(i) Were observational studies analyzing the longitudinal association between anxiety or depression (disorders as well as symptom severity) and QoL,
(ii) Analyzed samples without a specific disease or disorder other than anxiety and depression,
(iii) Applied appropriate, validated measures for the main variables (e.g., for anxiety/depression: psychiatric diagnosis according to criteria of the International Classification of Diseases (ICD), the Diagnostic and Statistical Manual of Mental Disorders (DSM), or using a valid self-report screening tool), and
(iv) Were published in English or German in a peer-reviewed journal.
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
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.
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 ].
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 ).
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.
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.
First Author (Year) | Disorder or Symptoms Analyzed; QoL Domains Analyzed | Research Question Regarding QoL | Methods | Results |
---|---|---|---|---|
Årdal (2013) [ ] | Controls and patients in the acute phase of recurrent MD and FU (DSM-IV, HDRS); SF-36 (physical functioning, role physical, vitality, bodily pain, mental health, role emotional, social functioning, general health, as well as summary scores PCS, MCS and total score) | (a) Whether QoL scores differ between MD patients and healthy comparisons across domains over time. (b) Whether QoL in patients with recurrent MDD differed between acute phase and recovery. | (a) ANOVA (b) Paired-sample -tests | (a) There was a significant interaction effect between time, QoL domain and group, indicating that QoL scores differed between MD patients and controls over time. Compared to the healthy control group, the MDD group had reduced QoL in all domains at BL and reduced QoL in several domains at FU (significant for general health, social, emotional role, mental health, PCS, MCS and total score). (b) In the MD group, QoL scores significantly improved during recovery from recurrent MDD in most domains (significant for physical functioning, physical role, vitality, social functioning, role emotional, mental health, PCS, MCS and total score). |
Buist-Bouwman (2004) [ ] | Onset, acute phase and subsequent remission from MDE (CIDI); comorbid anxiety disorder (CIDI); SF-36 (physical functioning, physical role, vitality, pain, psychological health, psychological role, social functioning and general health) | (a) Whether incident MDE and recovery from MDE are associated with changes in QoL and whether pre- and post-morbid QoL scores in the MD group differ from the comparison group without MD. (b) In the subgroup with worse QoL after MDE: whether the severity of depression and number of depressive episodes were associated with worse QoL. (c) Whether comorbid anxiety during MDE is associated with reduced QoL (i.e., lower QoL after MDE compared to before MDE). | (a)–(c) Multivariate logistic regression | (a) Incident MDE was associated with a drop in QoL (significant for vitality, psychological, psychological role and social functioning). Subsequent recovery from MDE was associated with an improvement in QoL (significant for physical role, vitality, psychological health, psychological role, social functioning and general health). Comparing pre- and post-morbid levels, QoL did not differ or was higher after MDE in some domains (significantly higher for psychological health and psychological role). Moreover, before MD onset, QoL was significantly lower compared to healthy controls in all domains. After remission from MDE, QoL scores in nearly all domains (not significant for psychological role) were significantly lower compared to healthy controls. (b) About 40% of the MDE group had worse QoL after recovery from MDE compared to pre-morbid levels. The severity of depression was associated with worse QoL only for the psychological health domain, but no other domains. The number of depressive episodes was not significantly associated with worsening QoL in any domain. (c) In the MDE cohort, comorbid anxiety was associated with a significant reduction in QoL (significant for physical role and psychological health). |
Cabello (2014) [ ] | Chronic MD (AUDADIS interview; summary score of the number of symptoms to identify severity); SF-12, “disability” (i.e., domain-specific reduced QoL, defined as score ≤ 25th percentile in the subscale; physical functioning, physical role, bodily pain, general health, vitality, social functioning, emotional role and mental health) | (a) Whether chronic MD is associated with the incidence/persistence of “disability” (i.e., reduced QoL) in a general population sample. (b) Whether the severity of depressive symptoms is associated with the incidence/persistence of “disability” (i.e., reduced QoL) in the MD subgroup of the sample. | Both (a) and (b) Generalized Estimating Equations and logistic regressions | (a) In the general population, chronic MD was a significant risk factor for the persistence of disability (i.e., reduced QoL) in all domains and of the incidence of disability (i.e., reduced QoL) in all domains except for the physical role. (b) In the chronic MD subgroup, the severity of depressive symptoms was associated with the persistence of disability (i.e., reduced QoL) (significant for general health, social functioning, emotional role and mental health) and not significantly associated with the incidence of reduced QoL in any domain. |
Cerne (2013) [ ] | Number of depressive episodes over time according to CIDI; number of episodes of panic and other anxiety syndromes over time (PHQ); SF-12 (PCS, MCS) | Whether the pooled number of (a) depressive episodes over time, (b) panic and anxiety episodes over time are are associated with the pooled QoL over time. | (a) and (b) Multivariate linear regression | (a) A higher number of depressive episodes over time was associated with lower pooled PCS and MCS. (b) a higher number of pooled panic episodes over time was associated with a lower mean MCS but not PCS. A higher number of pooled other anxiety syndrome episodes over time was not associated with the mean MCS or PCS. |
Chin (2015) [ ] | Depression according to PHQ-9 (>9), clinician’s diagnosis; SF-12v2 (PCS, MCS) | (a) Whether depressive symptoms and a clinician’s detection of depression at BL are associated with QoL at FU. (b) Whether a clinician’s detection of depression at BL is associated with a change in QoL. | (a) Multivariable non-linear mixed-effects regression (b) Independent -tests | (a) Depressive symptoms and a clinician’s detection of depression at BL were not predictive of QoL at FU. (b) A clinician’s detection of depression at BL was related to change (improvement) in MCS, but not PCS over time in a primary care sample screened as positive for depression. |
Chung (2012) [ ] | Depression diagnosis and symptoms (DSM-IV, HRSD depression scale, HADS depression scale); anxiety symptoms (HRSD anxiety scale, HADS anxiety scale; SF-36 (physical functioning, role physical, bodily pain, general health, vitality, social functioning, role emotional, mental health, PCS and MCS) | (a) Whether BL depressive symptoms are associated with QoL at FU. (b) Whether BL depressive symptoms or changes in depressive symptoms are associated with changes in QoL over time. (c) Whether BL anxiety symptoms are associated with QoL at FU. (d) Whether BL anxiety symptoms or changes in anxiety symptoms are associated with changes in QoL over time. | (a)–(d) Hierarchical regression | (a) BL depressive symptoms were not associated with any QoL domain at FU. (b) BL depressive symptoms were not associated with changes in any QoL domain over time. Changes in depressive symptoms were significantly associated with changes in some QoL domains over time (significant for: general health, vitality, mental health and MCS). (c) BL anxiety symptoms were not associated with any QoL domain at FU. (d) BL anxiety symptoms were not associated with changes in any QoL domain over time. Changes in anxiety symptoms were significantly associated with changes in some QoL domains over time (significant for: bodily pain, general health and mental health). |
