias (34, 37, 38, 56, 59, 61, 71, 74, 79, 85, 106, 108, 110, 135, 151, 163, 167, 171), recall bias (45, 46, 56, 57, 59, 82, 88, 89, 94, 111, 117, 128, 136), and low response rates attrition (112, 122, 166, 175) were also reported.\\u003c/p\\u003e\\u003cp\\u003eImportantly, psychometric limitations were mentioned in many papers, including the lack of evidence on test-retest reliability, linguistic validation, and responsiveness (34, 35, 47, 48, 58\\u0026ndash;60, 72, 78, 99, 106, 108, 116, 123, 126, 129, 138, 139, 160, 161, 179). The absence of population norms (66, 70, 86, 87, 101, 103, 106, 151) to allow comparison with their samples, and not including proxy reports where appropriate (72, 101), were also noted as potential limitations.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eTo the best of our knowledge, this scoping review is the first to systematically examine the use of generic HRQoL instruments among children and adolescents in LMICs. We identified a total of 152 papers derived from 145 distinct studies across 22 countries. Interestingly, 82% of these papers were published in the last 10 years, reflecting a growing interest in evaluating HRQoL in this population. This increase reflects the recent focus on assessing the well-being of children and adolescents in LMICs (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e), which is particularly important given the increasing global proportion of children and adolescents living in LMICs (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIt is well established that the HRQoL of children and adolescents should be assessed using HRQoL instruments specifically developed for the respective age groups of interest (199\\u0026ndash;201). In line with this, almost all studies included in this review used child-specific HRQoL instruments. A few studies (n\\u0026thinsp;=\\u0026thinsp;3) used adult instruments (i.e. EQ-5D-3L/5L and SF-36) to assess the HRQoL of adolescents, though it has been considered appropriate for individuals aged\\u0026thinsp;\\u0026gt;\\u0026thinsp;14 years to use such adult HRQoL instruments (189).\\u003c/p\\u003e\\u003cp\\u003eAmong the eight generic HRQoL instruments identified, the PedsQL was by far the most frequently reported. This is consistent with previous reviews among Indigenous children or youth (26) and children with Down syndrome (202), where the PedsQL was also most widely used. The less frequent use of some instruments, particularly the EQ-5D-Y-3L/Y-5L, may be attributed to their relatively recent adoption in LMICs as papers reporting the use of this family of instruments were all published within the past three years (48, 61, 116, 122, 133, 159, 160, 181). Furthermore, the licensing fees associated with the use of some HRQoL instruments, such as the HUI instruments, may limit their use in LMICs (203). The concentration of nearly half of the eligible papers coming from only two countries may have also skewed instrument selection towards specific HRQoL instruments.\\u003c/p\\u003e\\u003cp\\u003eEvaluating HRQoL in children and adolescents is essential for clinical evaluation, monitoring population health, identifying hidden morbidities, and informing health policy (17, 18, 204). With the growing burden of communicable and non-communicable diseases in LMICs, assessing HRQoL is vital for measuring health and prioritizing health interventions (205) (206). In this review, while the increase in HRQoL research in children and adolescent populations of LMICs is encouraging, this trend was not evenly distributed across all LMIC countries; in fact, papers were identified in only 22 (29%) of 75 LMICs. As mentioned, half the papers originated from only two countries (India and Egypt). Although this review was limited to generic HRQoL instruments, our findings suggest that more consideration should be given to including HRQoL assessments in future child and adolescent studies, particularly in underrepresented countries. It would be useful for future research to identify the barriers to assessing HRQoL in all LMICs, particularly given the health challenges facing children and adolescents globally \\u0026ndash; and inequitably in LMICs.