Measuring health-related quality of life in Africa: a systematic review of validated disease-specific and generic measurement tools

Abstract

Background:

This systematic review examines the evidence on the use of health-related quality of life (HRQoL) tools for African populations and evaluates their psychometric properties, cultural adaptation, and applicability.

Methods:

A systematic search was conducted across PubMed, Web of Science, Scopus, and gray literature from January 2015 to January 2025. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and COSMIN (Consensus-based Standards for the Selection of Health Measurement Instruments) frameworks. Duplicate screening and study selection were independently performed by multiple reviewers. Eligible studies included the development, adaptation, or validation of HRQoL for African populations. The protocol was submitted for registration to the International Prospective Register of Systematic Reviews (PROSPERO) under the identification number CRD42025639055.

Results:

Forty studies met the inclusion criteria, with 31 (77.5%) focusing on adults and minimal attention given to the pediatric population. East Africa had the highest representation, with 17 (42.5%), while West Africa accounted for 7 (17.5%). Internal consistency (Cronbach’s alpha ≥ 0.70) was demonstrated in 33 (97.1%) out of the 34 tools. A total of 34 different HRQoL tools were identified, including 12 generic instruments. The SF-12 and WHOQOL-BREF were the most validated tools, whereas the EORTC QLQ-C30 was the most validated disease-specific tool. Cultural adaptation was a major focus, with 32 (80.0%) of the studies incorporating linguistic modifications to enhance contextual relevance. Most studies, 28 (70.0%), used cross-sectional designs. Overall, most tools demonstrated good reliability and cultural adaptability, although limitations such as small sample sizes, limited geographic coverage, and incomplete reporting of responsiveness and test–retest reliability were common.

Conclusion:

Significant progress has been made in developing and validating HRQoL tools for African populations. However, gaps remain, including the need for longitudinal studies, greater inclusion of children’s HRQoL assessments, and broader geographic representation. Strengthening research capacity will be pivotal in advancing culturally responsive HRQoL tools and integrating them into healthcare decision-making in Africa.

Systematic review registration:

https://www.crd.york.ac.uk/PROSPERO/view/CRD42025639055, Identifier: CRD42025639055.

Introduction

Health systems that provide good-quality care aim not only to prevent and treat diseases but also to improve the wellbeing and quality of life (QoL) of patients (WHO, 2020a). QoL is a multidimensional concept that refers to an “individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, and standards” and is affected by a person’s physical health and psychological state (The Whoqol Group, 1998).

The improvement of QoL and other outcomes through proper prevention and treatment mechanisms is at the heart of clinical science and all health systems. Outcomes could be economic, clinical, or humanistic. Clinical outcomes in patient care measure the impact of the treatment on the patient, especially their QoL. Hence, the assessment of QoL should consider multidimensional aspects of physical and psychological health (Vagetti et al., 2014).

Health-related quality of life (HRQoL), which is derived from QoL, is a multidimensional concept that reflects an individual’s perception of the impact of a disease state or intervention on their physical, psychological, and social aspects of life (Tran et al., 2012; European Medicine Agency, 2006). HRQoL is essential in inpatient follow-up and monitoring, as it provides valuable feedback from the perspective of patients about a disease condition and its accompanying interventions (Mafirakureva et al., 2016).

The HRQoL measurement has emerged as an essential health outcome in clinical trials, clinical practice improvement strategies, and healthcare services research and evaluation (Varni et al., 2003). Research on HRQoL often explores patient-reported outcome measures (PROMs) that can offer valuable insights into therapeutic interventions, health strategies, and health policy development (Brazier et al., 2002; Miller et al., 2021; Conrad and Barker, 2010). This patient-centered exploration of the experience of health is particularly important, as the disjuncture between patients’ subjective experience of treatment and wellbeing and clinical improvements has been observed (Brazier et al., 2002).