Diehr (2006) [ ] | Depression according to CIDI, CES-D (>16); QLDS, WHOQOL-Bref (environmental, physical, psychological and social), SF-12 (PCS, MCS) | (a) Whether the quartile of change in depressive symptoms is associated with changes in QoL. (b) Whether the remission of depression at FU is associated with changes in QoL. | Regression | (a) No/little change in CES-D associated with changes in QoL over time (significant for SF-12 MCS). Every other quartile of change in depressive symptoms was significantly associated with changes in QoL in most scales/domains (significant for: QLDS, all domains of WHOQOL-Bref and SF-12 MCS), meaning a higher reduction in depressive symptoms was associated with a higher increase in QoL, and more severe depressive symptoms were associated with a reduction in QoL. (b) Remission of depression at FU was associated with improvement in all QoL measures and domains (SF-12, QLDS and WHOQOL-Bref). There was no significant change in QoL in those with persistent clinical depression at FU. |
Hajek (2015) [ ] | Depressive symptoms (GDS); EQ-VAS | Whether an initial change in depressive symptoms is associated with a subsequent change in QoL in the whole sample and by sex. | Vector autoregressive models | No significant association between an initial change in depression score and a subsequent change in QoL was found for the whole sample or stratified by sex. |
Hasche (2010) [ ] | Depression status at BL (according to DIS diagnosis and CES-D ≥ 9); SF-8 (PCS, MCS) | (a) Whether depression status groups at BL differed according to QoL at FU. (b) Whether depression status groups at BL differed according to QoL changes in score over time. | (a) -tests (b) Linear mixed effects regression models | (a) At 6- and 12-month FU, those with and without depression at BL differed significantly in QoL scores, with the depression group reporting lower QoL at FUs (significant for MCS and PCS). (b) While depression at BL was significantly related to improvements in MCS (but not PCS) scores over time, those with depression still reported lower QoL compared to those without. |
Heo (2008) [ ] | Depression (BDI ≥ 10); SF-36 (decrease in total score over time) | Whether FU depression is associated with a reduction in QoL over time. | Binary logistic regression | Depression at FU was associated with a significant reduction in QoL total score over time. |
Ho (2014) [ ] | Depression (according to GDS ≥ 5); SF-12 (PCS, MCS) | Whether depression at BL is associated with QoL at FU. | Linear regression | BL depression was associated with lower QoL at FU (significant for MCS and PCS). |
Hussain (2016) [ ] | Depressive disorders (SCID, MINI); current PTSD, specific phobias, other anxiety disorders (SCID, MINI); WHOQOL-Bref (general QoL and hrqol) | (a) Whether current depressive disorders at BL predict QoL at FU. (b) Whether current PTSD, specific phobias and other anxiety disorders at BL predict QoL at FU. | (a) and (b) Multiple linear regression | (a) Depressive disorders at BL predicted reduced QoL at FU (significant for general QoL and hrqol). (b) PTSD, but not specific phobias or other anxiety disorders, predicted reduced general QoL at FU. None of the anxiety disorders predicted hrqol at FU. |
Joffe (2012) [ ] | Lifetime history of depression (according to SCID); anxiety disorder (according to SCID); SF-36 (impaired QoL according to 25th percentile of SF-36; social functioning, role emotional, role physical, pain and vitality) | (a) Whether a lifetime history of depression is associated with impaired QoL during FU. (b) Whether a prior lifetime history of anxiety disorder (compared to no depression or anxiety) is associated with reduced QoL during FU. (c) Whether a lifetime history of comorbid depression and anxiety is associated with impaired QoL during FU. | (a)–(c) Repeated measure multilevel regression | (a) A history of depression only was associated with reduced QoL during FU (significant for social functioning and pain). (b) Prior lifetime history of anxiety disorder was associated with reduced QoL (significant for physical role). (c) A history of comorbid anxiety and depression was associated with reduced QoL during FU (significant for social functioning, emotional role, physical role and pain). |
Johansen (2007) [ ] | Level of PTSD symptoms according to IES-15; WHOQOL-Bref (physical health, psychological health, social relationships and environment) | Whether PTSD symptoms predict QoL at FU. | Structural equation model | More severe PTSD symptoms predicted QoL at FU (significant positive association between FU1 and FU2). |
Kramer (2003) [ ] | Current or lifetime depression/PTSD (according to Q-DIS); SF-36 (energy/fatigue, emotional role, general health, mental health, pain, physical functioning, physical role and social) | Whether QoL outcomes over time differed among the disorder groups. | Random/fixed effects model | There was no significant interaction between time and diagnostic group (no depression/PTSD, PTSD, depression and comorbid depression/PTSD) on QoL. Comparing the adjusted means for all three times among the disorder groups showed significant differences between the groups in most domains. In comparison, those with depression at BL reported reduced QoL over time in several domains compared to the PTSD group and the group without PTSD/depression. In comparison, those with PTSD only showed higher QoL compared to those with depression or comorbid depression/PTSD in several domains. |
Kuehner (2009) [ ] | Depressive symptoms (MADRS); WHOQOL (overall, physical, psychological, social and environmental) | Whether the lag in levels of depressive symptoms predicts future levels of QoL and whether the association differs by group (formerly depressed inpatients vs. community controls). | Time-lagged linear models | Higher depressive symptoms predict future lower QoL (significant for social). The association was not moderated by group status. |
Kuehner (2012) [ ] | Depression score (according to MADRS, FDD-DSM-IV); WHOQOL-Bref (physical, psychological, social and environment) | Whether the lag in depressive symptoms predicted QoL at FU. | Hierarchical, time-lagged linear models | Higher depressive symptoms significantly predicted lower QoL at FU (significant for physical and psychological). |
Lenert (2000) [ ] | Remission or persistent depression (according to DSM-III criteria, DIS); SF-12 (PCS, MCS) | Whether the remission of depression (compared to no remission) is associated with changes in QoL over time. | OLS regression | Remission of depression was associated with improved QoL (significant for MCS) at FU1 and FU2. |
Mars (2015) [ ] | Asymptomatic, mild and high symptoms of depression (according to SCAN); EQ-5D (without anxiety/depression item) | Whether depression symptom trajectories over time (asymptomatic, mild symptoms and chronic–high symptoms) are associated with QoL at FU. | Latent class growth analysis with distal outcome models | QoL at FU differed significantly among different depression symptom trajectories, with persons from the the chronic–high depressive symptom class showing lower QoL scores relative to the asymptomatic class. |
Moutinho (2019) [ ] | Depression at BL (according to DASS cut-off: 9); anxiety at BL (according to DASS anxiety scale cutoff: 7); WHOQOL-Bref at FU (physical, psychological, social and environment) | (a) Whether BL depression predicted QoL at FU. (b) Whether BL anxiety predicted QoL at FU. | (a) and (b) Stepwise linear regression | (a) Depression at BL was significantly associated with reduced QoL at FU (significant for psychological functioning, social functioning and environmental). (b) Anxiety at BL was associated with reduced QoL at FU (significant for physical). |
Ormel (1999) [ ] | Depression at BL (according to CIDI); “disability” (i.e., reduced QoL according MOS SF 6-item physical functioning scale ≥ 2) | Whether depression at BL is associated with the onset of disability (i.e., reduced QoL) during FU. | Logistic regression models | Compared to the non-depressed group, people with depression at BL showed higher odds for the onset of disability (i.e., reduced QoL) during FU (significant for 12-month FU, but not 3-month FU). |
