• Open access
  • Published: 16 November 2012

Systematic review of health-related quality of life models

  • Tamilyn Bakas 1 ,
  • Susan M McLennon 1 ,
  • Janet S Carpenter 1 ,
  • Janice M Buelow 1 ,
  • Julie L Otte 1 ,
  • Kathleen M Hanna 1 ,
  • Marsha L Ellett 1 ,
  • Kimberly A Hadler 1 &
  • Janet L Welch 1  

Health and Quality of Life Outcomes volume  10 , Article number:  134 ( 2012 ) Cite this article

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A systematic literature review was conducted to (a) identify the most frequently used health-related quality of life (HRQOL) models and (b) critique those models.

Online search engines were queried using pre-determined inclusion and exclusion criteria. We reviewed titles, abstracts, and then full-text articles for their relevance to this review. Then the most commonly used models were identified, reviewed in tables, and critiqued using published criteria.

Of 1,602 titles identified, 100 articles from 21 countries met the inclusion criteria. The most frequently used HRQOL models were: Wilson and Cleary (16%), Ferrans and colleagues (4%), or World Health Organization (WHO) (5%). Ferrans and colleagues’ model was a revision of Wilson and Cleary’s model and appeared to have the greatest potential to guide future HRQOL research and practice.

Conclusions

Recommendations are for researchers to use one of the three common HRQOL models unless there are compelling and clearly delineated reasons for creating new models. Disease-specific models can be derived from one of the three commonly used HRQOL models. We recommend Ferrans and colleagues’ model because they added individual and environmental characteristics to the popular Wilson and Cleary model to better explain HRQOL. Using a common HRQOL model across studies will promote a coherent body of evidence that will more quickly advance the science in the area of HRQOL.

Introduction

Health-related quality of life (HRQOL) has been identified as a goal for all people across all life stages by leading health organizations [ 1 – 3 ]. HRQOL, that is, quality of life relative to one’s health or disease status, is a concern of policymakers, researchers, and health care practitioners [ 4 ]. Especially important is the need to align HRQOL research priorities with the needs and values of patients and their families. Because of the multidimensional aspects of HRQOL, and the varied use of this term across many different health and disease conditions, researchers have used a variety of HRQOL models to guide their research. A conceptual model is a schematic representation of a theory that acts as a heuristic device to provide a better understanding of a phenomenon (e.g., HRQOL) by depicting interrelationships among concepts [ 5 ]. The term conceptual model has been used interchangeably as “conceptual framework, theoretical model, or theoretically based conceptual model [ 6 ].”

There are many HRQOL models applied across different health and illness conditions, across the lifespan, and among individuals, their families, and communities. HRQOL is commonly conceived as dynamic, subjective, and multidimensional, and the dimensions often include physical, social, psychological, and spiritual factors [ 7 ]. However, the specific dimensions are labeled differently by different authors [ 7 ]. For example, these broad dimensions subsume more specific dimensions such as emotions, cognitive function, economic status, and intelligence [ 8 ], and they may incorporate friends and family [ 9 ]. While the theoretical underpinnings of HRQOL may be consistent across models, variations in terminology for analogous concepts make comparison across studies very difficult. Differing conceptualizations of HRQOL limit the ability to have a coherent body of evidence to guide further HRQOL research and practice. Common HRQOL models provide essential structure to the conceptualization of HRQOL using common language that can be shared across studies. Identification and evaluation of common HRQOL models can help guide research and practice toward promoting or attaining optimum HRQOL for populations of interest. Thus, the purposes of this paper were to (a) identify the most frequently used HRQOL models found in the literature over the past ten years and (b) review and critique the most commonly used models using established criteria by Bredow [ 10 ]. Although Bredow’s [ 10 ] criteria were developed to critique middle-range theories in nursing research, they represent a comprehensive approach to theory analysis for review and critique of HRQOL models [ 10 ]. The overall goal was to determine the relevance of HRQOL models to research and practice aimed at improving HRQOL.

Several search engines were used to locate relevant articles. Initially, PubMed, MEDLINE, CINAHL, and PsychINFO were searched using the keywords quality of life, health-related quality of life, conceptual framework, conceptual model, and theory. Both quality of life and health-related quality of life were searched because these terms have been used interchangeably in the literature.

We limited our search to English language articles published between January 1, 1999 and August 31, 2010. The inclusion criteria were published articles pertaining to HRQOL models that had been used to guide (a) literature reviews, (b) instrument development studies, (c) descriptive or correlational studies, (d) intervention studies, or (e) practice. Articles in which research findings were used to derive HRQOL models were also included. We did not limit our search to specific populations (e.g., children, adolescents, adults, older adults) because we wanted a broad representation of the use of HRQOL models. Exclusion criteria were articles that did not pertain to humans, were non-English, or involved studies and information published only as dissertations, abstracts, editorials, or clinical opinion. Relevant articles were identified from the literature search using a three-step process. In the first step, authors working in pairs reviewed the article titles based on inclusion/exclusion criteria. In cases in which there was a lack of consensus between the two reviewers, a third reviewer was sought, consistent with methods outlined by the Joanna Briggs Institute [ 11 ]. In the second step, titles that met the criteria were further evaluated. Authors, again working in pairs, reviewed abstracts and reached agreement about whether the abstracts met inclusion/exclusion criteria. In the third step, the identified articles were obtained and evaluated by the same pairs of authors. Full text articles were reviewed and again were included only if the pairs agreed the article met the criteria.

The paired authors then extracted and consolidated pertinent information from the articles into a review table. Column variables in the table were: author and date, country of origin, purpose, methods, design, and model. Each row represented a unique article. All authors engaged in group discussion to reach consensus on articles to be included in the review and to determine the format in which to present review findings. After reviewing articles, the most commonly used HRQOL models were identified, fulfilling the first purpose of this review. For purpose two, each of the most commonly used HRQOL models was critiqued by the author pairs using established criteria by Bredow [ 10 ]. After considering several alternatives, we chose Bredow because he incorporated the most comprehensive criteria for evaluating theories, frameworks, and models [ 10 ]. Although these criteria are used to evaluate middle range theories in nursing research, they are also useful in evaluating quality of life theories [ 10 ]. A description of Bredow’s [ 10 ] criteria appears in the first column of Table 1 , and is summarized below.

Bredow’s [ 10 ] criteria for evaluating theories were organized around two major areas: internal and external criticism. Internal criticism involves a judgment about the internal components of the theory, whereas external criticism involves a judgment about the match between the theory and context of its use. When evaluating internal criticism, the evaluator assesses the adequacy (thoroughness in addressing topic), clarity (clearness of statements), consistency (congruency in semantics, etc.), logical development (support from evidence), and level of theory development. To make judgments about external criticism, the evaluator assesses the complexity (number of concepts/variables, from parsimonious to complex), discrimination (uniqueness), reality convergence (relevant assumptions), pragmatism (ability to use in the real world), scope (narrow to broad use for practice), significance (impact of theory), and utility (ability to produce hypotheses). Critique information for each of the commonly used models was summarized in a table after consensus had been reached by two (or sometimes three) authors.

The disposition of the search results is shown in Figure 1 as a PRISMA flow diagram [ 14 ]. Searching the three databases with the selected keywords yielded a total of 1,602 records. Author review excluded 50 records because they were duplicates, books, dissertations, presentations, or could not be located. This left 1,552 titles to screen. Author pairs excluded 1,334 titles because they did not meet inclusion criteria. This left 218 abstracts to be screened, of which 70 did not meet inclusion criteria; 148 progressed to the full text assessment for eligibility. Of the 148 full text articles assessed, 48 were eliminated because a HRQOL model had not been derived from or used to guide the research, review, and/or findings. This process resulted in a total of 100 articles being included in this review (see Figure 1 ).

figure 1

Literature search flow diagram.

Of the 100 articles, 46 were quantitative. Of the remaining 54, 16 were qualitative research, 1 was mixed methods research, 15 involved instrument development, 20 were literature reviews, 1 described a model revision, and 1 was a consensus paper. The 46 quantitative studies were mainly descriptive studies ( n = 31), with a few being correlational ( n = 13), or randomized controlled trials ( n = 2). Sample sizes ranged from 10 [ 15 ] to 69,031 participants [ 16 ]. The 100 articles came from 21 different countries including Australia ( n = 4), Austria ( n = 1), Brazil ( n = 3), Canada ( n = 12), China ( n = 3), Finland ( n = 1), Germany ( n = 4), India ( n = 1), Ireland ( n = 1), Israel ( n = 1), Italy ( n = 2), Japan ( n = 1), Netherlands ( n = 7), Norway ( n = 2), Spain ( n = 3), Sweden ( n = 2), Taiwan ( n = 1), Thailand ( n = 4), Ukraine ( n = 1), United Kingdom ( n = 10), and the United States ( n = 49). Of these 100 articles, 9 involved more than one country.

