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Anxiety, Depression and Quality of Life—A Systematic Review of Evidence from Longitudinal Observational Studies

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This review aimed to systematically review observational studies investigating the longitudinal association between anxiety, depression and quality of life (QoL). A systematic search of five electronic databases (PubMed, PsycINFO, PSYNDEX, NHS EED and EconLit) as well as forward/backward reference searches were conducted to identify observational studies on the longitudinal association between anxiety, depression and QoL. Studies were synthesized narratively. Additionally, a random-effects meta-analysis was performed using studies applying the mental and physical summary scores (MCS, PCS) of the Short Form Health Survey. The review was prospectively registered with PROSPERO and a study protocol was published. n = 47 studies on heterogeneous research questions were included, with sample sizes ranging from n = 28 to 43,093. Narrative synthesis indicated that QoL was reduced before disorder onset, dropped further during the disorder and improved with remission. Before onset and after remission, QoL was lower in comparison to healthy comparisons. n = 8 studies were included in random-effects meta-analyses. The pooled estimates of QoL at follow-up (FU) were of small to large effect sizes and showed that QoL at FU differed by disorder status at baseline as well as by disorder course over time. Disorder course groups differed in their MCS scores at baseline. Effect sizes were generally larger for MCS relative to PCS. The results highlight the relevance of preventive measures and treatment. Future research should consider individual QoL domains, individual anxiety/depressive disorders as well as the course of both over time to allow more differentiated statements in a meta-analysis.

1. Introduction

The World Health Organization [ 1 ] estimates that 264 million people worldwide were suffering from an anxiety disorder and 322 million from a depressive disorder in 2015, corresponding to prevalence rates of 3.6% and 4.4%. While their prevalence varies slightly by age and gender [ 1 ], they are among the most common mental disorders in the general population [ 2 , 3 , 4 , 5 , 6 ]. During the COVID-19 pandemic, multiple challenges have arisen for many, such as loneliness [ 7 ] or financial hardship. A meta-analysis showed a prevalence of anxiety of about 32% (95% CI: 28–37) and a prevalence of depression ( n = 14 studies) of about 34% (95% CI: 28–41) in general populations during the COVID-19 pandemic [ 8 ].

Anxiety and depression have been associated with adverse societal and individual correlates, including higher health care costs [ 9 , 10 , 11 ] and an increased risk for physical comorbidities, such as cardiovascular illnesses [ 12 , 13 ]. Moreover, they have been linked to a reduced quality of life (QoL) in numerous cross-sectional as well as longitudinal studies in which they significantly predicted QoL outcomes [ 14 , 15 , 16 , 17 , 18 ]. Other studies have reported a reverse association, whereby QoL was predictive of mental health outcomes [ 19 ] or a bi-directional association [ 20 , 21 ]. Some very recent studies also examined these associations among quite different samples (e.g., [ 22 , 23 , 24 , 25 ]).

Looking at longitudinal rather than cross-sectional data from observational studies has several advantages. It allows for the identification of trajectories over time within the same individuals rather than focusing on group differences at one point in time only [ 26 ]. Moreover, when appropriate methods are applied to longitudinal data, intraindividual heterogeneity can be taken into account, resulting in more consistent estimates [ 27 ]. This has previously been demonstrated in QoL research [ 28 ]. A need to analyze longitudinal changes in QoL domains in QoL research in people with mental disorders has also been previously identified [ 29 ]. Beyond individual longitudinal studies suggesting a link between anxiety or depression and QoL, several systematic reviews have synthesized longitudinal evidence on these associations and mostly reported negative associations between the variables. These reviews have tended to focus on specific age groups, such as older adults [ 30 ], samples with specific diseases [ 31 , 32 ], or have investigated the effect of specific treatments on QoL in patients with anxiety [ 33 ]. Investigating these associations in samples without these limitations could reduce the effect of specific conditions and treatments on the association and strengthen the conclusions that can be drawn.

In light of the previous findings, this study aims to add to the present literature by systematically synthesizing evidence from observational studies on the longitudinal association between anxiety, depression and QoL across all age groups in samples who do not have other specific illnesses and do not receive specific treatments.

2. Materials and Methods

This review was registered with PROSPERO (CRD42018108008) and a study protocol was published [ 34 ].

2.1. Search Strategy

Five electronic databases from several fields of research (PubMed, PsycINFO, PSYNDEX, NHS EED and EconLit) were examined until December 2020. Where possible, search terms were entered as Medical Subject Headings (MeSH) or as keywords in the title/abstract. The PubMed search strategy was: (anxi*[Title/Abstract] or depress*[Title/Abstract] or anxiety disorder[MeSH] or depressive disorder[MeSH]) and quality of life[MeSH] and longitudinal study[MeSH]. Please note that “*” is a truncation symbol. Time or location were not restricted. In addition, we applied backward and forward reference searches of included studies to identify additional references. The forward reference search was conducted until January 2021 using Web of Science to identify cited papers.

2.2. Study Selection Process

The study selection process is displayed in Figure 1 . Most identified studies were screened in a two-step process (title/abstract; full-text screening) independently by two reviewers (J.K.H., E.Q.) against defined criteria (see Table 1 ). The last updated literature screening before submission was conducted by one reviewer (J.K.H.) and encompassed 9% of the studies included for title/abstract screening. Before the final criteria were applied, they were pretested and refined. Disagreements during the selection process were resolved through discussion or by the inclusion of a third party (A.H.) if a consensus could not be reached.

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Study flow (PRISMA flow chart).

Study selection criteria.

Abbreviations: QoL = quality of life; ICD = International Classification of Diseases; DSM = Diagnostic and Statistical Manual of Mental Disorders; BL = study baseline; KIDSCREEN = Health Related Quality of Life Questionnaire for Children and Young People and their Parents; KINDL = German generic quality of life instrument for children

2.3. Data Extraction and Synthesis

We extracted information regarding the study design, operationalization of the variables, sample characteristics, statistical methods and results regarding the research question of interest. If several analyses were presented for the same research question, we extracted the final covariate-adjusted model for narrative synthesis. Data were extracted by one reviewer (J.K.H.) and cross-checked by a second reviewer (E.Q.). If needed, extracted data were standardized (e.g., by calculating the weighted average means when combining groups) to present comparable information. If clarification was needed, the corresponding authors were contacted.

For the narrative synthesis, all studies were first grouped by research question, e.g., whether disorders or the degree of symptoms were analyzed, which comparison groups were used, which QoL domains were considered, and at which waves the variables of interest were considered in the analyses. Because research questions and analyses were heterogeneous, a concise narrative synthesis of the main results of all studies was not feasible. Therefore, we provide an overview of all identified studies in the tables and a detailed narrative synthesis of those studies, analyzing trajectories of disorders or changes in symptoms in association with changes in QoL over time.

Additionally, we examined whether data were appropriate for meta-analysis. The specific research questions, the operationalization of main variables and statistical methods were heterogeneous across studies and not all the statistical estimates needed could be obtained from covariate-adjusted analyses. Therefore, to enhance the comparability of the underlying data and the interpretation of the pooled estimates, we used descriptive information. Because most papers applied variations of the Short Form Health Survey and analyzed mental and physical component scores (MCS, PCS), we considered these studies as eligible for meta-analysis. The necessary information could be obtained for 8 publications. Random-effects meta-analysis was used for pooling. Heterogeneity was assessed by means of I 2 , with higher values representing a larger degree of heterogeneity in terms of variability in effect size estimates between studies [ 41 ]. Pooled estimates are reported as Hedge’s g standardized mean difference (SMD), representing the difference in mean outcomes between groups relative to outcome measure variability [ 42 ]. According to Cohen (as cited in [ 43 ]), SMDs can be grouped into small ≤0.20, medium = 0.50 and large effects ≥0.80. Stata 16 was used for meta-analyses.

2.4. Quality/Risk of Bias Assessment

Two reviewers (J.K.H., E.Q.) independently assessed the quality and risk of bias of the included studies using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, which was developed by the National Heart, Lung, and Blood Institute [ 44 ].

3.1. Selection Process

The literature search yielded 4027 unique references. After title/abstract screening, 215 studies were included for full-text screening. Finally, 47 publications were included in the final synthesis. During full-text screening, most studies were excluded because they exclusively analyzed data on a cross-sectional level (56.5%). For further details, see the PRISMA flow chart ( Figure 1 ).

3.2. Overview of Included Studies

Descriptive characteristics and quality/risk of bias assessment of the included studies are provided in Table S1 (Supplementary Material) . In short, sample size ranged from 28 to 43,093. Most studies focused on adults; only four analyzed children/adolescents. Regarding the settings, 17 of the analyzed samples were exclusively recruited in a health care setting, 12 of the studies analyzed general population samples, 14 recruited in another or in several settings, and all studies on children/adolescents recruited in schools ( n = 4). Twenty studies (42.6%) applied data from the same seven underlying datasets. Most studies reported on depression ( n = 36), less reported on anxiety ( n = 20) and some reported on the comorbidity between depression and anxiety ( n = 7). To assess mental disorders, half (48.9%) used structured interviews. Regarding QoL, most studies applied variations of the Short Form Health Survey (SF, n = 27) or the WHOQOL ( n = 12). A total of 38.3% of the studies were rated as “good”, 55.3% as “fair” and 6.4% as “poor” in the quality assessment.