Pan (2012) [ ] | Depressive symptoms (CES-D); WHOQOL-Bref-TW (overall score, physical, psychological, social and environmental) | Whether depressive symptoms were associated with QoL over time. | Linear mixed-effects models | Higher depressive symptoms were associated with lower QoL in MDD patients (significant for overall score, physical, psychological, social and environmental). |
Panagioti (2018) [ ] | Depressive symptoms (MHI-5); WHOQOL-Bref (physical, psychological, environmental and social) | Whether depressive symptoms at BL are associated with changes in QoL over time. | Multivariate regression models | Higher depressive symptoms at BL were associated with a decline in QoL over time (significant for physical and psychological). |
Pakpour (2018) [ ] | Dental anxiety at BL (MDAS); PedsQL 4.0 general hrqol and oral hrqol scale at FU | Whether dental anxiety at BL predicted oral- and general-health-related QoL at FU. | Structural equation modeling | Dental anxiety at BL was no significant direct predictor of generic QoL at FU and was significantly associated with worse oral-health-related QoL at FU. |
Pyne (1997) [ ] | MD-diagnosis (SCID/SADS) and depressive symptoms (HAM-D); QWB | Whether group status over time (community controls, continuously non-depressed patients, incident depression patients and continuously depressed patients) is associated with changes in QoL. | Repeated measure analysis (ANOVA) | There was no significant interaction term between group status and time, indicating that changes in QoL did not differ between the groups. At both points in time, QoL differed significantly among all groups, except between the incident depression and continuous depression group. |
Remmerswaal (2020) [ ] | OCD course (SCID), Y-BOCS, BDI, BAI over time; EQ-5D over time | (a) Whether OCD symptom severity and QoL over time were associated. (b) Whether QoL over time differs between OCD course groups (chronic, intermittent and remitting) and general population norms. (c) Whether OCD symptom severity, anxiety and depressive symptoms over time are associated with changes in QoL over time in patients with OCD. | (a) Pearson’s correlation (b)–(c) Linear mixed models | (a) QoL over time and OCD symptom severity were significantly correlated. (b) The QoL of OCD patients was significantly lower compared to general population norms, except the QoL of the intermittent OCD group at FU1, where there was no significant difference compared to the general population. When comparing the OCD course groups, the chronic OCD group had a significantly lower QoL over time compared to the other groups. The remitting group had moderately improved until FU1 and a small QoL improvement between FU1 and FU2 relative to the chronic group. (c) In those with a remitting OCD, only more severe symptoms of comorbid anxiety and depressive symptoms, but not OCD symptom severity over time, were significantly associated with a lower QoL over time. |
Rhebergen (2010) [ ] | MD-/dysthymia-/DD diagnosis at BL and subsequent recovery at FU (according to CIDI); comorbid anxiety at BL (CIDI); SF-36 (physical health summary score) | Whether QoL trajectories over time differ between: (a) different depression status groups who achieved remission (MDD, dysthymia and double depression) and a comparison group without mental health disorders. (b) The different depression status groups. (c) Whether comorbid anxiety at BL in a sample recovering from depression is associated with changes in QoL. | (a)–(c) Linear mixed models | (a) There was a significant interaction between group status and time. More specifically, compared to changes in QoL over time in people without a mental health diagnosis, QoL improved over time in those with MDD and DD, but not dysthymia. All depression diagnosis groups showed a significantly lower QoL compared to the no diagnosis group at all waves. (b) Considering the depression groups, only the interaction term between dysthymia and time until FU1 was significant. Those with dysthymia had a significantly lower QoL compared to those with MDD at FU1. This difference was not significant at FU2. (c) Comorbid anxiety disorder at BL in people who recovered from depression over time was not associated with a significant change in QoL over time. |
Rubio (2014) [ ] | First episode of incident MDD (AUDADIS-IV) at FU; incident GAD, social anxiety disorder, PD, specific phobia (AUDADIS-IV); SF-12 (MCS) | Whether incident MDD is associated with changes in QoL over time compared to: (a) people without history of MDD, (b) without history of any mental health disorder, (c) and whether the association differed by gender. Whether incident anxiety disorders are associated with changes in QoL over time: (d) compared to no history of the specific anxiety disorder, (e) compared to no history of any psychiatric disorder, (f) and whether the association differed by gender. | Linear regression model | (a) Incidence of MDD (compared to no MDD) was associated with a significant decrease in QoL until FU. (b) Incidence of MDD (compared to no mental health disorder) was associated with a significant decrease in QoL until FU. (c) The association did not vary by gender. (d) Incidence of all anxiety disorders (with comorbid disorders; ref: no history of anxiety disorder) was associated with a significant decrease in QoL over time. (e) Incident anxiety disorders were not significantly associated with QoL when only considering “pure” anxiety without any comorbidities (ref: no history of any psychiatric disorder). (f) The association did not vary by gender. |
Rubio (2013) [ ] | Remission from MDD, dysthymia (AUDADIS-IV); Remission from GAD, PD, SAD, specific phobia (AUDADIS-IV); SF-12 (MCS) | Whether remission from depression (MDD, dysthymia) is associated with: (a) changes in QoL over time (compared to non-remitted cases), (b) QoL at FU (compared to people with no history of a specific depressive disorder), (c) QoL at FU, when only considering depressive disorders without any psychiatric comorbidity (compared to people without any lifetime psychiatric diagnosis). Whether remission from anxiety disorders are associated with: (d) changes in QoL over time (compared to non-remitted cases), (e) QoL at FU (compared to people with no history of a specific anxiety disorder), (f) QoL at FU, when only considering anxiety disorders without any psychiatric comorbidity (compared to people without any lifetime psychiatric diagnosis). | (a)–(f) Linear regression models | (a) Remission from MD and dysthymia was associated with a significant positive change in QoL compared to non-remitted cases. (b) Remission of MD and dysthymia was associated with significantly lower QoL at FU compared to people without history of a specific diagnosis. (c) Remission of MD and dysthymia was associated with significantly lower QoL at FU compared to people without any lifetime psychiatric diagnosis. (d) Remission from SAD and GAD was associated with significant positive changes in QoL compared to non-remitted cases. (e) Remission of PD, SAD, specific phobia and GAD was associated with significantly lower QoL at FU compared to people without history of a specific diagnosis. (f) Remission of “pure” PD, SAD, specific phobias and GAD was associated with significantly lower QoL at FU compared to people without any lifetime psychiatric diagnosis. |
Rozario (2006) [ ] | Depressive symptoms (GDS); SF-12 (MCS and PCS) | Whether depressive symptom severity was associated with QoL change profiles over time (no change, declined and improved groups). | Multinomial logistic regression | There was no significant association between depressive symptom severity and QoL change score profiles at FU. |
