Figures
Abstract
Background
Treating comorbid depression does not always improve outcomes for people with type 2 diabetes. Evidence is lacking on potential psychological and behavioural intermediaries of the impact of depression on diabetes outcomes.
Objective
To synthesise evidence on the impact of comorbid depression on self-efficacy, illness perceptions, and self-management in people with type 2 diabetes.
Data sources
We searched PubMed, Embase, PsycINFO, and Global Health databases from inception up to 29th March 2023.
Study eligibility criteria
Only prospective studies (cohort or intervention studies) were included, with no restrictions on language. The outcomes were self-efficacy, illness perceptions, and self-management.
Risk of bias
The risk of bias was assessed using the Effective Public Health Practice Project (EPHPP) quality assessment tool for quantitative studies.
Results
Twenty-five studies were included, all from high-income countries. Depression was associated with lower self-efficacy (2 studies), poor illness perception (1 study), and poor self-management practices (17 studies) in people with type 2 diabetes. In 6/7 studies, depressive symptoms predicted less adherence to dietary recommendations, 8/10 studies found depressive symptoms were associated with poor medication adherence, 1/3 study found that depressive symptoms were associated with poor weight control, 3/4 with less physical exercise, and 2/3 with general self-care practices.
Citation: Derese A, Gebreegzhiabhere Y, Medhin G, Sirgu S, Hanlon C (2024) Impact of depression on self-efficacy, illness perceptions and self-management among people with type 2 diabetes: A systematic review of longitudinal studies. PLoS ONE 19(5): e0302635. https://doi.org/10.1371/journal.pone.0302635
Editor: Dickens Akena, Makerere University CHS: Makerere University College of Health Sciences, UGANDA
Received: October 18, 2023; Accepted: April 9, 2024; Published: May 6, 2024
Copyright: © 2024 Derese et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: CH receives support from the National Institute of Health Research through grant NIHR200842 and the NIHR Global Health Research Unit on Health System Strengthening in Sub-Saharan Africa, King's College London (GHRU 16/136/54) using UK aid from the UK Government. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, or the Department of Health and Social Care, England. CH, AD, and YG also receive support from AMARI as part of the DELTAS Africa Initiative [DEL- [15-01].
Competing interests: The authors have declared that no competing interests exist.
Introduction
Non-communicable diseases (NCDs) are among the leading causes of death globally, accounting for 71% of all deaths worldwide [1]. Diabetes mellitus (DM) is one of the five priority diseases targeted for action by world leaders at the United Nations General Assembly [2]. Diabetes is a chronic metabolic disorder characterised by persistent hyperglycemia [3]. Individuals with type 2 diabetes account for 90% of diabetes cases and have insulin resistance, usually accompanied by relative insulin deficiency [3]. Poorly controlled DM leads to severe complications, including stroke, blindness, kidney failure, and limb amputation [4].
Good glycemic control is crucial in avoiding diabetes complications. It can be achieved through practical diabetes self-management activities, such as regular plasma glucose monitoring, medication adherence, and diet and lifestyle modifications [5]. However, these self-care management activities are often complex, and many people with diabetes do not perform them as recommended [5,6].
People with diabetes are at a higher risk of mental health problems due to biological, environmental, social, behavioral, and emotional factors [7–9]. Depression often co-occurs with diabetes and has been found to adversely affect diabetes outcomes directly and indirectly [10–13]. A systematic review and meta-analysis of observational studies found that depression in people with diabetes mellitus ranged from 2% to 88%, with a pooled prevalence of 28% [14]. However, the nature of the relationship between depression and diabetes outcomes may be affected by intermediate factors [15–17], including psychosocial and behavioural factors such as self-efficacy [17,18], self-management [15,16,18], illness perception [19,20] and social support [21].
In systematic reviews and meta-analyses of comorbid depression and diabetes, interventions targeting depression have been found to reduce depressive symptom severity, but treating depression does not consistently improve glycemic control [22–25]. The reason might be that intermediate psychosocial and behavioural factors, specifically self-efficacy, illness perceptions, and self-care management, are negatively affected by depression but do not necessarily improve with interventions specific to depressive symptoms [15,18]. Systematic reviews have been published on the impact of comorbid depression on medication adherence in diabetes [26] and commitment to lifestyle changes [27], but not on the broader range of psychosocial and behavioural factors that may mediate the impact of depression on diabetes outcomes. Furthermore, these reviews are now outdated.