3.3. Overview of Studies on the Association between Anxiety/Depression as Independent Variables and QoL Outcomes

Detailed results on all studies investigating the association between anxiety/depression as independent variables and QoL outcomes are reported in Table 2 . As described in the methods section, the following paragraphs give an overview of those studies focusing on disorder trajectories/changes in symptoms over time and changes in QoL outcomes over time, because they allow for more differentiated interpretations.

Studies on depression/anxiety as independent variables and QoL outcomes.

Abbreviations: QoL = quality of life; MD = major depression; FU = follow-up; DSM = Diagnostic and Statistical Manual of Mental Disorders; HDRS = Hamilton Depression Rating Scale; PCS = Physical Component Score; MDS = Mental Component Score; MDD = major depressive disorder; ANOVA = analysis of variance; BL = baseline; MDE = major depressive episode; CIDI = Composite International Diagnostic Interview; SF-36 = Short Form 36; AUDADIS = Alcohol Use Disorders and Associated Disabilities Interview Schedule; SF-12 = Short Form 12; PHQ = Patient Health Questionnaire; SF-12v2: Short Form 12, Version 2; HRSD = Hamilton Rating Scale for Depression; HADS = Hospital Anxiety and Depression Scale; QLDS = Quality of Life in Depression Scale; EQ-VAS = EQ Visual Analogue Scale; DIS = Diagnostic Interview Schedule; BDI = Beck Depression Inventory; SCID = Short Children’s Depression Inventory; MINI = Mini-International Neuropsychiatric Interview; PTSD = post-traumatic stress disorder; hrqol = health-related quality of life, IES-15 = Impact of Event Scale 15; Q-DIS = Quick Version of the Mental Health’s Diagnostic Interview Schedule; MADRS = Montgomery–Åsberg Depression Rating Scale; FDD-DSM-IV = Fragebogen zur Depressionsdiagnostik nach Diagnostic and Statistical Manual of Mental Disorders IV; SCAN = Schedule for Clinical Assessment in Neuropsychiatry; DASS = Depression Anxiety Stress Scales; MOS SF = Medical Outcomes Study Short Form; CES-D = Center for Epidemiological Studies Depression Scale; WHOQOL-Bref-TW = WHOQOL-Bref Taiwan Version; MHI-5 = Mental Health Inventory 5; OCD = obsessive compulsive disorder; Y-BOCS = Yale–Brown Obsessive Compulsive Scale; BAI = Beck Angst Inventar; DD = depressive disorder; PD = psychiatric disorder; SAD = social anxiety disorder; Q-LES-Q = Quality of Life Enjoyment and Satisfaction Questionnaire; GHQ-28 = General Health Questionnaire 28; PCL-S = Post-traumatic Stress Disorder Checklist Scale; VETR-PTSD = Vietnam Era Twin Registry Posttraumatic Stress Disorder; DRPST = Disaster-Related Psychological Screening Test; SCL-90 = Symptomcheckliste bei psychischen Störungen 90; SASC = SpLD Assessment Standards Committee; QOLS = Quality of Life Scale; CDI = Children’s Depression Inventory.

Depression as independent variable and QoL as outcome. One study investigated QoL at several time points during the entire course of an episode of MD .

Buist-Bouwman, Ormel, de Graaf and Vollebergh [ 46 ] analyzed an MD group from a general population setting (NEMESIS) with data on SF-36 domains in the onset, acute and recovery phase of the depressive episode. The onset of MD was associated with a significant drop in several QoL domains and recovery with a significant increase. Pre- and post-morbid QoL levels were not significantly different for most domains, and post-morbid QoL was even higher for the psychological role functioning and psychological health domains. In comparison to a group without MD, pre- and post-morbid QoL levels in the MD group were significantly lower, except for the psychological role functioning domain, where no significant differences were found. Additionally, it should be noted that 40% of the sample had lower post-morbid QoL compared to pre-morbid levels.

Two studies investigated changes in QoL for people experiencing an onset of depression relative to different comparison groups over two points in time.

One study investigated incident MD in a general population sample (NESARC; Rubio, Olfson, Perez-Fuentes, Garcia-Toro, Wang and Blanco [ 14 ]). Here, incident MD (compared to those without a history of MD as well as to a group without any mental disorder) was associated with a significant drop in QoL (SF-12 MCS). Additionally, analyzing two waves, Pyne, Patterson, Kaplan, Ho, Gillin, Golshan and Grant [ 67 ] compared the QoL (Quality of Well-Being scale) between MD patients and community controls. The patient group was further divided into those continuously not receiving an MD diagnosis, those who continuously received the diagnosis and those who only received the diagnosis at FU (onset). The authors found that changes in QoL did not differ between the groups. At both points in time, QoL scores differed significantly between the groups, except for the incident and the continuous depression group [ 67 ].

Six studies investigated different courses of depression over time in people with depression at BL with or without a healthy comparison group as reference.

Two primary care studies analyzed groups with clinical depression at BL with different FU depression statuses (remission, no remission). One study [ 51 ] analyzed changes in generic QoL measures (SF-12, WHOQOL-Bref) and the disease-specific Quality of Life in Depression Scale. In this study, remission was associated with an improvement in all QoL domains, whereas QoL did not change significantly over time for the non-remitted group. Another study [ 60 ] investigated SF-12 MCS and PCS scores and reported a significant increase in MCS over time in the remitting group. MCS scores in the continuously depressed group and PCS scores in both groups improved, albeit not significantly.

Another study [ 47 ] investigated whether chronic MD in a general population sample (NESARC) was associated with domain-specific reduced QoL (SF-12). They found that chronic MD was a significant risk factor for persistently reduced QoL in all domains and for the onset of reduced QoL at FU in all domains except for physical role.

Two population-based studies further differentiated between the depressive disorders. Analyzing MCS scores (NESARC), Rubio, Olfson, Villegas, Perez-Fuentes, Wang and Blanco [ 15 ] reported a significant increase in QoL for those who remitted from MD and from dysthymia relative to those who had a persistent disorder. Rhebergen, Beekman, de Graaf, Nolen, Spijker, Hoogendijk and Penninx [ 69 ] differentiated between people with MD, double depression or dysthymia at BL who remitted until FU relative to a group without a mental health diagnosis (NEMESIS). Physical health (SF-36) was lowest at BL for double depression, dysthymia and then the MD group. Over time, the MD and double depression groups improved significantly in their physical health, while the dysthymia group did not improve significantly. QoL was significantly lower relative to healthy comparisons for all depression groups at all waves. There were no significant differences regarding physical health trajectories over time among the depressive disorder groups.

Stegenga, Kamphuis, King, Nazareth and Geerlings [ 75 ] investigated more than two MD course groups over time (remitted, intermittent and chronic MD) in association with SF-12 MCS and PCS over time in a primary care-recruited sample with BL MD (Predict study). MCS increased over time in all groups, while changes in PCS were small. Compared to those who remitted, MCS at BL was significantly lower for the chronic course group. While the intermittent group also displayed a lower mean MCS at BL, the coefficient was not significant.

Three studies investigated changes in depressive symptom levels as the independent variable and changes in QoL as outcomes in adults.

One study found no significant association between an initial change in depressive symptoms and subsequent change in QoL (EQ-VAS) in older adults recruited in primary care [ 21 ]. The two other studies analyzed changes in depressive symptoms in samples with MD at BL [ 50 , 51 ]. Chung, Tso, Yeung and Li [ 50 ] found that changes in depressive symptom levels was associated with changes in several QoL domains (SF-36: general health, vitality, social functioning, mental health and MCS). Diehr, Derleth, McKenna, Martin, Bushnell, Simon and Patrick [ 51 ] investigated whether quartiles of change in depressive symptoms were associated with changes in QoL (SF-12, QLDS and WHOQOL-Bref). Those without any change in depressive symptoms generally showed no change in QoL. For all QoL domains and scores except for SF-12 PCS, improvement in depressive symptoms over time was associated with a significant increase in QoL, while a reduction in depressive symptoms was associated with a significant reduction in QoL. Those who had the largest reduction in depressive symptoms also had the largest improvement in QoL measures.

Anxiety as an independent variable and QoL as an outcome. Two publications used a general population sample (NESARC) to investigate incident anxiety disorders [ 14 ] and the remission of anxiety disorders [ 15 ] in association with SF-12 MCS. Both studies separated generalized anxiety disorder (GAD), social anxiety disorder (SAD), panic disorder (PD) and social phobia (SP). All incident disorders were associated with a significant reduction in QoL compared to people without a history of the specific disorders. When the analysis was restricted to incident cases without comorbidities, QoL levels were not significantly different compared to people without a history of any psychiatric disorder [ 14 ]. Those who remitted from SAD showed a significant increase in QoL compared to persistent cases. While QoL improved for all remitting anxiety disorders, change scores for PD and SP were not significant [ 15 ].