Sareen (2013) [ ] | Depression trajectory groups over time (according to AUDADIS-IV); anxiety disorder trajectory groups over time (according to AUDADIS-IV); SF-12 (MCS and PCS) | (a) Whether depression trajectory groups (no past year disorder/no suicide attempt at FU, remission without treatment, persistent disorder/comorbidity/suicide attempt/treatment) differed according to QoL at FU. (b) Whether anxiety disorder trajectory groups (no past year disorder/no suicide attempt at FU, remission without treatment, persistent disorder/comorbidity/suicide attempt/treatment) differed according to QoL at FU. | (a) and (b) Multiple linear regression models | (a) QoL at FU differed among the different depression trajectory groups (MCS was significant for all groups: no disorder > remitted disorder > persistent disorder; PCS: no disorder > remitted disorder; remitted disorder < persistent disorder). (b) QoL at FU differed among the different anxiety trajectory groups (MCS was significant for all groups: no disorder > remitted disorder > persistent disorder; PCS: no disorder > persistent disorder, remitted disorder > persistent disorder). |
Shigemoto (2020) [ ] | PTSD symptoms (PCL-C); Q-LES-Q (psychosocial and physical) | Whether previous PTSD symptoms are associated with QoL at FU. | Longitudinal structural equation model | Previous PTSD symptoms were associated with physical QoL at FU1, but not FU2 or psychosocial QoL at both FUs. |
Siqveland (2015) [ ] | Depressive symptoms (according to the depression scale from the GHQ-28); PTSD symptoms (PCL-S); WHOQOL-Bref (global and hrqol) | (a) Whether depressive symptoms at BL are associated with QoL at FU. (b) Whether PTSD symptoms at BL are associated with QoL at FU. | (a) and (b) Multiple mixed effects regression analyses | (a) Higher depressive symptoms at BL were associated with reduced QoL at FU. (b) PTSD levels at BL were not significantly associated with reduced QoL at FU. |
Spijker (2004) [ ] | Depression status (CIDI); Comorbid anxiety (CIDI); SF-36 (social, role emotional) | (a) Whether depression status over time (non-depressed, recovered or depressed (including persistent, relapsing course)) is associated with QoL at FU. Whether comorbid anxiety is associated with QoL at FU (b) in a group with persistent depression and (c) in a group recovered from depression. | ANOVA | (a) QoL at FU was significantly reduced in depressed samples compared to the non-depressed group, and lower in the persistently depressed compared to the recovered group (significant for: role emotional and social). Among the depressed subgroups, there was no significant difference between a persistent or a relapsing course regarding QoL at FU. (b) In the persistently depressed group, comorbid anxiety was significantly associated with reduced QoL at FU (significant for role emotional and social). (c) In those who recovered from depression, comorbid anxiety was significantly associated with reduced QoL (significant for role emotional). |
Stegenga (2012) [ ] | MDD status according to CIDI (remitted, intermittent and chronic); SF-12 (PCS and MCS) | Whether MDD course (remitted, intermittent and chronic) is associated with QoL over time. | Random coefficient analysis | While change in QoL over time did not differ between course groups, QoL at BL (MCS) was lower in those with a chronic course compared to those who remitted from BL. |
Stegenga (2012) [ ] | MDD (CIDI); anxiety syndromes (panic disorder and others, PHQ); SF-12 (PCS) | (a) Whether MDD at BL predicts change in QoL over time. (b) Whether anxiety syndrome at BL (compared to no psychiatric diagnosis) predict changes in QoL over time. (c) Whether comorbid anxiety and MDD at BL (compared to no psychiatric diagnosis) predict changes in QoL over time. | (a)–(c) Random coefficient model | (a) While changes in QoL over time did not differ significantly between those with MDD at BL and those without any psychiatric diagnosis, QoL at BL was lower in those with depression. (b) While changes in QoL over time did not differ significantly between those with anxiety syndrome at BL and those without any psychiatric diagnosis, QoL at BL was lower in those with anxiety compared to those without any psychiatric diagnosis. (c) While changes in QoL over time did not differ significantly between those with comorbid anxiety and MDD at BL and those without any psychiatric diagnosis, QoL at BL was lower in those with comorbid anxiety and MDD compared to those without any psychiatric diagnosis. |
Stevens (2020) [ ] | Posttraumatic stress symptoms (VETR-PTSD); SF-36 (MCS, PCS, physical functioning, bodily pain, general health, role physical, role emotional, mental health, vitality and social functioning) | Whether PTSS at BL is associated with QoL at FU. | Generalized estimating equations | Higher BL PTSS was significantly associated with lower QoL (PCS and MCS) at FU. Using a Bonferroni-corrected alpha value, only the domains of mental health, vitality and social functioning at FU were significantly associated with BL PTSS symptoms. The interaction between time and PTSS at BL was not significant, indicating that PTSS had the same effect on QoL outcomes at both FUs. |
Tsai (2007) [ ] | Increased post-traumatic stress symptoms (DRPST); MOS SF-36 (physical functioning, role physical, pain, general health, vitality, social functioning, role emotional, mental health, PCS and MCS) | (a) Whether different PTSS trajectory groups over time (persistent PTSS, recovered, delayed and persistently healthy) differed in QoL at FU. (b) Whether increased post-traumatic stress symptoms at BL predicted QoL at FU. | (a) ANOVA (b) Multiple regression models | (a) At FU, those who were persistently healthy had the highest QoL scores (significantly higher compared to the persistent group in all domains; significantly higher than the recovered group for: pain, general health, vitality, mental health and MCS; significantly higher compared to delayed PTSS in all domains). In addition, those with delayed PTSS (significantly lower than the recovered group in all domains except physical functioning) and those with persistent PTSS (significantly lower than recovered group in all domains) had the lowest QoL overall. (b) Increased PTSS at BL was not significantly associated with QoL at FU. |
Vulser (2018) [ ] | Depressive symptom levels (CES-D score), depression status (CES-D ≥ 19); SF-12v2 (role emotional and social) | Whether depressive symptoms or depression status at BL are associated with QoL at FU. | Generalized linear models | Both the level of depressive symptoms at BL as well as depression status at BL were associated with QoL at FU (significant for: role emotional and social). |
Wang (2000) [ ] | Depressive symptoms (SCL-90 subscale); anxiety symptoms (SCL-90 subscale); WHOQOL-Bref (total) | (a) Whether depressive symptoms at BL were associated with QoL at FU. (b) Whether anxiety symptoms at BL were associated with QoL at FU. | (a) and (b) Stepwise regression | (a) Higher depressive symptoms at BL were associated with reduced QoL at FU. (b) Anxiety symptoms BL were not included in the final stepwise regression model. |
Wang (2017) [ ] | Depressive disorder course groups (CIDI); anxiety disorder course (CIDI); SF-36 (MCS, PCS) | (a) Whether QoL at FU differs between three different course groups of depressive disorders (1. no disorder at BL and no suicide attempt until FU; 2. remitted without treatment; 3. persistent disorder/treatment/developed psychiatric co-morbidity/suicide attempt until FU). (b) Whether QoL at FU differs between three different course groups of anxiety disorders (1. no disorder at BL and no suicide attempt until FU; 2. remitted without treatment; 3. persistent disorder/treatment/developed psychiatric co-morbidity/suicide attempt until FU). | (a) and (b) Multiple linear regression | (a) Those with depression at BL that remitted without treatment had lower QoL at FU (significant for MCS and PCS) than those without the disorder and higher QoL at FU (significant for MCS) than those with a persistent disorder. (b) Those with anxiety at BL that remitted without treatment over time had lower QoL at FU than those without the disorder and higher QoL (MCS, but not PCS) than those with a persistent disorder. |
Wu (2015) [ ] | Depressive symptoms according to CDI; social anxiety symptoms (SASC); QOLS | (a) Whether depressive symptoms at BL are associated with QoL at FU. (b) Whether social anxiety symptoms at BL are associated with QoL at FU. | (a) and (b) Multivariate stepwise forward regression | (a) Higher depressive symptoms at BL were significantly associated with reduced QoL at FU. (b) Higher social anxiety symptoms at BL were not significantly associated with QoL at FU. |
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.
