Accepted Manuscript Title: Psychosocial Interventions for People with Diabetes and Co-morbid Depression. A Systematic Review Author: Jing Li Amy Kok Allison Williams Lin Zhao PII: DOI: Reference:
S0020-7489(15)00189-3 http://dx.doi.org/doi:10.1016/j.ijnurstu.2015.05.012 NS 2577
To appear in: Received date: Revised date: Accepted date:
19-6-2014 7-4-2015 29-5-2015
Please cite this article as: Kok, J.L.A., Williams, A., Zhao, L.,Psychosocial Interventions for People with Diabetes and Co-morbid Depression. A Systematic Review., International Journal of Nursing Studies (2015), http://dx.doi.org/10.1016/j.ijnurstu.2015.05.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Psychosocial Interventions for People with Diabetes and Co-morbid Depression. A
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Systematic Review.
Jing Li Amy Kok, Monash University, BNur (Hon)
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Allison Williams, Associate Prof, Monash University, PhD, MN, Ba AppSc Nur, Grad Dip
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Adv Nur, RN
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Lin Zhao, Lecturer, Monash University, PhD, MN, GCHPE, RN
Correspondence: Jing Li Amy Kok, [email protected] (email)
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Structured Abstract Aims and objectives: To examine the effectiveness of psychosocial interventions on depressive symptoms and glycaemic control of adults with Type 1 or 2 diabetes and co-
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morbid depression.
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Background: Diabetes is a chronic disease that affects as many as 382 million people in the
world. Diabetes management is a challenging daily task which can be overwhelming and lead
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to depression. Both diabetes and co-morbid depression have a negative bidirectional influence on each other, which is detrimental for the individual's quality of life. This co-
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morbidity places a huge burden on the individual, family, health care system, and the
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economy, with an associated 50-75% increase in health care costs. Design: A systematic review
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Methods: Four electronic databases were searched including Cochrane Library, CINAHL,
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MEDLINE, and PsycINFO for articles written in English from the year 1998 to 2013. Data
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extraction of vital information from included studies was conducted and the effect sizes were calculated for the outcomes.
Results: Ten interventional primary studies were retrieved from the search; six were randomised studies. Nine out of ten studies reported that psychosocial interventions were effective for depression with effect sizes ranging from small (-0.24) to large (-1.74). No effect was found for the glycaemic control outcome. The interventions came in a myriad of intervention type, delivery method, duration, and intensity, therefore a meta-analysis was not conducted. The review found that nurses were competent in delivering psychosocial interventions for this population. Methodological quality was below average amongst the study with various biases present.
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Conclusions: The results and effect sizes were promising but due to the high number of bias risks, it cannot be determined if psychosocial interventions were found to be effective in people with diabetes and co-morbid depression.
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Relevance to clinical practice: Psychosocial interventions have the potential to make
improvements in depression, alleviating the global burden on people with diabetes, keeping
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in mind the best intervention modality tailoring to the client’s needs and preferences. Nurses
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and other health professionals involved in caring for this group are in a good stead to carry
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out these interventions.
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Keywords: co-morbidity; depression; depressive symptoms; diabetes mellitus; glycaemic
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control; nurses; psychosocial interventions ; systematic review
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Introduction Background Diabetes is a chronic metabolic endocrine disorder characterised by the inability of
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the body to metabolise glucose effectively (Torpy, 2011). Type 1 and Type 2 diabetes are the
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most common types of diabetes requiring long term management, with the occurrence of
Type 1 diabetes being approximately 5% and Type 2 diabetes accounting for 90-95% of all
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diabetes diagnoses (Centers for Disease Control and Prevention, 2011). Although Type 1 and Type 2 diabetes are pathophysiologically different, their clinical symptoms are to a certain
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extent similar and require similar chronic management needs. Both diabetes types are associated with chronic hyperglycaemia that necessitates adequate management to prevent
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long term complications including nephropathy, neuropathy, and retinopathy (American
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Diabetes Association, 2008).
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The incidence of diabetes has reached epidemic levels in recent years, with 382 million people living with diabetes globally in 2013 and a projected increase to 592 million in
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2035 as the population continues to grow, age, and urbanise alongside decreasing physical activity and increasing obesity rates, posing a public health threat worldwide (International Diabetes Federation [IDF], 2013a; Wild, Roglic, Green, Sicree, & King, 2004). People with diabetes often liken having this chronic disease to a life sentence (Barnett, 2010), as managing diabetes requires a daily commitment of monitoring one’s diet, physical activity, general health, stress levels, blood sugar levels, and medication taking; tablets and/or insulin (Golden et al., 2008). Managing diabetes is more than just glycaemic control; it involves one’s emotional health as well (Fisher, Thorpe, Devellis, & Devellis, 2007). Sometimes, coping with diabetes becomes an insurmountable challenge, and the individual with diabetes may become overwhelmed and burdened by the demands of disease management, leading to 4 Page 4 of 53
feelings of anxiety, worry, guilt, helplessness, defeat, and overall feelings of depression (Katon, 2008). Depression is a common co-morbidity that accompanies Type 1 and Type 2 diabetes,
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and both diabetes and depression have negative bidirectional influence on each other that can be detrimental for the individual living with both conditions (Peyrot et al., 2013). Forty-three
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million people with diabetes worldwide were found to have clinical or sub-clinical depression
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in 2000 (World Federation for Mental Health, 2013). Depression occurs three times more frequently in people with Type 1 and two times more frequently in people with Type 2
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diabetes, compared to those without this chronic disease (Roy & Lloyd, 2012). Co-morbid depression in diabetes is often associated with poorer self-care and non-adherence to diabetes
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management, leading to increased episodes of hyperglycaemia, microvascular and macrovascular complications, and an associated increase in morbidity and mortality (Egede
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& Ellis, 2008; Petrak & Herpertz, 2009). Poorly controlled diabetes, along with short and/or
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long term complications can then lead to feelings of defeat, frustration, and powerlessness over the disease, exacerbating the depression (Coupe, Garrett, & Gask, 2012). Despite the
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burgeoning prevalence of this co-morbidity, depression often remains under-diagnosed and undertreated in individuals with diabetes (Campayo, Gómez-Biel, & Lobo, 2011; Cepoiu et al., 2008). Depression can be difficult to recognise and diagnose by health professionals because it shares many common symptoms with poorly managed diabetes such as fatigue, sleep disturbances, and weight and appetite changes (Egede & Ellis, 2009). The World Health Organisation [WHO] (2008) predicts that in 2030, depression and diabetes will be the first and tenth leading cause of the global burden of disease respectively. Co-morbid depression in people with diabetes not only affects their quality of life, it also impinges on healthcare resources and the economy. There is an increased need for medical attention, hospitalisation, and overall increased healthcare expenditure to manage the 5 Page 5 of 53
worse diabetes outcomes associated with co-morbid depression (Egede & Ellis, 2008). Simon et al (2005) estimated an increased burden of 50-75% of healthcare costs – including medical visits, prescriptions, laboratory tests, mental health treatments, and surgery, when depression
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co-existed in the person with diabetes. The co-morbidity is also associated with a greater decrease in work productivity, an increase in absenteeism, and a loss of disability-adjusted
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life years (McIntyre, Liauw, & Taylor, 2011). All these, by and large, place a substantial
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economic burden on the individual and the society.
Depression is commonly treated with a combination of pharmacotherapy and
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psychosocial therapy with good effect (Cuijpers, van Straten, Warmerdam, & Andersson, 2009). People who have diabetes, however, usually find it difficult to adhere to self-care
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regimens to manage their diabetes, exhibiting the second lowest adherence to care rate among 17 other chronic conditions (DiMatteo, 2004), and an additional task of taking
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antidepressants is likely to be met with poor adherence (Rizzo, Creed, Goldberg, Meader, &
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Pilling, 2011). Research has also found that adherence to anti-depressants is low in primary care and patients have a preference towards psycho-interventions (van Schaik et al., 2003).
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Psychosocial therapy has been found to be robust in treating co-morbid depression seen in people with chronic medical diseases by addressing maladaptive coping responses, dysfunctional beliefs and emotions, and helping clients to adjust to and manage their health (Koenig, 2012; Serfaty et al., 2009). Since co-morbid depression is associated with poor diabetes outcomes including hyperglycaemia, it is also worth exploring the result of depression interventions on glycaemic control, which can give an indication of the individual’s management of their diabetes (Lustman et al., 2000). This review, therefore, examined the effectiveness of psychosocial interventions on depressive symptoms and glycaemic control of adults with Type 1 or Type 2 diabetes and co-morbid depression.
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A brief literature review revealed a number of primary research studies that explored psychosocial treatments for people with diabetes and co-morbid depression. There were, on the other hand, also a significant number of existing reviews that highlighted the need for
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more research in this field. There were the reviews conducted by Wang, Tsai, Chou, & Chen (2007), Petrak & Herpertz (2009), van der Feltz-Cornelis et al. (2010), Markowitz, Gonzalez,
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Wilkinson, & Safren (2011), Krishnadev et al (2011), and the Cochrane review by
Baumeister et al (2012) that looked at pharmacological, psychological, and collaborative
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treatment for the population with diabetes and co-morbid depression. These reviews were,
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however, dissimilar as they did not solely focus on psychosocial treatment and included few studies with this treatment option. Another review by van Straten, Geraedts, Leeuw,
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Andersson, & Cuijpers (2010) looked at the psychological treatment for a selection of medical illnesses and co-morbid depression, but only two studies included diabetes.
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Lastly, the more recent review by Balhara & Verma (2013) focused on psychosocial
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interventions on people with diabetes and co-morbid depression, and was the most similar to this review. However, the interventions of the included studies in this review were not purely
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psychosocial and consisted of collaborative interventions involving pharmacology. Keywords used in Balhara and Verma’s (2013) study included diabetes, depression, cognitive behavioral therapy, collaborative care, depression, diabetes, psycho-social interventions, behavioral therapy, treatment or management or intervention or trial, collaborative care, primary care, psycho-social, and psychotherapy, and did not provide a flow chart to show why papers were included or excluded. In addition, P values were calculated without effect sizes, and the risk of bias was not taken into consideration. Keywords used in Balhara and Verma’s (2013) study included diabetes, depression, cognitive behavioral therapy, collaborative care, depression, diabetes, psycho-social interventions, behavioral therapy, treatment or management or intervention or trial, collaborative care, primary care, 7 Page 7 of 53
psycho-social, and psychotherapy, and did not provide a flow chart to show why papers were included or excluded. Finally, Balhara and Verma (2013) identified only three studies that included purely psychosocial interventions for the population, whereas a literature review
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conducted in the current study found many more in the similar timeframe, providing justification to conduct this study. Key terms were refined (See Appendix 1) in an endeavour
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to clearly identify the knowledge gap of the effects of psychosocial interventions on people
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with diabetes and co-morbid depression. Aims
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This systematic review’s primary aim is to examine the effectiveness of psychosocial interventions on depressive symptoms in the adult population with Type 1 or Type 2 diabetes
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and co-morbid depression. The secondary aim is to examine the effectiveness of psychosocial
Method
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interventions on glycaemic control in this population.
Inclusion Criteria
Primary research studies investigating psychosocial interventions were included.
Studies had to also be conducted in English and published within the last 15 years; from 1998 to 2013. This review aimed to include recent data whilst including evidence from when research on psychosocial interventions on people with diabetes and co-morbid depression was first pioneered by Lustman, Griffith, Freedland, Kissel, & Clouse (1998). Participants had to be 18 years or older, and have an established diagnosis of Type 1 or Type 2 diabetes, and co-morbid depression. Depressive disorders are diagnosed by standard criteria such as those stated in the Diagnostic and Statistical Manual of Mental 8 Page 8 of 53
Disorder, Fourth Edition (DSM-IV) (American Psychiatric Association, 2000) or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) (WHO, 2013b). The term ‘depression’ in this review also includes those
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of subclinical depression or significantly elevated depressive symptoms. Although it is recognised that subclinical depression is less severe than clinical depression, subclinical
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depression also contributes to the co-morbidity and economic burden, and left untreated, it
can lead to clinical depression (Beck et al., 2011; Chisholm et al., 2003). Elevated depressive
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symptoms can be diagnosed using certain established depression screening tools commonly
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used in General Practices (GPs) with validated cut-off scores such as the Beck Depression Inventory (BDI) and Patient Health Questionnaire (PHQ) (Watnick, Wang, Demadura, &
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Ganzini, 2005).
In this review, interventions had to be psychosocial in nature, aimed at reducing
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depression, and comprising of therapies such as supportive counselling, Cognitive
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Behavioural Therapy (CBT), interpersonal therapy, Problem Solving Therapy (PST), or behavioural therapy (Cuijpers, Andersson, Donker, & van Straten, 2011). Other depression
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treatments like pharmacotherapy or electroconvulsive therapy were excluded as the focus of this review was to examine the effects of psychosocial interventions only. No restrictions were posed on study design and study duration including length of follow-up. Although Randomised Controlled Trials (RCTs) are the gold standard for research evidence, a brief literature review revealed a dearth of RCTs, therefore this review broadened the inclusion criteria to include non-randomised studies (NRS), such as observational studies, quasiexperimental trials, pretest-posttest study, to locate all possible existing evidence for the research question. NRS, when evaluated for risk of bias rigorously, can be very valuable in the search of answers to the research question (Peinemann, Tushabe, & Kleijnen., 2013;
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Shrier et al., 2007). In addition, NRS can guide recommendations for conducting subsequent clinical trials (Cochrane Non-Randomised Studies Methods Group., 2008). For the outcomes, changes in depressive symptoms were assessed and quantified by
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established depression questionnaires such as the BDI or PHQ to provide interval data,
through self-report or interview at baseline, post-intervention, and follow-up (Watnick et al.,
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2005). Changes in glycaemic control were measured by the Glycated Haemoglobin (GHb)
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test, such as the common Haemoglobin A1c (HbA1c) test.