\\u003c/p\\u003e\\u003cp\\u003eLongitudinal studies that monitor changes in HRQoL over time and identify factors affecting HRQoL outcomes are important for understanding children's and adolescents' general health, as well as the impact of specific diseases and injuries (14\\u0026ndash;16). However, in this review, only four (2.6%) papers reported findings from longitudinal cohort studies and one quasi-experimental design. This suggests a paucity of research assessing changes in HRQoL over time or evaluating the long-term effects of interventions. An increase in the number of longitudinal studies in LMICs will enable the improved identification of factors affecting HRQoL, the identification of vulnerable or marginalized groups within the population, and the monitoring of population well-being. Such research would align well with the United Nations Sustainable Development Goal 3, which aims to ensure the well-being of all children (207, 208). Again, it would be important to identify and address the barriers to this important longitudinal research being undertaken. We hypothesise that in some countries, the funding to support research activities is very constrained relative to need.\\u003c/p\\u003e\\u003cp\\u003eIn the current review, although most of the included papers assessed HRQoL in children and adolescents with a range of conditions (e.g. cancer, blood disorders, and infectious diseases), very few focused on general populations, especially adolescents (57, 103, 105, 117, 127, 136, 149). Only one study reported PedsQL population norm data for children and adolescents in India (139). Despite the widespread use of generic HRQoL instruments in clinical settings, their application in the child and adolescent general population remains limited. Notably, in this review, the KIDSCREEN instrument was used in a few large population studies to assess HRQoL in general adolescent populations (57, 105, 117, 136). KIDSCREEN and EQ-5D-Y-3L/5L have been used to generate population norm data in developed countries (206, 209). Such data serve as essential benchmarks for monitoring health changes over time and comparing with clinical populations (205, 210). Therefore, future research should also focus on general children and adolescent populations in LMICs.\\u003c/p\\u003e\\u003cp\\u003eAnother important component to consider in assessing HRQoL is who completes the questionnaires. Where possible, HRQoL should be self-reported by children and adolescents (211, 212). In this review, most studies (n\\u0026thinsp;=\\u0026thinsp;101) used self-reported measurements, with 53 of these including both self-reports and proxy reports. This could be attributed to the fact that the age distribution of the samples, i.e. 65 papers included children and adolescents aged 8\\u0026ndash;18 years and 41 papers focused on those aged 5\\u0026ndash;18 years, aligned with recommendations for self-reported data from the age of 8 years (213), and proxy-reports recommended for younger children or those with an intellectual disability (214). In 17 studies, proxy-parents or caregivers\\u0026rsquo; reports were used, with one study reporting proxy data from physicians (75), mainly due to the younger age of children or intellectual disability (36, 41, 52, 53, 69, 107, 118, 141, 146, 147, 169, 182, 184, 198). Previous studies have reported discrepancies between self- and proxy-reports. In nine of the included papers, proxy-parent or caregiver respondents reported poorer HRQoL than the children themselves (36, 38, 43, 79, 85, 96, 97, 99), better (100, 114), or no significant difference (66, 92, 166). These inconsistencies suggest that while proxy reports may be necessary in some cases, they should be interpreted with caution and ideally complement child self-reports (215).\\u003c/p\\u003e\\u003cp\\u003eIn terms of the administration method, a previous review recommended the use of electronic measures (216). A review of the use of PROMs/PREMs within routine care of children and young people found that most of the reports were collected through electronic devices (17). In contrast, our review found that most of the HRQoL data were collected in LMICs using pen-and-paper. Only nine papers employed electronic methods such as PCs, tablets or smartphones, mostly during the time of the COVID-19 pandemic. This finding aligns with recent evidence highlighting multiple barriers to implementation of digital endpoint in LMICs, including limited infrastructure, affordability challenges, and technological literacy (217). This highlights a gap in digital data collection in LMICs, despite the recent digitalized methods of health research data collection. Nevertheless, our review points the importance of pen-and-paper method of HRQoL data collection in LMICs, ensuring inclusivity and feasibility, and future research should consider retaining it alongside digital tools to maximize data collection in LMICs.