HRQoL instruments are commonly grouped into generic and disease-specific measures, each serving distinct purposes in health assessment (Lipton et al., 2013). Generic instruments, such as the SF-36, SF-12, EQ-5D, and WHOQOL-BREF, assess broad domains of functioning, such as physical, emotional, and social, allowing comparisons across diseases and populations (Hand, 2016). These tools are widely used in Africa, and several validation studies report acceptable psychometric properties, particularly in internal consistency and construct validity. For example, the WHOQOL-BREF has demonstrated reliability across multiple African languages; however, cultural discrepancies have been reported regarding items related to social relationships, spirituality, and environmental context (Colbourn et al., 2012; Bowden et al., 2002; Price et al., 2020). These findings suggest that although generic tools are broadly applicable, they may overlook culturally embedded expressions of wellbeing. In contrast, disease-specific instruments—such as the EORTC QLQ-C30 for cancer, the KDQOL for kidney disease, or HIV-specific QoL measures—are tailored to capture symptoms and functional limitations unique to particular conditions (Glover et al., 2011; Namisango et al., 2007). Although these tools generally show stronger clinical sensitivity, many lack extensive validation in African populations. Several studies note inconsistencies in factor structures, challenges in linguistic adaptation, and reduced responsiveness due to cultural variations in symptom reporting (Ngwira et al., 2021; Soto et al., 2015; Crawford, 2012). The distinction between generic and disease-specific tools is, therefore, essential, as their adequacy in Africa varies and depends on rigorous local validation.

However, owing to the need for high-quality, specifically designed questionnaires based on patient-reported outcomes (PROs) in clinical practice, the instruments are usually translated into different languages (Ware, 2000; Herdman et al., 2011). Evidence shows that the reliability and validity of an instrument are influenced by socioeconomic factors, such as education, literacy, and rural or urban living, which were often associated with populations’ cultural backgrounds and historical racial inequalities (Wissing et al., 2010; O’Keefe and Wood, 1996; Mullin et al., 2000; Nelson et al., 2020).

The HRQoL can be measured using generic or disease-specific instruments. Generic tools such as the SF-12, SF-36, EQ-5D, and WHOQOL-BREF capture broad aspects of physical, psychological, and social wellbeing and allow comparison across different diseases and populations. However, evidence from African studies shows that although these tools often demonstrate acceptable reliability, several items may not fully align with local cultural norms, particularly in domains related to social relationships, spirituality, and environmental context (Olsen et al., 2013; Gladstone et al., 2008). Disease-specific instruments, such as the EORTC QLQ-C30 for cancer or diabetes-specific QoL scales, provide more clinically sensitive assessments. However, many have undergone limited validation in African settings, with challenges reported in linguistic adaptation, conceptual equivalence, and responsiveness (Naamala et al., 2021; Marsh and Truter, 2021). This distinction matters because the adequacy of each type of tool in Africa depends on rigorous and context-specific validation.

Validating HRQoL instruments typically involves assessing their reliability, validity, and responsiveness, as well as ensuring cultural and linguistic appropriateness. This is especially important in Africa, where cultural norms, language diversity, and shifting health burdens from infectious to chronic non-communicable diseases may influence how individuals interpret and respond to HRQoL items (Marsh and Truter, 2021). Despite increasing use of these tools, synthesized evidence on their validation in African populations is lacking, underscoring the need for a systematic review to map existing instruments, highlight gaps, and guide future adaptation or development.

To support this assessment, this review uses the COSMIN (Consensus-based Standards for the Selection of Health Measurement Instruments) framework, which provides internationally recognized criteria for evaluating the methodological quality of studies on PROMs (Mokkink et al., 2016). Although alternative guidelines exist, such as ISOQOL standards or the FDA PRO guidance, COSMIN offers the most comprehensive and structured approach for evaluating psychometric properties, making it particularly suitable for this review (Lorente et al., 2020). Despite the growing use of HRQoL instruments in African health research, there remains limited consolidated evidence on how these tools have been developed, adapted, and psychometrically validated for use across the continent’s diverse cultural and linguistic contexts. No prior systematic review has comprehensively synthesized this evidence, even though such information is essential for ensuring that PROMs are conceptually appropriate, reliable, and meaningful for African populations. Therefore, the objective of this systematic review is to identify and critically appraise all studies that have developed, adapted, or validated generic or disease-specific HRQoL instruments for African populations. Guided by the PROSPERO-registered protocol (CRD42025639055), the review aims to answer the following research question: “Which HRQoL measurement tools have been validated or culturally adapted for use in African populations, and what is the quality of the evidence supporting their psychometric properties and contextual relevance?” This study also seeks to highlight the methodological strengths and limitations of the included studies to inform future research and to promote more robust and culturally appropriate HRQoL measurement across African settings.

Methods

Design

This study was conducted as a systematic review to identify, evaluate, and document HRQoL measurement tools developed or validated for use in African populations. This systematic review assessed the psychometric properties, cultural adaptation, and validation of HRQoL tools across diverse populations in Africa. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The protocol was formally registered with the International Prospective Register of Systematic Reviews (PROSPERO), registration number CRD42025639055.

Eligibility criteria

Studies were eligible for inclusion if they met the following criteria:

• Studies conducted among children or adult populations residing in Africa.