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Exclusion Criteria
Studies were excluded if subjects had grief depression, suicidal ideations, drug and
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alcohol problems, and/or a diagnosis of other mental illnesses such as bipolar disorder, schizophrenia, or mental illnesses requiring ECT. Studies with participants who have other
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forms of diabetes besides Type 1 or Type 2 were also excluded. People who were in active
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treatment for other co-morbidities such as cancer, heart failure, or renal failure were excluded because the focus of this review was on diabetes and co-morbid depression only. Participants
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who have been prescribed anti-depressants recently (over the last six months) were excluded as it was assumed that these patients’ depression had not been fully stabilised with medication. However, those who were on anti-depressants for longer than six months indicating routine treatment were included as this review examined the effects of psychosocial interventions on current depressive symptoms. Search Strategy Four databases were searched, namely CINAHL, Cochrane Library, MEDLINE, and PsycINFO, with keywords and their associated Medical Subject Headings (MeSH) terms (See Appendix 1). These search terms were then exploded to increase the search yield. Each stage
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of the search and elimination process was documented in the adapted version of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow-chart (Moher, Liberati, Tetzlaff, Altman, & The PRISMA Group, 2009). This screening process was
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conducted independently by the reviewers to minimise bias.
Data Collection and Analysis Methods
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Relevant data of the studies’ characteristics were extracted and placed in the Cochrane
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data extraction template (Higgins & Green, 2011). All data extraction was conducted independently by the reviewers, with the main reviewer collating the data together onto the
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table. Effectiveness of the interventions is measured by quantifying and calculating the effect size of the outcomes, and examining its statistical significance (Cohen, 1988). Effect sizes
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were calculated to analyse the effectiveness of psychosocial interventions on the outcomes of
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depressive symptoms and glycaemic control (Diamond & Kaul, 2013). As different studies often use different instruments to measure outcomes – especially for depressive symptoms,
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calculation of the effect size puts the strength and direction of the treatment effect in a numerical form on the same scale, allowing the various treatment effects to be essentially comparable (Hedges, 2008). There are several different effect size indexes, depending on the data available for each study (Littell, Corcoran, & Pillai, 2008). The Mean Difference (MD) was calculated for randomised studies that used similar
outcome measures, while Standardised Mean Difference (SMD), or Cohen’s d was calculated for those with different outcome measures. For SMD, an effect size of 0.2 was considered small, 0.5 was moderate, and 0.8 was large (Cohen, 1988). In non-randomised studies, the Mean Gain Score (MGS) (Lipsey & Wilson, 2001) and Standardised Mean Gain Score (SMGS) (Becker, 1988) were calculated from studies using the same or different outcome 11 Page 11 of 53
measure respectively. The various effect sizes were calculated with their corresponding 95% Confidence Interval (CI) for the outcomes depressive symptoms and glycaemic control. A clinical judgement would subsequently be made regarding the pooling of the
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various effect size indexes in a meta-analysis. Pooling of the effect sizes can greatly increase the generalisability and statistical power of the results to detect a real effect that can be used
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to apply to the target population (Higgins & Green, 2011). It could, however, be unfeasible in
heterogenous (The Cochrane Collaboration, 2002).
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Methodological Quality Assessment
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some cases when the nature of interventions, sample populations, and settings are
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The included articles were critically appraised on six different criteria to evaluate the research evidence quality. The risk of bias within each research process, whether intentional
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or unintentional, that could threaten the scientific integrity and validity of the studies’
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findings, were assessed (Grimes & Schulz, 2002). This was done by using the Cochrane Collaboration’s tool for assessing risk of bias which includes selection, performance,
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detection, attrition, reporting, and other bias.
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Results
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Search Results
Figure 1: PRISMA – PRISMA flow chart providing an overview of the selection of papers examining the effectiveness of psychosocial interventions on depressive symptoms in the adult population with Type 1 or Type 2 diabetes and co-morbid depression
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The database search was last conducted on the 3/11/2013 and 198 references were identified; 49 papers were from CINAHL, 54 from Cochrane, 83 from MEDLINE, and 11 from PsycINFO. One of the included articles was a study protocol and a further search on the
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internet found the full-text of the completed research study report (de Groot et al., 2012) which was added to the result list. These 199 references were then screened and 60 duplicates
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were removed, 91 were excluded as they were not relevant to the review’s adult diabetes and co-morbid depression population, 18 more were excluded as they were not psychosocial
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interventional studies, leaving 30 potential references (See Figure. 1). Out of the 30 studies,
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two were protocols of potential studies that have yet to be completed or published (Coventry et al., 2012; Petrak et al., 2010), nine were reviews that examined the treatment for people
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with diabetes and co-morbid depression which will be mentioned further in the discussion
Study Design
Setting
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First Author
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Included Studies
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chapter, and the remaining nine were excluded due to the exclusion criteria.
Participants
Intervention
(Year)
de Groot
Single-arm
Physician
n=50, ≥18yo,
Program
(2012)
repeated-
practices and
ambulatory,
Appalachians
measures
media
Type 2 diabetes
Coming
intervention
throughout
≥1yr, current
Together to
design
southeastern
major
Increase
Ohio and
depression
Exercise
western West
disorder, 57yo,
(ACTIVE): 10
Virginia
68% F, 100%
wks CBT, 12
White, 100% T2
wks exercise
Depressive
Glycaemic
Symptoms
Control
Tool: BDI
Tool: DCA2000+
B: 26.8±10.4 PI: 16.1±12.9
Measure: HbA1c B: 7.6±1.8 PI: 7.2±1.5 FU: 7.3±1.3
FU: 16.7±13.1 Effect Size: -0.91[-1.32, -0.49] -0.85[-1.26, -0.44]
Effect Size: -0.40[-1.05, -0.25] -0.30[-0.92, 0.32] (MGS)
(SMGS) Georgiades
Single-group
Physician
n=90, ≥18yo,
16 sessions of
Tool: HAM-D
(2007)
open-label
practices and
Type 1 or 2
CBT delivered
B: 9.8±5.8
prepost
clinics within
diabetes, BDI
in small
PI: 3.6±4.2
-
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quasi-
Duke Uni
score ≥10, 51yo,
groups over
Effect Size:
experimental
Health
71% F, 67%
14 wks
-1.22[-1.57, -0.88]
design
Systems
White, 69% T2
Gonzalez
Case series
Massachusetts
n=5, 18-70yo,
1 visit with
Tool: BDI, CGI,
Tool: -
(2010)
study
General
Type 2 diabetes
nurse diabetes
MADRS
Measure: HbA1c
Hospital
with HbA1c>7.0,
educator, 2
B: 15.2±7,
B: 8.8±2
Diabetes
major depressive
visits with
4.2±0.8, 24.8±5.4
PI: 7.6±1
Clinic
disorder or
dietician, 10-
PI: 11.0±8.2,
Effect Size:
dysthymia,
12 CBT
2.4±1.1, 11.2±8.3
-1.20[-3.16, 0.76]
50yo, 40% F,
sessions
cr Effect Size:
(MGS)
-0.55[-1.81, 0.71]
80% White non-
-1.87[-3.36, 0.39]
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Hispanic, 100%
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(SMGS)
-1.94[-3.44, -0.44]
T2
(SMGS)
Randomised
Physician
n=51, 21-70yo,
I: 10 wks of
Tool: BDI
Tool: Pierce
(1998)
Controlled
practices
able to give
diabetes
B: 24.9±10.2(I),
Glyco-test
Trial
within the
informed
education and
Washington
consent, Type 2
10 weeks of
PI: 71%(I),
Uni Sch of
diabetes, major
CBT
22%(C) achieved
Medicine and
depression, ≥14
C: 10 weeks
depression
PI: 10.2, 9.9
BJC
on BDI, 53(I)
of diabetes
remission, and
FU: 9.5, 10.9
Healthcare
56(C)yo, 40%(I)
education
67%(I), 30%(C)
System, St
40%(C) F,
≥50% decrease in
Louis,
85%(I) 77%(C)
BDI score
Missouri
White, 100% T2
FU: 58%, 26%
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M
an
Lustman
21.1±6.8(C)
Measure: GHb B:10.2±3.6(I), 10.4±3.1(C)
and 58%, 30%
Penckofer
Randomised
Diabetes
n=74, women
I: Group CBT
Tool: CES-D
Tool: Bayer DCA
(2012)
Controlled
centre and
aged ≥18yo,
and psycho-
B: 27.7±9.3(I),
Measure: HbA1c
Trial
primary care
Type 2 diabetes
education for
clinics in a
>6mths, average
8 wks and
major
score ≥16 on
booster
Midwestern
CES-D scale
sessions at
medical centre
from phone and
wks 14 and 22
baseline screen,
C: Provided a
55(I), 54(C)yo,
list of
63%(I), 69%(C)
resources and
White, 100% T2
offered intervention
28.9±9.5(C) MI: 15.2±7.9(I), 23.1±11.4(C) PI: 12.6±8.0(I), 21.5±10.2 Effect Size:
B: 7.8±1.8(I), 7.9±2.0(C) MI: 7.4±1.3(I), 7.8±1.8(C) PI: 7.4±1.3(I), 7.8±1.6(C) Effect Size: -0.40[-1.15, 0.35]
-0.81[-1.31, -0.30] -0.97[-1.51, -0.43]
-0.40[-1.13, 0.33] (MD)
post study
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(SMD) Randomised
Vuk Vrhovac
n=50, diabetes,
I: 4 group
Tool: CES-D
Tool: Olympus
Okanovic
Controlled
Uni Clinic in
PHQ-9: 10-14
psycho-
B: 26(22-30)(I),
AU600 analyser
(2009)
Trial
Croatia
points, 55(I),
education
24(18-35)(C)
Measure: HbA1c
58(C)yo,
delivered over
(median and
B: 7.5(6.4-8.3)(I),
64%(I), 84%(C)
6 wks
interquartile
7.7(6.6-8.9)(C)
F, 100% T2
C: Informed
range)
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Pibernik-
PI: 18(12.5-28.5),
treatments
20(16.5-27)
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of available
PI: 7.3(6.3-7.6), 6.9(6.2-8.2)
FU: 7.0(6.0-7.6),
FU: 19(11-26),
7.0(5.9-7.9)
us
19(15-26)
Randomised
Community-
n=339, ≥21yo,
I: Telephone-
Tool: BDI
Tool: DCA2000
(2011)
Controlled
based non-
physician
delivered CBT
B: 26.7±7.7(I),
point-of-care
Trial
profit, Uni,
authorisation for
and walking
and Veterans
special
Affairs (VA)
26.5±9.9(C)
analyser
PI: 14.2±10.3(I),
participants,
C: Books and
18.6±10.7(C)
healthcare
Type 2 diabetes,
educational
Effect Size:
system, and
prescription for
materials for
-0.42[-0.65, -0.19]
community
antihyperglycae
depression,
(SMD)
health
mia, PHQ-9≥11,
walking, and
Effect Size:
55(I), 56(C)yo,
diabetes, and a
0[-0.40, 0.40]
52% F, 84%
list of local
(MD)
White, 100% T2
resources for
system’s
te
diabetes
d
for 12 months
M
an
Piette
learning
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centre
Measure: HbA1c B: 7.5±1.7(I), 7.7±1.7(C) PI: 7.7±1.8(I), 7.7±1.7(C)
depression
Rungreangk
Quasi-
Diabetic clinic
n=64, Type 2
I: Buddhist
Tool: 9Q Thai I-
ulkij (2011)
experimental
in Nakae
diabetes, ≥7 out
group therapy
san dialects
using
Hospital
of 9 on 9
for 6 wks
B: 11.8±2.74(I),
matching
Question Thai I-
C: -
techniques
san dialects
PI: 1.7±2.7(I),
(9Q), 50(I),
5.9±2.1(C)
11.5±2.5(C)
47(C)yo, 94% F,
Effect Size:
100% Thai,
-1.74[-2.32, -1.15]
100% T2
(SMD)
Simson
Randomised
In-patient
n=30, diabetic
I: Supportive
Tool: HADS
(2008)
Controlled
German
foot syndrome,
psychotherapy
B: 11.7±2.7(I),
Trial
Diabetes
HADS≥8, 58(I),
wkly for the
Centre
63(C)yo, 43% F,
duration of in-
80% T2
patient admission
-
-
10.6±2.9(C) PI: 10.1±3.5(I), 10.9±3.3(C) Effect Size:
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C: -
-0.24[-0.95, 0.48] (SMD)
Randomised
Various media
n=255, adult,
I: 8
Tool: Dutch
Tool: -
Bastelaar
Controlled
in The
have an email
consecutive
validated version
Measure: HbA1c
(2011)
Trial
Netherlands
address and
web-based
of CES-D
B: 7.4±1.6(I),
and Belgium
internet access,
CBT
B: 29±7(I),
7.3±1.6(C)
≥16 on the CES-
C: Given
D, 48(I),
access to
PI: 37%, 19%
51(C)yo, 61% F,
intervention
clinically
89% White,
after the study
ip t
van
PI: -
cr
28±7(C)
significant
improvement
55% T2
us
FU: 41%, 24% Effect Size:
an
-0.29[-0.40, -0.17] (1-month FU) (SMD)
Post-Intervention, FU: Follow-Up
M
I: Intervention Group, C: Control Group, F: Female, B: Baseline, MI: Mid-Inervention, PI:
Table 1: Data extraction table – Data extraction from the included studies with data extraction
Ac ce p
Study Characteristics
te
d
template adopted from the Cochrane group.