Most frequently used HRQOL models

Of the 100 articles, 57 used an existing HRQOL model as a guide and 25 derived a HRQOL model. Interestingly, 18 articles used an existing HRQOL model as an initial guide and then also derived a revised model based on the findings. Figure 1 shows that of the 100 full-text articles included in the review, 77 either derived a HRQOL model (n = 25) or were guided by a HRQOL model that was used only once or twice (n = 52). There was little consensus among the models used, with each article essentially citing a different model. These 77 articles are summarized in Additional file 1 : Tables SA and SB included as an appendix for this paper. Of the 25 articles that derived a HRQOL model, 24 were disease-specific, and 1 was a consensus paper on HRQOL (Additional file 1 : Table SA). The disease-specific models were classified as using a uniquely derived HRQOL model based on the findings. For example, Barr and Schumacher [ 17 ] identified six categories of HRQOL specific to individuals receiving medical nutrition therapy. Similarly, Klassen, Pusic, Scott, Klok, &; Cano [ 18 ] examined the impact of breast conditions and surgery to develop a quality of life framework specific to breast surgery patients. Because there was such a wide variation in disease states, HRQOL domains, and particular characteristics, findings could not be adequately synthesized. Of the 52 articles that were guided by a HRQOL model that was used only once (n = 46) or twice (n = 6), only three HRQOL models were cited twice (Additional file 1 : Table SB). Those used twice were Maslow’s hierarchy of needs (n = 2), Mishel’s Uncertainty in Illness Theory (n = 2), and Stewart’s conceptual model of factors affecting dying patients and families (n = 2) (See Additional file 1 : Table SB for details).

As depicted at the bottom of Figure 1 , there remained a total of 23 articles that cited the same model 4 or more times. As shown in Table 2 (and in Figure 1 ), the most common existing HRQOL models found in the literature were those by Wilson and Cleary [ 12 ] ( n = 14), Ferrans and colleagues [ 13 , 19 ] ( n = 3), and the World Health Organization (WHO) [ 20 , 21 ] ( n = 4). Two additional articles used a combination of two of these models. Ferrans et al. [ 13 ] used the Wilson and Cleary [ 12 ] model as a guide to derive a revised model of HRQOL [ 13 ]. Valderas and Alonso [ 22 ] used both Wilson and Cleary [ 12 ] and the WHO [ 20 ] models. Schematic diagrams for each of the three most common HRQOL models have been published in Wilson and Cleary [ 12 ], Ferrans and colleagues [ 13 ], and the World Health Organization [ 20 , 21 ], and are described in more detail in the results section. The largest group ( n = 10) of the 23 articles in Table 2 reported observational studies (descriptive or correlational) and focused on patients with chronic illness, with sample sizes ranging from 61 [ 23 ] to 917 [ 24 ]. Literature reviews ( n = 6) and instrument development studies ( n = 3) were also found. Only one randomized controlled trial was found [ 25 ], along with one mixed-methods study [ 26 ], one qualitative study [ 27 ], and one article that described a model revision [ 13 ].

Critical analysis of predominant HRQOL models

Table 1 details the critique of the three most commonly used HRQOL models found in the literature over the past 10 years using criteria by Bredow [ 10 ]. Wilson and Cleary’s [ 12 ] model of HRQOL combines two paradigms, biomedical and social science. Their model is a taxonomy that includes five major well-defined domains: biological, symptoms, function, general health perception, and overall HRQOL. However, the definitions for two other domains, individual and environmental characteristics, were not made explicit. Each domain is related to the others, and reciprocal relationships may exist. The authors suggest that environmental and individual factors are associated with outcomes, thus affecting total HRQOL.

Ferrans, Zerwic, Wilbur, and Larson [ 13 ] published a revision of Wilson and Cleary’s [ 12 ] HRQOL model. The five major domains of the original model were retained. Ferrans and colleagues [ 13 ] made explicit the definitions for individual and environmental characteristics, and they simplified the depiction of the model by removing non-medical factors and labels on the arrows portraying the relationships in the figure. In addition, they contributed further theoretical background about the main concepts in the model [ 19 ] and provided examples of instruments to enhance measurement. According to Ferrans et al. [ 13 ], the model depicts dominant causal associations; however, reciprocal relationships are implied. An explicit assumption is that understanding relationships among these components will lead to the design of optimally effective clinical interventions. The revised conceptual model could be applied to any health care discipline.

The World Health Organization International Classification of Functioning, Disability, and Health (WHO ICF) is a model designed to provide a description of health and health states, while providing a unified and standard language that can be used across disciplines and cultures [ 3 , 20 , 21 ]. The WHO ICF has evolved over time from a focus on “consequences of disease” in 1980 to “components of health” in 2001 [ 20 , 21 ]. The more recently developed WHO ICF-CY covers infants, children, and adolescents [ 3 ]. The WHO has conceptualized HRQOL as an individual’s perception of his or her health and health-related domains of well-being [ 3 , 21 , 43 ]. Health and health-related domains have been further conceptualized in terms of functioning within the WHO ICF model. The WHO ICF model includes components within two main parts. Part 1 focuses on functioning and disability (body functioning and structures, activities, and participation), whereas Part 2 addresses contextual factors (environmental and personal). The main concepts are well-defined overall, with explicit propositions and assumptions. However, unlike the models by Wilson and Cleary [ 12 ] and Ferrans and colleagues [ 13 ], the WHO ICF is not specific to HRQOL. Cieza and Stuki [ 43 ] assert that the WHO ICF categories under functioning can serve as the basis for the operationalization of HRQOL but are not the only potential application of the WHO ICF. For example, Miller and colleagues [ 44 ] used the WHO ICF as a framework to organize a comprehensive overview of nursing and interdisciplinary care of the stroke patient. The WHO ICF serves more as a mapping and classification framework than as a guide for hypothesis generation in the area of HRQOL.

Critique of the internal components of the HRQOL models using the Bredow criteria [ 10 ] indicated many similarities and some differences. All were fairly complete in the descriptions and definitions of HRQOL, with some gaps in the influence of management of therapeutic regimens and self-management on quality of life. Most existing models focus on the influence of symptoms rather than on management related to the condition. For example, for those with diabetes, both symptoms (such as hypoglycemia) and management (such as frequent checking of glucose levels) are important influential factors for HRQOL. Within the WHO ICF model [ 3 ], there was some definitional overlap between activities and participation. The Ferrans et al. [ 13 ] model was more complete and clear than that of Wilson and Cleary [ 12 ] because of the revisions and because of better definitions for individual and environmental factors. The relationships among the concepts were less clear in the Wilson and Cleary model [ 12 ], whereas the Ferrans et al. model [ 13 ] added clarity. Both the Wilson and Cleary [ 12 ] and Ferrans et al. [ 13 ] models implied potential reciprocity. In contrast, the WHO ICF [ 3 ] was explicit with the depiction of causal and reciprocal relationships.

There were greater variations among the three models when critiquing model fit with operational application. The models were similarly parsimonious, yet the complexities of multiple relationships were described. They made sense for use in real-world settings and have been used to guide research and practice. A major difference is that the Wilson and Cleary [ 12 ] and Ferrans et al. [ 13 ] models specifically explain HRQOL, whereas the WHO ICF model [ 3 ] describes health related to functioning and disability. In addition, though the Wilson and Cleary [ 12 ] and Ferrans et al. [ 13 ] models were primarily intended for application to individuals, the WHO ICF model [ 3 ] could be used to explain the health of individuals, families, communities, populations, and cultures. With the former, adaptations may be needed for use with families, communities, and individuals unable to report their own HRQOL, such as infants and young children and those with cognitive impairment. Empirical evidence for use of the models for intervention research is limited. However, the Ferrans et al. [ 13 ] model and the WHO ICF model [ 3 ] have robust potential for guiding the design of interventions that could be tested and applied in practice settings. The WHO ICF may be more applicable to practice situations for needs assessments, matching treatments with conditions, and evaluating outcomes because it is primarily a classification and mapping system. All three models were at a similar level of development emerging from the two paradigms of biomedical and social sciences.

Discussion and recommendations

There are two important findings from this review. First, there has been little consistency in HRQOL models within the literature of the past 10 years. Approximately three-fourths of the articles reviewed used an existing HRQOL model as a guide; however, most of these applied a variety of different models, rather than using a common model found in the literature such as the Wilson and Cleary model [ 12 ]. Thus, there were wide variations in terminology for analogous HRQOL concepts, making cross-study comparisons virtually impossible. This seriously limits the ability to have a coherent body of evidence to guide further HRQOL research and practice. Second, the most commonly used models were based on work by Wilson and Cleary [ 12 ], the revised model by Ferrans and colleagues [ 13 , 19 ], and the WHO [ 3 , 20 , 21 ]. A majority of the researchers using these models could be doing so because of an absence of better alternatives. However, based on our findings, we recommend that authors consider the advantages of using one of the three commonly used global models in research to more quickly advance the science in the area of HRQOL. Our findings show that Wilson and Cleary [ 12 ], as well as the revisions of Wilson and Cleary’s model proposed by Ferrans et al. [ 13 ], together are the most frequently referenced in the HRQOL literature, representing nearly a quarter of all of the articles reviewed. Ferrans and colleagues’ [ 13 ] model provides clear conceptual and operational definitions, and it also clarifies relationships among concepts to guide research and practice. The WHO ICF model [ 3 ] may be useful in specific HRQOL studies; however, it has more potential for application to studies of an epidemiological, sociological, or educational nature.