• Studies that focused on the development, cross-cultural or linguistic adaptation, or psychometric validation of an HRQoL tool. Adaptation was defined as any modification made to an existing HRQoL instrument to improve its cultural, linguistic, or contextual relevance to an African setting. This includes translation, back-translation, and pilot testing. Validation was defined as the evaluation of one or more psychometric properties of the HRQoL instrument, such as reliability (e.g., internal consistency and test–retest), construct validity, criterion validity, responsiveness, or factor structure, based on the COSMIN guidelines.

• Quantitative, qualitative, cross-sectional, longitudinal, and mixed-method studies.

• Peer-reviewed primary studies with sufficient methodological detail from 1 January 2015 to 1 January 2025.

Exclusion criteria

The study’s exclusion criteria were as follows:

• Studies that exclusively focused on non-human populations.

• Studies that used HRQoL tools without developing, adapting, or validating them for African populations.

• Unpublished theses and retrospective analyses of secondary datasets.

Search strategy

A comprehensive literature search was conducted across PubMed, Web of Science, Scopus, and gray literature sources. Additional sources included the reference lists of relevant articles. The search was designed to retrieve studies published to date, using a combination of MeSH terms and free-text keywords related to HRQoL measurement tools, their development, validation, and use in Africa. Boolean operators (AND/OR) and truncation (*) were applied where necessary to refine the search.

The primary search terms included: (“tool” OR “instrument” OR “scale*” OR “questionnaire*” OR “measure*” OR “assessment tool*” OR “survey*”) AND (“health-related quality of life” OR “HRQoL” OR “QoL” OR “quality of life” OR “health preference*”) AND (“measurement” OR “assessment” OR “evaluation” OR “validation” OR “development”) AND (“Africa” OR “Sub-Saharan Africa” OR “African countries” OR “African region”)**. The search was conducted without language restrictions, but studies had to meet specific eligibility criteria to be included. The full search strategy from gray literature and databases (PubMed, Web of Science, and Scopus) is provided in Supplementary File 1.

Study selection and screening

The search results were imported into Rayyan, a web-based tool for systematic reviews, where duplicate records were automatically removed after being assessed by AI. Title and abstract screening were independently conducted by EJU and CNI, with conflicts resolved by the third reviewer, AI. Similarly, full-text screening was performed by EJU and CNI, with discrepancies resolved by AI.

The study selection process is illustrated in Figure 1.

Figure 1

Data extraction

A structured data extraction form was developed and managed using Microsoft Excel based on the study objectives and COSMIN guidelines. An artificial intelligence (AI) tool was also used to assist in data extraction. Any discrepancies in extracted data were discussed and resolved by consensus. The form was piloted on a random sample of three included studies to ensure clarity and completeness. Necessary revisions were made before full-scale data extraction. Two independent reviewers (EJU and CNI) extracted the following details:

• Study characteristics (authors, year, country, design).

• Participant characteristics (sample size, age group, setting).

• Tool characteristics (name, type, language, mode of administration, domains, validation process).

• Key findings, psychometric properties, and policy implications.

Any discrepancies in the extracted data were discussed and resolved by consensus, and when needed, a third reviewer (AI) acted as an arbitrator. This dual-coding and consensus-based approach ensured the reliability of the data extraction process. A quality assessment of the 40 included articles was conducted using the 8-item checklist for analytical cross-sectional studies by the Joanna Briggs Institute. Each of the eight questions was used to appraise the articles using the options “yes,” “no,” “unclear,” and “not applicable” (Supplementary File 2).

Data synthesis

A narrative synthesis was conducted to summarize findings across studies. We conducted the synthesis using AI, categorizing the HRQoL tools into generic and disease-specific instruments and identifying trends in validation, adaptation, and psychometric evaluation. Quantitative metrics, including frequency distributions and proportions, were reported to enhance clarity.

Findings were structured according to the characteristics of the measurement tools, the characteristics of the first authors of the included studies, the psychometric properties of the tools, and their application across African settings.

Results

A total of 3,922 records were retrieved from databases, of which 3,425 remained after duplicate removal. After title and abstract screening, 43 articles were retained for full-text review. Three studies were excluded at this stage: one unpublished thesis, one retrospective dataset analysis, and one study on an Ivorian population residing in the United States. Ultimately, 40 studies were included in this systematic review, with the majority (82.5%, n = 33) employing a cross-sectional design alone (Colbourn et al., 2012; Bowden et al., 2002; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012), while only 15% (n = 6) used a longitudinal approach alone (Scott et al., 2017; Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019). Notably, 80.0% (n = 32) of the studies focused on the translation and cultural adaptation of tools to align with local contexts (Colbourn et al., 2012; Bowden et al., 2002; Van Biljon et al., 2015; Reba et al., 2019; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Kulich et al., 2008; Gadisa et al., 2019).