Table 1 summarizes the characteristics of the included studies. Ten studies were
found to examine the effectiveness of psychosocial interventions on depressive symptoms in people with diabetes, of which eight studies were reported in the last five years. Six of the studies were RCTs and the remaining four were NRSs; one was a quasi-experimental trial, two were pre-post test studies, and one was a case series descriptive study. Six studies were conducted in the United States, three were in Europe, and one was in Thailand. The sample sizes were in general small, with eight studies having fewer than 100 participants. For RCTs, this ranged from 30 participants in Simson et al’s (2008) study to 339
17 Page 17 of 53
in Piette et al’s (2011), whereas for NRSs, the range was smaller with five participants in Gonzalez et al’s (2010) study to 90 in Georgiades et al’s (2007) study. The rates of attrition varied amongst the studies. The RCT by Simson et al (2008) which conducted psychotherapy
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on hospitalised patients, had no attrition while the van Bastelaar et al (2011) RCT web-based study had the highest attrition rate of 42%.
cr
Overall, the participants were mainly 50-60 years old and a large majority were
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Caucasians. A significant number of participants had Type 2 diabetes, with seven studies recruiting participants with Type 2 diabetes only. The remaining three studies had 69%
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(Georgiades et al., 2007), 80% (Simson et al., 2008), and 55% (van Bastelaar et al., 2011) of participants with Type 2 diabetes.
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Most of the studies employed CBT in their psychosocial interventions, addressing maladaptive behaviours and negative emotions by focusing on problems and difficulties that
d
the clients were facing and developing strategies to address them (Lustman et al., 1998). The
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six RCTs and one quasi-experimental trial contained a comparison control group that
Ac ce p
received usual standard diabetes care from the primary physician and in some cases, additional brochures, information, resources, and/or psychoeducation for depression. Interventions were conducted in a myriad of ways including individual therapy, group therapy, and telepsychiatry via the telephone and internet. The two RCT studies with interventions involving telepsychiatry correspondingly had the largest sample size of 339 (Piette et al., 2011) and 255 (van Bastelaar et al., 2011). These studies, however, also had the highest attrition with only half of the participants completing 17 out of 21 sessions in the Piette et al (2011) study while the van Bastelaar et al (2011) study had 42% attrition. There were two studies that had additional exercise interventions; the NRS by de Groot et al (2012) and the RCT by Piette et al (2011). The interventions were carried out by a number of health professionals including psychologists and mental health nurses. 18 Page 18 of 53
The intervention duration was varied with the shortest being conducted by the RCT by Pibernik-Okanovic et al (2009), lasting six weeks, and the longest was conducted by the RCT by Piette et al (2011), lasting twelve months. The studies correspondingly had the least
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and most number of interventions of four and twenty-one sessions respectively.
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Outcome Results
Depressive symptoms. A number of different tools were used by the studies to
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measure depressive symptoms. Based on their respective validated tools, it was found that participants of the included studies had a mean indicative of mild to major depression at
an
baseline. Post-intervention, the NRS by Rungreangkulkij et al (2011) and the RCT by Penckofer et al (2012) had mean scores indicative of depression remission in their
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intervention group, while the rest of the studies that reported post-intervention mean scores had improved depression scores. Nine out of ten of the studies reported that psychosocial
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interventions were effective for depression in their study report. The RCT conducted by
Ac ce p
group.
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Pibernik-Okanovic et al (2009), however, reported no effect when compared with the control
Effect size. Three studies did not report their participant’s mean and SD scores,
therefore their effect sizes could not be calculated. These were the RCTs by Lustman et al (1998), Pibernik-Okanovic et al (2009), and van Bastelaar et al (2011). SMD was calculated for the three RCTs with two studies having a small effect of -0.42 (Piette et al., 2011) and 0.24 (Simson et al., 2008), while the other one having a large effect of -0.81 (Penckofer et al., 2012). SMD for quasi-experimental trial for Rungreangkulkij et al (2011) was -1.74.. SMGS was calculated for the three NRS and the effect sizes were mostly large with -0.91 for de Groot (2012), -1.22 for Georgiades et al (2007), and -0.55, -1.87, and-1.94 for Gonzalez et al (2010) with the respective depression scales. 19 Page 19 of 53
Out of the seven effect sizes calculated, however, only five were statistically significant. NRS by Gonzalez et al (2010) and RCT by Simson et al (2008) had CIs that included 0, indicating that the intervention had no effect. These two studies also had the
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smallest sample sizes of five and thirty respectively. Four studies conducted follow-up assessments whilst only two reported on their
cr
scores. The RCT by van Bastelaar et al’s (2011) had a small SMD effect size of -0.29 at one
us
month follow-up and the de Groot et al’s (2012) NRS had a large SMGS effect size of -0.85 at three months, and both results were statistically significant.
an
Glycaemic control. Six studies included glycaemic control as their outcomes. Of which, two were NRSs and four were RCTs. At baseline, the study participants had a mean
M
HbA1c score of 7.4% to 9.47% and Lustman et al’s (1998) participants had a mean GHb score of 10.3%. Post intervention, the mean HbA1c scores of the participants remained above 7.0%.
d
Four studies demonstrated slight improvement, one had deterioration, while the last showed
te
no change in the mean score. Apart from the NRS by de Groot et al (2012) , that had a
Ac ce p
moderate effect of -0.40, the effect sizes of the studies that reported the SD were all statistically insignificant.
For follow-up assessment, three studies that conducted follow-up reported improved
scores. The scores however, were either statistically insignificant (de Groot et al., 2012) or their statistical significance could not be determined because the SD scores were not provided (Lustman et al., 1998; Pibernik-Okanovic et al., 2009). The psychosocial interventions presented in this review were disparate in their intervention type, delivery method, duration, and intensity. Although a meta-analysis can increase the statistical power of the results and broaden the generalisability of the interventions to the target population (Borenstein, Hedges, Higgins, & Rothstein, 2009), the 20 Page 20 of 53
diversity of the interventions in this review made it implausible to combine the effect sizes into a meta-analysis. It was therefore decided not to conduct a meta-analysis.
H
H
L
Blinding of participants and personnel
H
H
H
Blinding of outcome assessment
H
H
Incomplete outcome data
L
Selective reporting
Other bias
L
L
U
U
L
H
L
L
U
U
L
H
H
H
H
H
H
M
d
te
H
van Bastelaar 2011
H
ip t
Allocation concealment
Simson 2008
L
cr
L
Rungreangkulkij 2011
Penckofer 2012
H
Piette 2011
Lustman 1998
H
us
Gonzalez 2010
H
Pibernik-Okanovic 2009
Georgiades 2007
Random sequence generation
an
de Groot 2012
Bias Assessment
L
U
U
U
L
U
U
H
U
L
L
H
H
L
L
L
L
L
U
H
U
H
U
U
U
H
H
H
L
L
L
L
L
L
H
L
Ac ce p
H
H: High risk, L: Low risk, U: Unclear risk
21 Page 21 of 53
Table 2: Risk of bias assessment for included studies
As can be seen in Table 2 above, the methodological qualities were in general poor
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across the included studies with some studies having more significant numbers of risk for
cr
bias than others. Some lapses in the quality were inevitable due to the inherent nature of the
study design and intervention. The selection bias was high for the NRSs due to confounding
us
factors that cannot be distributed to a control group – as in the case of randomised studies (Kunz, Vist, & Oxman, 2008). Performance bias was high for all the included studies because
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blinding of participants and some research personnel was not possible due to participants’ direct contact with the therapist delivering the intervention. Five out of six RCTs did not
M
report on the blinding of outcome assessors and hence the risk is unclear. Some studies also failed to report on attrition of participants from the time of enrolment to the end of the study,
d
making it difficult to assess attrition bias. In addition, a few studies did not carry out
te
Intention-To-Treat (ITT) analysis, leading to attrition bias. Up to six studies did not have a
Ac ce p
study protocol available, making it difficult to assess reporting bias. All studies had at least two risks for bias. The strongest study was conducted by Lustman (1998) who had only two high risks of bias but the non-randomised studies had on average five out of the seven high risk of bias categories.
Discussion Key Results Depressive symptoms. With the exception of the Pibernik-Okanovic et al (2009) RCT, all included studies concluded that psychosocial interventions were effective in 22 Page 22 of 53
improving depression. The effect sizes calculated for a number of studies revealed that the interventions were generally consistent with this conclusion. The two studies (Gonzalez et al., 2010; Simson et al., 2008) with statistically insignificant effect could be attributed to their
ip t
small sample sizes which generally have lower statistical power and predictive value (Button et al., 2013). Only two studies provided data to calculate follow-up assessment results and the
cr
evidence suggested that psychosocial interventions have some lasting effect as the
us
participants’ depressive symptoms remained improved over the next few months.
However, taking the methodological quality of the included studies into account, this
an
conclusion is at best overconfident. There were quite a few less than rigorous RCTs in this review that had high numbers of risk of bias and under-reporting of blinding and the
M
availability of protocol, which could cause an overestimation of the effect and consequently a false conclusion (Detsky, Naylor, O’Rourke, McGeer, & L’Abbé, 1992). In addition, the non-
d
randomised studies unequivocally scored high in selection biases, compromising the
Ac ce p
2003).
te
methodological quality of the study, and hence, their results can be misleading (Deeks et al.,
Psychosocial interventions in this review were delivered in a myriad of ways. This
included individual therapy, group therapy, the internet, and the telephone. The RCT interventions delivered via technology by Piette et al (2011) – telephone – and by van Bastelaar et al (2011) – web-based – were collectively termed as telepsychiatry. Telepsychiatric studies have the largest sample size in this review, with both studies
recruiting over 250 participants, increasing statistical power. However, telepsychiatry can have favourable and unfavourable factors. It eliminates geographical barriers – which can be convenient and reach out to a larger group as well as provide real time support, it can increase disclosure – which can be helpful for clients who find face-to-face contact confronting, and it 23 Page 23 of 53
is cost-effective (Christensen, Griffiths, & Jorm, 2004; Mohr et al., 2005; Proudfoot et al., 2004). It could, on the other hand, also be impersonal, lacking in accountability, difficult to assess non-verbal cues, such as body language, which compromise the therapeutic bond, and
ip t
is associated with high attrition rates (Bee, Lovell, Lidbetter, Easton, & Gask, 2010; Eysenbach, 2005). Nevertheless, telepsychiatry was generally reported to be effective for
cr
those who completed the intervention (Piette et al., 2011; van Bastelaar et al., 2011), but the ITT effect sizes were small due to high drop-out rates. Research also found that
us
telepsychiatry could be an effective adjunct to enhance adherence to medical management of
(Clough & Casey, 2011; Hunkeler et al., 2000).
an
chronic diseases and the effectiveness of face-to-face psychotherapy in improving depression
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The three studies with a larger statistically significant effect size involved group therapy. They were NRSs conducted by Georgiades et al (2007) and Rungreangkulkij et al
d
(2011), and RCT by Penckofer et al (2011). In a group setting, there is camaraderie and
te
people feel less isolated in their struggle with diabetes and co-morbid depression as they receive feedback from one another (White, 2000). Sharing of experiences with others not
Ac ce p
only helps the participants identify and support each other, but it is also cathartic, reducing their stress and sense of hopelessness, and alleviating their depression (Gilbert & Procter, 2006). In addition, group therapy facilitates peer learning, as everyone in a group brings in various perspectives in a safe environment supervised by a trained professional (Corey, 2012). The common criticism about group therapy, however, is that it is not tailored to individual needs and some people may find it more confronting to disclose to a group than to a mental health professional individually, who is less likely to judge them (Morrison, 2001). Notwithstanding this criticism, people with diabetes and co-morbid depression have very specific needs that are likely to be similar to each other and can therefore gain more support and benefit from group therapy, as evident from the results of the studies. In a practical sense, 24 Page 24 of 53
group therapy is also a more cost-effective option as fewer resources and manpower are needed per client, as compared to individual therapy (Tucker & Oei, 2007). Besides the delivery method, the optimal number, frequency, and intensity of
ip t
psychotherapy sessions for people with diabetes and co-morbid depression are unknown in
this review. However evidence suggests that there are many factors such as the nature of the
cr
therapy, the severity of the co-morbid diseases, and the progress of the client that come into
us
play (Barkham et al., 2006). Evidence also suggests that longer and more intensive treatments lead to improved depression but there is a limit where further treatments can lead to
an
diminishing gains (Cuijpers, Huibers, Ebert, Koole, & Andersson, 2013; Cuijpers et al., 2010; Hansen, Lambert, & Forman, 2002). This was reflected in the two RCTs with the shortest
M
contact time in this review – Pibernik-Okanovic et al (2009) and Simson et al (2008), which
d
had consequently little or no significant effect on participants’ depression status.
te
The two studies; NRS by de Groot et al (2012) and Piette et al (2011), that included an exercise intervention in conjunction with CBT reported positive results, but it was unclear
Ac ce p
which intervention brought the desired outcome. Exercise is generally recognised to have some anti-depressant effects and is beneficial for the glucose metabolism in people with diabetes (De Feo et al., 2006; Rimer et al., 2012). A mix of health professionals were employed to carry out the psychosocial
interventions in this review including psychologists and nurses trained in psychiatry. Mental health resources are grossly inadequate globally and there is a stark gap between service users and service providers (Saraceno & Saxena, 2002; WHO, 2013c). The success of nurses administering the interventions was reported in the various included studies (Penckofer et al., 2012; Piette et al., 2011; Rungreangkulkij et al., 2011). In psychosocial interventions, the therapeutic alliance that a therapist has with the individual is one of the pivotal factors in 25 Page 25 of 53
determining a successful treatment (Feller & Cottone., 2003). This important bond formed between the therapist and client helps the individual work towards agreed upon goals and/or tasks (Del Re, Flückiger, Horvath, Symonds, & Wampold, 2012). As nurses are important
ip t
partners of healthcare for people with diabetes, and hence have a therapeutic relationship with them, they are befitting to receive extra training to facilitate psychosocial interventions to
cr
help alleviate the mental health resource burden of people with co-existing diabetes and
us
depression (Peyrot, Rubin, & Siminerio, 2006).