There are a great many models in the HRQOL literature that have not been adequately tested or refined. Cross-comparisons across diseases could be done if authors had at least used a global HRQOL model as a starting point. In fact, many single-use models included the same concepts as the three global HRQOL models but labeled them differently. In the future, when a common global HRQOL model is not used, authors should clearly delineate why a context- or disease-specific model is preferred. Increasing the consistency in models used across studies would help increase our understanding of this important concept.

Of the 23 articles citing the three most common HRQOL models, most articles were descriptive, correlational, or literature reviews. Importantly, future HRQOL research should involve comparisons of intervention outcomes. Only one randomized controlled trial was found that used the most commonly cited Wilson and Cleary [ 12 ] model [ 25 ]. Although disease-specific or situation-specific models may be better for testing interventions, the global models should still be useful as a template and a jumping-off point for adaptations to specific contexts. In addition, using an existing model can advance the state of the science of HRQOL by contributing new information about the applicability of the selected model to research and practice, thus leading to model refinement such as the revised model proposed by Ferrans and colleagues [ 13 ]. This underscores the need to start with the best available HRQOL models and build upon them, rather than creating new models.

Limitations

Our search strategies were limited to selected databases (PubMed, MEDLINE, CINAHL, and PsychINFO) and keywords (e.g., quality of life, health-related quality of life, conceptual framework, conceptual model, and theory). Given that standard keywords were used within each search engine, any article indexed by that search engine would have been captured; however, follow up manual searches and review of reference lists might have revealed additional citations. The search strategies were specifically designed to capture articles that were guided by or derived HRQOL models that were further analyzed in detail by the reviewers. All reviewers were doctorally-prepared and a research librarian assisted with the searches. Because the aim of our paper was to identify the most frequently-used HRQOL models found in the literature over the past 10 years, a complete synthesis of disease-specific models was not undertaken. Future work to analyze uniquely derived disease-specific HRQOL models may provide unique HRQOL domains that might further inform the three more commonly used HRQOL models. For example, Klassen et al. [ 18 ] qualitatively derived a HRQOL model for women who had undergone breast surgery. Their model consisted of six themes (satisfaction with breasts, satisfaction with process of care, satisfaction with overall outcome, psychosocial well-being, sexual well-being, and physical well-being). The satisfaction with process of care theme further informs both Ferrans and colleagues’ model [ 13 ] and the WHO ICF [ 3 , 20 , 21 ] as an important characteristic of the environment. Sexual well-being could further inform the functional status domains in all three models. Identifying domains that are unique to disease-specific models, or particular characteristics such as feedback or recursive patterns to address dynamic changes in HRQOL with time, may further inform or strengthen the rationale for using the three existing HRQOL models.

In summary, based on this systematic review of the literature, Ferrans et al., [ 13 ] revision of Wilson and Cleary’s [ 12 ] model appears to have the greatest potential to guide HRQOL research and practice. We recommend Ferrans and colleagues’ [ 13 ] model because they added individual and environmental characteristics to the popular Wilson and Cleary [ 12 ] model to better explain HRQOL. Although the WHO ICF model has been considered a model of HRQOL, it is more of a mapping and classification framework than a guide for hypothesis generation in the area of HRQOL. Use of one model, such as Ferrans et al. [ 13 ] revised HRQOL model, will provide more opportunities for testing and refinement of the model and more evidence about which relationships among HRQOL concepts are common to different populations. Finally, and maybe most importantly, using one model will help in comparing HRQOL across studies and populations, contribute to the development of more intervention studies, and more quickly advance the science in the area of HRQOL.

Abbreviations

Health-related Quality of Life

Quality of Life

World Health Organization

World Health Organization International Classification of Functioning, Disability, and Health.

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Acknowledgements

Funding for this study was provided by Indiana University School of Nursing Research Investment Funds and the Center for Enhancing Quality of Life in Chronic Illness. The authors thank Phyllis Dexter, RN, PhD, Assistant Scientist and Editor, Indiana University School of Nursing Center for Nursing Research, for her editorial assistance. The authors also wish to thank Randi L. Stocker, Research Librarian at Indiana University Purdue University at Indianapolis, for her assistance with literature searches.

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TB provided overall leadership and contributed to the conception and design, participated in the review and critique process, drafted sections of the manuscript, and revised it critically for intellectual content. SMM, JSC, JMB, JLO, KMH, MLE, &; JLW contributed to the conception and design, participated in the review and critique process, drafted sections of the manuscript, and revised it critically for intellectual content. KAH acquired articles for review, abstracted findings to tables, contributed to analysis and interpretation, provided reference management, and drafted sections of the manuscript. All authors read and approved the final manuscript.

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Bakas, T., McLennon, S.M., Carpenter, J.S. et al. Systematic review of health-related quality of life models. Health Qual Life Outcomes 10 , 134 (2012). https://doi.org/10.1186/1477-7525-10-134

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Quality of life and mortality in the general population: a systematic review and meta-analysis

  • Aung Zaw Zaw Phyo 1 ,
  • Rosanne Freak-Poli 1 , 2 ,
  • Heather Craig 1 ,
  • Danijela Gasevic 1 , 3 ,
  • Nigel P. Stocks 4 ,
  • David A. Gonzalez-Chica 4 , 5 &
  • Joanne Ryan   ORCID: orcid.org/0000-0002-7039-6325 1 , 6  

BMC Public Health volume  20 , Article number:  1596 ( 2020 ) Cite this article

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Quality of life (QoL) is multi-dimensional concept of an individual’ general well-being status in relation to their value, environment, cultural and social context in which they live. This study aimed to quantitatively synthesise available evidence on the association between QoL and mortality in the general population.

An electronic search was conducted using three bibliographic databases, MEDLINE, EMBASE and PsycINFO. Inclusion criteria were studies that assessed QoL using standardized tools and examined mortality risk in a non-patient population. Qualitative data synthesis and meta-analyses using a random-effects model were performed.

Of 4184 articles identified, 47 were eligible for inclusion, involving approximately 1,200,000 participants. Studies were highly heterogeneous in terms of QoL measures, population characteristics and data analysis. In total, 43 studies (91.5%) reported that better QoL was associated with lower mortality risk. The results of four meta-analyses indicated that higher health-related QoL (HRQoL) is associated with lower mortality risk, which was consistent for overall HRQoL (HR 0.633, 95% CI: 0.514 to 0.780), physical function (HR 0.987, 95% CI: 0.982 to 0.992), physical component score (OR 0.950, 95% CI: 0.935 to 0.965), and mental component score (OR 0.980, 95% CI: 0.969 to 0.992).

These findings provide evidence that better QoL/HRQoL was associated with lower mortality risk. The utility of these measures in predicting mortality risk indicates that they should be considered further as potential screening tools in general clinical practice, beyond the traditional objective measures such as body mass index and the results of laboratory tests.

Peer Review reports

Quality of life (QoL) is a multi-dimensional concept of an individual’s general well-being status in relation to the value, environment, cultural and social context in which they live [ 1 ]. Since QoL measures outcomes beyond biological functioning and morbidity [ 2 ], it is recognised as an important measure of overall [ 1 ]. The origin of the term QoL dates back to the early 1970s, as a measure of wellness with linkage to health status like diseases or disability [ 3 , 4 ]. Since then, interest in QoL has increased considerably [ 5 ]. As life expectancy increases, more emphasis has been placed on the importance of better QoL, and the maintenance of good health for as long as possible [ 6 , 7 , 8 , 9 ]. Indeed, global leading health organizations have emphasized the importance of QoL and well-being as a goal across all life stages [ 10 , 11 , 12 ].

Moreover, QoL has increasingly been used in the wider context to monitor the efficacy of health services (e.g. patient reported outcome measures, PROMs), to assess intervention outcomes, and as an indicator of unmet needs [ 13 , 14 , 15 ]. Several studies have reported that QoL is negatively associated with rehospitalization and death in patients with diseases such as coronary disease [ 16 , 17 ], and pulmonary diseases [ 18 ]. Further, QoL is also predictive of overall survival in patients affected by cancer, chronic kidney disease or after coronary bypass graft surgery [ 19 , 20 , 21 , 22 ]. In recent years, an increasing number of studies have investigated whether QoL is also a predictor of mortality risk in the general population [ 23 , 24 , 25 , 26 , 27 ].

To date, there has been only one pooled analysis of eight heterogeneous-Finnish cohorts. That study of 3153 older adults, focused exclusively on the prognostic value of the validated 15-dimentional (15D) health-related QoL (HRQoL) measures [ 28 ] for predicting all-cause mortality [ 29 ]. However, there has been no systematic review investigating the association between QoL measured by different instruments and all-cause mortality in population-based samples which could be used to monitor health changes in the general population. A broad and comprehensive systematic review of the prognostic value of QoL for all-cause mortality prediction is needed to determine the utility of this QoL measure as a potential screening tool in general clinical practice. Therefore, this systematic review and meta-analysis was conducted with the aim of determining whether QoL is predictive of mortality in the general population which includes individuals with or without a range of health conditions.

Search methods

This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 30 ]. The protocol for this review was registered with the International Prospective Register of Ongoing Systematic Reviews (PROSPERO) [ 31 ], under the registration number: CRD42019139994 [ 32 ]. The electronic bibliographic databases, MEDLINE, EMBASE and PsycINFO (through OVID) were searched from database inception until June 21, 2019. The search strategy was developed in consultation with a Senior Medical Librarian. The MeSH terms and key-words were developed for MEDLINE (through OVID) and were translated to EMBASE and PsycINFO using the OVID platform (See Supplementary Tables S1-S3, Additional File  1 ). When the full text of an article was not available, all attempts were made to obtain it by contacting the authors directly. To identify further potentially relevant studies, another search was also developed with those specific QoL / HRQoL measures which were found in this review (See Supplementary Table S4, Additional File 1 ). Additionally, the bibliography lists of the included articles were also hand searched.