Geographically, South Africa (Van Biljon et al., 2015; Gqada et al., 2021; Brandt et al., 2016; Scott et al., 2017) and Ethiopia (Reba et al., 2019; Jikamo et al., 2021; Muhye and Fentahun, 2023; Araya et al., 2019; Getu et al., 2022; Gadisa et al., 2019) were the most represented countries, contributing 10.0% (n = 4) and 15% (n = 6) of the studies, respectively. Among studies on disease-specific tools, breast cancer and diabetes (Reba et al., 2019; Ehab et al., 2021; Uwizihiwe et al., 2022; Kidayi et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Getu et al., 2022; Gadisa et al., 2019) were the most frequently studied conditions, representing 20% (n = 8) of the included studies (Table 1).

Quality assessment of the included articles using the Joanna Briggs Institute checklist

The vast majority of studies demonstrated robust practices, with 34 (85%) clearly defining their inclusion criteria (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Scott et al., 2017; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019) and 33 (82.5%) providing a detailed description of the study subjects and setting (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Smith and Morris-Eyton, 2023; Scott et al., 2017; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019). Measurement and analysis were particularly strong, as nearly all studies, 37 (97.5%), reported measuring outcomes in a valid and reliable way and employing appropriate statistical analysis (Colbourn et al., 2012; Bowden et al., 2002; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Scott et al., 2017; Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019). However, the handling of confounding factors was a notable exception. Of the 33 studies for which this criterion was applicable, only 8 (24.2%) adequately identified potential confounders (Van Biljon et al., 2015; Reba et al., 2019; Ibrahim et al., 2020; Mbada et al., 2015; Borissov et al., 2022; Osman et al., 2018; Smith and Morris-Eyton, 2023; Getu et al., 2022) (see Supplementary Tables 2a–c).

Characteristics of first authors of the included studies

Among the 40 included studies, 22 (55%) of the first authors were men (Colbourn et al., 2012; Bowden et al., 2002; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Muhye and Fentahun, 2023; Duracinsky et al., 2012; Uwizihiwe et al., 2022; Kidayi et al., 2023; Borissov et al., 2022; Kondo et al., 2023; Olasehinde et al., 2024; Nkurunziza et al., 2016; El Alami et al., 2021; Guermazi et al., 2012; Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019), while 13 (32.5%) were women (Namisango et al., 2007; Mgbeojedo et al., 2022; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Brandt et al., 2016; El Fakir et al., 2014a; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Scott et al., 2017), indicating a gender disparity in authorship of HRQoL research in Africa. The majority of the first authors (n = 32, 80.0%) were affiliated with African institutions (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ehab et al., 2021; Gqada et al., 2021; Onagbiye et al., 2018; Kidayi et al., 2023; Brandt et al., 2016; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Okello et al., 2018; Gadisa et al., 2019), while 7 (17.5%) were affiliated with institutions outside Africa (Ravens-Sieberer et al., 2010; Duracinsky et al., 2012; Uwizihiwe et al., 2022; Borissov et al., 2022; Ohrnberger et al., 2020; Kulich et al., 2008; Getu et al., 2022; Bowden, 2002). The United Kingdom (Borissov et al., 2022; Ohrnberger et al., 2020; Bowden, 2002) (n = 3, 7.5%) and France (Duracinsky et al., 2012) (n = 1, 2.5%) were represented among non-African affiliations. South Africa (Van Biljon et al., 2015; Gqada et al., 2021; Onagbiye et al., 2018; Brandt et al., 2016; Smith and Morris-Eyton, 2023; Scott et al., 2017) (n = 6, 15.0%), Nigeria (Ibrahim et al., 2020; Mbada et al., 2015; Mgbeojedo et al., 2022; Olasehinde et al., 2024; Odetunde et al., 2020; Owolabi, 2010) (n = 6, 15.0%), and Ethiopia (Reba et al., 2019; Jikamo et al., 2021; Muhye and Fentahun, 2023; Araya et al., 2019; Gadisa et al., 2019) (n = 5, 12.5%) had the highest representation of first-author institutional affiliations. Dual institutional affiliations were observed in 14 (35.0%) of the studies, reflecting interdisciplinary and cross-institutional research collaborations (Colbourn et al., 2012; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Duracinsky et al., 2012; Gqada et al., 2021; Uwizihiwe et al., 2022; Olasehinde et al., 2024; El Alami et al., 2021; Westmoreland et al., 2018; Guermazi et al., 2012; Okello et al., 2018; Getu et al., 2022) (see Supplementary Tables 3a–c).