\\u003c/p\\u003e\\u003cp\\u003eAll the instruments identified in this review were originally developed in English. Given that English is not the primary language in most LMICs, rigorous translation and cross-cultural adaptation are critical before their use in these settings. Such adaptation should follow standardized guidelines to ensure conceptual, semantic, and cultural equivalence of items and response formats. Following adaptation, psychometric evaluation should be used to establish the reliability and validity of these instruments in the target population (210). Our review identified 19 papers that describe the evaluation of five generic HRQoL instruments, yet none of the studies evaluated all nine psychometric properties recommended by the COSMIN guidelines (32).\\u003c/p\\u003e\\u003cp\\u003eAcross all the instruments, key psychometric properties such as test-retest reliability, content validity, and structural validity were only sporadically reported. Moreover, psychometric properties such as measurement error and responsiveness that are essential for evaluating sensitivity to change were not reported in any of the studies. The findings of our review indicate that there is limited evidence about the psychometric properties of instruments used to assess HRQoL of children and adolescents in LMICs. The lack of comprehensive psychometric testing raises concerns about the appropriateness of using these instruments to capture HRQoL across culturally diverse populations. Future research should focus on comprehensive psychometric validation of HRQoL instruments prior to their widespread use.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStrengths and limitations of the review\\u003c/h2\\u003e\\u003cp\\u003eA strength of this review is the systematic search of relevant databases: Medline, Embase, PubMed, Scopus, CINAHL, and Web of Science using a rigorous dual-review screening process. The review adhered to predefined objectives and systematically mapped the use and evaluation of generic HRQoL instruments in LMICs. Additionally, the review identified the application of HRQoL instruments in the general children and adolescent populations. However, this scoping review had some limitations. As the aim of this review was to identify papers reporting generic HRQoL instruments, there will be a range of condition-specific HRQoL measures that were not within the scope of this review. Although the search was focused on major databases, literature from regional databases and grey literature may potentially have been overlooked. Restricting eligible papers to those published in English potentially excluded papers published in other languages.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eImplications of the review\\u003c/h2\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eAlmost all studies used HRQoL instruments that were specifically developed for or applicable to child and adolescent populations.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eResearch was identified from only 29% of LMICS, and there is a notable lack of studies conducted in low-income countries, highlighting a geographical research gap.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eVery few studies have included very young children (\\u0026le;\\u0026thinsp;4 years) suggesting the need to include younger age groups in future research.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eOnly a small number of studies focused on the general child or adolescent populations, and only one study reported population norm data.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eEvidence on the psychometric properties of the identified instruments is limited, highlighting the need for further validation studies.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe use of generic HRQoL instruments as a health outcome measure for children and adolescents in LMICs, has increased in recent years. These instruments, predominantly the PedsQL, have been applied across a range of health conditions and to a lesser extent in the general child and adolescent populations. However, despite their growing use, gaps remain in terms of population coverage, psychometric evidence, and the geographic distribution of countries reporting HRQoL using generic instruments.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eLMICs: Low- and middle-income countries, HRQoL: Health-related quality of life, Prisma-ScR: Preferred Reporting Items for Systematic Reviews and Scoping Reviews, RCTs: Randomized controlled trials\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe acknowledge Christy Ballard, Health Sciences subject librarian at the University of Otago for her assistance in the development of search terms and strategies for this scoping review. We thank Gebretsadkan Gebremedhin for his counter-review of the papers through each of the screening stages.