Inclusion and exclusion criteria

Articles were included if they: (a) involved adults aged 18 years and older; (b) were general population-based samples with or without a range of health conditions; (c) assessed mortality from any cause or cause-specific mortality using a longitudinal design; and (d) included a QoL / HRQoL measure using a standard tool. QoL, the general well-being of individuals, consists of a range of contexts – health, education, employment, wealth, politics and the environment [ 33 ]. HRQoL, the self-perceived health status, includes physical, mental, emotional, and social domains [ 33 ]. We excluded papers not written in English, reviews, or studies including only specific groups of patients (e.g. patients on dialysis, those with fractures, after surgery, or individuals with a terminal illness).

Study selection

The screening of articles for eligibility according to title and abstract was undertaken independently by two reviewers (AZZP and HC). All relevant full-text articles were independently reviewed by two reviewers (AZZP and HC) for eligibility against inclusion criteria. The inter-coder reliability among two reviewers (AZZP and HC) was 98%. Discrepancies and disagreements between two reviewers (AZZP and HC) were resolved through discussion with a third reviewer (JR). The screening process was undertaken using Covidence online software [ 34 ] and EndNote X9 software.

Data extraction

A standard data extraction form was used which included the following fields – title, authors, year of publication, setting/country, name of the study and design, sample size, follow-up period, participant characteristics (age and sex), specific QoL measure, cause of death (if available), and results (risk estimates including 95% confidence intervals, CI) which were standardized in term of 1-unit increase or 1-SD increase for continuous risk estimate, or high vs. low for categorical risk estimates. The first reviewer (AZZP) completed the data extraction form and a second reviewer (HC) verified the extracted information. All efforts were made to contact authors when there was missing information.

Quality appraisal

The quality of included studies was appraised using ‘the Newcastle – Ottawa Quality Assessment Scale (NOS)’ [ 35 ]. The NOS includes eight items, categorized into three dimensions (a) Selection, (b) Comparability, and (c) Outcome. The NOS scale uses a star system to evaluate the quality of each study, and they can be accredited a maximum of one star for each item within the Selection and Outcome dimension and two stars for the Comparability item. When considering the comparability of each study, a star was provided for studies which controlled for relevant covariates – age, sex (where appropriate), socioeconomic status or proxy (including socioeconomic position, education level or income), and some measure of co-morbidity (for example a specific health condition). An additional star was given for studies which considered other factors associated with QoL and mortality, including clinical measures, BMI, or lifestyle factors (i.e. smoking, alcohol, physical activity). The range of NOS scoring was from 0 to 9 stars, with higher scores indicating less susceptibility to bias. The methodological quality of included studies was rated by one reviewer (AZZP) and verified by a second reviewer (HC). Disagreements were resolved through discussion with a third reviewer (JR).

Data synthesis

The clinical and methodical heterogeneity of the studies was examined, in particular considering the measure of QoL used, and the effect estimates reported (Hazard Ratio (HR), Relative Risk (RR) or Odds Ratio (OR)). Where studies were considered too methodically heterogeneous to enable pooling, the results were summarized quantitatively in tables according to related categories with risk estimates; and 95% CIs.

  • Meta-analysis

A meta-analysis was performed when there was a sufficient number of studies (four or more) which used the same domain of QoL measure and equivalent effect estimate parameters. In the present study, four meta-analyses were conducted for a pooled risk estimate of studies using (a) physical component score (PCS) of 36-item Short Form (SF-36) and OR / RR; (b) physical function domain of SF-36 and HR; (c) mental component score (MCS) of SF-36 and OR / RR; and (d) the 15-dimensional measure (15D) and HR. A DerSimonian-Laird random-effects model was chosen given heterogeneity in the studies in terms of population characteristics and varying health status. When more than one risk estimate was reported in the study, the fully adjusted/final regression model was included. In addition, when the included studies from the same cohorts with the same follow-up were eligible for meta-analysis, only one study with larger sample size was chosen for meta-analysis. Effect estimates were standardized where possible, so all values corresponded to a 1-unit increase in SF-36 or a 1-SD increase in 15D (single index number). A pooled risk estimates of less than one indicates a decreased risk of mortality with higher QoL. Statistical heterogeneity was evaluated by using the I 2 statistic, and the results were interpreted based on the Cochrane guidelines (0–40% = no heterogeneity; 30–60% = moderate heterogeneity; 50–90% = substantial heterogeneity; and 75–100% = considerable heterogeneity) [ 36 ]. In addition, when the I 2 statistic showed considerable heterogeneity (≥ 75%), the influence of individual studies on the pooled risk estimate was assessed using the metaninf command of STATA. Funnel plots and Egger’s test were used to assess publication bias. Data analysis was undertaken using STATA statistical software, version 15.0 (StataCorpLP, College Station, TX, USA).

Search result

A total of 4175 articles were identified from the systematic database search, and six additional articles were found via searching the reference list of included articles (Fig.  1 ). After removing duplicates, 3140 records remained for review. After title and abstract screening, 3058 articles were excluded and the full-text of the remaining 82 articles were evaluated for eligibility. A total of forty-four (44) articles met all inclusion criteria. Excluded articles with reasons for exclusion are presented in Supplementary Table S5, Additional File 1 . Moreover, three articles from additional search were also added in this review. Therefore, a total of forty-seven (47) articles were included in this systematic review.

figure 1

Flow Diagram of Review Process

Description of included studies

Table  1 presents the characteristics of the 47 included studies. The earliest study was published in 1993 while the remaining included articles were published between 2002 and 2019, with 28% published in the past 5 years. All studies except the retrospective cohort study of Ul-Haq et al., [ 75 ] were prospective cohort studies. The included studies were conducted in USA (34%), UK (9%), Australia (6%), Canada (6%), Spain (6%), Taiwan (6%), Belgium (4%), Finland (4%), Scotland (4%), Sweden (4%), Bangladesh (2%), China (2%), Germany (2%), South Korea (2%), Italy (2%), Norway (2%), and South Africa (2%). The sample sizes of the included studies ranged from 171 [ 41 ] to 559,985 [ 40 ]; 14 studies had a sample size of less than 1000, 17 studies between 1000 and 10,000, 13 studies between 10,000 and 100,000, and the remaining three studies [ 38 , 40 , 53 ] has a sample size of more than 100,000 participants. Five studies included only males [ 41 , 42 , 54 , 71 , 73 ] and three studies only females [ 56 , 59 , 74 ]. The remaining 39 studies recruited between 3 to 78% of women. The follow-up periods of the studies varied between 9 months [ 72 ] and 18 years [ 73 ].

This review included a variety of different QoL measures and half of the included studies (24 studies) measured QoL using the Short Form 36 (SF-36) (Tables  1 and 2 ). Of the 47 articles included in this review (Table 1 ), some studies involved the same cohorts and, in several cases, likely the same participants. Subsequent publications often reported effect estimates over different lengths of follow-up or using different QoL tools. Two published articles of De Buyser et al. reported the results of the same population-based cohort study [ 41 , 42 ], three published articles by De Salvo et al. and Fan et al. were from the same study and included participants enrolled in the Veterans Affairs Ambulatory Care Quality Improvement Project [ 24 , 43 , 47 ], two published studies of Mold et al. and Lawler et al. used the same community-dwelling cohort [ 57 , 61 ], two published studies of Higueras-Fresnillo et al. and Otero-Rodriguez et al. were from the same Spanish cohort [ 52 , 67 ], two published studies of Feeny et al. and Kaplan et al. were from the same Canadian cohort [ 48 , 55 ]; and Myint et al. published three articles [ 26 , 64 , 65 ] with different perspectives on the same population-based study. Additionally, Liira et al.’s study [ 29 ], included eight individual cohorts, however, only five of the cohorts met the inclusion criteria for this current systematic review, and thus are shown in Table 1 .

Risk of Bias assessment

The methodological quality of included studies based on NOS ranged between five and nine stars. Among the included studies, seven were of high methodological quality, with nine stars. Across the ten studies with less than seven stars, they were scored most poorly on the items assessing how representative the cohort was in relation to the overall population being sampled and whether they adjusted for potential confounding factors in their analysis (See Supplementary Table S6-S7, Additional File 1 ).

Qualitative synthesis

Of the total 47 included studies, 43 (91.5%) studies reported for at least one of the domains examined, that better QOL was associated with lower mortality risk (Table 1 ). Of 33 studies which assessed physical HRQoL (nine exclusively assessed physical HRQoL), 30 studies (91%) reported better HRQoL was associated with lower mortality risk. Among the 23 studies which examined mental HRQoL (one exclusively assessed MCS), 13 studies (57%) reported that higher mental HRQoL was associated with decreased mortality risk (Table 1 ). The five studies [ 49 , 52 , 57 , 59 , 76 ] that measured HRQoL using SF-36 or SF-20 reported not only the physical functioning and mental health domains, but also general health perception, bodily pain, vitality, and social functioning. The findings were generally consistent in general health perception and social functioning; and it was reported that better level of general health perception and social functioning was associated with decreased mortality risk (Table 1 ).