Descriptive characteristics of sample sizes and populations whose HRQoL were assessed

Most studies (77.5%, n = 31) focused on adult populations aged 18 years and above (Colbourn et al., 2012; Namisango et al., 2007; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Guermazi et al., 2012; Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019). In comparison, only 7.5% (n = 3) included children and adolescents alongside the adult population (Mgbeojedo et al., 2022; Ravens-Sieberer et al., 2010; Westmoreland et al., 2018), suggesting a significant emphasis on adult HRQoL in African settings.

Sample sizes varied considerably, with 80.0% (n = 32) of studies enrolling fewer than 300 participants (Namisango et al., 2007; Van Biljon et al., 2015; Ibrahim et al., 2020; Jikamo et al., 2021; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ehab et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019; Gqada et al., 2021), and only 7.5% (n = 3) involving cohorts exceeding 1,000 participants (Younsi and Chakroun, 2014; Mbada et al., 2015; Ohrnberger et al., 2020).

Recruitment settings were predominantly hospital-based (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Ehab et al., 2021; Duracinsky et al., 2012; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019; Gqada et al., 2021) (77.5%, n = 31), with only 22.5% (n = 9) conducted in community settings (Bowden et al., 2002; Younsi and Chakroun, 2014; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Onagbiye et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Ohrnberger et al., 2020), indicating a potential bias toward healthcare facility-based populations.

Regional distribution revealed East Africa as the leading contributor (Colbourn et al., 2012; Bowden et al., 2002; Namisango et al., 2007; Reba et al., 2019; Jikamo et al., 2021; Muhye and Fentahun, 2023; Uwizihiwe et al., 2022; Kidayi et al., 2023; Borissov et al., 2022; Kondo et al., 2023; Nkurunziza et al., 2016; Araya et al., 2019; Westmoreland et al., 2018; Ohrnberger et al., 2020; Okello et al., 2018; Getu et al., 2022; Gadisa et al., 2019) (42.5%, n = 17), with West Africa (Ibrahim et al., 2020; Mbada et al., 2015; Mgbeojedo et al., 2022; Duracinsky et al., 2012; Olasehinde et al., 2024; Odetunde et al., 2020; Owolabi, 2010) (17.5%, n = 7) contributing the least proportion of studies. Gender-specific studies were limited, with only 17.5% (n = 7) exclusively targeting women, highlighting a notable gap in gender-focused HRQoL research (Jikamo et al., 2021; Kidayi et al., 2023; Brandt et al., 2016; El Fakir et al., 2014a; Olasehinde et al., 2024; Araya et al., 2019; Osman et al., 2018) (see Table 2).

Characteristics of the HRQoL tools

A total of 34 tools were reported in the included studies. Of the 12 generic HRQoL instruments used in the studies, SF-12 (Younsi and Chakroun, 2014; Ibrahim et al., 2020; Ohrnberger et al., 2020) and WHOQOL-BREF (Colbourn et al., 2012; Reba et al., 2019; Jikamo et al., 2021) were reported in three studies each. Among the 22 disease-specific instruments, the different versions of the QoL in cancer tool, the EORTC QLQ, were validated in seven studies (Ehab et al., 2021; Kidayi et al., 2023; Araya et al., 2019; El Alami et al., 2021; Getu et al., 2022; Gadisa et al., 2019; Gqada et al., 2021), with the EORTC QLQ-C30 accounting for four of the studies (Kidayi et al., 2023; Araya et al., 2019; El Alami et al., 2021; Gadisa et al., 2019), emphasizing their widespread use in African health research. Most studies (82.5%, n = 33) translated tools into local languages, including Afrikaans, Chi Chew, Igbo, Yoruba, and Arabic, ensuring linguistic and cultural appropriateness (Colbourn et al., 2012; Bowden et al., 2002; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Westmoreland et al., 2018; Guermazi et al., 2012; Ohrnberger et al., 2020; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019). Administration methods varied, with 45% (n = 18) of the included studies adopting self-administration of 13 tools (Mgbeojedo et al., 2022; Gqada et al., 2021; Onagbiye et al., 2018; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; Odetunde et al., 2020; Araya et al., 2019; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Gadisa et al., 2019), while 30.0% (n = 12) adopted interviews in data collection using 10 tools (Colbourn et al., 2012; Bowden et al., 2002; Namisango et al., 2007; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Duracinsky et al., 2012; Borissov et al., 2022; El Fakir et al., 2014a; Osman et al., 2018; Ohrnberger et al., 2020), reflecting differences in participant literacy and accessibility. Validation efforts demonstrated robust reliability, with internal consistency (Cronbach’s alpha) exceeding 0.70 in the majority of the tools (see Table 3).