Another study investigated different courses (intermittent, chronic or remitting) of obsessive compulsive disorder (OCD) and course in QoL (EQ-5D) as well as a comparison group from the general population [ 68 ]. They found that the OCD groups mostly reported a lower QoL compared to the general population. Moreover, the course groups differed regarding their QoL over time, with remitters reporting small to moderate improvements compared to the chronic group.

One study investigated changes in anxiety symptoms in association with changes in all SF-36 domains and both summary scores over time in a sample with MD at BL [ 50 ]. Changes in anxiety symptoms were significantly associated with changes in bodily pain, general health and the mental health domain.

3.4. Overview of Studies on the Association between QoL as Independent Variable and Anxiety/Depression as Outcomes

Additionally, we identified publications operationalizing QoL as the independent variable and anxiety/depression as outcomes with details on all studies reported in Table 3 . Only one study reported on change in QoL over time and change/trajectories in mental health outcomes over time. This study operationalized change in QoL as a predictor of future change in depressive symptoms over time and reported that an initial improvement in EQ-VAS was associated with a future reduction in depressive symptoms in older adults [ 21 ].

Studies on QoL as the independent variable and depression/anxiety as outcome.

Abbreviations: CES-D-20 = Center for Epidemiological Studies Depression Scale 20; BL = baseline; FU = follow-up; QoL = quality of life; CIDI = Composite International Diagnostic Interview; QLDS = Quality of Life in Depression Scale; SF-12 = Short Form 12; PCS = Physical Component Score; MCS = Mental Component Score; GDS = Geriatric Depression Scale; EQ-VAS = EQ Visual Analogue Scale; MD = mental disorder; AUDADIS-IV = Alcohol Use Disorders and Associated Disabilities Interview Schedule; SF-12v2 = Short Form 12 Version 2; PTSD = post-traumatic stress disorder; IES-15 = Impact of Event Scale 15; MADRS = Montgomery–Åsberg Depression Rating Scale; MDD = major depressive disorder; PHQ = Patient Health Questionnaire; SASC = SpLD Assessment Standards Committee; QOLS = Quality of Life Scale; CDI = Children’s Depression Inventory.

3.5. Meta-Analyses on Anxiety, Depression and SF Summary Scores

In total, eight studies on adults were included in a supplementary meta-analyses of several research questions on SF PCS and MCS in association with anxiety and depressive disorders. Forest plots for the analyses are provided in the supplementary materials (Figures S1–S10) .

Differences in SF summary scores at FU among adults with and without depressive disorders at BL. Based on a pooling of four studies [ 45 , 49 , 52 , 54 ], those with depression at BL showed lower MCS scores at FU compared to a group without depression at BL with a large effect size (SMD = −0.96, 95% CI: −1.04 to −0.88, p < 0.001, I 2 = 0.0%). PCS scores at FU were lower for the depression group compared to the non-depression group with a medium effect size (SMD = −0.68, 95% CI: −1.06 to −0.30, p < 0.001, I 2 = 94.6%). Excluding the study rated “poor” in the quality/risk of bias assessment from the pooling did not substantially affect the results (MCS: SMD = −0.96, 95% CI: −1.03 to −0.88, p < 0.001, I 2 = 0.01%; PCS: SMD = −0.63, 95% CI: −1.08 to −0.19, p < 0.01, I 2 = 96.8%).

BL differences in SF summary scores among adults with MD at BL with and without remitting courses over time. Based on a pooling of two studies [ 19 , 84 ] of samples with MD at BL, those with persistent MD at FU had significantly lower MCS at BL (SMD = −0.25, 95% CI: −0.41 to −0.10, p = 0.001, I 2 = 74.95) and PCS scores at BL (SMD = −0.24, 95% CI: −0.39 to −0.09, p = 0.002, I 2 = 73.14) compared to those who achieved remission until FU. Effect sizes were small for both summary scores.

FU differences in SF summary scores among adults with depressive and anxiety disorders at BL with and without remitting courses . Based on the pooling of two studies [ 71 , 81 ] of samples with MD and/or dysthymia, the group where the disorder had persisted/a co-morbid condition was present/had a suicide attempt until FU had significantly lower MCS scores at FU compared to the group where the disorder had remitted without treatment until FU, with a medium effect size for depressive disorders (SMD = −0.59, 95% CI: −0.75 to −0.42, p < 0.001, I 2 = 37.72) and a small effect size for anxiety disorders (SMD = −0.44, 95% CI: −0.58 to −0.30, p < 0.001, I 2 = 58.87). The SMD for PCS scores at FU was negligible in terms of effect size for both disorder groups (depressive disorders: SMD = 0.02, 95% CI: −0.24 to 0.27, p = 0.90, I 2 = 73.65; anxiety disorders: SMD = −0.09, 95% CI: −0.17 to −0.01, p = 0.03, I 2 = 0.01).

4. Discussion

4.1. main results.

This review adds to the present literature by providing an overview of longitudinal observational studies investigating the association between depression, anxiety and QoL in samples without other specific illnesses or specific treatments. Additional meta-analyses investigated group differences according to SF MCS and PCS.

While a concise synthesis of all the identified studies is challenging due to heterogeneity, the following picture emerges from studies investigating change–change associations: before the onset of disorders, QoL is already lower in disorder groups in comparison to healthy comparisons. The onset of the disorders further reduces the QoL. Remission is associated with an increase in QoL, mostly to pre-morbid levels. Additionally, some studies show that remission patterns are relevant for QoL outcomes as well. Moreover, a bi-directional effect was reported, whereby QoL is also predictive of mental health outcomes.

Evidence for a bi-directional association as well as studies showing lower QoL across the entire course of the disorders (before onset, during disorder, after disorder) relative to a healthy comparison group seem to suggest that impairments in QoL may result from a certain pre-disorder vulnerability in these groups. Longitudinal studies using general population data have investigated different hypotheses on (QoL) impairments after remission of anxiety disorders and MD [ 87 , 88 ]. One hypothesis suggests that impairments after the illness episode reflect a pre-disorder vulnerability (vulnerability or trait hypothesis), while the another states that impairments develop during the mental health episode and remain as a residual after recovery (scar hypothesis). Generally, both studies favored the vulnerability hypothesis [ 87 , 88 ]. For subgroups with recurrent anxiety disorders, scarring effects were also found for mental functioning [ 88 ]. Yet, it has to be noted that it was not the aim of our review to gather evidence for these hypotheses using QoL as an indicator, which represents an opportunity for future research.

To be able to investigate possible domain-specific differences across studies, we aimed to conduct a meta-analysis on all studies on the same research question which reported on QoL subdomains (e.g., using WHOQOL and SF). However, as described in the Methods section above, only eight studies reported comparable information on different research questions and could be included in meta-analyses. Due to the limited number of studies included in each meta-analysis, the focus on SF MCS and PCS scores, and most studies reporting on depression, the results of the meta-analyses should be viewed with caution. Keeping this in mind, our results indicate that both mental and physical QoL are significantly impacted by anxiety and depressive disorders and that the course of the disorder is also relevant for QoL outcomes. Not surprisingly, effect sizes for MCS were larger compared to PCS for most research questions. A pooling of two studies on different courses of anxiety and depressive disorders found that effect sizes for MCS at FU were of moderate size for depressive (SMD = −0.59) and of small size for anxiety disorders (SMD = −0.44), while SMDs for PCS at FU were negligible in size.

Overall, effect sizes from meta-analyses ranged from negligible to large, and heterogeneity varied considerably (I 2 between 0% and 95%). Because of the small number of studies, possible influential study-level factors (e.g., setting, operationalization of the variables, length of FU) could not be investigated in further detail by means of a meta-regression, which remains a question for future research.

4.2. Implications for Future Research

Based on the results described and study heterogeneity discussed above, we provide recommendations for future research.

First recommendation: future research should differentiate between individual disorders and focus on anxiety disorders. The majority of the studies investigated depressive disorders or symptoms. On the level of individual disorders, most focused on MD, while two studies additionally reported on dysthymia [ 15 , 69 ]. One of these investigated double depression [ 69 ]. On the level of anxiety disorders, three publications differentiated between individual anxiety disorders within the same study [ 14 , 15 , 63 ]. While it was not possible to conduct a meta-analysis comparing different anxiety disorders in our case, individual studies suggest possible disorder-specific differences when analyzing changes in QoL over time: Rubio, Olfson, Villegas, Perez-Fuentes, Wang and Blanco [ 15 ] suggest that QoL significantly improved for those remitting from GAD and SAD (compared to non-remission). QoL improved for PD and SP as well, but differences in change scores were smaller and did not reach statistical significance. The incidences of all of these disorders were associated with a significant drop in QoL [ 14 ]. In summary, future longitudinal studies should focus on anxiety disorders and generally differentiate between individual disorders to investigate possible disorder-specific differences.

Second recommendation: future research should consider trajectories of disorders/change in symptoms and changes in QoL over time. We would have liked to include a meta-analysis of disorder trajectories and change scores in QoL over time. Because of the small, diverse number of studies on this association in general and the number of assumptions that would have had to have been made for a meta-analysis, we refrained from pooling effects for this research question. In total, 17 studies investigated changes in independent variables associated with changes in outcomes. This approach has several advantages. On the one hand, different disorder or symptom trajectories can be identified. Several studies reported that QoL outcomes differ according to disorder course and the degree of change in symptoms. The focus on the change in characteristics over time in future research could additionally reduce the problem of unobserved time-constant heterogeneity in observational studies when appropriate methods are applied [ 26 ].