The mortality risk estimates of the studies which were not included in the meta-analyses are shown in Tables  3 , 4 and 5 . The 18 out of 20 studies which measured the PCS using the SF-36 or SF-12 or the physical functioning subscale using SF-36, RAND-36, or SF-20 reported these to be a predictor of mortality risk, with better physical health being associated with lower mortality risk (Table  3 ). Nine out of 16 studies which assessed the MCS or mental health subscale using SF-36 or SF-12, showed that better mental health was associated with lower mortality risk (Table  4 ). The 12 out of the 15 studies that measured the association between QoL and mortality risk, found that higher QoL scores were associated with lower mortality risk (Table  5 ).

Meta-analyses

Four studies including 53,642 participants [ 23 , 24 , 60 , 70 ] measured QoL using the SF-36 and examined the association between the PCS and all-cause mortality and provided estimates from logistic regression analysis (OR or RR). With an average 1.8-year follow-up, one unit increase in the SF-36 PCS was associated with a 5% decrease in all-cause mortality (pooled OR/RR = 0.950; 95% CI: 0.935 to 0.965; P -value < 0.001). There was substantial heterogeneity between studies (I 2  = 82.1%; P -value = 0.001) (Fig.  2 -a).

figure 2

Forest plot of all-cause mortality risk per one unit increase in a SF-36 PCS, b SF-36 Physical-Functioning, c SF-36 MCS. CI = confidence interval; FU (yrs) = follow-up in years; N = sample size; OR = odds ratio; RR = relative risk; HR = hazard ratio

Six studies including 22,570 participants [ 42 , 46 , 57 , 59 , 68 , 76 ] measured QoL using the SF-36 and investigated the association between the physical functioning and all-cause mortality using time-to-event survival analysis. With an average 8.7-year follow-up, one unit increase in the SF-36 PF was associated with a 1.3% decrease in time to death (pooled HR = 0.987; 95%CI: 0.982 to 0.992; P -value < 0.001). There was substantial heterogeneity between studies (I 2  = 83.8%; P -value < 0.001) (Fig. 2 -b).

Four studies including 53,642 participants [ 23 , 24 , 60 , 70 ] measured QoL using the SF-36 and examined the association between the MCS and all-cause mortality reported estimates on logistic regression analysis (OR or RR). With an average 1.8-year follow-up, one unit increase in the SF-36 MCS was associated with a 2% decrease in all-cause mortality (pooled OR/RR = 0.980; 95% CI: 0.969 to 0.992; P -value = 0.001). There was substantial heterogeneity between studies (I 2  = 75.9%; P -value = 0.01) (Fig. 2 -c).

Given the heterogeneity identified in the three meta-analyses described above, the influence of individual studies on the pooled risk estimate was assessed. The removal of no single study affected the association (Supplementary Table S8 – S10, Additional File 1 ).

Five Finnish individual cohorts of the Liira et al. study including 2377 [ 29 ] measured QoL using the 15D index and explored its association with all-cause mortality using time-to-event survival analysis. With an average 2-year follow-up, one SD (0.14) increase in the 15D index was associated with a 36.7% decrease in all-cause mortality (pooled HR = 0.633; 95%CI: 0.514 to 0.780; P -value < 0.001). There was moderate heterogeneity between studies (I 2  = 49.4%; P -value = 0.10) (Fig.  3 ).

figure 3

Forest plot of all-cause mortality risk per one-SD (0.14) increase in 15D index. CI = confidence interval; FU (yrs) = follow-up in years; HR = hazard ratio; N = sample size

Visual inspection of the funnel plots which were used to assess for publication bias were presented in the Supplementary Figures S1-S4, Additional File 1 . For three of the four meta-analyses, there was no strong evidence of publication bias, however for the meta-analysis of MCS, this test was statistically significant ( P  = 0.04).

This systematic review is the first to investigate the association between QoL and mortality in community-dwelling individuals with or without health conditions rather than patients in a hospital or people living in assisted living. It summarizes the findings from 47 studies including approximately 1,200,000 individuals aged predominantly 65 years and older (age range 18–101 years), with 46 studies (98%) conducted in high-income or upper-middle-income countries. Overall thirteen different instruments were used to assess the association between QoL or more specifically HRQoL and mortality risk after 9 months to 18 years of follow-up, with the SF-36 or its derivatives (RAND-36, SF-20, SF-6D) most commonly used. Overall, 43 (91.5%) studies of the 47 included studies reported for at least one of the domains examined, that better QoL was associated lower mortality risk, which was also supported by the results of four meta-analyses (11 studies, n  = 78,589) of PCS, physical function and MCS domains of the SF-36, and 15D HRQoL.

Our findings are in line with a previous study that used pooled analysis [ 29 ] of eight heterogenous Finnish cohorts using the 15D HRQoL measure and included a wide range of both community-dwelling participants with or without morbidity, such as cardiovascular disease, dementia, and hospitalized patients with delirium. They also found that the 15D HRQoL measure was associated with two-year survival, with a slightly higher hazard ratio than that found in our study (HR per 1-SD = 0.44, 95% CI 0.40 to 0.48) [ 29 ]. These differences may relate to their inclusion of patient groups in generally poorer health, while our systematic review focused on the community dwelling population. Moreover, our findings in the general non-patient population are also comparable with studies investigating people with specific diseases such as cancer and chronic kidney disease, which reported QoL to be a predictor of mortality risk [ 19 , 20 , 21 ].

The findings of the present study are also consistent with those of recent population-based systematic review which investigated on the association between QoL and multimorbidity [ 78 ]. In their recent study, Makovski et al. (2019) systematically reviewed the evidence on the relationship between QoL and multimorbidity. They observed a stronger relationship between the PCS of QoL and multimorbidity (overall decline in QoL per additional disease = − 4.37, 95%CI − 7.13% to − 1.61% for WHOQoL-BREF physical domain and − 1.57, 95%CI − 2.70% to − 0.44% for WHOQoL-BREF mental domain) [ 78 ]. These findings also align with the results of the present study, where the meta-analysis indicated a stronger effect size for PCS compared to MCS using the SF-36 tool (pooled OR/RR = 0.950; 95% CI: 0.935 to 0.965 for PCS; and pooled OR/RR = 0.980; 95%CI: 0.969 to 0.992 for MCS). Since physical health is generally recognised as a strong risk factor for comorbidity, hospitalisations and mortality [ 79 , 80 , 81 , 82 ], our findings add further support to the predictive capacity of physical HRQoL for mortality risk. Like other objective health measures such as body mass index, glycaemia, and blood pressure, these findings highlight the utility of assessing physical HRQoL in general clinical practice to help identify individuals at greatest risk of death [ 83 ].

Given the evidence regarding the longitudinal relationship between QoL and mortality risk, the utility of a QoL tool in general care may improve patient’ health which in turn would decrease mortality. Furthermore, mental health issues such as depression or anxiety could also be identified through QoL measures and this would enable initiation of early interventions for mental health which in turn could improve long term QoL of individuals. Hence, the finding of this review can help to increase the efficacy of disease prevention strategies in older people through identifying individuals at higher risk for adverse health outcomes in general practice / primary health settings. Thus, the mortality risk prediction by QoL might not be very relevant to younger healthy populations although QoL generic measures were designed to be used across a wide range of populations [ 84 ]. There is a need for further studies however, in particular to better understand the influence of gender on these associations, and whether differences could be observed for males and females. Understanding these specific relationships could help identify which particular groups are most at risk and enable specific targeting of interventions to these individuals.

Strengths of the review

Strengths of this systematic review are that it was performed in a rigorous manner, adhering to strict systematic review guidelines. The protocol was registered with the International prospective register of systematic reviews (PROSPERO), and the review was undertaken in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. A reproducible and rigorous search strategy using three electronic databases was used, which helped ensure that all relevant articles were included. The literature screening was independently performed by two reviewers, who were also involved in the process of data extraction and methodological quality assessment of the included studies in accordance with NOS. Based on the NOS, all studies received greater than or equal to five out of nine stars, which indicates that there was generally a low risk of bias. Similarly, most studies provided risk estimates that controlled for important factors including current health and socio-economic status. Since our review criteria were not limited to articles with the commonly used QoL (or HRQoL) tools such as the SF-36, this has increased the generalisability of the findings. Therefore, this review has a broad and comprehensive perspective, with results that are rigorous and can be reproduced.