The objective of this review was to synthesise the evidence on the impact of comorbid depression on self-efficacy, illness perceptions, and self-management in people with diabetes.
Materials and methods
Registration and protocol
We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [28] for reporting this review. The review protocol was registered on the PROSPERO International Register of Systematic Reviews [CRD42019136249].
Search strategy
We searched PubMed, Embase, PsycINFO, and Global Health databases from inception to 29th March 2023 without language restriction. We did forward and backward searches of the citations of included studies using Google Scholar and hand-searched for unpublished literature from University repositories. The search terms comprised key terms, MeSH terms, and Emtree terms for depression, diabetes mellitus, self-efficacy, illness perception, and self-management. The detailed search strategy is available in (S1 File).
Eligibility criteria
The following criteria were used to include studies in the review:
Study design: Prospective cohort studies or intervention studies.
Study setting: Community, primary care, general and/or specialist medical outpatient settings. Studies conducted in in-patient settings were not eligible for this review.
Participants: Adults (age ≥ 18 years) with a clinical diagnosis (using HbA1C or plasma glucose) of type 2 diabetes mellitus. Studies that were restricted to special populations, including pregnant women, people with dementia or, psychosis, or HIV, were not eligible for this review.
Exposure/intervention: For observational studies, the exposure was the presence of depression assessed with either [A] diagnostic categories of depressive disorder based on the International Classification of Diseases (ICD-11 or earlier versions) or Diagnostic and Statistical Manual of Mental Disorders (DSM-V or earlier versions) criteria or [B] depressive symptoms identified using standardised scales like the Patient Health Questionnaire (PHQ-9) or Centre for epidemiologic studies depression scale (CES-D).
Intervention studies were included if they evaluated any intervention for participants with comorbid depression (diagnosed by a standardised tool or meeting diagnostic criteria). These interventions could be psychological, pharmacological, or a combination of both. For the intervention studies, our interest was in how changes in the exposure of depression (through intervention) affected the outcomes of self-efficacy, illness perceptions or self-management.
Comparator: People with a clinical diagnosis of type 2 diabetes mellitus without depression or elevated depressive symptoms.
Outcomes: The effect of depression on the constructs of self-efficacy, illness perceptions, or self-management, measured using standardised scales and defined as follows:
Self-efficacy is confidence in one’s ability to achieve intended results, according to Albert Bandura [29,30]. People differ in their efficacy across different domains of functioning. In the case of diabetes, self-efficacy is the judgment of one’s capabilities to monitor, plan, and perform self-management activities [31].
Self-management or self-care refers to daily activities that people with chronic diseases carry out to control their illnesses and cope with their illness’s psychosocial consequences [32]. Self-management comprises activities related to diet, exercise, blood glucose monitoring, medication adherence, and foot care [33].
Illness perceptions and cognitions are terms used to describe a range of cognitive processes underlying attention, interpretation, and behavior in response to illness-related information [34]. A positive perception of diabetes controllability, coupled with good knowledge about the disease, empowers individuals to adhere to treatment regimens and self-management practices like diet and exercise20].
Screening
The identified references were first exported into Endnote reference management software [35], and duplicates were removed. The titles and abstracts were then independently screened for eligibility by two reviewers (AD and YG). The article was included for full-text review if screened in by either reviewer. Full manuscripts were checked independently against inclusion criteria by two authors, and any differences were reconciled through discussions. Excluded articles and reasons for exclusions were documented.
Data extraction
Data were extracted by AD and YG independently into a custom-designed Excel spreadsheet with the following domains: author, publication year, country, study design, sample size, outcomes, measures, and critical findings. As before, any discrepancies were reconciled through discussions.