Glycaemic control outcome. Six studies measured the effect of the interventions on
an
glycaemic control, and the results were mostly statistically insignificant. All the studies used Glycosylated Haemoglobin to operationalise glycaemic control and this blood test measures
M
the average blood glucose level over about three months (Sacks et al., 2011). It could be difficult to determine the effects of psychosocial interventions on glycaemic control when
Ac ce p
Limitations
te
take place.
d
most interventions were three months or less, giving less than sufficient time for the effect to
The overall evidence is limited in its applicability due to the following aspects
explored below.
Firstly, although the search yield was high, almost half of the studies consisted of
participants that were not relevant to our review. It is possible that the search terms were not specific enough as a large proportion of screened papers were also not included, with only ten out of 139 papers included. However, the experience of conducting this literature review has shown us that search terms are no guarantee for locating all the relevant literature as this review located papers that other reviews did not. The search was repeated many times by the authors, independently, over the course of the study to obtain all the relevant literature. 26 Page 26 of 53
The studies included in this review were mostly conducted in the United States and Europe. The recent IDF diabetes atlas found that the regions with the highest prevalence of diabetes are actually in the Western Pacific and South East Asia region (IDF, 2013a). This
ip t
review could therefore be limited in its generalisability as the demographics, healthcare system, culture, and social environment differs across the world, and many regions of the
cr
world are under-represented (Watts, Phillips, Petticrew, Harden, & Renton, 2011).
us
There were also predominantly more female than male participants in this review despite diabetes having similar prevalence rates in men and women (IDF, 2013a), and one
an
study (Penckofer et al., 2012) focused its intervention solely on women. Research has shown that women were more likely to seek help and get diagnosed for their depression than men
M
(Branney & White, 2008; Ogrodniczuk & Oliffe, 2010). Also, women in the 50 to 60 years age group were likely to be going through menopause, or be in their early postmenopausal
d
years which could represent a time of vulnerability, increased psychological distress, and an
te
increased risk of depression (Bromberger et al., 2001; Soares & Zitek, 2008). Therefore, it is
Ac ce p
vital to consider gender sensitivity when implementing interventions to help people with diabetes and co-morbid depression. There were significantly more participants with Type 2 diabetes in this review than
Type 1, with seven out of ten studies having participants with Type 2 diabetes only. Although at least nine out of ten cases of diabetes are Type 2 worldwide (IDF, 2013b), more than half of the included studies were aimed and tailored for people with Type 2 diabetes only, limiting the applicability to the population with Type 1 diabetes. Considering the population with Type 1 diabetes would have been living with their chronic disease a few decades longer than their Type 2 counterparts, their psychological and support needs could be different. Furthermore, the prevalence of depression is higher in those with Type 1 diabetes (Roy & Lloyd, 2012). Research evidence exploring psychological interventions in patients with Type 27 Page 27 of 53
1 diabetes found that it was ineffective in improving glycaemic control and psychological distress in adults (Winkley, Landau, Eisler, & Ismail, 2006). Since the three studies in this review that had both Type 1 and 2 diabetes population did not report on the differential
interventions were effective across the two types of diabetes.
ip t
outcome for the respective diabetes types, it cannot be determined if psychosocial
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Another limitation that affects the generalisability of the results is the small sample
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size of the studies, with eight out of ten studies having fewer than 100 participants. Small samples can lead to underpowered studies, risking type 1 or type 2 errors. These issues when
an
combined with an inability to conduct a meta-analysis contribute to the need to be cautious when applying the findings (Borenstein et al., 2009).
M
The chief psychosocial intervention used in this review was mainly CBT and other psychotherapeutic techniques like problem-solving (PST), motivational, and social therapy
d
were not explored. Different psychotherapeutic techniques have various theoretical models
te
underpinning its approaches and hence leading to varying emphasis in therapy (National
Ac ce p
Collaborating Centre for Mental Health & National Institute for Health and Clinical Excellence., 2011). With the majority of studies using CBT as their psychosocial intervention, other psychological approaches like social therapy which focuses on the client’s social interaction, environment, and network, or PST which aids in identifying complicated issues in the client’s life and developing a structured approach to solve it were not explored. This can limit the applicability of the broad term ‘psychosocial interventions’ when replicating the results of this review. The control group in the various randomised studies received usual care from their respective GPs and this differs from each country and healthcare system. Therefore, the control groups across the included studies were not similar and this non-standard control 28 Page 28 of 53
group could act to enhance or diminish the effects of the intervention, limiting the validity of the review’s results. Many studies did not conduct follow-up assessments after the interventions and this
ip t
limits our knowledge on the long-term effects of the psychosocial interventions. Studies have found that up to 80% of people with diabetes and co-morbid depression will have a
cr
depressive symptoms relapse over five years (Katon, 2008). Since follow-up assessments
us
were not conducted, it cannot be determined if psychosocial interventions can prevent
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relapses of depression for these individuals.
Another important limitation to this review was the considerable number of studies
M
with missing and/or had incomplete post-intervention results. Although most studies reported on baseline mean and SD for the outcome to be measured, three studies failed to report on
d
post-intervention mean and SD, making the calculation of effect size impossible, affecting the
te
completeness of this review’s analysis.
Ac ce p
Lastly, the question of cost-effectiveness was not addressed. Examining the costeffectiveness of an intervention and its potential resource utilisation before implementation is both practical and prudent especially when health care resources are scarce (Williams & Bryan, 2007).
Quality of Evidence
One major bias in this review was the lack of a research protocol. Without a protocol, the review could appear unplanned with the potential bias of selective reporting and adjustment of inclusion criteria, and can therefore compromise the quality of the review (Chan et al., 2013). Some bias were also observed in the search process. Only general 29 Page 29 of 53
purpose databases were sourced for research studies for this review. Other sources of evidence like grey literature, unpublished study reports, hand-searching of research journals, or culling the reference lists of relevant research articles may have provided a different search
ip t
yield (Schlosser, 2007). Published research has a high propensity to be more positive and overestimate the effect size than unpublished research, and research with non-significant
cr
findings tend to take longer to achieve publication than those that has positive results, or not
get published at all (Dirnagl & Lauritzen, 2010; Ioannidis, 2005). Overreliance on published
us
literature, therefore, may not give us a comprehensive understanding of what works for the
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target population, and the results of this review may be misleading (Dwan et al., 2008). Also, only English language articles were included in this review which can narrow the
M
generalisability of the findings to populations with an English speaking background which does not represent the totality of the evidence available for the problem area.
d
The included studies have varying methodological quality – some of which were
te
inevitable due to the nature of the study such as high selection bias risk in NRSs and a high
Ac ce p
performance bias risk across all studies due to the nature of psychosocial intervention. All randomised studies, in general, had less bias than the non-randomised studies due to the act of randomisation which lowers selection bias by distributing confounders thus increasing comparability (Kunz, Vist, & Oxman., 2008). The participants with diabetes and co-morbid depression who signed up for the research study, for example, were probably motivated for a change and believed in the effectiveness of psychosocial interventions. These characteristics in the participants could serve as confounders as motivation is vital for treatment effectiveness in psychotherapy (Ryan, Lynch, Vansteenkiste, & Deci, 2011), and could make them unrepresentative of the target population (Mein et al., 2012). Many randomised studies, however, failed to report on blinding of the outcome assessment. Neglecting to report on blinding is common in many research studies but insufficient information regarding the 30 Page 30 of 53
blinding process or whether it was conducted can be misleading (Schulz & Grimes, 2002). If it was the case that blinding was not carried out, differential treatment of the participants by the outcome assessors could occur and influence the psychological response of participants
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(Supino, 2012). Some randomised studies were quite poorly conducted and/or neglectful in their reporting such as that of the RCTs by Pibernik-Okanovic et al (2009), Simson et al
cr
(2008), and van Bastelaar et al (2011). The many high risks of bias in the included RCTs
challenges the notion that RCTs are the gold standard for research evidence and can provide
us
an inaccurate sense of security with regards to their results (Peinemann et al., 2013). One
an
NRS(de Groot et al., 2012), albeit high in selection bias, was fairly well-conducted detailing the attrition data and providing most of the vital information, following their protocol, while
M
the other two NRSs were not as well conducted and had multiple high bias risks. As most of the studies were carried out within the same healthcare site and system,
d
contamination of the intervention and control group could have occurred. Participants in the
te
control group received their care as usual from their GPs who had the opportunity to come into contact with the intervention group participants attending the same clinic, resulting in the
Ac ce p
diffusion of treatment, and causing the results to be biased. This is with the exception of van Bastelaar et al (2011) who advertised their intervention to the public instead of relying on a particular health site to advertise their study. The results of this review need to be taken with caution due to the biases previously
mentioned. Some of the study findings may well be equivocal but are still worth reporting as they contribute to the knowledge base on this important topic. Ultimately, health professionals need to tap into their clinical expertise and judgement, and use best practice evidence, regarding what works for each of their individual, taking into account their condition, predicament, and preferences to determine the intervention’s appropriateness (Evans, 2003). 31 Page 31 of 53
Potential Biases in the Review Process
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Other Reviews and Ongoing Studies The other reviews identified in the introduction had similar conclusions to this review,
cr
stating that psychosocial interventions have a positive impact on depression in people with
diabetes, but no observable impact on glycaemic control. In addition, more primary research
us
studies are needed to inform the evidence. This review included the most number of
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psychosocial studies, having at most three similar studies to the previous reviews, and therefore adds to the knowledge base. The two relevant study protocols discovered in the
M
search are yet to be published and will contribute to our understanding of interventions to
Conclusions
d
support the target population.
te
While the conclusions of the included studies and previous reviews, and the effect
Ac ce p
sizes calculated in this review reported the effectiveness of psychosocial interventions for people with diabetes and co-morbid depression, the high risks of bias found in the included studies in this review made it difficult to agree with this standpoint. The effect of the interventions on glycaemic control, remained inconclusive. Group therapy stands out in its effectiveness and practicality in terms of limited
mental health resources. In spite of the effectiveness of the group therapy modality, however, comparative methods research study would need to be conducted to confirm its superiority over other delivery methods (Hanneman, 2008). Telephone and web-based interventions, albeit associated with high attrition, are nevertheless convenient and may be considered as an option for people living in remote areas or who have mobility difficulties. After all, the 32 Page 32 of 53
clients’ modality preference is worth considering when choosing a psychosocial intervention to ensure better outcomes and lower attrition rates (Swift, Callahan, Ivanovic, & Kominiak, 2013). Health professionals, especially nurses who already have developed a rapport with
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clients, can be trained in psychotherapeutic techniques and effectively carry out these psychosocial treatments.
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The generalisability of the results was found to be limited and the methodological
us
quality was lacking in most studies. Therefore, replication of the interventions needs to be taken with caution. More well-conducted RCTs addressing the effectiveness of psychosocial
an
interventions for people with diabetes and co-morbid depression, with measures taken to lower biases and utilise ITT analysis, need to be conducted. If possible, studies should
M
routinely offer the control group the intervention after the study is completed to make them feel valued, and also match baseline characteristics to increase equivalence between the
d
groups. It is recommended that these RCTs examine the effectiveness of group therapy, and if
te
possible, compare this method to other delivery methods and/or psychosocial techniques.
Ac ce p
Over the last 15 years, only ten studies that researched this topic were identified and ironically, there were a number of reviews in recent years that identified this problem area and similarly concluded that more original research studies needs to be conducted. What is promising, however, is that eight of these ten studies were conducted in the last five years and two more ongoing studies were also identified, highlighting the research priority of this problem area. Ultimately, health professionals need to draw on their own experiences when managing people living with diabetes and co-morbid depression, and take into account their individual characteristics and preferences before making an informed decision regarding treatment options (Jenicek, 2006) as there is no panacea for the management of diabetes and co-morbid depression.
33 Page 33 of 53
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Diabetes Care, 31(S1),S55-S60. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental
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disorders: DSM-IV-TR. Washington, DC: American Psychiatric Association.
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Balhara, Y., & Verma, R. (2013). Management of depression in diabetes: a review of psychosocial interventions. Journal of Social Health and Diabetes, 1(1), 22-26.
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Baumeister, H., Hutter, N., & Bengel, J. (2012). Psychological and pharmacological interventions for depression in patients with diabetes mellitus and depression. The
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Cochrane Library 2012, (12),1-119.
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Barkham, M., Connell, J., Stiles, W. B., Miles, J. N. V., Margison, F., Evans, C., & Mellor-
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Clark, J. (2006). Dose-effect relations and responsive regulation of treatment duration:
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Appendix 1: Database Search Results CINAHL Search term entered
Hits
1.