Limitations of the review

Among included articles, large heterogeneity was observed in terms of country-of-origin, participant characteristics, and evaluation of QoL. The majority of the included articles were conducted in English speaking counties, and restriction to English language articles as part of our inclusion criteria, may impact the generalisability of these findings. Since the different QoL standard tools examine different aspects [ 33 , 85 ] and are not directly comparable, this made comparison of included studies in data synthesis difficult. There were also some differences in the way the data analysis was performed and the results were presented, reporting OR versus HR for example. In addition, some articles reported the risk estimates by comparing categorical QoL groups while others provided the risk estimates per 1 or more units change in the continuous scale. Hence, the different nature of each QoL scale and inconsistency in risk comparison precluded us from including some articles in the meta-analyses. As such, only 11 studies were included across the four meta-analyses of this systematic review, and the meta-analyses still showed substantial heterogeneity. Therefore, caution should be taken with the interpretation of the overall effect estimates. Moreover, since the numbers of studies included in each meta-analysis were fewer than 10 studies, the results of funnel plots or Egger’s test should also be interpreted with caution. Of particular interest here, it has commonly been reported that gender differences exist in QoL and women of all age groups have lower QoL than their male counterparts [ 86 , 87 , 88 , 89 , 90 ]. However, in this review, it was not possible to perform statistical pooling by gender and age groups due to the different reporting strategies of the reviewed studies. Finally, it is important to consider that although studies of mortality are not directly affected by reverse causation, individuals with severely declining health prior to death, would likely report a decreased HRQoL. An ideal study design would involve excluding individuals who died in the first year of the study, or at least, to run sensitivity analysis to ensure these early deaths were not driving the results. Most of the studies included in this review, did not undertake such analyses. Furthermore, around 10% of the included studies have very short follow-up periods of less than 2 years.

This is the first systematic review and meta-analysis that has determined whether QoL is associated with mortality in the general non-patient population. In summary, the findings provide evidence that better QoL or HRQoL measured by different tools were associated with lower mortality risk in the general population. Therefore, our findings could be applied more generally to QoL or HRQoL assessed using different instruments. Our unique and first review indicates that QoL measures can be considered as potential screening tools beyond the existing traditional clinical assessment of mortality risk. Additionally, our result also encourages clinicians to incorporate QoL measure into routine data collection of health system which in turn could enable initiation of early primary health care for people at high risk of premature death. Furthermore, this study also adds further support to the predictive capacity of physical HRQoL for mortality risk. Additional research is needed to determine whether these associations differ across gender, and other populations in low- and lower-middle-income countries, who have suffered of a double burden of infectious and chronic diseases, with having difficulties for accessing quality health services. Ultimately these findings suggest the utility of QoL measures to help identify populations at greatest risk of mortality and who might benefit most from routine screening in general practice and possible interventions.

Availability of data and materials

All data generated or analysed during this study are included in this published article (and its supplementary information files).

Abbreviations

15-dimentional

Confidence intervals

Euroqol-5 dimension

Hazard ratio

  • Health-related quality of life

Health utilities index 3

Mental component score

NEWCASTLE-Ottawa quality assessment scale

Physical component score

Preferred reporting items for systematic reviews and meta-analyses

Patient reported outcome measures

International prospective register of systematic reviews

  • Quality of life

Relative risk

Standard deviation

12-items short form

20-item short form

36-item short form

Six-dimension utility index

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Acknowledgements

We would like to thank Lorena Romero, the Senior Medical Librarian, Alfred Health, and Cassandra Freeman, the Subject Librarian, Faculty of Medicine, Nursing and Health Sciences, Monash University Library for technical support involved in developing the search strategy.

This work was supported by Monash International Tuition Scholarship and Monash Graduate Scholarship. AZZP is supported by Monash International Tuition Scholarship (Medicine, Nursing, and Health Sciences) and Monash Graduate Scholarship (30072360). JR is supported by a National Health and Medical Research Council Dementia Research Leader Fellowship (APP1135727). None of the funders were involved in the design of the study, in the collection, analysis, and interpretation of data and in the writing of the manuscript.

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RFP conceived the study. JR and AZZP designed the study. AZZP undertook the literature searches, screened the articles, extracted the data, performed quality assessment and data analysis. HC was the independent assessor, also completing all data screening, extraction and quality assessment. AZZP and JR interpreted the data, with input from DAGC, DG, and NPS. AZZP wrote the initial manuscript draft. All authors provided critical comments and approved the final version.

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Additional file 1: figure s1..

Funnel plot of all-cause mortality risk per one unit increase in SF-36 PCS. Figure S2. Funnel plot of all-cause mortality risk per one unit increase in SF-36 Physical-Functioning. Figure S3 . Funnel plot of all-cause mortality risk per one unit increase in SF-36 MCS. Figure S4. Funnel plot of all-cause mortality risk per one-SD (0.14) increase in 15D index. Table S1. Search Strategy using Ovid MEDLINE 1946 to June 212,019. Table S2. Search Strategy using Embase Classic 1947 to June 212,019. Table S3. Search Strategy using PsycINFO 1806 to June Week 32,019. Table S4. Additional Search Strategy up to June Week 32,019. Table S5. The list of excluded articles and reasons for exclusion ( n  = 38). Table S6. Appraisal Standard of Newcastle/Ottawa Scale. Table S7. Quality appraisal of included studies based on the Newcastle–Ottawa Quality Assessment Scale. Table S8. One study removed analysis for all-cause mortality risk per one unit increase in SF-36 PCS. Table S9. One study removed analysis for all-cause mortality risk per one unit increase in SF-36 Physical-Functioning. Table S10. One study removed analysis for all-cause mortality risk per one unit increase in SF-36 MCS.

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Phyo, A.Z.Z., Freak-Poli, R., Craig, H. et al. Quality of life and mortality in the general population: a systematic review and meta-analysis. BMC Public Health 20 , 1596 (2020). https://doi.org/10.1186/s12889-020-09639-9

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Quality of Life —a Review of the Literature

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MIDGE FOWLIE, JOHN BERKELEY, Quality of Life —a Review of the Literature, Family Practice , Volume 4, Issue 3, September 1987, Pages 226–234, https://doi.org/10.1093/fampra/4.3.226

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This review of the literature looks at the meaning of the concept ‘quality of life’, the use to which it has been put in various branches of medicine, its meaning in particular in cancer patients, especially those for whom there is no longer expectation of cure, and the instruments already developed for its measurement and their relevance to terminal care. The review concentrates mainly on patients with terminal malignant disease as the quality of life of other patients is a much broader issue.

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Quality of Life in Caregivers of Cancer Patients: A Literature Review

María dolores guerra-martín.

1 Department of Nursing, Faculty of Nursing, Physiotherapy and Podiatry, University of Seville, 41009 Seville, Spain

María Del Rocío Casado-Espinosa

2 Faculty of Nursing, Physiotherapy and Podiatry, University of Seville, 41009 Seville, Spain

Yelena Gavira-López

Cristina holgado-castro.

3 Valme Hospital, Andalusian Health Service, 41014 Seville, Spain

Inmaculada López-Latorre

4 Quirónsalud Maternal and Child Hospital, 41013 Seville, Spain

Álvaro Borrallo-Riego

Associated data.

Not applicable.

(1) Background: Cancer constitutes one of the principal causes of morbi-mortality in the world and generates an important loss of patients’ self-sufficiency. People who are their caregivers usually become the main care providers, which impacts their quality of life; (2) Aim: Analyze the different problems (physical, emotional, social, and financial) faced by people who are caregivers of adults with cancer and describe the strategies required to improve their quality of life; (3) Method: A literature review was conducted on the following database: PubMed, Cinahl, PsycINFO, and Scopus. The following eligibility criteria were specified: (a) research studies of quantitative, qualitative, or mixed methods, (b) consistent with objective, and (c) published in the English language or Spanish during the last five years; (4) Results: 36 studies were selected from those found in the literature. Regarding the problems mentioned: eight studies described physical issues, 26 emotional effects, 10 social implications, and seven financial strains. Twenty-eight studies described strategies to improve the quality of life of caregivers; (5) Conclusions: Caregivers are usually women around the age of 50. Problems faced are mostly emotional in nature, followed by social, physical, and financial ones. In order to cope with this burden, there are some strategies that can be developed to help to build skills to manage both the disease and the impact derived from it, therefore improving their quality of life.

1. Introduction

Cancer is considered the disease of the 21st century, as it is a leading cause of mortality and morbidity in the world [ 1 ]. According to the WHO, cancer is one of the leading causes of death through disease and its cure is still unsolved despite the advances in diagnosis and treatment [ 2 ]. The International Agency for Research on Cancer estimated that in the year 2020, 18.1 million cases of cancer were diagnosed worldwide, and this number will increase in the following two decades, reaching 27 million [ 3 ].

Cancer, as other chronic diseases, has numerous sequelae and an important limitation of self-sufficiency, thus requiring support from other people, usually from their immediate circle. Those people are known as informal or non-professional carers. The role of these caregivers is key in aiding and supporting cancer patients, however, they may experience a significant burden at numerous levels, causing an impact in their quality of life [ 4 , 5 ].

According to different authors, there is an inverse correlation between burden and quality of life in the caregivers of patients with cancer [ 6 ]. At the same time, being the caregiver of a care-dependent person implies 86% risk of suffering anxiety, and 65% of suffering depression [ 7 ].

Diverse studies have profiled non-professional caregivers, concluding that they are mostly women, aged 45 to 65, homemakers, and the daughter or spouse of the patient [ 8 , 9 ]. Besides, 39% of caregivers have no education and the average time devoted to provide care is 10.9 h per day [ 5 ].

Cancer causes devastation at physical, emotional, and social levels to people who suffer it and their families [ 10 ]. Epidemiologic data show that 40–50% of people diagnosed with cancer suffer from a high degree of physical and psychosocial pain along the whole oncological process, where 30% of patients asked for professional support. Family caregivers undergo an adaptation and acceptance process with regard to the disease, facing numerous tasks with high level of stress, which result in physical, emotional, social, and financial draining, therefore affecting their quality of life [ 11 , 12 ].