Assessment of bias
Two of the co-authors (AD and YG) assessed the risk of bias independently using the Effective Public Health Practice Project (EPHPP) quality assessment tool for quantitative studies [36]. This tool has eight sections, of which six (selection bias, allocation bias, control of confounders, blinding of outcome assessors, data collection methods, and withdrawals and dropouts) were used in the global rating of the study. The global rating provides an overall methodological rating of strong, moderate, or weak [36]. While EPHPP dictionary provides detailed guidance for rating initial six components, it does not offer specific criteria for component ratings of intervention integrity and analysis appropriateness. For this study, we focused on evaluating each domain individually, to provide comprehensive understanding of the potential for bias. The issue was solved by discussion among the two authors (AD and YG) in case of discrepancies.
Synthesis method
We used narrative synthesis. The included studies were critically appraised to assess their quality and relevance. Key findings were extracted from each study. Then, the studies were grouped together based on study design. The findings are then summarised in a narrative format, considering each study’s strengths and limitations.
Results
Study selection
A total of 7272 records were obtained from four databases. After removing 1269 duplicates, 6003 records underwent title and abstract screening. Of these, 118 were included for full-text review, and 97 were excluded as they did not fulfill the inclusion criteria. Three further articles were identified from the forward and backward citation search, and one study was added through a hand search of unpublished databases. A total of 25 articles from 21 studies were included in the final analysis, as shown in the PRISMA diagram (Fig 1).
Characteristics of the studies
All studies were from high-income countries (HIC) and were published from 2004 to 2022. Among the 25 included studies, the majority (17/25) were from the USA. The remaining studies were conducted in Canada (n = 4), Denmark, Germany, Japan, and the UK (one from each country).
Regarding study design, 18 were cohort studies, and seven were randomised controlled trials (RCTs). The sample size of each study ranged from 85 to 87,650. Most (n = 21) of the studies were facility-based, and only four were community-based.
The reviewed studies assessed the impact of comorbid depression on self-efficacy (n = 2), illness perceptions (n = 1), and different aspects of self-management (total n = 23), including medication adherence (n = 13), adherence to dietary recommendations (n = 11), exercise (n = 6), self-monitoring of blood glucose (SMBG)(n = 4), foot care (n = 3), weight change (n = 2), and compliance with regular visits (n = 1). (See Tables 1–3 below).
Seven screening tools and four approaches to diagnostic measurement were used to define depression. The screening scales used were the Patient Health Questionnaire (PHQ-9), Center for Epidemiologic Studies Depression Scale (CES-D), Depression, Anxiety and Stress Scale (DASS), Diabetes Wellbeing Questionnaire (DWBQ), ESA-Questionnaire (ESA-Q), Depression Screening Questionnaire (DSQ), Hopkins Symptom Checklist 90 (HSC-90), Harvard Department of Psychiatry/National Depression Screening Day Scale (HANDS) and 5-item mental health sub-scale from Medical Outcomes Study (MOS-36 items). The diagnostic instruments for depression were the Composite International Diagnostic Interview (CIDI), Emotional Self-Awareness Questionnaire, and Structured Clinical Interview for DSM (SCID) or trained clinicians applying international diagnostic criteria.
The studies used diverse measures, methods, and analytic approaches, meaning a meta-analysis could not be carried out.
Risk of bias within studies
Most studies received “strong” ratings for data collection methods and handling of confounders. Withdrawals and dropouts led to weak ratings and a risk of attrition bias. (See S2 File).
Effect of depression on self-efficacy
Two studies reported the effect of depression on self-efficacy [37,38]. Both studies were from the USA and were longitudinal analyses nested within a trial. The studies reported either change in depressive symptoms or baseline depression being negatively and significantly associated with self-efficacy at follow-up after adjusting for relevant covariates.
Effect of depression on illness perception
Only one cohort study reported the impact of depression on illness perceptions after six months of follow-up. In the study by Hudson et al. [39], depression and anxiety symptoms at baseline were prospectively associated with specific diabetes illness perceptions. Participants with higher depression scores and who were more anxious at baseline were more likely to perceive that diabetes was an unpredictable condition at six months follow-up [39].