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.
First Author (Year) | Disorder or Symptoms Analyzed; QoL Domains Analyzed | Research Question | Methods | Results |
---|---|---|---|---|
Chou (2011) [ ] | Depressive sympt oms (CES-D-20 score); WHOQOL-Bref (total) | Whether QoL at BL is associated with depressive symptoms at FU. | Multiple regression | Lower QoL at BL was associated with higher depressive symptoms at FU. |
De Almeida Fleck (2005) [ ] | Depression status (remission vs. no complete remission, CIDI and CES-D-20 cutoff >16); QLDS, WHOQOL-Bref (physical, psychological, social and environment), SF-12 (PCS, MCS) | Whether QoL at BL is associated with course of depression (complete remission vs. non-complete remission) in a depressed sample. | Stepwise multiple logistic regression | Disease-specific QoL measure at BL significantly predicted the remission of depression at FU (significant for QLDS). |
Hajek (2015) [ ] | Depressive symptoms (GDS); EQ-VAS | Whether an initial change in QoL is associated with subsequent changes in depressive symptoms. | Vector autoregressive model | Initial changes in QoL were associated with a subsequent reduction in depression score (significant for total sample and women). |
Hoertel (2017) [ ] | MD (according to AUDADIS-IV): SF-12v2 (PCS and MCS) | Whether QoL at BL predicted recurrence (vs. remission) or persistence (vs. remission) of MD over time. | Structural equation model | Lower QoL at BL was a predictor of risk of persistence (PCS and MCS) and recurrence of MDE over time. |
Johansen (2007) [ ] | PTSD symptoms according to IES-15; WHOQOL-Bref (total) | Whether QoL predicted PTSD symptoms at FU. | Structural equation model | QoL did not significantly predict PTSD symptoms at FU. |
Kuehner (2009) [ ] | Depressive symptoms (MADRS); WHOQOL (overall, physical, psychological, social and environmental) | Whether the lag of levels of QoL predicts future levels of depressive symptoms and whether the association differs by group (formerly depressed inpatients vs. community controls) | Time-lagged linear models | Lower levels of QoL were associated with higher future depressive symptoms (significant for physical, psychological, environmental and overall). The association was not moderated by group status. |
Stegenga (2012) [ ] | MDD (CIDI); anxiety syndromes (panic disorder and others, PHQ); SF-12 (PCS) | (a) Whether “dysfunction” (i.e., reduced QoL) at BL (mildly reduced, moderately reduced or severely reduced; compared to no reduced QoL) predicts MDD onset over time. (b) Whether “dysfunction” (i.e., reduced QoL) at BL (mildly reduced, moderately reduced or severely reduced; compared to no reduced QoL) predicts anxiety syndrome onset over time. (c) Whether “dysfunction” (i.e., reduced QoL) at BL (mildly reduced, moderately reduced or severely reduced; compared to no reduced QoL) predicts onset of comorbid anxiety and MDD over time. | (a)–(c) Multinomial logistic regressions | (a) Dysfunction (i.e., reduced QoL) at BL was associated with higher odds of onset of MDD over time in the sample of people without a diagnosis at BL (significant for severely reduced QoL). (b) Dysfunction (i.e., reduced QoL) at BL was associated with higher odds of onset of anxiety syndrome over time in the sample of people without a diagnosis at BL (significant for moderately and severely reduced QoL). (c) Dysfunction (i.e., reduced QoL) at BL was associated with higher odds of onset of comorbid anxiety and depression over time in the sample of people without a diagnosis at BL (significant for mild, moderately and severely reduced QoL). |
Wu (2016) [ ] | Elevated social anxiety symptoms (SASC cutoff 9); QOLS | Whether QoL is associated with changes in elevated social anxiety symptoms over time. | Generalized Estimating Equation | Higher QoL was associated with a decreased risk for developing elevated social anxiety symptoms over time. |
Wu (2017) [ ] | Elevated depressive symptoms (according to CDI ≥19); QOLS | Whether QoL at BL is associated with elevated depressive symptoms at FU. | Multiple stepwise logistic regression | QoL at BL was not significantly related to depressive symptoms at FU. |
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.
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).
4.1. 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.
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.
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.
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.
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).
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.
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.
Data availability statement, conflicts of interest.
The authors declare no conflict of interest.
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Methods section.
Education is expected to have appositive importance on the student’s life by enhancing their capability to think and improving their competency. However, it often acts as a source of stress that affects students’ mental health adversely. This causation of academic stress often emanates from the need to have high grades, the requirement to change attitude for success, and even pressures put by various school assignments.
These pressures introduced by education can make the student undergo a series of anxiety, depression, and stress trying to conform to the forces. The causes of academic stress are well-researched but there is still no explanation why the rate of strain increases despite some measures being implemented to curb student stress. This research explores this niche by using 100 participants who study at my college.
Nowadays there are many reasons that cause stress among growing number of students who might not know they are going through the condition most of the time. Hence, undiscovered discouragement or uneasiness can cause understudies to feel that they are continually passing up unique open doors. It prompts substance misuse; self-destruction is the second most typical reason for death among undergrads. The main hypothesis of this article is that college and university students have higher depression rates.
This proposal undercovers how the problem of anxiety and depression is progressing if not addressed. With such countless youngsters experiencing undiscovered tension, it may be challenging for them to appreciate school. Understudies’ emotional well-being is risked when pressure and trouble go unnoticed, which can prompt social and educational issues (Nelson & Liebel, 2018). Educators might battle to perceive uneasiness since these circumstances manifest themselves contrastingly in different people.