Third recommendation: future research should investigate individual QoL domains. Several systematic reviews on cross-sectional studies found that effect sizes differed by QoL domains [ 32 , 89 ]. For example, Olatunji, Cisler and Tolin [ 89 ] reported that health and social functioning were most impaired for anxiety disorders (compared to non-clinical controls). Comparing individuals with diabetes and depressive symptoms to those with diabetes only, Schram, Baan and Pouwer [ 32 ] reported that while SF pain scores were mild to moderately impaired, role and social functioning displayed moderate to severe impairments in those with comorbid depressive symptoms. The other scores were moderately impaired. As described above in detail, a meta-analysis using all subdomains was not feasible in this review. Further research differentiating between QoL domains would thus allow future meta-analyses to investigate whether the observed domain-specific differences reported in previous reviews of cross-sectional data can be observed in longitudinal studies as well.

Fourth recommendation: future research should consider bi-directional effects. While investigating QoL as the outcome measure and anxiety/depression as independent variables seems relatively straightforward, ten studies investigated QoL as the independent variable and anxiety/depression as outcomes. In light of possible bi-directional effects and pre-existing vulnerability suggested by individual studies, future research considering QoL as an independent variable could inform a deeper understanding of this complex association.

4.3. Strengths and Limitations

A strength of this work is the transparent methodological process: the review was prospectively registered with PROSPERO and a study protocol was published [ 34 ]. Two reviewers were included in screening, data extraction and quality assessment processes. There were no limitations regarding the time or location of the publications. Moreover, all versions of the ICD/DSM and validated questionnaires were considered eligible to identify anxiety or depression. Another strength is the thorough literature search that enabled us to identify all relevant studies. Additionally, we did not limit the age range and were therefore able to shed light on studies investigating children/adolescents. Moreover, some studies could be pooled using random-effects meta-analyses, which allows for stronger conclusions regarding effect sizes compared to individual studies. Besides the content analysis, this review emphasizes difficulties in meta-analysis from observational, longitudinal studies. We hope that our work can facilitate discussion on this topic.

The study has some limitations. We did not limit our search to specific research questions, which led to the inclusion of heterogeneous studies. Heterogeneity particularly stemmed from the operationalization of the variables of interest. Due to this, a concise narrative synthesis of all results was not feasible. The positive aspect of this broad focus is that it allowed us to provide an overview of studies and research questions analyzed and to formulate more nuanced recommendations for future research. We have to acknowledge that there is an abundance of QoL assessments used in medicine and health sciences [ 37 ]. The list applied in this work was derived with respect to previous relevant reviews on QoL research. It was not designed to be fully comprehensive or exhaustive. Rather, it provided us with a working definition for this review and helped to enhance the transparency of our selection processes. Additionally, because we included validated QoL measures frequently used in research, we assume that exclusion would particularly have been the case for novel or study-specific measures. Finally, the focus on peer-reviewed literature means that studies in other languages and gray literature were not considered. Nonetheless, this focus on literature published in peer-reviewed journals should ensure a certain scientific quality.

5. Conclusions and Relevance for Clinical Practice

Overall, the results indicate that QoL is lower before the onset of anxiety and depressive disorders, further reduces upon onset of the disorders and generally improves with remission to pre-morbid levels. Moreover, disorder course (e.g., remitted, intermittent, chronic) seems to play an important role; however, only a few studies analyzed this. Changes in anxiety and depressive symptoms were also associated with changes in QoL over time. Meta-analyses found that effect sizes were larger for MCS relative to PCS, highlighting the relevance of differentiation between QoL domains. While our review identified some gaps in the current literature and made recommendations for future research, the following should be noted for clinical practice. On the one hand, an improvement in mental health is associated with better QoL, which emphasizes the relevance of support during the disorders. This is also shown by meta-analyses, which show that cognitive behavioral therapy additionally improves QoL [ 90 , 91 ]. Moreover, the results indicate reduced QoL even before disorder onset, highlighting the relevance of early preventive measures in vulnerable groups. In line with this, studies on school-based prevention programs show a significant reduction in anxiety and depressive symptoms [ 92 , 93 ], and psychosocial prevention programs may additionally improve QoL [ 94 ].

During the COVID-19 pandemic, it is of high relevance to tackle the arising challenges associated with this pandemic. For example, it is important to face the high prevalence rates of both depression and anxiety with appropriate measures.

Acknowledgments

The authors would like to thank Elzbieta Kuzma for her consultation (Albertinen-Haus Centre for Geriatrics and Gerontology, University of Hamburg, Hamburg, Germany; University of Exeter Medical School, Exeter, UK).

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijerph182212022/s1 , Table S1: detailed descriptive information for included studies ( n = 47); Figure S1: forest plot for differences in SF MCS at FU among adults with and without depressive disorders at BL; Figure S2: forest plot for differences in SF PCS at FU among adults with and without depressive disorders at BL; Figure S3: forest plot for differences in SF MCS at FU among adults with and without depressive disorders at BL (sensitivity analysis); Figure S4: forest plot for differences in SF PCS at FU among adults with and without depressive disorders at BL (sensitivity analysis); Figure S5: forest plot for BL differences in SF MCS among adults with MD at BL with and without remitting courses over time; Figure S6: forest plot for BL differences in SF PCS among adults with MD at BL with and without remitting courses over time; Figure S7: forest plot for FU differences in SF MCS among adults with depressive disorders at BL with and without remitting courses; Figure S8: forest plot for FU differences in SF PCS among adults with depressive disorders at BL with and without remitting courses; Figure S9: forest plot for FU differences in SF MCS among adults with anxiety disorders at BL with and without remitting courses; Figure S10: forest plot for FU differences in SF PCS among adults with anxiety disorders at BL with and without remitting courses.

Author Contributions

J.K.H.: conceptualization of research question; development of search strategy; study screening and selection; risk of bias/quality assessment; study synthesis; writing—original draft, review and editing; H.-H.K.: conceptualization of research question; writing—review and editing; E.Q.: study screening and selection; risk of bias/quality assessment; writing—review and editing; A.H.: conceptualization of research question; development of search strategy; study screening and selection (third party); study synthesis; writing—review and editing. All authors have read and agreed to the published version of the manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Institutional Review Board Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

  • Research article
  • Open access
  • Published: 06 November 2020

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 (QOL) is an important concept in the field of health and medicine. QOL is a complex concept that is interpreted and defined differently within and between disciplines, including the fields of health and medicine. The aims of this study were to systematically review the literature on QOL in medicine and health research and to describe the country of origin, target groups, instruments, design, and conceptual issues.

A systematic review was conducted to identify research studies on QOL and health-related quality of life (HRQOL). The databases Scopus, which includes Embase and MEDLINE, CINAHL, and PsycINFO were searched for articles published during one random week in November 2016. The ten predefined criteria of Gill and Feinstein were used to evaluate the conceptual and methodological rigor.

QOL research is international and involves a variety of target groups, research designs, and QOL measures. According to the criteria of Gill and Feinstein, the results show that only 13% provided a definition of QOL, 6% distinguished QOL from HRQOL. The most frequently fulfilled criteria were: (i) stating the domains of QOL to be measured; (ii) giving a reason for choosing the instruments used; and (iii) aggregating the results from multiple items.

QOL is an important endpoint in medical and health research, and QOL research involves a variety of patient groups and different research designs. Based on the current evaluation of the methodological and conceptual clarity of QOL research, we conclude that the majority QOL studies in health and medicine have conceptual and methodological challenges.

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Introduction

Quality of life (QOL) has become established as a significant concept and target for research and practice in the fields of health and medicine [ 1 ]. Traditionally, biomedical and not QOL outcomes have been the principal endpoints in medical and health research. However, during the past decades, more research has focused on patients’ QOL, and the use of QOL assessments has increased [ 2 ].

Understanding QOL is important for improving symptom relief, care, and rehabilitation of patients. Problems revealed by patients’ self-reported QOL may lead to modifications and improvement in treatment and care or may show that some therapies offer little benefit. QOL is also used to identify the range of problems that can affect patients. This kind of information can be communicated to future patients to help them anticipate and understand the consequences of their illness and its treatment. In addition, cured patients and long-term survivors may have continuing problems long after their treatment is completed. These late problems may be overlooked without QOL assessment. QOL is also important for medical decision-making because QOL is a predictor of treatment success and is therefore of prognostic importance. For instance, QOL has been shown to be a strong predictor of survival [ 1 ]. This prognostic ability suggests that there is a need for routine assessment of QOL in clinical trials [ 1 ].

Despite the importance of QOL in health and medicine, there is a continuing conceptual and methodological debate about the meaning of QOL and about what should be measured. There is no uniform definition of the concept; however, The World Health Organization (WHO) outlines one definition of QOL; “An individual’s perception of their position in the in the life in the context of the culture in which they live and in relation to their goals, expectations, standards and concerns” [ 3 ].