Diabet* (Search as Keyword) OR Diabetes Mellitus (MeSH;
111,003
Explode) OR Diabetes Mellitus, Type 1 (MeSH; Explode) OR Co-morbid Depression (Search as Keyword) OR Comorbid
cr
Diabetes Mellitus, Type 2 (MeSH) OR Diabetic Patients (MeSH) 2.
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No
59,012
us
Depression (Search as Keyword) OR Depression, Reactive (MeSH) OR Depression (MeSH; Explode)
Psychosocial Interventions (Search as Keyword) OR Support,
an
3.
83, 351
Psychosocial (MeSH; Explode) OR Psychosocial Care (MeSH; Explode) OR Rehabilitation, Psychosocial (MeSH, Explode) OR
M
Psychological interventions OR Behaviour Therapy (MeSH; Explode) OR Counseling (MeSH; Explode) OR Peer Counseling Psychoeducation (MeSH)
Glyc*mic control (Search as Keyword) OR Hemoglobin A,
te
4.
d
(MeSH) OR Teaching, Guidance, and Counseling (MeSH) OR 17, 599
Glycosylated (MeSH) OR Depress* symptom (Search as Keyword)
6.
1. AND 2. AND 3. AND 4.
51
Limiter: Published from 1998-2013
49
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5.
Cochrane Library No 1.
Search term entered
Hits
Diabet* (Search as Keyword) OR Diabetes Mellitus (MeSH;
31, 087
Explode) OR Diabetes Mellitus, Type 1 (MeSH; Explode) OR Diabetes Mellitus, Type 2 (MeSH) OR Diabetic Patients (MeSH)
2.
Co-morbid Depression (Search as Keyword) OR Comorbid
11, 539
Depression (Search as Keyword) OR Depression (MeSH; Explode) OR Depressive Disorder (MeSH; Explode) OR Depressive Disorder, Major (MeSH; Explode)
50 Page 50 of 53
3.
Psychosocial Interventions (Search as Keyword) OR Psychological
23, 605
Techniques (MeSH; Explode) OR Social Support (MeSH; Explode) OR Behaviour Therapy (MeSH; Explode) OR Nondirective Therapy (MeSH; Explode) OR Cognitive Therapy (MeSH; Explode) OR Explode) OR Psychotherapy, Group (MeSH; Explode) OR Explode) OR Peer Group (MeSH; Explode) 4.
Glyc*mic control (Search as Keyword) OR Hemoglobin A,
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Psychotherapy, Multiple (MeSH; Explode) OR Counseling (MeSH;
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Psychotherapy (MeSH; Explode) OR Psychotherapy, Brief (MeSH;
18, 102
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Glycosylated (MeSH; Explode) OR Depress* symptom (Search as 1. AND 2. AND 3. AND 4.
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Limiter: Published from 1998-2013
54 54
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5.
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Ovid MEDLINE Search term entered
1.
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Explode) OR Diabetes Mellitus, Type 1 (MeSH; Explode) OR
2.
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Diabetes Mellitus, Type 2 (MeSH) OR Diabetic Patients (MeSH) Co-morbid Depression (Search as Keyword) OR Comorbid
155,279
Depression (Search as Keyword) OR Depression (MeSH; Explode) OR Depressive Disorder (MeSH; Explode) OR Depressive Disorder, Major (MeSH; Explode)
3.
Psychosocial Interventions (Search as Keyword) OR Psychotherapy
302,831
(MeSH; Explode) OR Social Support (MeSH; Explode) OR Behaviour Therapy (MeSH; Explode) OR Cognitive Therapy (MeSH; Explode) OR Peer Group (MeSH; Explode) OR Counseling (MeSH; Explode) OR Psychotherapy, Group (MeSH; Explode) OR Patient Education as Topic (MeSH; Explode)
4.
Glycemic control (Search as Keyword) OR Hemoglobin A,
148,774
Glycosylated (MeSH; Explode) OR Blood Glucose (MeSH; 51 Page 51 of 53
Explode) OR Depress* symptom (Search as Keyword) 5.
1. AND 2. AND 3. AND 4.
92
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Limiter: Published from 1998-Current
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Diabet* (Search as Keyword) OR Diabetes Mellitus (MeSH; Explode) Co-morbid Depression (Search as Keyword) OR Comorbid
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2.
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No
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PsycINFO
Depression (Search as Keyword) OR Major Depression (MeSH; Reactive Depression (MeSH; Explode) 3.
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Explode) OR “Depression (Emotion)” (MeSH; Explode) OR
Psychosocial Interventions (Search as Keyword) OR Psychosocial
258,936
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Rehabilitation (MeSH; Explode) OR Psychotherapy (MeSH; Explode) OR Cognitive Behaviour Therapy (MeSH; Explode) OR Psychotherapeutic Techniques (MeSH; Explode) OR Counseling
d
(MeSH; Explode) OR Counseling Psychology (MeSH; Explode) OR
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Educational Counseling (MeSH; Explode) OR Group Counseling (MeSH; Explode) OR Peer Counseling (MeSH; Explode) OR
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Psychotherapeutic Counselling (MeSH; Explode) OR Group Psychotherapy (MeSH; Explode) OR Social Support (MeSH; Explode) OR Support Groups (MeSH; Explode) OR Psychoeducation (MeSH; Explode)
4.
Glycemic control (Search as Keyword) OR Blood Sugar (MeSH;
3,069
Explode) OR Depress* symptom (Search as Keyword)
5. 6.
1. AND 2. AND 3. AND 4.
11
Limiter: Published from 1998-2013
11
52 Page 52 of 53
Contribution of the Paper What is already known about the topic? - Psychosocial interventions are found to be effective in improving depression in people with
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diabetes and co-morbid depression
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What this paper adds?
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- This paper informs the global population with diabetes and co-morbid depression of the various treatment options for their depression
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to be the most effective in improving depression
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- Different treatment modalities were explored and compared, and group therapy was found
- Health professionals other than psychologists can also carry out these interventions to good
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effect
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- This paper provides a comprehensive summary of all psychosocial interventions, adding to the knowledge base of treatment for people with diabetes and co-morbid depression
53 Page 53 of 53
S0020-7489(15)00189-3 http://dx.doi.org/doi:10.1016/j.ijnurstu.2015.05.012 NS 2577
To appear in: Received date: Revised date: Accepted date:
19-6-2014 7-4-2015 29-5-2015
Please cite this article as: Kok, J.L.A., Williams, A., Zhao, L.,Psychosocial Interventions for People with Diabetes and Co-morbid Depression. A Systematic Review., International Journal of Nursing Studies (2015), http://dx.doi.org/10.1016/j.ijnurstu.2015.05.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Psychosocial Interventions for People with Diabetes and Co-morbid Depression. A
an
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Systematic Review.
Jing Li Amy Kok, Monash University, BNur (Hon)
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Allison Williams, Associate Prof, Monash University, PhD, MN, Ba AppSc Nur, Grad Dip
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Adv Nur, RN
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Lin Zhao, Lecturer, Monash University, PhD, MN, GCHPE, RN
Correspondence: Jing Li Amy Kok, [email protected] (email)
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Structured Abstract Aims and objectives: To examine the effectiveness of psychosocial interventions on depressive symptoms and glycaemic control of adults with Type 1 or 2 diabetes and co-
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morbid depression.
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Background: Diabetes is a chronic disease that affects as many as 382 million people in the
world. Diabetes management is a challenging daily task which can be overwhelming and lead
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to depression. Both diabetes and co-morbid depression have a negative bidirectional influence on each other, which is detrimental for the individual's quality of life. This co-
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morbidity places a huge burden on the individual, family, health care system, and the
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economy, with an associated 50-75% increase in health care costs. Design: A systematic review
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Methods: Four electronic databases were searched including Cochrane Library, CINAHL,
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MEDLINE, and PsycINFO for articles written in English from the year 1998 to 2013. Data
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extraction of vital information from included studies was conducted and the effect sizes were calculated for the outcomes.
Results: Ten interventional primary studies were retrieved from the search; six were randomised studies. Nine out of ten studies reported that psychosocial interventions were effective for depression with effect sizes ranging from small (-0.24) to large (-1.74). No effect was found for the glycaemic control outcome. The interventions came in a myriad of intervention type, delivery method, duration, and intensity, therefore a meta-analysis was not conducted. The review found that nurses were competent in delivering psychosocial interventions for this population. Methodological quality was below average amongst the study with various biases present.
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Conclusions: The results and effect sizes were promising but due to the high number of bias risks, it cannot be determined if psychosocial interventions were found to be effective in people with diabetes and co-morbid depression.
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Relevance to clinical practice: Psychosocial interventions have the potential to make
improvements in depression, alleviating the global burden on people with diabetes, keeping
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in mind the best intervention modality tailoring to the client’s needs and preferences. Nurses
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and other health professionals involved in caring for this group are in a good stead to carry
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out these interventions.
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Keywords: co-morbidity; depression; depressive symptoms; diabetes mellitus; glycaemic
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control; nurses; psychosocial interventions ; systematic review
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Introduction Background Diabetes is a chronic metabolic endocrine disorder characterised by the inability of
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the body to metabolise glucose effectively (Torpy, 2011). Type 1 and Type 2 diabetes are the
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most common types of diabetes requiring long term management, with the occurrence of
Type 1 diabetes being approximately 5% and Type 2 diabetes accounting for 90-95% of all
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diabetes diagnoses (Centers for Disease Control and Prevention, 2011). Although Type 1 and Type 2 diabetes are pathophysiologically different, their clinical symptoms are to a certain
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extent similar and require similar chronic management needs. Both diabetes types are associated with chronic hyperglycaemia that necessitates adequate management to prevent
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long term complications including nephropathy, neuropathy, and retinopathy (American
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Diabetes Association, 2008).
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The incidence of diabetes has reached epidemic levels in recent years, with 382 million people living with diabetes globally in 2013 and a projected increase to 592 million in
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2035 as the population continues to grow, age, and urbanise alongside decreasing physical activity and increasing obesity rates, posing a public health threat worldwide (International Diabetes Federation [IDF], 2013a; Wild, Roglic, Green, Sicree, & King, 2004). People with diabetes often liken having this chronic disease to a life sentence (Barnett, 2010), as managing diabetes requires a daily commitment of monitoring one’s diet, physical activity, general health, stress levels, blood sugar levels, and medication taking; tablets and/or insulin (Golden et al., 2008). Managing diabetes is more than just glycaemic control; it involves one’s emotional health as well (Fisher, Thorpe, Devellis, & Devellis, 2007). Sometimes, coping with diabetes becomes an insurmountable challenge, and the individual with diabetes may become overwhelmed and burdened by the demands of disease management, leading to 4 Page 4 of 53
feelings of anxiety, worry, guilt, helplessness, defeat, and overall feelings of depression (Katon, 2008). Depression is a common co-morbidity that accompanies Type 1 and Type 2 diabetes,
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and both diabetes and depression have negative bidirectional influence on each other that can be detrimental for the individual living with both conditions (Peyrot et al., 2013). Forty-three
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million people with diabetes worldwide were found to have clinical or sub-clinical depression
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in 2000 (World Federation for Mental Health, 2013). Depression occurs three times more frequently in people with Type 1 and two times more frequently in people with Type 2
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diabetes, compared to those without this chronic disease (Roy & Lloyd, 2012). Co-morbid depression in diabetes is often associated with poorer self-care and non-adherence to diabetes
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management, leading to increased episodes of hyperglycaemia, microvascular and macrovascular complications, and an associated increase in morbidity and mortality (Egede
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& Ellis, 2008; Petrak & Herpertz, 2009). Poorly controlled diabetes, along with short and/or
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long term complications can then lead to feelings of defeat, frustration, and powerlessness over the disease, exacerbating the depression (Coupe, Garrett, & Gask, 2012). Despite the
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burgeoning prevalence of this co-morbidity, depression often remains under-diagnosed and undertreated in individuals with diabetes (Campayo, Gómez-Biel, & Lobo, 2011; Cepoiu et al., 2008). Depression can be difficult to recognise and diagnose by health professionals because it shares many common symptoms with poorly managed diabetes such as fatigue, sleep disturbances, and weight and appetite changes (Egede & Ellis, 2009). The World Health Organisation [WHO] (2008) predicts that in 2030, depression and diabetes will be the first and tenth leading cause of the global burden of disease respectively. Co-morbid depression in people with diabetes not only affects their quality of life, it also impinges on healthcare resources and the economy. There is an increased need for medical attention, hospitalisation, and overall increased healthcare expenditure to manage the 5 Page 5 of 53
worse diabetes outcomes associated with co-morbid depression (Egede & Ellis, 2008). Simon et al (2005) estimated an increased burden of 50-75% of healthcare costs – including medical visits, prescriptions, laboratory tests, mental health treatments, and surgery, when depression
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co-existed in the person with diabetes. The co-morbidity is also associated with a greater decrease in work productivity, an increase in absenteeism, and a loss of disability-adjusted
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life years (McIntyre, Liauw, & Taylor, 2011). All these, by and large, place a substantial
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economic burden on the individual and the society.
Depression is commonly treated with a combination of pharmacotherapy and
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psychosocial therapy with good effect (Cuijpers, van Straten, Warmerdam, & Andersson, 2009). People who have diabetes, however, usually find it difficult to adhere to self-care
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regimens to manage their diabetes, exhibiting the second lowest adherence to care rate among 17 other chronic conditions (DiMatteo, 2004), and an additional task of taking
d
antidepressants is likely to be met with poor adherence (Rizzo, Creed, Goldberg, Meader, &
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Pilling, 2011). Research has also found that adherence to anti-depressants is low in primary care and patients have a preference towards psycho-interventions (van Schaik et al., 2003).