Anxiety and depression are complicated disorders with numerous elements that impact people differently. Teachers and staff must be well trained to deal with these unforeseen events. Understudies coming to college come from various financial foundations, which can prompt an assortment of psychological wellness chances (Li et al., 2021). Additionally, current works will be evaluated to differentiate the risk factors associated with stress among university undergraduates worldwide.
There are various reasons which might cause the onset of anxiety and depression. It can be absence of rest, terrible dietary patterns, and lack of activity add to the gloom in undergrads (Ghrouz et al., 2019). Scholarly pressure, which incorporates monetary worries, strain to track down a decent profession after graduation, and bombed connections, is sufficient to drive a few understudies to exit school or more awful.
Numerous parts of school life add to despondency risk factors. For example, understudies today are owing debtors while having fewer work prospects than prior. Discouraged kids are bound to foster the problems like substance misuse (Lattie et al., 2019). For adaptation to close-to-home trouble, discouraged understudies are more inclined than their non-discouraged companions to knock back the firewater, drink pot, and participate in unsafe sexual practices.
The central hypothesis for this study is that college students have a higher rate of anxiety and depression. The study will integrate various methodologies to prove the hypothesis of nullifying it. High rates of anxiety and depression can lead to substance misuse, behavioral challenges, and suicide (Lipson et al., 2018). Anxiety is one of the most critical indicators of academic success, it shows how students’ attitudes change, reflecting on their overall performance.
The study will use college students who are joining and those already in college. The research period is planned to last six months; college students are between the ages of 18 and 21 and life is changing rapidly at this age (Spillebout et al., 2019). This demography will come from the college where I study. The participants will be chosen randomly, the total number will be 100, both female and male, and from all races.
Some of the materials to be used in the study will include pencils, papers, and tests. Paper and pencils are typical supplies that students are familiar with, so using them will not cause additional stress. It will be used during the interview with the students and throughout the study will be in effect (Huang et al., 2018). These have been applied in various studies before, and, hence, they will be instrumental in this study.
The study will follow a step-wise procedure to get the required results. First, the students’ pre-depression testing results would be researched and recorded. Second, the students would undergo standardized testing in the same groups. Scholarly accomplishment is impacted by past intellectual performance and standardized testing (Chang et al., 2020). Third, the students’ levels of depression and anxiety would be monitored along with their test results.
The study will use a descriptive, cross-sectional design with categorical and continuous data. The sample demographic characteristics were described using descriptive statistics. Pearson’s proportion of skewness values and common mistakes of skewness was utilized to test the ordinariness of the persistent factors. The distinctions in mean scores between sociodemographic variables and stress will be examined using Tests (Lipson et al., 2018). The independent variable will be essential because it will provide the basis of measurement.
The 100 participants had different anxiety levels, as seen from the Test taken and the various evaluations. Forty-five of the participants had high levels, 23 had medium levels, while the remaining 32 had low levels (Lipson et al., 2018). The correlation and ANOVA, which had a degree of era margin of 0.05, were allowed (Lipson et al., 2018). This finding aligns intending to have clear and comprehensive outcomes.
If the results would be not significant, it means that students are not subjected to more pressure on average. If the study results in significant outcomes, this would mean that there is much that needs to be done to reduce student’s anxiety. The idea that scholarly accomplishment is indispensable to progress is built up in higher instructive conditions (Nelson & Liebel, 2018). Many colleges devote money to tutoring, extra instruction, and other support services to help students succeed.
The study will have to follow the APA ethical guidelines because it involves experimenting with humans. Some of the policies include having consent from the participant, debriefing the participant on the study’s nature, and getting IRB permission (Nelson & Liebel, 2018). Ethical guidelines should comply with proficient, institutional, and government rules. They habitually administer understudies whom they likewise instruct to give some examples of obligations.
The study also had some limitations, making it hard to get the desired outcomes. It was not easy to detect the population-level connections, but not causality. This case hardened the aspect of confounding and getting the relevant random assignment needed for the study had to access (Nelson & Liebel, 2018). For the right individuals for the investigation to be identified, the sampling was not easy.
This study would be essential as it will create a platform for future studies. The result that was gotten shows that many college students are undergoing the problem of anxiety and depression without knowing that it is happening. Educators will have an awareness on what aspects of academics they need to modify to ensure their students are not experiencing mental health challenges. Hence, it makes it possible for future researchers to conduct studies to provide possible solutions.
Chang, J., Yuan, Y., & Wang, D. (2020). Mental health status and its influencing factors among college students during the epidemic of COVID-19. Journal of Southern Medical University , 40(2), 171-176.
Ghrouz, A. K., Noohu, M. M., Manzar, D., Warren Spence, D., BaHammam, A. S., & Pandi-Perumal, S. R. (2019). Physical activity and sleep quality in relation to mental health among college students. Sleep and Breathing Journal , 23(2), 627-634.
Huang, J., Nigatu, Y. T., Smail-Crevier, R., Zhang, X., & Wang, J. (2018). Interventions for common mental health problems among university and college students: A systematic review and meta-analysis of randomized controlled trials. Journal of Psychiatric Research , 107, 1-10.
Lattie, E. G., Adkins, E. C., Winquist, N., Stiles-Shields, C., Wafford, Q. E., & Graham, A. K. (2019). Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: A systematic review. Journal of Medical Internet Research , 21(7), e12869.
Li, Y., Zhao, J., Ma, Z., McReynolds, L. S., Lin, D., Chen, Z.,… & Liu, X. (2021). Mental health among college students during the COVID-19 pandemic in China: A 2-wave longitudinal survey. Journal of Affective Disorders , 281, 597-604.
Lipson, S. K., Kern, A., Eisenberg, D., & Breland-Noble, A. M. (2018). Mental health disparities among college students of color. Journal of Adolescent Health , 63(3), 348-356.
Nelson, J. M., & Liebel, S. W. (2018). Anxiety and depression among college students with attention-deficit/hyperactivity disorder (ADHD): Cross-informant, sex, and subtype differences. Journal of American College Health , 66(2), 123-132.
Spillebout, A., Dechelotte, P., Ladner, J., & Tavolacci, M. P. (2019). Mental health among university students with eating disorders and irritable bowel syndrome in France. Journal of Affective Disorders , 67(5), 295-301.
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Cause And Effect Essay Writing
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Published on: Mar 13, 2020
Last updated on: Mar 25, 2024
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Are you struggling to wrap your head around cause and effect essays? Don’t worry; you’re not alone.
These essays might seem complex at first glance, but with the right approach, they can become easier to write.
In this comprehensive guide, we'll look into what cause and effect essays are, how to structure them, and provide valuable tips and examples to help you understand this type of writing.
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A cause and effect essay is a type of essay writing that explores the relationship between events, actions, or phenomena (causes) and their outcomes or consequences (effects) .
In this type of essay, the writer analyzes how one event leads to another, providing insights into the underlying causes and the resulting effects. Cause and effect essays aim to explain the connections between various occurrences and explain the reasons behind certain outcomes.