Strengths and limitations of HRQoL tools

Many of the HRQoL tools demonstrated strong contextual adaptability, with WHOQOL-BREF, SF-36, and EQ-5D-Y (Colbourn et al., 2012; Reba et al., 2019; Jikamo et al., 2021; Mbada et al., 2015; Ravens-Sieberer et al., 2010; Guermazi et al., 2012; Scott et al., 2017; Kulich et al., 2008) validated across multiple African languages and populations, ensuring relevance in diverse settings. Disease-specific instruments such as the EORTC QLQ-BR45 (Ehab et al., 2021; Kidayi et al., 2023; Getu et al., 2022) for breast cancer and the KCCQ (Okello et al., 2018) for heart failure provided highly specialized assessments, capturing condition-specific impacts more accurately than generic tools. Nonetheless, some instruments, such as the D-39 for diabetes (Uwizihiwe et al., 2022) and the EORTC QLQ-PAN26 for pancreatic cancer (Gqada et al., 2021), were lengthy and complex, posing challenges for use in clinical settings with time-constrained patients (see Table 4).

Overview of the findings, strengths, and limitations of the included studies in the review

Reliability and validity were good in 87.5% (n = 35) of the tools in African contexts (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Duracinsky et al., 2012; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gqada et al., 2021). Cultural adaptation was found to enhance the usability and acceptability of tools in 75.0% (n = 30) of the studies. Generic tools, such as the WHOQOL-BREF and SF-12, were validated across diverse populations and conditions, constituting 15.0% (n = 6) of the studies, highlighting their versatility (Colbourn et al., 2012; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Ohrnberger et al., 2020). However, 60.0% (n = 24) of studies reported limitations related to sample size, underscoring the need for larger, more representative cohorts to improve generalisability (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Jikamo et al., 2021; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ehab et al., 2021; Duracinsky et al., 2012; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Olasehinde et al., 2024; Farid et al., 2023; Odetunde et al., 2020; El Alami et al., 2021; Osman et al., 2018; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Owolabi, 2010; Gadisa et al., 2019). Policy implications from 80.0% (n = 32) of the studies emphasized the integration of HRQoL tools into healthcare systems to facilitate patient-centered care and inform resource allocation strategies (Colbourn et al., 2012; Bowden et al., 2002; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Nkurunziza et al., 2016; Farid et al., 2023; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Ohrnberger et al., 2020; Okello et al., 2018; Kulich et al., 2008; Getu et al., 2022) (see Table 5).

Summary of HRQoL tools following the consensus-based standards for the selection of health measurement instruments (COSMIN) guideline

A total of 34 HRQoL measurement tools were evaluated using the COSMIN framework. All the tools reported on feasibility, 33 (97.1%) tools assessed internal consistency using Cronbach’s alpha (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Duracinsky et al., 2012; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019; Gqada et al., 2021). Construct validity was evaluated in 32 (94.1%) tools using methods such as confirmatory factor analysis (CFA) and assessments of convergent and discriminant validity (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Duracinsky et al., 2012; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019; Gqada et al., 2021). Content validity, which assessed the relevance and comprehensiveness of the tools (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Duracinsky et al., 2012; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019; Gqada et al., 2021), was documented in 31 (91.2%) tools. Test–retest reliability and other indicators of stability over time were reported in 26 (76.5%) tools (Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Ravens-Sieberer et al., 2010; Duracinsky et al., 2012; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; Osman et al., 2018; Smith and Morris-Eyton, 2023; Guermazi et al., 2012; Scott et al., 2017; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022). Known-groups validity was assessed in 22 (64.7%) measurement tools (Colbourn et al., 2012; Bowden et al., 2002; Namisango et al., 2007; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Duracinsky et al., 2012; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; El Fakir et al., 2014a; Nkurunziza et al., 2016; Farid et al., 2023; Araya et al., 2019; El Alami et al., 2021; Westmoreland et al., 2018; Scott et al., 2017; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019). Only 13 (38.2%) tools measured responsiveness (Bowden et al., 2002; Borissov et al., 2022; El Fakir et al., 2014a; Odetunde et al., 2020; Osman et al., 2018; Smith and Morris-Eyton, 2023; Owolabi, 2010; Gadisa et al., 2019). Finally, ceiling and/or floor effects were documented in 21 (61.8%) tools, with some showing minimal issues (Colbourn et al., 2012; Van Biljon et al., 2015; Ibrahim et al., 2020; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Uwizihiwe et al., 2022; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Smith and Morris-Eyton, 2023; Scott et al., 2017; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008). Other information is found in Supplementary Tables 8a–e.