Moreover, the term health-related quality of life (HRQOL) is often described as: “A term referring to the health aspects of quality of life, generally considered to reflect the impact of disease and treatment on disability and daily functioning; it has also been considered to reflect the impact of perceived health on an individual’s ability to live a fulfilling life. However, more specifically HRQOL is a measure of the value assigned to duration of life as modified by impairments, functional states, perceptions and opportunities, as influenced by disease, injury, treatment and policy” [ 4 ].

QOL is a complex concept that is interpreted and defined in a number of ways within and between various disciplines. As a consequence, many different instruments are now used to assess QOL. These instruments were developed based mainly on empirical considerations and have not been developed from a definition or a conceptual model. Consequently, there is a lack of conceptual clarity about what QOL means and measures, which may pose a threat to the validity of QOL research [ 1 ].

Several conceptual and methodological analyses of QOL have been published [ 1 , 5 , 6 , 7 , 8 ]. For instance, with the aim of determining the range of conceptual and methodological rigor of studies and of identifying temporal trends, Bratt and Moons [ 7 ] conducted a systematic literature review of all empirical studies of QOL in patients with congenital heart disease published since 1974. They applied ten review criteria that had been previously developed by Gill and Feinstein in 1994 [ 5 ] and further refined by Moons et al. in 2004 [ 8 ]. Bratt and Moons found slight but nonsignificant temporal improvements in conceptual and methodological rigor and in the use of assessment methods. However, most of the papers had substantial conceptual and methodological deficits. Despite 40 years of research on QOL in people with congenital heart disease, the review identified the prevalence of major weaknesses in the methodological rigor. We reasoned that this might also be the case in research on QOL in general medical and health research. Therefore, the aim of the present study was to perform a systematic review of QOL research in the fields of medicine and health, and to describe the country of origin, target groups, instruments, design, and conceptual issues in the current research.

The review was designed as a systematic review with a short time frame, which was limited to one random week (a “snapshot”). Because a high number of QOL articles are published every year, it is not possible to review all. Therefore, a random selection can give a good picture of QOL research. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement) checklist to ensure rigor in conducting and the reporting of this systematic review [ 7 ]. The checklist comprises 27 items including those deemed essential for transparent reporting of systematic reviews. To evaluate the conceptual and methodological rigor, we used the same ten predefined criteria developed by Gill and Feinstein [ 5 ] and refined by Moons et al. [ 8 ].

Data search

Systematic literature searches for publications referring to QOL or health-related quality of life (HRQOL) were conducted in collaboration with a trained librarian. To ensure broad coverage, the search term used was “ Quality of life OR Health - related quality of life .” We searched for publications published during a randomly chosen week from November 19–26, 2016. The actual search was performed on November 26, and we searched for “the last 7 days” in the databases Scopus, which covers Embase and MEDLINE, CINAHL, and PsycINFO. The Scopus database allowed us to search for specific dates. The search resulted in 364 publications. To ensure that this week was not unique in terms of the number of articles published, we performed the same search strategy using the same databases for a random week 2 months later, in January 2017, which yielded a similar number of publications ( n  = 383).

Eligibility criteria

The inclusion and exclusion criteria were developed a priori. A data extraction form was created before the review to identify the key characteristics of studies that met the criteria for inclusion. The main inclusion criteria were that QOL or HRQOL should be mentioned in the title or abstract and that the included studies should be peer-reviewed original research publications. The exclusion criteria were: conference abstract, non-English publication, editorial, opinion article, scientific statement, guideline, protocol, or review article.

Data selection process

The literature searches resulted in 364 publications. After removing duplicates, 349 papers were eligible for screening. Twenty-four QOL researchers participated in the screening process, and all papers were screened independently by title and abstract by two reviewers, who worked in pairs. In total, 186 publications were excluded during the screening process. The remaining 163 publications were included, read in full, and then independently reviewed and scored by the two reviewers before agreeing in a consensus meeting. In case of disagreement, consensus was achieved by three main investigators, one of whom was involved in the original review. A flowchart detailing the study selection and inclusion is shown in Fig.  1 (An online supplement with all references is included in the appendix).

figure 1

Source: Reproduced From Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta- Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. https://doi.org/10.1371/journal.pmed1000097 . For more information, visit https://www.prisma-statement.org .

Flow chart of inclusion.

Data extraction forms to register the key characteristics of the studies were used, and the following variables were registered: country, study design, number of participants, age groups (children or adults), and QOL instrument(s) used.

Review criteria

In accordance with the aim of the study, we reviewed the included QOL publications in terms of country, study design, number of participants, age groups (children or adults), and QOL instrument(s) used. In addition, we reviewed the publications regarding how they dealt with conceptual issues and methodology [ 6 ] according to the criteria presented in Table  5 .

Description of QOL publications

Search results.

The studies included in this review all used QOL and/or HRQOL as a concept. Of the included studies, 60 were from Europe and had been conducted in 17 different European countries. The Netherlands had the most with nine studies, and Spain and Germany had eight studies each; 47 studies were from North America (USA and Canada), and 41 were from Asian countries (Table  1 ).

Sixty-one (38%) of the included studies had an experimental design involving either a randomized controlled trial (RCT) design or a quasi-experimental design. Fifty studies had a cross-sectional or descriptive design, and 37 had a cohort or longitudinal design. Six of the studies had a case-control design, seven studies were methodological or validation studies, one study had a qualitative design, and one study had a mixed-methods design (Table  2 ).

In 20 of the studies, the sample was children and/or adolescents. The other 143 studies included adults. The most prevalent patient groups studied were those with cancer (34 studies), mental illness (12 studies), heart disease (11 studies), gastrointestinal disease (11 studies), and chronic obstructive pulmonary disease (COPD) or asthma (seven studies). Seven studies included community samples or normal populations, and seven studies included older adults (Table  3 ).

The 163 papers reviewed used 51 different questionnaires, which were both generic and disease specific. Generic QOL questionnaires were used in 66 of the studies of adults. The generic instruments most commonly used were the Short Form-36 (SF-36), EQ 5D, EORTC QLQ C-30, WHOQOL-BREF, and SF-12. Child-specific instruments were used in most of the studies on children, although four studies used questionnaires for adults. Of the child-specific instruments used, 12 were generic and four were disease specific. The PedsQL was used most frequently. An overview of the instruments used is given in Table  4 .

Evaluation according to the criteria

The evaluation of methodological and conceptual quality or rigor according to the criteria of Gill and Feinstein [ 5 , 8 ] (Table 5) revealed that 22 (13%) of the 163 studies provided a definition of the concept QOL (criterion 1). In 57 of the papers (35%), the investigators stated the domains they measured as part of QOL (criterion 2). In 41 of the papers (25%), the investigators gave a specific reason for the choice of instrument to measure QOL (criterion 3). In 88 (53%) of the studies, the investigators had aggregated results from multiple items, domains, or instruments into a single composite score for QOL (criterion 4). However, few studies (9%) fulfilled criterion 5, concerning whether patients were asked to give their own global rating of QOL by a single item at the end of the questionnaire.

For criterion 6, in 11 (6%) of the included articles, QOL was distinguished from HRQOL. Evaluation of the studies showed that criteria 7–10 were not fulfilled; none of the studies provided an option for the participants to select additional items that are important to them. However, in one study, the respondents could indicate which of the given items are personally important to them, but the importance rates were not incorporated into the overall score.

The findings of this systematic snapshot review show that QOL research is truly international, involves a variety of target groups, and uses different research designs and many types of QOL measures. Moreover, few of the included studies provided a definition of the concept of QOL, and most articles had a low-quality score according to the criteria of Gill and Feinstein [ 5 , 8 ].

However, some trends were apparent. Studies of QOL have been conducted in all parts of the world, but the USA has the most published articles, followed by China. Several European countries follow; and if taken as a whole, Europe has produced more studies than the USA. Only three studies have been published from African countries. These trends suggest that QOL research is being conducted mainly in developed countries. A Chinese review of QOL studies from 2009 commented that such studies in China were rare and that the research was conducted predominantly in the West [ 9 ]. Shek [ 9 ] argued that this can be explained by the socioeconomic and political circumstances, in addition to cultural differences, such as different sets of values and philosophical foundations. It is possible that the concept of QOL is understood differently in different cultures, and the relevance from the cross-cultural context is unclear. Therefore, it is of interest to conduct more QOL studies in Asian and other non-Western cultures to understand QOL and its manifestation from the cross-cultural context. Our snapshot review suggests that the situation is changing and that QOL research is expanding in China.

The studies included in our review show that QOL research has involved primarily patient groups with specified diseases, especially different kinds of cancer and other long-term diseases. Improved medical treatment means that more people are living with disease and chronic conditions. This has led to an increasing interest in QOL research by focusing not only on treatment options and effect, but also on the effects on people’s lives. Fewer studies have focused on community samples and children. Only 12% of the included studies involved children or adolescents. There are several possible explanations for the focus on adults, primarily that the prevalence of disease and long-term conditions is much lower in children than in adults. There are also challenges in the assessment of QOL in children and adolescents, including conceptual, methodological, and practical aspects. Ravens-Sieberer et al. [ 10 ] identified issues such as the relevance and age-appropriate tools to measure QOL in children, challenges in using proxy-rated QOL measures in children, and cross-cultural comparison of the dimensions of QOL.