They often require critical thinking, careful analysis, and the use of evidence and examples to support arguments.
You may confuse cause-and-effect essays with compare and contrast essays . While cause and effect essays focus on analyzing the relationship between events, compare and contrast essays examine similarities and differences between two or more subjects or ideas.
There are two main structural types commonly used to write a cause and effect essay: the block structure and the chain structure.
In the block structure, the writer first discusses all the causes of the event in one section, followed by a separate section dedicated to discussing all the effects.
This cause and effect essay format allows for a clear separation between the causes and effects, making it easier for the reader to understand the relationships between them.
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Use the block structure when:
In the chain structure, each cause is followed immediately by its corresponding effect(s), creating a chain-like sequence of events.
This structure emphasizes the direct relationship between each cause and its effect, providing a more immediate and interconnected narrative.
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Use the chain structure when:
Creating an outline is essential for organizing your thoughts and structuring your cause and effect essay effectively.
Here's a basic outline to guide you through the writing process:
Start with an attention-grabbing statement or question to engage the reader. Provide context and background information on the topic. Clearly state the main causes and effects you will discuss in your essay.
Introduction to Causes Introduce the first cause you will discuss. Provide an explanation of the cause and its significance. Support your explanation with relevant examples or evidence.Introduce the effects resulting from the first cause. Discuss the consequences or outcomes of the cause. Provide examples or evidence to illustrate the effects. Repeat the above structure for each additional cause and its corresponding effects. Summarize the main causes and effects discussed in the essay. Highlight the connections between the causes and effects. End with a thought-provoking statement or suggestion for further exploration of the topic. |
Need a detailed outline guide? Be sure to check out our blog on " Cause and Effect Essay Outline " for a comprehensive breakdown of how to organize your essay.
Writing a cause and effect essay involves examining the reasons (causes) and outcomes (effects) of a particular event, phenomenon, or situation. Here's a step-by-step guide to help you craft an effective cause and effect essay:
Start by selecting a topic that interests you and has clear cause-and-effect relationships. It could be a social issue, scientific phenomenon, historical event, or any other subject with identifiable causes and effects.
For example, "The Effects of Climate Change on Wildlife Populations" or "Causes of Obesity in Developed Countries."
Gather relevant information and evidence to support your thesis statement . Look for credible sources such as academic journals, books, government reports, and reputable websites.
Ensure you have a thorough understanding of both the causes and effects related to your chosen topic.
Craft a clear and concise thesis statement that outlines the main causes and effects you will discuss in your essay. Your thesis should provide a roadmap for the reader and clearly state your position on the topic.
For instance, "The rise in carbon emissions from human activities is leading to severe consequences for global ecosystems."
Create a structured outline that organizes your ideas and arguments logically. Divide your essay into introduction , body paragraphs (each discussing a specific cause or effect), and conclusion .
Each body paragraph should focus on one cause or effect and provide supporting details and evidence.
Begin with an engaging introduction that provides background information on the topic and introduces your thesis statement.
Hook the reader's attention with an interesting fact, statistic, or anecdote related to your topic. Clearly state the purpose of your essay and preview the main points you will discuss.
In recent years, the proliferation of social media platforms has revolutionized the way people communicate, connect, and consume information. While these platforms offer numerous benefits such as instant communication and global networking, they have also been associated with various negative effects on mental health. This essay explores the causes behind the rise of social media and its detrimental effects on individuals' mental well-being. |
In the body paragraphs, explore the causes or effects of the topic in detail. Start each paragraph with a topic sentence that introduces the cause or effect you will be discussing.
Then, provide evidence and examples to support your claim. Use data, statistics, expert opinions, and real-life examples to strengthen your arguments. Make sure to explain the causal relationship between the factors you're discussing.
One of the primary causes behind the surge in social media usage is the widespread availability of smartphones and internet access. With the advent of affordable smartphones and widespread internet connectivity, people have constant access to social media platforms, leading to increased usage. Additionally, the addictive nature of social media interfaces, characterized by endless scrolling and notifications, further fuels this phenomenon. As individuals spend more time on social media, they become increasingly dependent on these platforms for social validation, entertainment, and information.
The excessive use of social media has been linked to various detrimental effects on mental health, including increased feelings of anxiety, depression, and loneliness. Constant exposure to carefully curated images and lifestyles on social media can create unrealistic expectations and foster feelings of inadequacy among users. Moreover, the prevalence of cyberbullying and online harassment on these platforms can exacerbate existing mental health issues and lead to social withdrawal. Studies have shown a correlation between heavy social media usage and poor sleep quality, as individuals often sacrifice sleep to engage with online content, further compromising their mental well-being. |
Use transition words and sentences to smoothly transition between paragraphs and maintain coherence throughout your essay.
These transitions help guide the reader through your arguments and ensure a logical flow of ideas.
Summarize the main points of your essay in the conclusion and restate your thesis statement. Reflect on the significance of your findings and emphasize the importance of understanding the causes and effects of the topic.
Avoid introducing new information in the conclusion; instead, offer insights or suggestions for further research or action.
In conclusion, the rise of social media has had profound implications for individuals' mental health, driven by factors such as increased smartphone usage and the addictive nature of social media platforms. While social media offers unparalleled opportunities for communication and connection, its negative effects on mental well-being cannot be ignored. It is essential for individuals to strike a balance between online and offline interactions and practice mindfulness while using social media to mitigate its adverse effects on mental health. Additionally, further research and awareness efforts are needed to address the underlying causes and consequences of excessive social media usage in society. |
Review your essay for clarity, coherence, and grammatical accuracy. Make sure each paragraph contributes to the overall argument and that your ideas are well-supported by evidence.
Once you've made revisions and edits, finalize your essay by formatting it according to the guidelines provided by your instructor or publication.
Double-check citations and references to ensure they are accurate and properly formatted according to the required citation style (e.g., APA, MLA).
When writing a cause and effect essay for the first time, it is recommended to go through a few examples. It will help you understand the structure and how to use a method effectively.
The following are some of the great cause and effect examples free to use.
Cause and Effect Essay
Cause and Effect Essay Sample
Climate Change Cause and Effect Essay
Poverty Cause and Effect Essay
Air Pollution Cause and Effect Essay
Here are some cause and effect essay topics:
These topics reflect current societal concerns and offer opportunities for in-depth analysis of cause-and-effect relationships. If you need more such ideas check out our cause and effect essay topics blog!
Here are additional tips for writing a cause and effect essay:
To conclude, writing a cause and effect essay can be a rewarding experience that allows you to look into complex issues. By following the guidelines outlined in this guide and applying your critical thinking skills, you can create compelling essays that inform and engage your audience.
But if you are in a time crunch do not hesitate to take professional help. CollegeEssay.org provides a top cause and effect essay writing service for those students who are having a hard time meeting deadlines. We'll help you with your cause and effects essays for the best grades.
Reach out to avail amazing discounts and get our custom essay writing help in no time. As a plus, you can use our AI writing tool if you need a quick fix to beat the deadline stress!