Discussion

Summary of key findings

This systematic review identified a diverse range of generic and disease-specific HRQoL instruments developed, adapted, or validated for use across African populations between 2015 and 2025. Although several tools demonstrated acceptable psychometric performance, particularly in internal consistency and construct validity, many studies lacked comprehensive assessments of measurement properties, including responsiveness, structural validity, and test–retest reliability. Cultural and linguistic adaptation methods varied widely, and several studies reported challenges related to conceptual equivalence and contextual relevance. Overall, the evidence demonstrates growing use of PROMs in Africa but highlights persistent gaps in methodological rigor, geographical coverage, and reporting quality.

Comparison with previous studies

More than 90% of the studies reviewed used a cross-sectional methodological design (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Jikamo et al., 2021; Mbada et al., 2015; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Guermazi et al., 2012), indicating a significant reliance on single-point data collection, which captures snapshots of HRQoL and limits the ability to track changes over time or assess the impact of interventions (Setia, 2016). Few studies used a longitudinal study design (Ohrnberger et al., 2020; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019), which is essential given the dynamic nature of the HRQoL, especially in the context of chronic diseases such as diabetes, cardiovascular diseases, and cancer. There is a need for future research to prioritize longitudinal designs to provide more robust evidence for policy and clinical decision-making. The balance between generic (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Onagbiye et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Ohrnberger et al., 2020) and disease-specific (Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019), HRQoL tools reflects a dual interest in quality-of-life assessment tools. Generic tools such as the SF-8 (Onagbiye et al., 2018), SF-12 (Younsi and Chakroun, 2014; Ibrahim et al., 2020; Ohrnberger et al., 2020), SF-36 (Mbada et al., 2015; Guermazi et al., 2012), and WHOQoL-BREF (Colbourn et al., 2012; Reba et al., 2019; Jikamo et al., 2021) offer the advantage of comparability across different populations and disease conditions. However, disease-specific tools such as the validated EORTCQLQ-BR45 (Ehab et al., 2021; Kidayi et al., 2023) for breast cancer provide more insights into their impact on the QoL of particular health conditions. The validation of these tools across diverse African populations and their translation into local languages is a significant step toward ensuring cultural and linguistic relevance (Efstathiou, 2019), this cultural adaptation of studies is essential for improving the usability and acceptability of HRQoL tools in African settings, where the health perceptions and outcomes are significantly influenced by the cultural context (Guillemin et al., 1993). For instance, adaptations of the Hausa SF-12 (Ibrahim et al., 2020) and Igbo OPQoL-35 (Mgbeojedo et al., 2022) highlight efforts to bridge the gap between culture and language, ensuring that HRQoL assessments are grounded in the lived experience of African populations. This cultural adaptation helps inform healthcare policies and improve outcomes, which could be tailored to the specific needs of the African population (Kaplan and Hays, 2022).

In this review, Southern (Van Biljon et al., 2015; Ravens-Sieberer et al., 2010; Gqada et al., 2021; Onagbiye et al., 2018; Brandt et al., 2016; Smith and Morris-Eyton, 2023; Scott et al., 2017; Kulich et al., 2008) and Eastern Africa (Namisango et al., 2007; Uwizihiwe et al., 2022; Kidayi et al., 2023; Borissov et al., 2022; Kondo et al., 2023; Araya et al., 2019; Okello et al., 2018; Getu et al., 2022; Gadisa et al., 2019) were the most represented African regions, indicating a concentration of research efforts in South and East Africa. This regional bias may reflect disparities in research capacity and funding across other African regions, such as Western Africa (Ibrahim et al., 2020; Mbada et al., 2015; Mgbeojedo et al., 2022; Duracinsky et al., 2012; Olasehinde et al., 2024; Odetunde et al., 2020; Owolabi, 2010) and Northern Africa (Younsi and Chakroun, 2014; Ehab et al., 2021; Guermazi et al., 2012), which were underrepresented. These disparities highlight the need for equitable distribution of research efforts to ensure HRQoL findings are generalizable across different African regions (Mberu et al., 2014).