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Psychosocial therapy has been found to be robust in treating co-morbid depression seen in people with chronic medical diseases by addressing maladaptive coping responses, dysfunctional beliefs and emotions, and helping clients to adjust to and manage their health (Koenig, 2012; Serfaty et al., 2009). Since co-morbid depression is associated with poor diabetes outcomes including hyperglycaemia, it is also worth exploring the result of depression interventions on glycaemic control, which can give an indication of the individual’s management of their diabetes (Lustman et al., 2000). This review, therefore, examined the effectiveness of psychosocial interventions on depressive symptoms and glycaemic control of adults with Type 1 or Type 2 diabetes and co-morbid depression.
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A brief literature review revealed a number of primary research studies that explored psychosocial treatments for people with diabetes and co-morbid depression. There were, on the other hand, also a significant number of existing reviews that highlighted the need for
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more research in this field. There were the reviews conducted by Wang, Tsai, Chou, & Chen (2007), Petrak & Herpertz (2009), van der Feltz-Cornelis et al. (2010), Markowitz, Gonzalez,
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Wilkinson, & Safren (2011), Krishnadev et al (2011), and the Cochrane review by
Baumeister et al (2012) that looked at pharmacological, psychological, and collaborative
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treatment for the population with diabetes and co-morbid depression. These reviews were,
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however, dissimilar as they did not solely focus on psychosocial treatment and included few studies with this treatment option. Another review by van Straten, Geraedts, Leeuw,
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Andersson, & Cuijpers (2010) looked at the psychological treatment for a selection of medical illnesses and co-morbid depression, but only two studies included diabetes.
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Lastly, the more recent review by Balhara & Verma (2013) focused on psychosocial
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interventions on people with diabetes and co-morbid depression, and was the most similar to this review. However, the interventions of the included studies in this review were not purely
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psychosocial and consisted of collaborative interventions involving pharmacology. Keywords used in Balhara and Verma’s (2013) study included diabetes, depression, cognitive behavioral therapy, collaborative care, depression, diabetes, psycho-social interventions, behavioral therapy, treatment or management or intervention or trial, collaborative care, primary care, psycho-social, and psychotherapy, and did not provide a flow chart to show why papers were included or excluded. In addition, P values were calculated without effect sizes, and the risk of bias was not taken into consideration. Keywords used in Balhara and Verma’s (2013) study included diabetes, depression, cognitive behavioral therapy, collaborative care, depression, diabetes, psycho-social interventions, behavioral therapy, treatment or management or intervention or trial, collaborative care, primary care, 7 Page 7 of 53
psycho-social, and psychotherapy, and did not provide a flow chart to show why papers were included or excluded. Finally, Balhara and Verma (2013) identified only three studies that included purely psychosocial interventions for the population, whereas a literature review
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conducted in the current study found many more in the similar timeframe, providing justification to conduct this study. Key terms were refined (See Appendix 1) in an endeavour
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to clearly identify the knowledge gap of the effects of psychosocial interventions on people
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with diabetes and co-morbid depression. Aims
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This systematic review’s primary aim is to examine the effectiveness of psychosocial interventions on depressive symptoms in the adult population with Type 1 or Type 2 diabetes
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and co-morbid depression. The secondary aim is to examine the effectiveness of psychosocial
Method
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interventions on glycaemic control in this population.
Inclusion Criteria
Primary research studies investigating psychosocial interventions were included.
Studies had to also be conducted in English and published within the last 15 years; from 1998 to 2013. This review aimed to include recent data whilst including evidence from when research on psychosocial interventions on people with diabetes and co-morbid depression was first pioneered by Lustman, Griffith, Freedland, Kissel, & Clouse (1998). Participants had to be 18 years or older, and have an established diagnosis of Type 1 or Type 2 diabetes, and co-morbid depression. Depressive disorders are diagnosed by standard criteria such as those stated in the Diagnostic and Statistical Manual of Mental 8 Page 8 of 53
Disorder, Fourth Edition (DSM-IV) (American Psychiatric Association, 2000) or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) (WHO, 2013b). The term ‘depression’ in this review also includes those
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of subclinical depression or significantly elevated depressive symptoms. Although it is recognised that subclinical depression is less severe than clinical depression, subclinical
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depression also contributes to the co-morbidity and economic burden, and left untreated, it
can lead to clinical depression (Beck et al., 2011; Chisholm et al., 2003). Elevated depressive
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symptoms can be diagnosed using certain established depression screening tools commonly
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used in General Practices (GPs) with validated cut-off scores such as the Beck Depression Inventory (BDI) and Patient Health Questionnaire (PHQ) (Watnick, Wang, Demadura, &
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Ganzini, 2005).
In this review, interventions had to be psychosocial in nature, aimed at reducing
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depression, and comprising of therapies such as supportive counselling, Cognitive
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Behavioural Therapy (CBT), interpersonal therapy, Problem Solving Therapy (PST), or behavioural therapy (Cuijpers, Andersson, Donker, & van Straten, 2011). Other depression
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treatments like pharmacotherapy or electroconvulsive therapy were excluded as the focus of this review was to examine the effects of psychosocial interventions only. No restrictions were posed on study design and study duration including length of follow-up. Although Randomised Controlled Trials (RCTs) are the gold standard for research evidence, a brief literature review revealed a dearth of RCTs, therefore this review broadened the inclusion criteria to include non-randomised studies (NRS), such as observational studies, quasiexperimental trials, pretest-posttest study, to locate all possible existing evidence for the research question. NRS, when evaluated for risk of bias rigorously, can be very valuable in the search of answers to the research question (Peinemann, Tushabe, & Kleijnen., 2013;
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Shrier et al., 2007). In addition, NRS can guide recommendations for conducting subsequent clinical trials (Cochrane Non-Randomised Studies Methods Group., 2008). For the outcomes, changes in depressive symptoms were assessed and quantified by
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established depression questionnaires such as the BDI or PHQ to provide interval data,
through self-report or interview at baseline, post-intervention, and follow-up (Watnick et al.,
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2005). Changes in glycaemic control were measured by the Glycated Haemoglobin (GHb)
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test, such as the common Haemoglobin A1c (HbA1c) test.
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Exclusion Criteria
Studies were excluded if subjects had grief depression, suicidal ideations, drug and
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alcohol problems, and/or a diagnosis of other mental illnesses such as bipolar disorder, schizophrenia, or mental illnesses requiring ECT. Studies with participants who have other
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forms of diabetes besides Type 1 or Type 2 were also excluded. People who were in active
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treatment for other co-morbidities such as cancer, heart failure, or renal failure were excluded because the focus of this review was on diabetes and co-morbid depression only. Participants
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who have been prescribed anti-depressants recently (over the last six months) were excluded as it was assumed that these patients’ depression had not been fully stabilised with medication. However, those who were on anti-depressants for longer than six months indicating routine treatment were included as this review examined the effects of psychosocial interventions on current depressive symptoms. Search Strategy Four databases were searched, namely CINAHL, Cochrane Library, MEDLINE, and PsycINFO, with keywords and their associated Medical Subject Headings (MeSH) terms (See Appendix 1). These search terms were then exploded to increase the search yield. Each stage
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of the search and elimination process was documented in the adapted version of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow-chart (Moher, Liberati, Tetzlaff, Altman, & The PRISMA Group, 2009). This screening process was
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conducted independently by the reviewers to minimise bias.
Data Collection and Analysis Methods
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Relevant data of the studies’ characteristics were extracted and placed in the Cochrane
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data extraction template (Higgins & Green, 2011). All data extraction was conducted independently by the reviewers, with the main reviewer collating the data together onto the
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table. Effectiveness of the interventions is measured by quantifying and calculating the effect size of the outcomes, and examining its statistical significance (Cohen, 1988). Effect sizes
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were calculated to analyse the effectiveness of psychosocial interventions on the outcomes of
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depressive symptoms and glycaemic control (Diamond & Kaul, 2013). As different studies often use different instruments to measure outcomes – especially for depressive symptoms,
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calculation of the effect size puts the strength and direction of the treatment effect in a numerical form on the same scale, allowing the various treatment effects to be essentially comparable (Hedges, 2008). There are several different effect size indexes, depending on the data available for each study (Littell, Corcoran, & Pillai, 2008). The Mean Difference (MD) was calculated for randomised studies that used similar
outcome measures, while Standardised Mean Difference (SMD), or Cohen’s d was calculated for those with different outcome measures. For SMD, an effect size of 0.2 was considered small, 0.5 was moderate, and 0.8 was large (Cohen, 1988). In non-randomised studies, the Mean Gain Score (MGS) (Lipsey & Wilson, 2001) and Standardised Mean Gain Score (SMGS) (Becker, 1988) were calculated from studies using the same or different outcome 11 Page 11 of 53
measure respectively. The various effect sizes were calculated with their corresponding 95% Confidence Interval (CI) for the outcomes depressive symptoms and glycaemic control. A clinical judgement would subsequently be made regarding the pooling of the
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various effect size indexes in a meta-analysis. Pooling of the effect sizes can greatly increase the generalisability and statistical power of the results to detect a real effect that can be used
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to apply to the target population (Higgins & Green, 2011). It could, however, be unfeasible in
heterogenous (The Cochrane Collaboration, 2002).
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Methodological Quality Assessment
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some cases when the nature of interventions, sample populations, and settings are
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The included articles were critically appraised on six different criteria to evaluate the research evidence quality. The risk of bias within each research process, whether intentional
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or unintentional, that could threaten the scientific integrity and validity of the studies’
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findings, were assessed (Grimes & Schulz, 2002). This was done by using the Cochrane Collaboration’s tool for assessing risk of bias which includes selection, performance,
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detection, attrition, reporting, and other bias.
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Results
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Search Results
Figure 1: PRISMA – PRISMA flow chart providing an overview of the selection of papers examining the effectiveness of psychosocial interventions on depressive symptoms in the adult population with Type 1 or Type 2 diabetes and co-morbid depression
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The database search was last conducted on the 3/11/2013 and 198 references were identified; 49 papers were from CINAHL, 54 from Cochrane, 83 from MEDLINE, and 11 from PsycINFO. One of the included articles was a study protocol and a further search on the
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internet found the full-text of the completed research study report (de Groot et al., 2012) which was added to the result list. These 199 references were then screened and 60 duplicates
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were removed, 91 were excluded as they were not relevant to the review’s adult diabetes and co-morbid depression population, 18 more were excluded as they were not psychosocial
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interventional studies, leaving 30 potential references (See Figure. 1). Out of the 30 studies,
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two were protocols of potential studies that have yet to be completed or published (Coventry et al., 2012; Petrak et al., 2010), nine were reviews that examined the treatment for people
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with diabetes and co-morbid depression which will be mentioned further in the discussion
Study Design
Setting
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First Author
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Included Studies
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chapter, and the remaining nine were excluded due to the exclusion criteria.