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Subject: English
Age range: 11-14
Resource type: Worksheet/Activity
Last updated
29 August 2024
Welcome, 8th graders and higher grades, to an exciting journey into the world of writing! In this activity, we’ll explore the art of crafting a “Cause and Effect Essay” , which is a type of writing that allows you to investigate and explain the reasons behind events or actions and their subsequent consequences.
Why do certain things happen, and what happens because of them? That’s what this 11 page activity is all about—discovering the connections and patterns in our world in one hour.
During this 30 Multiple Choice activity, you will learn the essential elements of a Cause and Effect Essay, how to structure it effectively, and how to use transitional words to guide your readers through the causal relationships. You’ll also explore common mistakes to avoid and techniques to make your essay compelling and insightful thanks to the Answers included.
By the end of this English activity, you’ll be well-equipped to write your own Cause and Effect Essays and impress your teachers with your writing skills. Let’s dive in!
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COMMENTS
Bullying is a very common cause of anxiety among young people today. People who are affected by some form of bullying often results them having recurring thinking patterns e.g. experiencing flashbacks, having nightmares, panic attacks, and a lot of them live in the fear that they will experience it again which in some ways never switches these ...
They have a sedating effect on the brain because when you have an anxiety disorder, you have a chemical imbalance in the brain which involves serotonin. A lack of serotonin causes the individual to not be able to relax and alters the person's sense of well-being. Although medications will not cure the disorder, by altering the brains ...
Social anxiety is also known as social phobia. Social anxiety disorder is the most common anxiety disorder, affecting over 10 million Americans. This disorder can develop as early in childhood, mid-teens, or even in adulthood. Social anxiety can be inherit usually through family history.
Causes and Effects of Anxiety. Most people experience anxiety in certain situations, such as before a big interview or when faced with an important decision. Indeed, anxiety is a normal response to stress, and is believed to have evolved to help organisms cope with dangerous environments or respond to potential threats.
Anxiety disorders are normally brained reactions to stress as they alert a person of impending danger. Most people feel sad and low due to disappointments. Feelings normally overwhelm a person leading to depression, especially during sad moments such as losing a loved one or divorce. When people are depressed, they engage in reckless behaviors ...
Causes of Social Anxiety Disorder Essay Sample, Example. People are exposed to huge amounts of stress each day. Problems at work, dysfunctional relationships, insurance issues, taxes, children's misbehavior, and so on—these typical misfortunes can upset anyone. However, there is a psychological problem that can make one's life excessively ...
3.) Panic Disorder. 4.) Post-Traumatic Stress Disorder (PTSD) 5.) Social Phobia (or Social Anxiety Disorder) What must be understood is that anxiety disorders are physiological and psychological manifestations of the effect stressors have on the body. In average cases where anxiety is present people feel varying degrees of nervousness ...
95 essay samples found. Anxiety is a common problem faced by many college students. This emotion of feeling worried can be triggered by a variety of factors, including stress from academic work, social pressures, chronic depression, or unease due to personal relationships. Writing a short college essay about anxiety can be challenging, but ...
1. 2. Next. The true cause of anxiety is being a human being, gifted with the capacity to imagine a future. As a mental state of apprehension about what might, or might not, lie ahead, anxiety ...
Table of Contents. Anxiety is the emotion that causes severe physical changes, can negatively affect social contacts, and even lead to depression. Here we've gathered top research questions about anxiety disorder as a mental health issue, as well as anxiety essay examples. Get inspired with us!
Research and Gather Evidence: Gather relevant data, statistics, examples, and expert opinions to support your arguments. Strong evidence enhances the credibility of your essay. Outline Your Essay: Create a structured outline that outlines the introduction, body paragraphs, and conclusion. This will provide a clear roadmap for your essay and ...
The effects of stress on individuals can be profound and far-reaching, impacting both physical and mental health. Chronic stress has been linked to a range of health problems, including heart disease, high blood pressure, and digestive issues. The constant activation of the body's stress response can lead to a weakened immune system, making ...
A cause and effect essay is a type of expository essay that explores its topic by discussing the issue's causes and consequences. For example, a cause and effect essay about deforestation's role in climate change might discuss a few of deforestation's specific causes, like a demand for wood and the clearing of land for grazing pastures ...
1114 Words 5 Pages. Social anxiety affects one 's life negatively by bringing negative emotions and feelings. Anyone who has social anxiety tends not to show their full potential because they 're afraid of social situations. Also, Social anxiety is not considered a normal facet of life like shyness is.
For people with social anxiety, activities that induces feelings of anxiety even for normal people, will be extremely stressful for them, completely overwhelming to the point of leading to a panic attack. Common symptoms include inhibition of speech, frequent slips of tongue, difficult breathing, and nausea (U.Penn).
Anxiety disorders usually run through the genetic line. Sometimes it's because of familial history including a failure to learn different types of skills, having unlike behaviors and abuse. According to K. L. Lerner & B. W. Lerner, there are approximately one-fourth of first-degree relatives that will be affected.
Here are ten simple steps to help you write an engaging essay that looks into how things are connected. 1. Select a Specific Topic. Choose a cause and effect relationship that sparks your interest. Ensure your topic is focused and manageable for a thorough exploration. 2.
Overall, the essay provides a solid overview of social anxiety disorder, its symptoms, causes, effects, and treatments. However, there are some areas where the essay could be improved. One shortcoming of the essay is that it doesn't provide enough examples or case studies to illustrate the symptoms and effects of social anxiety disorder.
A person's health and wellbeing may also contribute to frequency. Stress is common in most all Americans lives, possibly why anxiety is so common. Levels of stress, as well as, the environment around an individual can lead to the cause of a panic attack. Panic attacks are often associated with being hereditary as well as, genetically associated.
Depression is a mood-affective disorder that causes a persistent feeling of sadness, loneliness, and starting loss of interest in things. A major depressive disorder or clinical depression is that which affects feeling, thinking, and behavior and can lead to a variety of emotional and physical problems. Depression causes feelings of sadness and ...
1. Introduction. The World Health Organization [] 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 [], they are among the most common mental disorders in the general population [2,3,4,5,6].
The central hypothesis for this study is that college students have a higher rate of anxiety and depression. The study will integrate various methodologies to prove the hypothesis of nullifying it. High rates of anxiety and depression can lead to substance misuse, behavioral challenges, and suicide (Lipson et al., 2018).
Cause And Effect Essay Structure. Introduction: Hook: Start with an attention-grabbing statement or question to engage the reader. Background Information: Provide context and background information on the topic. Thesis Statement: Clearly state the main causes and effects you will discuss in your essay. Body Paragraphs: Paragraph 1: Introduction to Causes Topic Sentence: Introduce the first ...
Welcome, 8th graders and higher grades, to an exciting journey into the world of writing! In this activity, we'll explore the art of crafting a "Cause and Effect Essay", which is a type of writing that allows you to investigate and explain the reasons behind events or actions and their subsequent consequences.. Why do certain things happen, and what happens because of them?