The research designs of the included studies included descriptive, longitudinal, and experimental designs. QOL is increasingly used as an endpoint in clinical trials, often as part of an evaluation of different treatment or intervention outcomes. It is noteworthy that many of the interventions described in the included studies are not intended to increase QOL and therefore, QOL appears as an important, but secondary, outcome. Including QOL as a secondary outcome emphasizes the importance of such issues when assessing the benefits of different treatment options; that is, researchers are interested in both the medical outcomes as well as the effects of treatment on patients’ lives. This can provide information to clinicians and policymakers about how best to prioritize and allocate resources within health care.

One of the critiques of QOL research is the lack of conceptual clarity and a uniform definition of QOL [ 6 ]. Using a clearer and definitive definition of QOL research and research that includes QOL measures may increase the conceptual understanding, which will help researchers plan and conduct more rigorous QOL research studies [ 6 ].

Only one study in the review had a mixed-methods design, and only one was purely qualitative. Mixed methods involve the collection and analysis of both quantitative and qualitative data [ 11 ]. Traditionally, QOL research has been quantitative and there are few qualitative studies, although during the past years, an increasing number of qualitative QOL studies have added an important dimension to QOL research [ 12 ]. However, because of the few qualitative studies and the limited search (1 week), we have not been able to identify whether the number of qualitative studies has increased in recent years.

QOL measures can be categorized into three subtypes according to the type of report (self-report vs. proxy report), scores (single indicator, profile, or battery approach), and population (generic vs. condition specific), which allows for classification based on the scope and applicability of the study [ 13 ]. This review found that a diverse number of different measures are used to evaluate QOL. Most of the studies included a condition-specific measure, which is not surprising given that various disease populations were the target groups in most of the included studies. Generic measures of QOL are used either alone or in combination with a condition-specific instrument. Using both generic and condition-specific instruments has an advantage, because generic instruments can be used to compare QOL between health conditions, and condition-specific measures specifically address the health condition and appear to be more clinically relevant [ 14 ]. The choice of the type of measure clearly depends on the aim(s) of the study. The findings of our review indicate that a measure seems to exist for every disease. The challenge is to find instruments that can be widely used but have good psychometric properties for every health condition. The generic measures used in the included studies are well known and widely used and have been well validated across cultures. Examples are the SF-36, EQ-5D, and WHOQOL-BREF for adults, and Kidscreen, CHQ, and PedsQL for children.

QOL research has been criticized for a lack of conceptual clarity and clear definition of QOL [ 8 , 15 , 16 , 17 ]. In this snapshot review, most articles had a low-quality score according to the criteria of Gill and Feinstein [ 5 , 8 ]. Surprisingly, only 13% of the articles provided a definition of the concept of QOL. This is lower than that reported in the survey of Bratt and Moons [ 7 ], which found that 27% of the studies of congenital health disease from 2005 to 2014 provided a definition of QOL. A definition of QOL should state clearly what the authors mean by QOL and how it is related to other concepts [ 18 ]. The criteria fulfilled most frequently in our study were stating the domains of QOL to be measured, giving a reason for choosing the instruments used, and aggregating the results from multiple items. This is consistent with the results of Bratt and Moons [ 7 ]. It is important to give the reason for choosing an instrument. Valid measurements methods require that the instruments employed are suitable for the intended task [ 7 ]. Our results showed that in 25% of the studies, the authors gave reasons for choosing an instrument. For instance, pointed Hubert-Dibon et al. [ 17 ] out that they chose the KIDSCREEN-27questionnaire because the instrument provides a broad perspective on understanding of HRQOL, it includes five dimensions and requires only 10–15 min to complete, but still permits evaluation of the main components of HRQOL [ 17 ]. However, few studies have distinguished QOL from HRQOL, only 6% of the articles found in our study did so. According to Moons et al. [ 19 ], it is important to report and state clearly whether overall QOL or HRQOL has been measured. The majority of the included studies measured HRQOL, and only few articles distinguished between the terms. Cuerda et al. [ 20 ] argued for instance that they preferred to study HRQOL because it is a dynamic variable, which evaluates the subjective influence of health status, health care, and preventive health activities [ 20 ]. The terms health, HRQOL, and QOL are often used interchangeably in the literature. However, these terms have different definitions and intended use, and it is problematic that some researchers fail to distinguish between them. Further, it is debated whether many of the instruments used to measure HRQOL actually measure self-perceived health status and that the term (HR)QOL is unjustified [ 21 ].

Based on our evaluation of methodological and conceptual clarity, we conclude that most QOL studies in health and medicine have conceptual and methodological limitations. In general, theories and theoretical frameworks improve the understanding of QOL. The use of theoretical perspectives in empirical research deepens understanding and can help to establish new knowledge about QOL [ 22 ]. Theory is a presupposition for the ability to compare results from different studies and is important in the development and testing of QOL measures. Basing research on theory also improves the conceptual clarity and therefore the validity of the measures. The application of theoretical thinking leads to hypothesis generation, which makes research cumulative instead of atomistic. However, theoretical thinking needs to be interwoven in all stages of research. Its absence might engender a static concept of QOL by continuing to test the same parameters. Both qualitative and theoretical approaches to QOL are needed to open up the concept for discussion and change.

Strengths and limitations

One strength of this snapshot is that we searched widely in databases: Scopus, which covers Embase and MEDLINE, CINAHL, and PsycINFO. Another strength is that the selection process and review were performed independently by pairs of researchers and that agreement was reached in a consensus meeting.

However, the present study has some limitations. First, this study was designed as a snapshot and aimed to analyze and describe QOL research in one random week. Admittedly, a snapshot of a single week might not be representative of QOL research in general. However, a large number of QOL studies are published every year. A random selection can give a good picture of QOL research. To ensure that this week was not unique in terms of the number of articles published, we performed the same search strategy of the same databases for one random week 2 months later, and this search yielded nearly the same number of articles and showed the same trends in the type of articles, countries of origin, and study design. Second, searches were limited to English language only. It is possible that similar studies may have been published in other languages than English.

Third, the criteria used were developed in 1994, and one may question whether these remain relevant in 2018. However, the criteria were refined by Moons in 2004 and, to our knowledge, no other criteria for assessing the conceptual rigor in QOL studies have been published.

Knowledge about QOL is important for understanding the consequences of illness and treatment, and for medical decision-making across age groups and culture. QOL is an important endpoint in medical and health research, and QOL research involves a variety of target groups and research designs. However, based on the current evaluation of the methodological and conceptual clarity of QOL research, we conclude that many QOL studies in health and medicine have conceptual and methodological challenges. There is a need for improvements in this field, and researchers should pay closer attention to methodological and conceptual issues when planning QOL studies.

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Haraldstad, K., Wahl, A., Andenæs, R. et al. A systematic review of quality of life research in medicine and health sciences. Qual Life Res 28 , 2641–2650 (2019). https://doi.org/10.1007/s11136-019-02214-9

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Quality of life issues in patients with bone metastases: A systematic review

Affiliations.

  • 1 Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
  • 2 Department of Oncology, Princess Margaret Hospital, Hospital Authority, Kowloon, Hong Kong.
  • 3 Department of Radiation Oncology, Bank of Cyprus Oncology Centre, Nicosia, Cyprus.
  • 4 Department of Radiation Oncology, University of Lübeck, Lübeck, Germany.
  • 5 Department of Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
  • 6 Department of Radiation Oncology, National University Cancer Institute, National University Hospital, Singapore, Singapore.
  • 7 Department of Clinical Oncology, Tuen Mun Hospital, New Territories West Cluster, Hospital Authority, New Territories, Hong Kong.
  • 8 The University of Manchester, Manchester, UK.
  • 9 Department of Clinical Oncology, The Christie HNS Foundation Trust, Manchester, UK.
  • 10 Department of Radiation Oncology, University Hospitals Leuven, Louvain, Belgium.
  • 11 Radiotherapy Oncology Centre, Santa Maria Hospital, Terni, Italy.
  • 12 Department of Medicine and Surgery, University of Perugia, Perugia, Italy.
  • 13 Radiation Oncology Unit, Department of Biomedical, Dental Science and Morphological and Functional Images, University of Messina, Messina, Italy.
  • 14 Department of Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
  • 15 Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China.
  • 16 Department of Radiation Oncology, Hospital Sírio-Libanês, Sao Paulo, Brazil.
  • 17 Department of Orthopedics, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • 18 Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, the Netherlands.
  • 19 Department of Radiotherapy, Leiden University Medical Center, University of Leiden, Leiden, Holland.
  • 20 Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada. [email protected].
  • PMID: 38091116
  • DOI: 10.1007/s00520-023-08241-0

Introduction: Bones are frequent sites of metastatic disease, observed in 30-75% of advanced cancer patients. Quality of life (QoL) is an important endpoint in studies evaluating the treatments of bone metastases (BM), and many patient-reported outcome tools are available. The primary objective of this systematic review was to compile a list of QoL issues relevant to BM and its interventions. The secondary objective was to identify common tools used to assess QoL in patients with BM, and the QoL issues they fail to address.