Participants
Intervention
(Year)
de Groot
Single-arm
Physician
n=50, ≥18yo,
Program
(2012)
repeated-
practices and
ambulatory,
Appalachians
measures
media
Type 2 diabetes
Coming
intervention
throughout
≥1yr, current
Together to
design
southeastern
major
Increase
Ohio and
depression
Exercise
western West
disorder, 57yo,
(ACTIVE): 10
Virginia
68% F, 100%
wks CBT, 12
White, 100% T2
wks exercise
Depressive
Glycaemic
Symptoms
Control
Tool: BDI
Tool: DCA2000+
B: 26.8±10.4 PI: 16.1±12.9
Measure: HbA1c B: 7.6±1.8 PI: 7.2±1.5 FU: 7.3±1.3
FU: 16.7±13.1 Effect Size: -0.91[-1.32, -0.49] -0.85[-1.26, -0.44]
Effect Size: -0.40[-1.05, -0.25] -0.30[-0.92, 0.32] (MGS)
(SMGS) Georgiades
Single-group
Physician
n=90, ≥18yo,
16 sessions of
Tool: HAM-D
(2007)
open-label
practices and
Type 1 or 2
CBT delivered
B: 9.8±5.8
prepost
clinics within
diabetes, BDI
in small
PI: 3.6±4.2
-
14 Page 14 of 53
quasi-
Duke Uni
score ≥10, 51yo,
groups over
Effect Size:
experimental
Health
71% F, 67%
14 wks
-1.22[-1.57, -0.88]
design
Systems
White, 69% T2
Gonzalez
Case series
Massachusetts
n=5, 18-70yo,
1 visit with
Tool: BDI, CGI,
Tool: -
(2010)
study
General
Type 2 diabetes
nurse diabetes
MADRS
Measure: HbA1c
Hospital
with HbA1c>7.0,
educator, 2
B: 15.2±7,
B: 8.8±2
Diabetes
major depressive
visits with
4.2±0.8, 24.8±5.4
PI: 7.6±1
Clinic
disorder or
dietician, 10-
PI: 11.0±8.2,
Effect Size:
dysthymia,
12 CBT
2.4±1.1, 11.2±8.3
-1.20[-3.16, 0.76]
50yo, 40% F,
sessions
cr Effect Size:
(MGS)
-0.55[-1.81, 0.71]
80% White non-
-1.87[-3.36, 0.39]
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Hispanic, 100%
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(SMGS)
-1.94[-3.44, -0.44]
T2
(SMGS)
Randomised
Physician
n=51, 21-70yo,
I: 10 wks of
Tool: BDI
Tool: Pierce
(1998)
Controlled
practices
able to give
diabetes
B: 24.9±10.2(I),
Glyco-test
Trial
within the
informed
education and
Washington
consent, Type 2
10 weeks of
PI: 71%(I),
Uni Sch of
diabetes, major
CBT
22%(C) achieved
Medicine and
depression, ≥14
C: 10 weeks
depression
PI: 10.2, 9.9
BJC
on BDI, 53(I)
of diabetes
remission, and
FU: 9.5, 10.9
Healthcare
56(C)yo, 40%(I)
education
67%(I), 30%(C)
System, St
40%(C) F,
≥50% decrease in
Louis,
85%(I) 77%(C)
BDI score
Missouri
White, 100% T2
FU: 58%, 26%
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te
d
M
an
Lustman
21.1±6.8(C)
Measure: GHb B:10.2±3.6(I), 10.4±3.1(C)
and 58%, 30%
Penckofer
Randomised
Diabetes
n=74, women
I: Group CBT
Tool: CES-D
Tool: Bayer DCA
(2012)
Controlled
centre and
aged ≥18yo,
and psycho-
B: 27.7±9.3(I),
Measure: HbA1c
Trial
primary care
Type 2 diabetes
education for
clinics in a
>6mths, average
8 wks and
major
score ≥16 on
booster
Midwestern
CES-D scale
sessions at
medical centre
from phone and
wks 14 and 22
baseline screen,
C: Provided a
55(I), 54(C)yo,
list of
63%(I), 69%(C)
resources and
White, 100% T2
offered intervention
28.9±9.5(C) MI: 15.2±7.9(I), 23.1±11.4(C) PI: 12.6±8.0(I), 21.5±10.2 Effect Size:
B: 7.8±1.8(I), 7.9±2.0(C) MI: 7.4±1.3(I), 7.8±1.8(C) PI: 7.4±1.3(I), 7.8±1.6(C) Effect Size: -0.40[-1.15, 0.35]
-0.81[-1.31, -0.30] -0.97[-1.51, -0.43]
-0.40[-1.13, 0.33] (MD)
post study
15 Page 15 of 53
(SMD) Randomised
Vuk Vrhovac
n=50, diabetes,
I: 4 group
Tool: CES-D
Tool: Olympus
Okanovic
Controlled
Uni Clinic in
PHQ-9: 10-14
psycho-
B: 26(22-30)(I),
AU600 analyser
(2009)
Trial
Croatia
points, 55(I),
education
24(18-35)(C)
Measure: HbA1c
58(C)yo,
delivered over
(median and
B: 7.5(6.4-8.3)(I),
64%(I), 84%(C)
6 wks
interquartile
7.7(6.6-8.9)(C)
F, 100% T2
C: Informed
range)
ip t
Pibernik-
PI: 18(12.5-28.5),
treatments
20(16.5-27)
cr
of available
PI: 7.3(6.3-7.6), 6.9(6.2-8.2)
FU: 7.0(6.0-7.6),
FU: 19(11-26),
7.0(5.9-7.9)
us
19(15-26)
Randomised
Community-
n=339, ≥21yo,
I: Telephone-
Tool: BDI
Tool: DCA2000
(2011)
Controlled
based non-
physician
delivered CBT
B: 26.7±7.7(I),
point-of-care
Trial
profit, Uni,
authorisation for
and walking
and Veterans
special
Affairs (VA)
26.5±9.9(C)
analyser
PI: 14.2±10.3(I),
participants,
C: Books and
18.6±10.7(C)
healthcare
Type 2 diabetes,
educational
Effect Size:
system, and
prescription for
materials for
-0.42[-0.65, -0.19]
community
antihyperglycae
depression,
(SMD)
health
mia, PHQ-9≥11,
walking, and
Effect Size:
55(I), 56(C)yo,
diabetes, and a
0[-0.40, 0.40]
52% F, 84%
list of local
(MD)
White, 100% T2
resources for
system’s
te
diabetes
d
for 12 months
M
an
Piette
learning
Ac ce p
centre
Measure: HbA1c B: 7.5±1.7(I), 7.7±1.7(C) PI: 7.7±1.8(I), 7.7±1.7(C)
depression
Rungreangk
Quasi-
Diabetic clinic
n=64, Type 2
I: Buddhist
Tool: 9Q Thai I-
ulkij (2011)
experimental
in Nakae
diabetes, ≥7 out
group therapy
san dialects
using
Hospital
of 9 on 9
for 6 wks
B: 11.8±2.74(I),
matching
Question Thai I-
C: -
techniques
san dialects
PI: 1.7±2.7(I),
(9Q), 50(I),
5.9±2.1(C)
11.5±2.5(C)
47(C)yo, 94% F,
Effect Size:
100% Thai,
-1.74[-2.32, -1.15]
100% T2
(SMD)
Simson
Randomised
In-patient
n=30, diabetic
I: Supportive
Tool: HADS
(2008)
Controlled
German
foot syndrome,
psychotherapy
B: 11.7±2.7(I),
Trial
Diabetes
HADS≥8, 58(I),
wkly for the
Centre
63(C)yo, 43% F,
duration of in-
80% T2
patient admission
-
-
10.6±2.9(C) PI: 10.1±3.5(I), 10.9±3.3(C) Effect Size:
16 Page 16 of 53
C: -
-0.24[-0.95, 0.48] (SMD)
Randomised
Various media
n=255, adult,
I: 8
Tool: Dutch
Tool: -
Bastelaar
Controlled
in The
have an email
consecutive
validated version
Measure: HbA1c
(2011)
Trial
Netherlands
address and
web-based
of CES-D
B: 7.4±1.6(I),
and Belgium
internet access,
CBT
B: 29±7(I),
7.3±1.6(C)
≥16 on the CES-
C: Given
D, 48(I),
access to
PI: 37%, 19%
51(C)yo, 61% F,
intervention
clinically
89% White,
after the study
ip t
van
PI: -
cr
28±7(C)
significant
improvement
55% T2
us
FU: 41%, 24% Effect Size:
an
-0.29[-0.40, -0.17] (1-month FU) (SMD)
Post-Intervention, FU: Follow-Up
M
I: Intervention Group, C: Control Group, F: Female, B: Baseline, MI: Mid-Inervention, PI:
Table 1: Data extraction table – Data extraction from the included studies with data extraction
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Study Characteristics
te
d
template adopted from the Cochrane group.
Table 1 summarizes the characteristics of the included studies. Ten studies were
found to examine the effectiveness of psychosocial interventions on depressive symptoms in people with diabetes, of which eight studies were reported in the last five years. Six of the studies were RCTs and the remaining four were NRSs; one was a quasi-experimental trial, two were pre-post test studies, and one was a case series descriptive study. Six studies were conducted in the United States, three were in Europe, and one was in Thailand. The sample sizes were in general small, with eight studies having fewer than 100 participants. For RCTs, this ranged from 30 participants in Simson et al’s (2008) study to 339
17 Page 17 of 53
in Piette et al’s (2011), whereas for NRSs, the range was smaller with five participants in Gonzalez et al’s (2010) study to 90 in Georgiades et al’s (2007) study. The rates of attrition varied amongst the studies. The RCT by Simson et al (2008) which conducted psychotherapy
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on hospitalised patients, had no attrition while the van Bastelaar et al (2011) RCT web-based study had the highest attrition rate of 42%.
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Overall, the participants were mainly 50-60 years old and a large majority were
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Caucasians. A significant number of participants had Type 2 diabetes, with seven studies recruiting participants with Type 2 diabetes only. The remaining three studies had 69%
an
(Georgiades et al., 2007), 80% (Simson et al., 2008), and 55% (van Bastelaar et al., 2011) of participants with Type 2 diabetes.
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Most of the studies employed CBT in their psychosocial interventions, addressing maladaptive behaviours and negative emotions by focusing on problems and difficulties that
d
the clients were facing and developing strategies to address them (Lustman et al., 1998). The
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six RCTs and one quasi-experimental trial contained a comparison control group that
Ac ce p
received usual standard diabetes care from the primary physician and in some cases, additional brochures, information, resources, and/or psychoeducation for depression. Interventions were conducted in a myriad of ways including individual therapy, group therapy, and telepsychiatry via the telephone and internet. The two RCT studies with interventions involving telepsychiatry correspondingly had the largest sample size of 339 (Piette et al., 2011) and 255 (van Bastelaar et al., 2011). These studies, however, also had the highest attrition with only half of the participants completing 17 out of 21 sessions in the Piette et al (2011) study while the van Bastelaar et al (2011) study had 42% attrition. There were two studies that had additional exercise interventions; the NRS by de Groot et al (2012) and the RCT by Piette et al (2011). The interventions were carried out by a number of health professionals including psychologists and mental health nurses. 18 Page 18 of 53
The intervention duration was varied with the shortest being conducted by the RCT by Pibernik-Okanovic et al (2009), lasting six weeks, and the longest was conducted by the RCT by Piette et al (2011), lasting twelve months. The studies correspondingly had the least
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and most number of interventions of four and twenty-one sessions respectively.
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Outcome Results
Depressive symptoms. A number of different tools were used by the studies to
us
measure depressive symptoms. Based on their respective validated tools, it was found that participants of the included studies had a mean indicative of mild to major depression at
an
baseline. Post-intervention, the NRS by Rungreangkulkij et al (2011) and the RCT by Penckofer et al (2012) had mean scores indicative of depression remission in their
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intervention group, while the rest of the studies that reported post-intervention mean scores had improved depression scores. Nine out of ten of the studies reported that psychosocial
d
interventions were effective for depression in their study report. The RCT conducted by
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group.
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Pibernik-Okanovic et al (2009), however, reported no effect when compared with the control
Effect size. Three studies did not report their participant’s mean and SD scores,
therefore their effect sizes could not be calculated. These were the RCTs by Lustman et al (1998), Pibernik-Okanovic et al (2009), and van Bastelaar et al (2011). SMD was calculated for the three RCTs with two studies having a small effect of -0.42 (Piette et al., 2011) and 0.24 (Simson et al., 2008), while the other one having a large effect of -0.81 (Penckofer et al., 2012). SMD for quasi-experimental trial for Rungreangkulkij et al (2011) was -1.74.. SMGS was calculated for the three NRS and the effect sizes were mostly large with -0.91 for de Groot (2012), -1.22 for Georgiades et al (2007), and -0.55, -1.87, and-1.94 for Gonzalez et al (2010) with the respective depression scales. 19 Page 19 of 53
Out of the seven effect sizes calculated, however, only five were statistically significant. NRS by Gonzalez et al (2010) and RCT by Simson et al (2008) had CIs that included 0, indicating that the intervention had no effect. These two studies also had the
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smallest sample sizes of five and thirty respectively. Four studies conducted follow-up assessments whilst only two reported on their
cr
scores. The RCT by van Bastelaar et al’s (2011) had a small SMD effect size of -0.29 at one
us
month follow-up and the de Groot et al’s (2012) NRS had a large SMGS effect size of -0.85 at three months, and both results were statistically significant.
an
Glycaemic control. Six studies included glycaemic control as their outcomes. Of which, two were NRSs and four were RCTs. At baseline, the study participants had a mean
M
HbA1c score of 7.4% to 9.47% and Lustman et al’s (1998) participants had a mean GHb score of 10.3%. Post intervention, the mean HbA1c scores of the participants remained above 7.0%.
d
Four studies demonstrated slight improvement, one had deterioration, while the last showed
te
no change in the mean score. Apart from the NRS by de Groot et al (2012) , that had a
Ac ce p
moderate effect of -0.40, the effect sizes of the studies that reported the SD were all statistically insignificant.
For follow-up assessment, three studies that conducted follow-up reported improved
scores. The scores however, were either statistically insignificant (de Groot et al., 2012) or their statistical significance could not be determined because the SD scores were not provided (Lustman et al., 1998; Pibernik-Okanovic et al., 2009). The psychosocial interventions presented in this review were disparate in their intervention type, delivery method, duration, and intensity. Although a meta-analysis can increase the statistical power of the results and broaden the generalisability of the interventions to the target population (Borenstein, Hedges, Higgins, & Rothstein, 2009), the 20 Page 20 of 53
diversity of the interventions in this review made it implausible to combine the effect sizes into a meta-analysis. It was therefore decided not to conduct a meta-analysis.
H
H
L
Blinding of participants and personnel
H
H
H
Blinding of outcome assessment
H
H
Incomplete outcome data
L
Selective reporting
Other bias
L
L
U
U
L
H
L
L
U
U
L
H
H
H
H
H
H
M
d
te
H
van Bastelaar 2011
H
ip t
Allocation concealment
Simson 2008
L
cr
L
Rungreangkulkij 2011
Penckofer 2012
H
Piette 2011
Lustman 1998
H
us
Gonzalez 2010
H
Pibernik-Okanovic 2009
Georgiades 2007
Random sequence generation
an
de Groot 2012
Bias Assessment
L
U
U
U
L
U
U
H
U
L
L
H
H
L
L
L
L
L
U
H
U
H
U
U
U
H
H
H
L
L
L
L
L
L
H
L
Ac ce p
H
H: High risk, L: Low risk, U: Unclear risk
21 Page 21 of 53
Table 2: Risk of bias assessment for included studies
As can be seen in Table 2 above, the methodological qualities were in general poor
ip t
across the included studies with some studies having more significant numbers of risk for
cr
bias than others. Some lapses in the quality were inevitable due to the inherent nature of the
study design and intervention. The selection bias was high for the NRSs due to confounding
us
factors that cannot be distributed to a control group – as in the case of randomised studies (Kunz, Vist, & Oxman, 2008). Performance bias was high for all the included studies because
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blinding of participants and some research personnel was not possible due to participants’ direct contact with the therapist delivering the intervention. Five out of six RCTs did not
M
report on the blinding of outcome assessors and hence the risk is unclear. Some studies also failed to report on attrition of participants from the time of enrolment to the end of the study,
d
making it difficult to assess attrition bias. In addition, a few studies did not carry out
te
Intention-To-Treat (ITT) analysis, leading to attrition bias. Up to six studies did not have a
Ac ce p
study protocol available, making it difficult to assess reporting bias. All studies had at least two risks for bias. The strongest study was conducted by Lustman (1998) who had only two high risks of bias but the non-randomised studies had on average five out of the seven high risk of bias categories.