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGM prepared the first draft of the scoping review manuscript and led the research within his programme of PhD research (supervised by SD, TS and AS). All authors (SD, TS, AS, M\\u0026Aring;, MH, and GA) provided input and comments on multiple iterations of this article. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis protocol has been prepared as part of the first author\\u0026rsquo;s (GM) PhD research at the University of Otago. The EuroQol Research Foundation provided GM with funding for a PhD stipend.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval and consent to participate\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval and consent to participate are not required for this scoping review as no human participants were involved in this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare they have no competing interests. Sarah Derrett, Trudy Sullivan, Mimmi \\u0026Aring;strom, and Michael Herdman are members of the EuroQol Group, which developed and is responsible for the EQ-5D instruments, including the EQ-5D-Y-3L and the EQ-5D-Y-5L. \\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eShinde S, Harling G, Assefa N, B\\u0026auml;rnighausen T, Bukenya J, Chukwu A, et al. Counting adolescents in: the development of an adolescent health indicator framework for population-based settings. EClinicalMedicine. 2023;61:102067.\\u003c/li\\u003e\\n\\u003cli\\u003eUNICEF. Adolescent Demographics- UNICEF Data. 2025;cited 2025 Jul 24. https://data.unicef.org/topic/adolescents/.\\u003c/li\\u003e\\n\\u003cli\\u003eUNICEF. Generation 2030 Africa 2.0 Prioritizing investments in children to reap the demographic dividend. New York: United Nations Children\\u0026rsquo;s Fund; 2014.\\u003c/li\\u003e\\n\\u003cli\\u003eLam CG, Howard SC, Bouffet E, Pritchard-Jones K. 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BMC Pediatrics. 2024;24(1):688.\\u003c/li\\u003e\\n\\u003cli\\u003eHorsman J, Furlong W, Feeny D, Torrance G. The Health Utilities Index (HUI): concepts, measurement properties and applications. Health Qual Life Outcomes. 2003;1:54.\\u003c/li\\u003e\\n\\u003cli\\u003eVarni JW, Burwinkle TM, Lane MM. Health-related quality of life measurement in pediatric clinical practice: An appraisal and precept for future research and application. Health and Quality of Life Outcomes. 2005;3(1):34.\\u003c/li\\u003e\\n\\u003cli\\u003eSzende A, Janssen, B., \\u0026amp; Cabases, J. . Self-reported population health: An international perspective based on EQ-5D. New York: Springer Open. Springer Open. 2014;New York.\\u003c/li\\u003e\\n\\u003cli\\u003eBefus EG, Helseth S, M\\u0026oslash;lland E, Westergren T, Fegran L, Haraldstad K. Use of KIDSCREEN health-related quality of life instruments in the general population of children and adolescents: a scoping review. Health Qual Life Outcomes. 2023;21(1):6.\\u003c/li\\u003e\\n\\u003cli\\u003eClark H, Coll-Seck AM, Banerjee A, Peterson S, Dalglish SL, Ameratunga S, et al. A future for the world\\u0026apos;s children? A WHO-UNICEF-Lancet Commission. The Lancet. 2020;395(10224):605-58.\\u003c/li\\u003e\\n\\u003cli\\u003eWorld Health Organization. Sustainable development goals. 2017([Accessed 15 Jul 2025]).\\u003c/li\\u003e\\n\\u003cli\\u003e\\u0026Aring;str\\u0026ouml;m M, Persson C, Lind\\u0026eacute;n-Bostr\\u0026ouml;m M, Rolfson O, Burstr\\u0026ouml;m K. Population health status based on the EQ-5D-Y-3L among adolescents in Sweden: Results by sociodemographic factors and self-reported comorbidity. Qual Life Res. 2018;27(11):2859-71.\\u003c/li\\u003e\\n\\u003cli\\u003eChen TH, Li L, Kochen MM. A systematic review: how to choose appropriate health-related quality of life (HRQOL) measures in routine general practice? J Zhejiang Univ Sci B. 2005;6(9):936-40.\\u003c/li\\u003e\\n\\u003cli\\u003eKhanna D, Khadka J, Mpundu-Kaambwa C, Lay K, Russo R, Ratcliffe J. Are We Agreed? Self- Versus Proxy-Reporting of Paediatric Health-Related Quality of Life (HRQoL) Using Generic Preference-Based Measures: A Systematic Review and Meta-Analysis. Pharmacoeconomics. 2022;40(11):1043-67.\\u003c/li\\u003e\\n\\u003cli\\u003eRavens-Sieberer U, Erhart M, Wille N, Wetzel R, Nickel J, Bullinger M. Generic health-related quality-of-life assessment in children and adolescents: methodological considerations. Pharmacoeconomics. 