Effect of depression on diabetes self-management
Twenty-two articles assessed the effect of depression on adherence to medication, dietary recommendations, physical exercise, health behaviors, glucose testing, weight loss, foot care, and reducing substance use. The findings are described as follows:
Effect of depression on adherence to medication
Ten studies (eight cohort studies and two RCTs) assessed the effect of depression on medication adherence. Baseline depressive symptoms were significant predictors of poor adherence to diabetes control medications [44,50,59–61], anti-hypertensive medications [59], and low-density lipoproteins (LDL) control medications [59]. Another study found that having depressive symptoms at baseline predicted problems with medication adherence at consecutive follow-ups [54]. A study in Denmark found that the presence of depression or treatment with an anti-depressant was associated with an increase in diabetes medication initiation and adherence when compared to those without depression treatment [49]. Another study also found that people who started anti-depressant therapy were less likely to have poor adherence compared with controls who did not take anti-depressants [48].
On the contrary, two studies found no longitudinal association between depressive symptoms and adherence to oral hypoglycemic agents [45,51]. In a randomised, controlled trial that assessed the effectiveness of depression care management to improve affective and diabetes outcomes in older adults, enhanced care was associated with reduced severity of depression but no impact on medication adherence [45].
Effect of depression on dietary recommendations
In six studies, baseline depressive symptoms significantly predicted poor adherence to general dietary recommendations [42,52–54,56,60], decreased spacing of carbohydrates [53,60], and lower consumption of fruits and vegetables [53,60] at follow-up, after controlling for baseline levels of self-care. Similarly, participants with moderate-to-severe depressive symptoms at baseline had significant improvements in dietary quality when their depressive symptoms decreased over time [43]. In contrast, depression care interventions in three studies did not affect adherence to dietary recommendations [44,45,47].
Effect of depression on physical exercise
In six studies, depressive symptoms were significantly associated with less physical exercise or weight loss [52–54,56,58,60]. In another study, depression care interventions increased the frequency of exercise [45], but a 12-month enhanced depression care intervention was not associated with improvement in physical activity [44].
Effect on substance use
In a cohort study, depression and sub-threshold depression at baseline significantly predicted increased use of substances, like cigarette smoking and alcohol consumption, at follow-up [54]. Lin et al. assessed the impact of a 12-month enhanced depression care intervention, which was not associated with improvement in smoking cessation [44].
Effect on health behaviors
In eight studies, depression longitudinally predicted less frequent self-monitoring of blood glucose [60], poorer compliance with regular visits [57], and worse foot care [42,60,62] at follow-up, controlling for baseline levels of self-care. In three studies, baseline depressive symptoms had a direct negative effect on overall self-care at follow-up [46,54,55]. In an RCT, a physical exercise intervention for depression significantly improved diabetes distress, diabetes self-care, and quality of life in individuals with diabetes. Additionally, the program led to reductions in triglycerides, total cholesterol and LDL-cholesterol [63]. However, depression care interventions in another trial did not affect glucose testing or glycemic control [45]. Similarly, one study demonstrated no significant relationship between depression and self-care activities [47].
Problem-solving therapy (PST) for depression was not associated with a change in the frequency of self-care behaviors [41]. PST intervention and depression remission (assessed using PHQ-9) from the baseline did not result in a significant change in the frequency of self-care behaviors in two prospective models (from baseline to 18 months and from baseline to 24 months) [41]. However, depression remission status assessed by SCL-20 significantly predicted a more regular healthy diet and exercise at follow-up [41].
Discussion
In this systematic review, we synthesised findings from studies on the impact of comorbid depression in people with type 2 diabetes mellitus on self-efficacy, illness perceptions, and self-management. All included studies were longitudinal, with either interventional or observational cohort designs, and were of good quality; however, none were conducted in LMICs. Comorbid depression in people with type 2 diabetes mellitus (T2DM) predicted low self-efficacy, pessimistic illness perception, poorer adherence to medication, and greater difficulty in following dietary recommendations in most, but not all, studies. Interventions for depression resulted in improvement in depressive symptoms. However, improvement in depressive symptoms did not consistently lead to improvements in DM self-management.
The two studies that assessed the impact of depression on diabetes self-efficacy indicated that depression at baseline was negatively associated with self-efficacy at follow-up. This is in keeping with the predictions of self-efficacy theory and highlights that emotional states can boost or undermine self-efficacy [64]. A reciprocal relationship between depression and specific diabetes cognitions over time, with those who were depressed more likely to perceive DM as unpredictable [39], is consistent with the Common Sense Self-Regulation Model (CS-SRM) [65]. This could be because a higher level of depression is associated with lower perceived personal and treatment control and more severe consequences of chronic disease [66]. However, a study from the UK that used structural equation modeling to investigate potential mechanisms found that depression was directly associated with specific diabetes cognitions over time but that these cognition domains did not mediate the effect of emotion on diabetes self-care [39]. Further prospective studies are needed to investigate how self-efficacy relates to depression and self-management activities.