Given the significant exhaustion of informal caregivers, studies regarding their quality of life have gained importance in the last few years within the health sphere [ 13 ]. The concept quality of life is greatly influenced by the social context experienced by the individual and the relationship this person establishes with everything that surrounds her or him [ 14 ]. In this sense, the WHO has defined the term quality of life as the perception that each individual has of her/his own position in the cultural context and system of values she/he is experiencing, and this is closely related to this person’s goals, expectations, life standards, and concerns [ 15 ].

Different authors have described both dimensions and indicators embodied by the quality of life, as follows: 1. Physical well-being dimension, being its indicators health, nutrition, leisure, mobility, and the capacity to develop daily life basic activities. 2. Emotional dimension, comprising indicators, such as happiness, sense of security, spirituality, self-concept, and absence of stress or fear. 3. Social dimension, with indicators based on interpersonal relationships, such as family, friends, received support, social status, work environment, role in the community, etcetera. 4. Dimension of material well-being, including indicators related to the individual’s rights, socioeconomic status, safety, and employment [ 16 , 17 , 18 , 19 ].

In this respect, the objective of this review is focused on analyzing both problems and improvement strategies which are associated with the quality of life of people who are caregivers of adults with cancer.

2. Materials and Methods

2.1. study design and search strategy.

A literature review was conducted on PudMed, Cinahl, PsycINFO, and Scopus during the months of February and May 2022. The studies’ search strategy was defined by consensus between the authors in order to avoid bias for not including relevant studies [ 20 ], namely: [(“Quality of Life” OR “Social Problem”) AND Neoplasm AND (Caregivers OR “Nursing Care”)].

2.2. Eligibility Criteria

Eligibility criteria for studies were presented with the greatest transparency and clarity in order to control selection bias [ 21 ]. Those eligibility criteria were: (1) Research studies following a quantitative, qualitative, or mixed method. (2) Regarding the issues associated to people who provide care to adults with cancer. (3) People aged 18 or older. (4) Published in the English or Spanish language during the past five years (2017–2022).

2.3. Selection of Articles

Studies were selected in four stages. The first of them consisted in the search and localization of studies on the database through the selected search strategy. The second stage comprised the elimination of duplicates. The third stage was devoted to titles, keywords, and abstracts reading of the selected studies, choosing those consistent with the review subject and according to the eligibility criteria. A fourth stage consisted of a critical reading of the full texts of the selected articles.

2.4. Data Analysis

Evidence drawn from selected studies included: 1. Author/s and year. 2. Objective. 3. Country/Period of the study. 4. Type of study/Instrument/Sample. 5. Main findings. Data were collected in a table following the recommendations of author Del Pino et al. [ 20 ].

3.1. Description of the Studies

Of the 1812 studies initially identified, 36 were selected as being consistent with the review objective and eligibility criteria ( Figure 1 ).

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Selection of studies flowchart.

With regard to their typology, 28 studies followed a quantitative research method. Twenty-two of them were descriptive, exploratory, or based on observation; four were randomized controlled trials; one was an experimental controlled trial; and one was a case–control study. In the remaining studies, seven followed a qualitative research method and one, a mixed method. Specific characteristics of these studies are detailed in Table 1 .

3.2. Study Outcome

3.2.1. characteristics of the sample of caregivers of adults with cancer.

The total sample comprised 7663 people caregivers with an average age of 54.42 years. However, it must be highlighted that 10 studies did not describe average age [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. With regard to sex, 66.72% of caregivers were women. As for family bonds, 15 studies described these caregivers as being mostly spouses or daughters/sons of the patients with cancer, usually sharing the same home [ 22 , 23 , 25 , 26 , 27 , 28 , 30 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ].

3.2.2. Problems (Physical, Emotional, Social, and Financial) of People Who Are Caregivers of Adults with Cancer

In relation to physical problems, eight studies [ 25 , 34 , 37 , 40 , 41 , 42 , 43 , 44 ] described that care work implies a greater risk for caregivers of suffering physical morbidity with considerable wear, which in fact increases as the care burden continues over time. It is also observable that physical and health problems are more frequent in female caregivers, and physical damage results in a worse quality of life perceived by caregivers.

At an emotional level, 26 studies [ 22 , 23 , 25 , 26 , 27 , 28 , 29 , 30 , 33 , 34 , 35 , 36 , 38 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ] alluded to the emotional and psychological problems suffered by caregivers, highlighting anxiety, depression, feelings of grief and distress, and high levels of stress. Therefore, showing that, the longer the care was needed over time, the greater the tension and emotional burden, resulting in worse quality of life.

With regard to social aspects, 10 studies [ 24 , 25 , 27 , 31 , 32 , 41 , 49 , 53 , 54 , 55 ] described how the patients’ disease process has affected the social interaction of caregivers, who manifest a great difficulty in reconciling their role as caregivers and the development of other daily activities, highlighting the deterioration of working conditions. The studies conclude that less social support is related to worse quality of life.

In the economic sphere, 10 studies [ 25 , 27 , 39 , 44 , 53 , 56 , 57 ] described the financial problems of people caregivers, resulting from the costs associated with the disease, the lack of financial support of other family members, or the difficulty of reconciling their role as caregivers with their work activity. The studies show that caregivers with higher income levels perceive a better quality of life.

3.2.3. Strategies to Improve the Quality of Life of Caregivers of Adults with Cancer

Among the strategies described, 10 studies [ 28 , 29 , 31 , 32 , 36 , 39 , 41 , 50 , 54 , 57 ] emphasized the need of enhancing social support by means of reinforcing the support networks and facilitating access to support groups and associations. Thirteen studies [ 23 , 24 , 27 , 30 , 32 , 35 , 37 , 38 , 45 , 46 , 47 , 49 , 54 ] indicated the need of developing coping strategies to improve the management and control of both the situation and the emotional strain, and also to help increase training in caregivers, thus improving their self-efficacy while reducing the emotional impact. One study [ 53 ] included the completion of breathing exercises and counseling sessions to reduce emotional strain. One study [ 34 ] suggested exercising practice to improve the perceived health levels on the part of caregivers. One study [ 39 ] pointed out the need of providing financial support to caregivers in order to improve their well-being. Four studies [ 22 , 24 , 44 , 48 ] highlighted the need of training properly health professionals on existential and spiritual distress management, as well as the need to identify groups at risk and reassigning tasks among all family members. One study [ 26 ] described that health professionals require cultural awareness in order to properly meet the needs of caregivers.

Characteristics of the selected studies.

BIS: Burden Interview Scale; Brief-POMS: Brief-Profile of Mood States; CAMI: Community Attitudes to Mental Illness; CarCOQol: Caregiver Oncology Quality of Life questionnaire; CASE-T: Cancer Self-Efficacy Scale-Transplant; CBS: Caregiver Burden Scale; CCI: Charlson Comorbidity Index; CES-D: Center of Epidemiological Studies of Depression Scale; CNAT-C: Comprehensive Needs Assessment Tool for Cancer Caregivers; CQLI-R: Caregiver Quality of Life Index-Revised; CQOLC: Caregiver Quality of Life Index-Cancer; CRA: Caregiver Reaction Assessment; CSI-M: Caregiver Strain Index-Malay; CSS: Crisis Support Scale; DAS-7: 7-Item Dyadic Adjustment Scale; EQ-5D-3L: EuroQol five-dimension three-level; FACT-G: Functional Assessment of Cancer Therapy—General; FCCT: Family Caregiver Communication Tool; GAD-7: Generalized Anxiety Disorder-7; GHQ-12: The 12-Item General Health Questionnaire; HADS: The Hospital Anxiety and Depression Scale; HLCS-C: Health Literacy of Caregivers Scale—Cancer; IES-R: Impact of Event Scale—Revised; IPAQ: International-Physical-Activity-Questionnaire; ISEL: Interpersonal Support Evaluation List; KPS: Karnofsky Performance Scale; LCSS: Lung Cancer Symptom Scale; MCQOL: Malay Caregiver Quality of Life scale; MCSDS: Marlowe-Crowne Social Desirability; MHI-18: Mental Health Inventory-18; MOCS: Measure of Current Status; MOS: Mean Opinon Scale; MOS-SSS: Medical Outcomes Study Sleep Scale; MSPSS: Multidimensional Scale of Perceived Social Support; MSPSS-M: Malay Multidimensional Scale of Perceived Social Support; OARS-IADL: Older Americans Resources and Services Scale—Instrumental Activities of Daily Living; PHQ: Patient Health Questionnaire; PSQI: Pittsburgh Sleep Quality Index; PSS: Perceived Stress Scale; PST: Personnel Selection Test; QLQ-C30: Quality of Life Questionnaire-Cancer 30; QoL-FV: Quality of Life-Family Version; QOLLTI-F: Quality of Life in Life Threatening Illness-Family Carer version; QOLS-N: Norwegian version of the Quality of Life Scale; SCID-CV: Structured Clinical Interview for Disorders—Clinical Version; SCNS-P&C-F: Psychometric validation of the French version of the Supportive Care Needs Survey for Partners and Caregivers of cancer patients; SF-SUN: Short Form- Survivor Unmet Needs Survey; SF: Short Form; SSRS: The Columbia-Suicide Severity Rating Scale; SSSS: Social Support Satisfaction Scale; UWQOL: University of Washington Quality of Life questionnaire; WPAI:GH: Work Productivity and Activity Impairment Questionnaire: General Health; ZBI: Zarit Burden Interview.