Methods: A search was conducted on Ovid MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials databases between 1946 and 27 January 2023 with the keywords "bone metastases", "quality of life", and "patient reported outcomes". Specific QoL issues in original research studies and the QoL tools used were extracted.

Results: The review identified the QoL issues most prevalent to BM in the literature. Physical and functional issues observed in patients included pain, interference with ambulation and daily activities, and fatigue. Psychological symptoms, such as helplessness, depression, and anxiety were also common. These issues interfered with patients' relationships and social activities. Items not mentioned in existing QoL tools were related to newer treatments of BM, such as pain flare, flu-like symptoms, and jaw pain due to osteonecrosis.

Conclusions: This systematic review highlights that QoL issues for patients with BM have expanded over time due to advances in BM-directed treatments. If they are relevant, additional treatment-related QoL issues identified need to be validated prospectively by patients and added to current assessment tools.

Keywords: Patient-reported outcome measures; Quality of life; Secondary bone neoplasms; Systematic review.

© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Publication types

  • Systematic Review
  • Anxiety / therapy
  • Bone Neoplasms* / secondary
  • Pain / etiology
  • Quality of Life*

Grants and funding

  • 005/2023/European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Group (QLG)

Quality Of Life Research Paper

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1. Quality Of Life

Since the 1980s or so there has been an increasing realization that traditional biologically based endpoints such as morbidity and mortality alone do not represent adequately the potential outcomes of medical interventions. Health status measurement has evolved to allow insight into patients’ experiences in such areas of function as mobility, mood, life satisfaction, sexuality, cognition, and ability to fulfil occupational, social, and family roles. Quality of life (QoL) has emerged as a broad term to describe this domain of measurement. The QoL construct may be viewed as a paradigm shift since it shifts the focus of attention from symptoms to functioning and establishes the primacy, or at least the legitimacy, of the patient perspective. QoL measures have many applications in medicine and healthcare. They are used to describe the subjectively perceived health and social status of given populations, to compare interventions and to assess the costs and benefits of treatments and health policies (Spilker 1996). Despite the increasing popularity of QoL, there is dissent about the meaning of the term, how it should be measured, and indeed whether it should be measured at all (Hunt 1997). In a critical review, Gill and Feinstein (1994) found that investigators defined what they meant by the term QoL in only 15 percent of the 75 articles reviewed. As Humpty Dumpty said to Alice: ‘when I use a word, it means exactly what I want it to mean, nothing more and nothing less!’

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Get 10% off with fall23 discount code, 2. health-related qol.

Because of the amorphous and multidimensional nature of QoL, most researchers in medicine and healthcare concern themselves with a subcomponent of QoL called health-related QoL (HRQoL). This is distinguished from QoL as a whole, which would also include adequacy of education, housing, income, and perceptions of the immediate environment. Health status and HRQoL measures increasingly are used in studies addressing the costs and benefits of services and treatments (Patrick and Erickson 1993). The US Congress established the Agency for Health Care Policy and Research to undertake research on the effectiveness of medical care in terms of its impact on patient outcomes including HRQoL. The US Food and Drug Administration encourages the collection of HRQoL data for new drugs and, in the UK, health status and the experiences of patients and carers are included explicitly in outcome assessments.

3. Definition Of Health-Related QoL

Patrick and Erickson (1993) defined HRQoL as:

the value assigned to the duration of life as modified by the social opportunities, perceptions, functional states and impairments that are influenced by disease, injuries, treatments or policy.

Assessment of HRQoL usually focuses on physical function, psychological state, social function, somatic symptoms, sexual function, occupational function, and, occasionally, on financial state. Many existing measures frequently are criticized for including only concepts at the negative or illness end of the continuum while neglecting concepts at the more positive end.

4. Assessment Of Health-Related QoL

There are a number of approaches to the assessment of HRQoL. Most of these involve some form of subjective multidimensional assessment preferably completed by the respondent whose QoL is under scrutiny since levels of agreement among doctors and between doctors’ and patients’ judgments are usually low (Sprangers and Aaranson 1992). Measures include generic instruments such as health profiles and utility measures, disease-specific questionnaires, and individually focused measures.

4.1 Generic Measures

Generic measures, such as the SF-36, The Nottingham Health Questionnaire, the McMaster Health Index Questionnaire, and The Sickness Impact Profile are broadly-based questionnaires and can be applied in a wide range of conditions and populations. The main limitation of generic questionnaires is that they are broad measures of health status. Consequently, they may not provide sufficient information on specific aspects of a given disease which are likely to be important in assessing disease impact and the outcome of treatment. It is common practice to use a generic measure together with a disease-specific measure. It is likely that the use of generic instruments will diminish as more sophisticated disease-specific measures are developed.

4.2 The World Health Organization QoL Instrument (WHOQOL)

The concept of HRQoL owes much to the original World Health Organization (WHO) definition of health as a state of complete physical, mental, and social well-being and not merely the absence of disease. The WHO established a multinational group to develop a measure that would allow for cross-cultural comparisons of QoL (WHOQOL 1993). The definition underlying the measure was:

QoL is defined as the 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, standards and concerns. It is a broad ranging concept affected in a complex way by a person’s physical health, psychological state, level of independence and their relationships to salient features of their environment.

The WHOQOL is a somewhat unusual measure of HRQoL because, in addition to measuring physical and psychological health and social relationships, it also measures spirituality and it deals with facets of the environment such as financial resources and home environment. In developing the WHOQOL, the authors unexpectedly found that the basic factors inherent in QoL did not differ substantially across cultures (Power et al. 1999).

4.3 Utility Measures

Utility measures are generic measures derived from economics and decision theory and represent patients’ preferences for different health states in the form of a single summary score. Their key characteristics are that they are based on preference judgments and usually are measured along a continuum from death (0.0) to perfect health (1.0), although scores less than 0 representing states worse than death are possible. Utility scores represent both health status and the value of that health status to the patient. They are used in cost-utility studies in which the cost of an intervention is related to the utility value afforded by the intervention. Various approaches to measuring utilities have been developed. In the Kaplan Index of Well-being Scale, case descriptions were compiled to illustrate combinations of functional levels, symptoms or problems and these cases were rated by random samples of the public who gave their preference ratings which were then used to determine utility values. In the somewhat controversial QALY (quality-adjusted life years) approach, improvements in the length and quality of life are amalgamated into a single index. In the standard-gamble approach, patients are asked to assess the risks they would tolerate for certain medical interventions. In the time-trade-off method, respondents are asked to estimate their relative preference for quality vs. quantity of life.

The utility approach assumes that individuals are capable of reporting their preferences and that they can predict accurately these preferences in relation to some future putative health scenario. It is further assumed that the preferences would not change if the patient were actually to experience such a health scenario. An additional problem relates to who should provide the utility values—the general public, healthcare providers, and/or patients and their families. One response to these problems has been the development of the Euroqol (Euroqol Group 1990). The aim of the Euroqol is to provide a standardized, nondisease specific survey instrument for describing HRQoL and to generate a single health index for each health state. The measure, however, suffers from a number of logistical and methodological limitations (Bowling 1995).

4.4 Disease-Specific Measures

The second approach to QoL assessment focuses on aspects of health status that are specific to the area of primary interest. It is assumed that responsiveness will be increased by including only important aspects of HRQoL that are relevant to the patients being studied. The measure may be specific to a disease (such as cancer or arthritis), to a population of patients (frail elderly), to a certain function (sexual function or sleep), or to a particular problem (such as pain). Specific measures focus on areas that are likely to be explored routinely by clinicians and are likely to be more responsive to small but clinically significant changes. The modular approach adopted by the European Organization for Research on Treatment of Cancer (EORTC) provides a core generic measure for use in a variety of cancers supplemented by diseasespecific modules for use in particular types of cancer.

4.5 Limitations Of Health-Related QoL

The restriction of QoL assessment to HRQoL and the use of health-status measures to assess HRQoL is not entirely satisfactory. Most of the questionnaires used are essentially measures of health status in a new guise and these place an overwhelming reliance on the assessment of functional capacity. As such, while purporting to incorporate patients’ perspectives, they represent implicitly a medical model that stresses the ability to perform everyday tasks and fulfil social and occupational roles. Such measures ignore the meaning and importance of such tasks and roles for the individual and often preserve the supremacy of professional judgements leading to the suppression of what is supposed to be under scrutiny (Joyce et al.1999, Hunt 1997).

5. Individual QoL Measures

A number of researchers have argued that QoL depends on the unique interpretation and perceptions of the individual and that questionnaires represent an oversimplification of what is a complex, multidimensional, subjective phenomenon. QoL is seen as a uniquely personal perception, denoting the way that individuals feel about their health status and/or nonmedical aspects of their lives. It can be measured suitably only by determining the opinions of patients and by supplementing (or replacing) the instruments developed by ‘experts.’ This/hermeneutic approach would seek to understand individuals as self-reflective beings who are responsible for their own actions and who are the best judges of their QoL. This approach has resulted in the development of a number of individualized measures of QoL such as the Schedule for the Evaluation of Individual QoL or SEIQoL (Joyce et al. 1999).