There were few generic or disease-specific HRQoL measures for children and adolescents (Ravens-Sieberer et al., 2010; Borissov et al., 2022; Westmoreland et al., 2018; Scott et al., 2017), unlike in adult populations (Colbourn et al., 2012; Namisango et al., 2007; Reba et al., 2019; Younsi and Chakroun, 2014; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Onagbiye et al., 2018; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Kondo et al., 2023; El Fakir et al., 2014a; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Guermazi et al., 2012; Scott et al., 2017; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019), indicating a gap in HRQoL research among pediatric populations. In Africa, there is a high burden of childhood diseases such as malaria, malnutrition, and other diseases among children and adolescents, which implies that there is an urgent need to validate HRQoL tools to reflect the QoL of younger populations (Akombi et al., 2017). Additionally, hospital-based validation studies (Colbourn et al., 2012; Namisango et al., 2007; Van Biljon et al., 2015; Reba et al., 2019; Ibrahim et al., 2020; Jikamo et al., 2021; Mbada et al., 2015; Ehab et al., 2021; Duracinsky et al., 2012; Gqada et al., 2021; Uwizihiwe et al., 2022; Kidayi et al., 2023; Brandt et al., 2016; Borissov et al., 2022; Kondo et al., 2023; El Fakir et al., 2014a; Olasehinde et al., 2024; Nkurunziza et al., 2016; Farid et al., 2023; El Fakir et al., 2014b; Odetunde et al., 2020; Araya et al., 2019; El Alami et al., 2021; Osman et al., 2018; Smith and Morris-Eyton, 2023; Westmoreland et al., 2018; Okello et al., 2018; Owolabi, 2010; Kulich et al., 2008; Getu et al., 2022; Gadisa et al., 2019) were predominant over community-based validation studies (Younsi and Chakroun, 2014; Mgbeojedo et al., 2022; Muhye and Fentahun, 2023; Ravens-Sieberer et al., 2010; Onagbiye et al., 2018; Guermazi et al., 2012; Scott et al., 2017; Ohrnberger et al., 2020), suggesting potential bias in recruitment because hospital-based samples may not fully represent the broader population. Community-based studies are essential for capturing the HRQoL of individuals who may not have access to formal healthcare services, particularly in rural areas. Additionally, in the psychometric evaluation, this review applied the COSMIN framework to summarize the methodological rigor of included instruments. The HRQoL tools showed strong validation practices for core psychometric properties, although responsiveness and discriminatory capacity across groups were assessed in fewer studies, highlighting areas for future methodological focus (Finch et al., 2015; Mateen et al., 2017). More than half of the studies reported sample size limitations as a major downside of their research, because small sample sizes can reduce statistical power and the ability to detect significant relationships between groups (Serdar et al., 2021). Therefore, a larger sample cohort enhances the generalisability of the findings. From a policy perspective, integration of HRQoL tools into healthcare systems is critical for advancing patient-centered care and informing resource allocation. HRQoL data can provide valuable insights into the effectiveness of interventions, the burden of disease, and the unmet needs of patients, thereby guiding the development of more responsive and equitable healthcare policies (WHO, 2020b). Generic tools, such as WHOQOL-BREF, SF-36, SF-12, and SF-8, have been validated across multiple populations and conditions, further supporting their utility in both clinical and research settings.

Strengths and limitations of the included studies

The included studies offered valuable insights into efforts to adapt and validate HRQoL tools across multiple African regions. Strengths included the use of established theoretical frameworks for adaptation, the engagement of bilingual experts, and the inclusion of diverse clinical populations. However, limitations were common. Many studies assessed only a narrow subset of psychometric properties, cultural adaptation methods were inconsistently applied, sample sizes were sometimes insufficient for factor analysis, and several papers lacked detailed reporting required for COSMIN-based appraisal. These gaps highlight the need for more rigorous, standardized approaches to instrument validation in African contexts.

Strengths and limitations of this review

This review followed PRISMA guidelines and used a registered PROSPERO protocol, enhancing transparency and reproducibility. Comprehensive screening, duplicate review, and structured data extraction further strengthen the credibility of the findings. However, the review has limitations. Conducting the search across PubMed, Web of Science, Scopus, and gray literature while restricting to English-language publications may have excluded relevant studies, particularly those published locally or in French, Arabic, or Portuguese. Additionally, heterogeneity in validation methodologies limited the feasibility of meta-analysis, making a narrative synthesis necessary instead. Despite these limitations, this review provides the most up-to-date and comprehensive mapping of HRQoL measurement validation in African populations.

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