The review findings indicated that depressive symptoms generally predicted lower engagement with self-management behaviors, but the evidence was stronger for some types of self-management behaviors than others. For example, depressive symptoms were consistently associated with lower adherence to medication, exercise and diet, compared to the association with weight control. This is in line with Beck’s cognitive model, which hypothesises that people’s emotions and behaviors are influenced by their perceptions of events [67]. According to the cognitive model, individuals’ perceptions are often distorted and dysfunctional when distressed or depressed, affecting their physiological and behavioral actions, including motivation to carry out self-care activities [67].
However, the review findings indicate that not all interventions targeting depression improved self-management behaviors. The pathway through which depression affects diabetes outcomes may not only be directly through its effect on self-management; other indirect mechanisms might also be involved through diabetes distress or self-efficacy [68]. The other explanation might be that the intensity of these interventions might not be sufficient to increase optimism sustainably and the DM-specific motivation required to bring change related to self-management behaviors, even if depressive symptoms have reduced.
There were different interventions targeting either depression alone or both depression and diabetes management. In an RCT by Lin et al., a collaborative depression intervention (pharmacotherapy, problem-solving treatment, or combination) was used. The intervention resulted in less severe depression over time but did not result in improved diabetes self-care behaviors when compared to the usual care [44]. On the contrary, Oh et al. found that Problem Solving Therapy (PST) for depression did not result in depression remission. Furthermore, depression remission and receiving PST were not associated with changes in self-care behaviors [41]. Another study using a diabetes self-management intervention resulted in a lower mean CES-D score over time than the usual care, indicating potential bi-directional association [43].
Williams et al. used enhanced care (that included education, PST, or support for anti-depressant management) for depression. They found that the intervention resulted in less severe depression and improved overall functioning compared to the usual care. Moreover, the intervention increased weekly exercise days but did not affect other aspects of self-care [45].
In this review, we focused on individual psychological constructs, which may be less salient in other sociocultural settings, particularly those more collectivistic. In such settings, self-management may rely more on others and available resources than just the individual, as seen in managing other chronic conditions [69]. General illness perceptions about diabetes mellitus may be more externalised in some non-Western settings (i.e., less in the control of the individual). Therefore, the interaction with depressive symptoms may have different consequences. Nonetheless, a qualitative study in Ethiopia among people with type 2 DM indicated that an individual’s perception of their illness and its treatment could negatively influence their experience and adherence to medication [70]. Further work is needed to understand salient contextual factors for people with DM and comorbid depression in LMICs and the potential mechanisms linking these constructs to DM outcomes.
Limitations
Our review identified no studies from LMICs or non-Western countries. There is, therefore, a critical gap in the research evidence. LMIC and non-Western settings are likely to differ in terms of social, cultural, and educational factors, which could influence the interplay between depression, self-efficacy, illness perceptions, and self-management. There were methodological differences in the included studies, which precluded meta-analysis and limited the conclusions that could be drawn from the review. There were very few studies on the impact of depression on illness perception and self-efficacy. The potential for selection bias, specifically attrition bias, also limited the review, as only studies published in English were included. This could have excluded studies that were published in other languages.
Conclusions
Observational studies generally suggest that depressive symptoms are associated with lower self-efficacy and engagement in self-management behaviours, but the evidence is mixed. Interventions designed to reduce depressive symptoms have also shown mixed results regarding their impact on self-management behaviours. Further research is needed to identify the pathways through which depression affects diabetes outcomes to inform intervention development. There is a particular need for studies from LMICs and non-Western settings where mechanisms linking comorbid depression with diabetes outcomes may differ.
Supporting information
S1 File. Search strategy used in the systematic review.
https://doi.org/10.1371/journal.pone.0302635.s001
(DOCX)
S2 File. Quality assessment for included studies.
https://doi.org/10.1371/journal.pone.0302635.s002
(DOCX)
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