4. Discussion

4.1. characteristics of the sample of caregivers of adults with cancer.

The total sample was made up of 7663 caregivers, 66.72% were women. In this sense, most studies coincide in stating that there are more women caregivers of patients with cancer than men [ 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. This data met the results obtained by other authors outside this review, such as Martínez et al. [ 58 ] who conducted research to define the burden of caregivers of patients with head and neck cancer, concluded that the profile consisted of 81% women. On the other hand, the study accomplished by Guijarro-Requena et al. [ 13 ], with the aim of improving the quality of life of caregivers through training initiatives, indicated as well a larger representation of women, reaching 91.9% of the sample. However, there were two studies that described a larger number of male caregivers with respect to women [ 28 , 31 ].

Regarding average age of caregivers of patients with cancer, most of the studies set the interval between ages 45–65 [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 55 , 56 ]. These results coincide with those described by other authors [ 59 , 60 ]. Notwithstanding, in the study by Peredo et al. [ 61 ] analyzing burnout syndrome, anxiety, and depression in caregivers of patients with cancer, 48.7% were among the age interval 20–40 years. In the study by Amador et al. [ 62 ] analyzing psycho-affective features and overburden levels in caregivers of oncologic patients, 48% were women aged 24–29 years.

With respect to the familiar bond, the role of caregiver is undertaken by spouses or daughters/sons of people with cancer and usually share the same home [ 22 , 23 , 25 , 26 , 27 , 28 , 30 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. These results are aligned with the conclusions described by other authors [ 5 , 8 , 58 , 63 ].

4.2. Physical, Emotional, Social, and Financial Strain Suffered by Caregivers of Adults with Cancer

Different review studies have revealed that care provision on the part of caregivers causes physical problems, increasing the risk of suffering physical morbidity the longer the care activity continues [ 25 , 34 , 37 , 40 , 41 , 42 , 43 , 44 ]. Authors outside this review described how the stressful factors related to care provision considerably affect the physical condition of caregivers, with this impact higher on male caregivers who are older and bearing a high demand of care [ 64 ]. Barrón and Alvarado [ 65 ] indicated that care provision, particularly if prolonged over time, may result in various physical conditions such as: fatigue, headaches, joint pain, dizziness, and worsening of sleep quality to trouble sleeping.

Most of the studies [ 22 , 23 , 25 , 26 , 27 , 28 , 29 , 30 , 33 , 34 , 35 , 36 , 38 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ] have referenced the emotional and psychological problems suffered by caregivers, describing feelings of distress, stress, high levels of anxiety, and even depression. Emotional strain was significantly higher when care provision extended over time. Results obtained are in line with those mentioned by other authors [ 64 , 66 ]. Besides, the study conducted by Rosado et al. [ 67 ], aimed to evaluate the support needs and quality of life of caregivers of pediatric patients with cancer, described that emotional performance was the most damaged in the total score of quality of life index.

Regarding social problems, review studies [ 24 , 25 , 27 , 31 , 32 , 41 , 49 , 53 , 54 , 55 ] described how social interactions of caregivers were affected to the point of finding it very difficult to reconcile their role of caregivers with the rest of activities, where deterioration of working conditions and lesser social event engagement were highlighted. Caregivers who declared having worse social support showed poorer quality of life indicators. These findings are aligned with other authors’ results, which also indicated deterioration of working conditions, and a decrease of free time to develop daily activities or taking time for themselves. Moreover, the lesser the social support is perceived on the part of the caregiver, the worse the consequences on her/his health, both physical and psychological [ 66 , 68 ].

Several review studies [ 25 , 27 , 39 , 44 , 53 , 56 , 57 ] mentioned the financial problems of caregivers, highlighting among the possible causes, the lack of financial support, the costs derived from the disease process itself, or the difficulty of developing their work activity. Caregivers who reported lower incomes showed worse quality of life indicators. Several authors outside the review reported that difficulties to reconcile the role of caregiver with work activities add multiple financial burdens to caregivers [ 66 ]. The study conducted by Cortijo-Palacios et al. [ 69 ] carried out the Zarit Burden Interview in assessing caregiving burden, revealing high scores in low spending capacity and feelings of financial distress.

4.3. Strategies to Improve the Quality of Life of Caregivers of Adults with Cancer

With respect to the strategies to improve the quality of life of caregivers, different studies have pointed out the need of promoting social support, facilitating the access to support networks [ 28 , 29 , 31 , 32 , 36 , 39 , 41 , 50 , 54 , 57 ]. Authors such as Martínez, Lorenzo, and Llantá [ 58 ] have indicated that in order to improve the quality of life of caregivers, both social and family support are key, as they not only allow to hand over some tasks and responsibilities, but they also provide emotional, spiritual, and material support.

Review studies have described the need of implementing coping strategies to allow patients to improve the management of their own disease, while developing skills to reduce the emotional and physical impact [ 23 , 24 , 27 , 30 , 32 , 34 , 35 , 37 , 38 , 45 , 46 , 47 , 49 , 53 , 54 ]. These findings coincide with those published by other authors such as Pino et al. [ 70 ] who described the need of implementing coping strategies to protect the quality of life of caregivers and thus overcome cognitive, emotional, and behavioral demands faced by both caregiver and family environment. Besides, Pérez et al. [ 71 ] indicated that caregivers of patients with cancer who were able to develop coping strategies, managed to decrease the emotional impact and fatigue feeling.

Review studies revealed the need of appraising the economic situation of caregivers [ 25 , 27 , 39 , 44 , 53 , 56 , 57 ]. Authors such as Carreño et al. [ 72 ] have also pointed out this need when facing the high economic burden imposed on caregivers, and that this is not only associated to medical services, but to the lack of labor productivity or even job loss. This financial burden is connected to higher levels of anxiety and distress suffered by caregivers.

Authors of the review pointed out that in order to develop the strategies to aid caregivers, it is necessary to properly train health professionals, both in the management of emotional and physical strain, as in covering the spiritual needs and facilitate access to support networks. This requires health professionals to able to identify the most vulnerable groups while considering providing specific health care to all the family [ 22 , 24 , 44 , 48 ]. Moreover, in order to comply with all these needs, health professionals require cultural awareness to understand better the complex requirements of caregivers [ 26 ]. These results are aligned with the findings described by other authors [ 73 ].

4.4. Limitations

This review shows some limitations. Shortening the search strategy to the last five years made it impossible to recover all the information available on this subject, though a rigorous process was followed in order to obtain the latest scientific findings. Furthermore, the number of subjects in the sample vary from one study to another. On the other hand, the diversity of the research designs that were employed also limits interpretations. Another limitation is that no tool was used for study quality assessment.

5. Conclusions

The vast majority of caregivers of adults with cancer are women around 50 years old, being most commonly spouses or daughters of the patient with whom they usually live. Caregiving generates physical, emotional, social, and financial problems, which cause a burden in caregivers that results in a decrease of their quality of life. To improve their quality of life, different strategies can be implemented: In relation to physical problems, it is recommended exercising practice to improve the perceived health levels on the part of caregivers. At the emotional level, it is recommended, on one hand, enhancing social support by means of reinforcing the support networks and facilitating access to support groups and associations; and, on the other hand, developing coping strategies which improve the management and control of both the situation and the emotional strain. Breathing exercises and counseling sessions to reduce emotional strain have also been described is this regard. In the economic sphere, it stands out the need to provide financial support to caregivers in order to improve their well-being. In order to implement these strategies, it is necessary to train health professionals.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, M.D.G.-M.; methodology, M.D.G.-M. and Á.B.-R.; formal analysis, M.D.R.C.-E., Y.G.-L., C.H.-C. and I.L.-L.; investigation, M.D.R.C.-E., Y.G.-L., C.H.-C. and I.L.-L.; data curation, M.D.R.C.-E., Y.G.-L., C.H.-C. and I.L.-L.; writing—original draft preparation, M.D.G.-M. and Á.B-R.; writing—review and editing, M.D.G.-M. and Á.B-R.; supervision, M.D.G.-M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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An Overview of Quality of Urban Life

  • First Online: 01 January 2011

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literature review on quality of life pdf

  • Robert W. Marans 3 &
  • Robert Stimson 4  

Part of the book series: Social Indicators Research Series ((SINS,volume 45))

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There is a long history of quality of life (QOL) studies based on two distinctive analytical approaches. One covers objective indicators involving the analysis of spatially aggregated secondary data whereas the other approach focuses on the analysis of subjective measures of QOL domains derived from household surveys. Aided by the use of GIS technology, efforts have been made to integrate the subjective approach with spatial objective data, especially where there is a focus on the analysis and modeling of quality of urban life (QOUL). The chapter presents an overview of the evolution of these approaches in QOL/QOUL studies and sets the stage for subsequent chapters.

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Marans, R.W., Stimson, R. (2011). An Overview of Quality of Urban Life. In: Marans, R., Stimson, R. (eds) Investigating Quality of Urban Life. Social Indicators Research Series, vol 45. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1742-8_1

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