6. Future Trends And The Need For Theoretical Models

Assessment of patient QoL facilitates improved clinical intervention, assists in treatment comparisons, and should prove increasingly important in the identification of services and facilities and in resource allocation. However, much of the research, to date, has been underpinned by somewhat tautological operational definitions of QoL. There is a growing need for theoretical models that capture the psychological reality and complexity of QoL. For example, most current measures fail to address adequately the dynamic nature of QoL. Patients confronted with a life-threatening or chronic disease are faced with the necessity to accommodate to their illness. An important mediator of this adaptation process is ‘response shift’ which has been defined by Schwartz and Sprangers (2000) as changing one’s internal standards, values, or one’s conceptualization of QoL. Response shift is but one example of a psychological process that underpins the essentially dynamic and phenomenological nature of QoL. Research in this area must incorporate psychological variables into theory construction in order to produce a body of research that more validly reflects the true nature of QoL.

QoL has, heretofore, been considered solely as an outcome measure in health research. However, exciting developments in psychoneuroimmunology and evidence from a number of clinical studies raise the intriguing possibility that QoL might influence pathological processes such as tumor progression. If this were proven, then interventions aimed at maximizing patient QoL might also influence patient recovery.

Bibliography:

  • Bowling A 1995 Measuring Disease: A Review of Disease Specific Quality Of Life Measurement Scales. Open University Press, Buckingham, UK
  • Euroqol Group (Buxton M, O’Hanlon M, Pekurinen M ym) 1990 Euroqol: A new facility for the measurement of health-related quality of life. Health Policy 16: 199–208
  • Gill T M, Feinstein A R 1994 A critical appraisal of the quality of QoL measurements. Journal of the American Medical Association 272: 619–26
  • Hunt S M 1997 The problem of QoL. Quality of Life Research 6: 205–12
  • Joyce C R B, O’Boyle C A, McGee H M 1999 Individual QoL: Approaches to Conceptualization and Assessment. Harwood, The Netherlands
  • Patrick D L, Erickson P 1993 Health Status and Health Policy. Oxford University Press, Oxford, UK
  • Power M, Bullinger M, Harper A, WHOQOL Group 1999 The World Health Organization WHOQOL-100: Tests of the universality of life in 15 different cultural groups worldwide. Health Psychology 18: 495–505
  • Schwartz C E, Sprangers M A G 2000 Adaptation to Changing Health: Response Shift in QoL Research. American Psychological Association, Washington, DC
  • Spilker B (ed.) 1996 Quality-of-life and Pharmacoeconomics in Clinical Trials, 2nd edn. Lippincott-Raven, Hagerstown, MD
  • Sprangers M A G, Aaranson N K 1992 The role of health care providers and significant others in evaluating the quality of life of patients with chronic disease: A review. Journal of Clinical Epidemiology 45: 743–60
  • WHOQOL Group 1993 Measuring the Quality of Life: The Development of the World Health Organization Quality of Life Instrument. WHO, Geneva, Switzerland

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  1. Quality of Life Research

    Quality of Life Research An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation - An Official Journal of the International Society of Quality of Life Research Publishing model Hybrid Submit your manuscript Editorial board Aims and scope Journal updates Overview

  2. A systematic review of quality of life research in medicine and health

    Purpose. Quality of life (QOL) is an important concept in the field of health and medicine. QOL is a complex concept that is interpreted and defined differently within and between disciplines, including the fields of health and medicine. The aims of this study were to systematically review the literature on QOL in medicine and health research ...

  3. Quality Of Life

    For example, common facets of QoL include personal health (physical, mental, and spiritual), relationships, education status, work environment, social status, wealth, a sense of security and safety, freedom, autonomy in decision-making, social-belonging and their physical surroundings.

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    A meta-analysis showed a prevalence of anxiety of about 32% (95% CI: 28-37) and a prevalence of depression ( n = 14 studies) of about 34% (95% CI: 28-41) in general populations during the COVID-19 pandemic [ 8 ].

  5. Quality of life and mortality in the general population: a systematic

    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....

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    Karen Mogendorff Quality of life in ICU survivors from 1991 to 2022: a bibliometric analysis based on CiteSpace Limei Fan Centering prayer in the treatment of Parkinson's disease preliminary...

  7. Applied Research in Quality of Life

    Applied Research in Quality of Life (ARQOL) presents conceptual, methodological and empirical papers dealing with quality-of-life studies in the applied areas of the natural and social sciences. It aims to publish papers that have direct implications for, or impact on practical applications of research on the quality of life.

  8. Quality of Life Research

    Issue 1 supplement March 2023 Proceedings of the 6th National Patient Reported Outcome Measures (PROMs) Annual UK Research Virtual Conference 2022 Issue 2 February 2023 Issue 1 January 2023 Volume 31 January - December 2022 Issue 12 December 2022 Issue 11 November 2022 Issue 2 supplement November 2022

  9. A systematic review of quality of life research in medicine ...

    PMCID: PMC6761255 DOI: 10.1007/s11136-019-02214-9 Abstract Purpose: Quality of life (QOL) is an important concept in the field of health and medicine. QOL is a complex concept that is interpreted and defined differently within and between disciplines, including the fields of health and medicine.

  10. Applied Research in Quality of Life: A Computational ...

    As quality of life (QoL) is a highly interdisciplinary topic with a multitude of related research areas, it is beneficial to avail researchers of an overview of the different streams explored in the field. Furthermore, knowledge of prominent sub-domains helps researchers identify links and overlaps between QoL and their fields of interest. To meet these needs, a text-mining-based computational ...

  11. How is quality of life defined and assessed in published research?

    Methods: We reviewed all Quality of Life Research articles published in 2017 and recorded whether they described health-related quality of life or quality of life as constructs of interest, and/or mentioned the term (s) patient-reported outcome (measures). We recorded definitions of (HR)QOL stated and questionnaires used.

  12. Quality of Life Research

    The journal's scope reflects the wide application of quality of life assessment and research in the biological and social sciences. All original work is subject to peer review for originality, scientific quality and relevance to a broad readership. This is an official journal of the International Society of Quality of Life Research.

  13. Quality of Life Research

    Quality of Life Research is an international, multidisciplinary journal devoted to the rapid communication of original research, theoretical articles and methodological reports related to the field of quality of life, in all the health sciences. The journal also offers editorials, literature, book and software reviews, correspondence and ...

  14. Applied Research in Quality of Life: A Computational ...

    The outcome provides the reader with a list of the twelve most heavily discussed topics: 1) consumption & materialism, 2) character strength, 3) spirituality, religiousness & personal beliefs, 4) inequality, 5) leisure & tourism, 6) health related QoL (HRQoL) I, 7) quality of working life (QWL), 8) childhood & adolescence, 9) disparity & develop...

  15. Quality of Life Research on JSTOR

    Quality of Life Research is an international, multidisciplinary journal devoted to the rapid communication of original research, theoretical articles and methodological reports related to the field of quality of life in all the health sciences. The journal also publishes editorials, literature, book and software reviews, correspondence and abstracts of conferences.

  16. Quality of Life Research

    The impact factor for Quality of Life Research as of July 2012 is 2.30, and the 5-year impact factor is 2.99. The metric has been relatively stable over the past 5 years. Other indices of impact include the H5-index, which reflect the Hirsch index or h-index for the articles published in Quality of Life Research in the last 5 years. Quality of ...

  17. A systematic review of quality of life research in medicine ...

    Quality of Life Research Aims and scope Submit manuscript K. Haraldstad, A. Wahl, R. Andenæs, J. R. Andersen, M. H. Andersen, E. Beisland, C. R. Borge, E. Engebretsen, M. Eisemann, L. Halvorsrud, T. A. Hanssen, A. Haugstvedt, T. Haugland, V. A. Johansen, M. H. Larsen, L. Løvereide, B. Løyland, L. G. Kvarme, P. Moons, T. M. Norekvål, L. Ribu,

  18. (PDF) QUALITY OF LIFE

    October 2019 Authors: Liubov Ben-Noun (Nun) Ben-Gurion University of the Negev Abstract Quality of life (QOL) is a multidimensional issue. It can be categorized within five dimensions: physical...

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    The objective of this paper is to define the quality of life (QOL) and the quality of working life (QOWL) con-ceptions and their components, to establish the quality of life...

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    Quality of life (QoL) is an important endpoint in studies evaluating the treatments of bone metastases (BM), and many patient-reported outcome tools are available. The primary objective of this systematic review was to compile a list of QoL issues relevant to BM and its interventions. The secondary objective was to identify common tools used to ...

  21. Quality Of Life Research Paper

    1. Quality Of Life Since the 1980s or so there has been an increasing realization that traditional biologically based endpoints such as morbidity and mortality alone do not represent adequately the potential outcomes of medical interventions.

  22. Quality of Life Research Papers

    Quality of Life. This a an Introduction idea for a research paper on the quality of life. Research studies often contain very ambiguous variables in order to illustrate a hypothesis. Quality of life is a variable that is measured and analyzed in a variety of fields and disciplines. Quality of life may be a variable in the following different ...