2006;24(12):1199-220.\\u003c/li\\u003e\\n\\u003cli\\u003eRiley AW. Evidence That School-Age Children Can Self-Report on Their Health. Ambulatory Pediatrics. 2004;4(4):371-6.\\u003c/li\\u003e\\n\\u003cli\\u003eGuyatt GH FD, Patrick DL. . Measuring health-related quality of life. . Ann Intern Med 1993;118(8):622-9.\\u003c/li\\u003e\\n\\u003cli\\u003ePickard AS, Knight SJ. Proxy evaluation of health-related quality of life: a conceptual framework for understanding multiple proxy perspectives. Med Care. 2005;43(5):493-9.\\u003c/li\\u003e\\n\\u003cli\\u003eCoombes L, Bristowe K, Ellis-Smith C, Aworinde J, Fraser LK, Downing J, et al. Enhancing validity, reliability and participation in self-reported health outcome measurement for children and young people: a systematic review of recall period, response scale format, and administration modality. Qual Life Res. 2021;30(7):1803-32.\\u003c/li\\u003e\\n\\u003cli\\u003eAl Meslamani AZ. Barriers to digital endpoints in data collection in low and middle-income countries. Expert Review of Pharmacoeconomics \\u0026amp; Outcomes Research. 2024;24(6):701-3.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"systematic-reviews\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"sysr\",\"sideBox\":\"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/sysr/default.aspx\",\"title\":\"Systematic Reviews\",\"twitterHandle\":\"@MedicalEvidence\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Health-related quality of life (HRQoL), Children, Adolescents, Generic instrument, Low- and middle-income countries (LMICs)\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7617141/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7617141/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eHealth-related quality of life (HRQoL) measures are widely applied in research and clinical practice; however, their use among children and adolescents in low- and middle-income countries (LMICs) has not been explored in depth to date. This is particularly important given that nearly two-thirds of the world\\u0026rsquo;s children and adolescents reside in LMICs. This scoping review aims to identify generic HRQoL instruments used in studies of children and adolescents in LMICs, and to describe their use and reported psychometric properties.\\u003c/p\\u003e\\u003ch2\\u003eMethod\\u003c/h2\\u003e\\u003cp\\u003eGuided by Arksey and O\\u0026rsquo;Malley\\u0026rsquo;s framework, a search for peer-reviewed papers published between 1 January 2000 and 31 December 2024 was conducted in six databases (Medline, Embase, PubMed, Scopus, CINAHL, and Web of Science). The review included papers reporting the use of generic HRQoL instruments among children and adolescents aged 0\\u0026ndash;19 years in LMICs.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eA total of 152 papers originating from 22 (of 75) LMICs met the inclusion criteria. Most papers were from two countries: India 50 (32.9%) and Egypt 25 (16.5%). Eight generic HRQoL instruments (PedsQL 4.0 GCS, KIDSCREEN-10/27/52, HUI-2/3, EQ-5D-Y-3L/5L, PROMIS-25, PedsQL Short Form (15-items), EQ-5D-3L/5L, and SF-36) were identified. Of these, PedsQL 4.0 GCS was reported in 78% of papers reviewed. Only eleven studies (7.2%) reported use of the instruments in the general population, and only one generated population norm data. Very few studies 2 (1.3%) were conducted in the younger age group (\\u0026le;\\u0026thinsp;4 years). Three-quarters of the studies (34.9%) included both self- and proxy-reported HRQoL data. Instruments identified were most often used for general health assessment (77.6%); only 9.9% of studies evaluated treatment or intervention outcomes, and 12.5% reported on psychometric testing of the instruments. Nineteen studies (12.5%) reported psychometric properties, but none evaluated all nine properties recommended by COSMIN.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eThe use of generic HRQoL instruments in studies of children and adolescents in LMICs, mainly with PedsQL, has increased in recent years, though almost half of the studies identified were carried out in only two countries. Despite their growing use, gaps remain in population coverage, generation of evidence on their psychometric performance in LMICs, and the geographic distribution of research in LMICs using generic HRQoL instruments.\\u003c/p\\u003e\\u003ch2\\u003eScoping review registration:\\u003c/h2\\u003e\\u003cp\\u003eThe protocol was submitted to Open Science Framework on 24 January 2025. 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