Discussion Key Results Depressive symptoms. With the exception of the Pibernik-Okanovic et al (2009) RCT, all included studies concluded that psychosocial interventions were effective in 22 Page 22 of 53
improving depression. The effect sizes calculated for a number of studies revealed that the interventions were generally consistent with this conclusion. The two studies (Gonzalez et al., 2010; Simson et al., 2008) with statistically insignificant effect could be attributed to their
ip t
small sample sizes which generally have lower statistical power and predictive value (Button et al., 2013). Only two studies provided data to calculate follow-up assessment results and the
cr
evidence suggested that psychosocial interventions have some lasting effect as the
us
participants’ depressive symptoms remained improved over the next few months.
However, taking the methodological quality of the included studies into account, this
an
conclusion is at best overconfident. There were quite a few less than rigorous RCTs in this review that had high numbers of risk of bias and under-reporting of blinding and the
M
availability of protocol, which could cause an overestimation of the effect and consequently a false conclusion (Detsky, Naylor, O’Rourke, McGeer, & L’Abbé, 1992). In addition, the non-
d
randomised studies unequivocally scored high in selection biases, compromising the
Ac ce p
2003).
te
methodological quality of the study, and hence, their results can be misleading (Deeks et al.,
Psychosocial interventions in this review were delivered in a myriad of ways. This
included individual therapy, group therapy, the internet, and the telephone. The RCT interventions delivered via technology by Piette et al (2011) – telephone – and by van Bastelaar et al (2011) – web-based – were collectively termed as telepsychiatry. Telepsychiatric studies have the largest sample size in this review, with both studies
recruiting over 250 participants, increasing statistical power. However, telepsychiatry can have favourable and unfavourable factors. It eliminates geographical barriers – which can be convenient and reach out to a larger group as well as provide real time support, it can increase disclosure – which can be helpful for clients who find face-to-face contact confronting, and it 23 Page 23 of 53
is cost-effective (Christensen, Griffiths, & Jorm, 2004; Mohr et al., 2005; Proudfoot et al., 2004). It could, on the other hand, also be impersonal, lacking in accountability, difficult to assess non-verbal cues, such as body language, which compromise the therapeutic bond, and
ip t
is associated with high attrition rates (Bee, Lovell, Lidbetter, Easton, & Gask, 2010; Eysenbach, 2005). Nevertheless, telepsychiatry was generally reported to be effective for
cr
those who completed the intervention (Piette et al., 2011; van Bastelaar et al., 2011), but the ITT effect sizes were small due to high drop-out rates. Research also found that
us
telepsychiatry could be an effective adjunct to enhance adherence to medical management of
(Clough & Casey, 2011; Hunkeler et al., 2000).
an
chronic diseases and the effectiveness of face-to-face psychotherapy in improving depression
M
The three studies with a larger statistically significant effect size involved group therapy. They were NRSs conducted by Georgiades et al (2007) and Rungreangkulkij et al
d
(2011), and RCT by Penckofer et al (2011). In a group setting, there is camaraderie and
te
people feel less isolated in their struggle with diabetes and co-morbid depression as they receive feedback from one another (White, 2000). Sharing of experiences with others not
Ac ce p
only helps the participants identify and support each other, but it is also cathartic, reducing their stress and sense of hopelessness, and alleviating their depression (Gilbert & Procter, 2006). In addition, group therapy facilitates peer learning, as everyone in a group brings in various perspectives in a safe environment supervised by a trained professional (Corey, 2012). The common criticism about group therapy, however, is that it is not tailored to individual needs and some people may find it more confronting to disclose to a group than to a mental health professional individually, who is less likely to judge them (Morrison, 2001). Notwithstanding this criticism, people with diabetes and co-morbid depression have very specific needs that are likely to be similar to each other and can therefore gain more support and benefit from group therapy, as evident from the results of the studies. In a practical sense, 24 Page 24 of 53
group therapy is also a more cost-effective option as fewer resources and manpower are needed per client, as compared to individual therapy (Tucker & Oei, 2007). Besides the delivery method, the optimal number, frequency, and intensity of
ip t
psychotherapy sessions for people with diabetes and co-morbid depression are unknown in
this review. However evidence suggests that there are many factors such as the nature of the
cr
therapy, the severity of the co-morbid diseases, and the progress of the client that come into
us
play (Barkham et al., 2006). Evidence also suggests that longer and more intensive treatments lead to improved depression but there is a limit where further treatments can lead to
an
diminishing gains (Cuijpers, Huibers, Ebert, Koole, & Andersson, 2013; Cuijpers et al., 2010; Hansen, Lambert, & Forman, 2002). This was reflected in the two RCTs with the shortest
M
contact time in this review – Pibernik-Okanovic et al (2009) and Simson et al (2008), which
d
had consequently little or no significant effect on participants’ depression status.
te
The two studies; NRS by de Groot et al (2012) and Piette et al (2011), that included an exercise intervention in conjunction with CBT reported positive results, but it was unclear
Ac ce p
which intervention brought the desired outcome. Exercise is generally recognised to have some anti-depressant effects and is beneficial for the glucose metabolism in people with diabetes (De Feo et al., 2006; Rimer et al., 2012). A mix of health professionals were employed to carry out the psychosocial
interventions in this review including psychologists and nurses trained in psychiatry. Mental health resources are grossly inadequate globally and there is a stark gap between service users and service providers (Saraceno & Saxena, 2002; WHO, 2013c). The success of nurses administering the interventions was reported in the various included studies (Penckofer et al., 2012; Piette et al., 2011; Rungreangkulkij et al., 2011). In psychosocial interventions, the therapeutic alliance that a therapist has with the individual is one of the pivotal factors in 25 Page 25 of 53
determining a successful treatment (Feller & Cottone., 2003). This important bond formed between the therapist and client helps the individual work towards agreed upon goals and/or tasks (Del Re, Flückiger, Horvath, Symonds, & Wampold, 2012). As nurses are important
ip t
partners of healthcare for people with diabetes, and hence have a therapeutic relationship with them, they are befitting to receive extra training to facilitate psychosocial interventions to
cr
help alleviate the mental health resource burden of people with co-existing diabetes and
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depression (Peyrot, Rubin, & Siminerio, 2006).
Glycaemic control outcome. Six studies measured the effect of the interventions on
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glycaemic control, and the results were mostly statistically insignificant. All the studies used Glycosylated Haemoglobin to operationalise glycaemic control and this blood test measures
M
the average blood glucose level over about three months (Sacks et al., 2011). It could be difficult to determine the effects of psychosocial interventions on glycaemic control when
Ac ce p
Limitations
te
take place.
d
most interventions were three months or less, giving less than sufficient time for the effect to
The overall evidence is limited in its applicability due to the following aspects
explored below.
Firstly, although the search yield was high, almost half of the studies consisted of
participants that were not relevant to our review. It is possible that the search terms were not specific enough as a large proportion of screened papers were also not included, with only ten out of 139 papers included. However, the experience of conducting this literature review has shown us that search terms are no guarantee for locating all the relevant literature as this review located papers that other reviews did not. The search was repeated many times by the authors, independently, over the course of the study to obtain all the relevant literature. 26 Page 26 of 53
The studies included in this review were mostly conducted in the United States and Europe. The recent IDF diabetes atlas found that the regions with the highest prevalence of diabetes are actually in the Western Pacific and South East Asia region (IDF, 2013a). This
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review could therefore be limited in its generalisability as the demographics, healthcare system, culture, and social environment differs across the world, and many regions of the
cr
world are under-represented (Watts, Phillips, Petticrew, Harden, & Renton, 2011).
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There were also predominantly more female than male participants in this review despite diabetes having similar prevalence rates in men and women (IDF, 2013a), and one
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study (Penckofer et al., 2012) focused its intervention solely on women. Research has shown that women were more likely to seek help and get diagnosed for their depression than men
M
(Branney & White, 2008; Ogrodniczuk & Oliffe, 2010). Also, women in the 50 to 60 years age group were likely to be going through menopause, or be in their early postmenopausal
d
years which could represent a time of vulnerability, increased psychological distress, and an
te
increased risk of depression (Bromberger et al., 2001; Soares & Zitek, 2008). Therefore, it is
Ac ce p
vital to consider gender sensitivity when implementing interventions to help people with diabetes and co-morbid depression. There were significantly more participants with Type 2 diabetes in this review than
Type 1, with seven out of ten studies having participants with Type 2 diabetes only. Although at least nine out of ten cases of diabetes are Type 2 worldwide (IDF, 2013b), more than half of the included studies were aimed and tailored for people with Type 2 diabetes only, limiting the applicability to the population with Type 1 diabetes. Considering the population with Type 1 diabetes would have been living with their chronic disease a few decades longer than their Type 2 counterparts, their psychological and support needs could be different. Furthermore, the prevalence of depression is higher in those with Type 1 diabetes (Roy & Lloyd, 2012). Research evidence exploring psychological interventions in patients with Type 27 Page 27 of 53
1 diabetes found that it was ineffective in improving glycaemic control and psychological distress in adults (Winkley, Landau, Eisler, & Ismail, 2006). Since the three studies in this review that had both Type 1 and 2 diabetes population did not report on the differential
interventions were effective across the two types of diabetes.
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outcome for the respective diabetes types, it cannot be determined if psychosocial
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Another limitation that affects the generalisability of the results is the small sample
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size of the studies, with eight out of ten studies having fewer than 100 participants. Small samples can lead to underpowered studies, risking type 1 or type 2 errors. These issues when
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combined with an inability to conduct a meta-analysis contribute to the need to be cautious when applying the findings (Borenstein et al., 2009).
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The chief psychosocial intervention used in this review was mainly CBT and other psychotherapeutic techniques like problem-solving (PST), motivational, and social therapy
d
were not explored. Different psychotherapeutic techniques have various theoretical models
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underpinning its approaches and hence leading to varying emphasis in therapy (National
Ac ce p
Collaborating Centre for Mental Health & National Institute for Health and Clinical Excellence., 2011). With the majority of studies using CBT as their psychosocial intervention, other psychological approaches like social therapy which focuses on the client’s social interaction, environment, and network, or PST which aids in identifying complicated issues in the client’s life and developing a structured approach to solve it were not explored. This can limit the applicability of the broad term ‘psychosocial interventions’ when replicating the results of this review. The control group in the various randomised studies received usual care from their respective GPs and this differs from each country and healthcare system. Therefore, the control groups across the included studies were not similar and this non-standard control 28 Page 28 of 53
group could act to enhance or diminish the effects of the intervention, limiting the validity of the review’s results. Many studies did not conduct follow-up assessments after the interventions and this
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limits our knowledge on the long-term effects of the psychosocial interventions. Studies have found that up to 80% of people with diabetes and co-morbid depression will have a
cr
depressive symptoms relapse over five years (Katon, 2008). Since follow-up assessments
us
were not conducted, it cannot be determined if psychosocial interventions can prevent
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relapses of depression for these individuals.
Another important limitation to this review was the considerable number of studies
M
with missing and/or had incomplete post-intervention results. Although most studies reported on baseline mean and SD for the outcome to be measured, three studies failed to report on
d
post-intervention mean and SD, making the calculation of effect size impossible, affecting the
te
completeness of this review’s analysis.
Ac ce p
Lastly, the question of cost-effectiveness was not addressed. Examining the costeffectiveness of an intervention and its potential resource utilisation before implementation is both practical and prudent especially when health care resources are scarce (Williams & Bryan, 2007).
Quality of Evidence
One major bias in this review was the lack of a research protocol. Without a protocol, the review could appear unplanned with the potential bias of selective reporting and adjustment of inclusion criteria, and can therefore compromise the quality of the review (Chan et al., 2013). Some bias were also observed in the search process. Only general 29 Page 29 of 53
purpose databases were sourced for research studies for this review. Other sources of evidence like grey literature, unpublished study reports, hand-searching of research journals, or culling the reference lists of relevant research articles may have provided a different search
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yield (Schlosser, 2007). Published research has a high propensity to be more positive and overestimate the effect size than unpublished research, and research with non-significant
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findings tend to take longer to achieve publication than those that has positive results, or not
get published at all (Dirnagl & Lauritzen, 2010; Ioannidis, 2005). Overreliance on published
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literature, therefore, may not give us a comprehensive understanding of what works for the
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target population, and the results of this review may be misleading (Dwan et al., 2008). Also, only English language articles were included in this review which can narrow the
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generalisability of the findings to populations with an English speaking background which does not represent the totality of the evidence available for the problem area.
d
The included studies have varying methodological quality – some of which were
te
inevitable due to the nature of the study such as high selection bias risk in NRSs and a high
Ac ce p
performance bias risk across all studies due to the nature of psychosocial intervention. All randomised studies, in general, had less bias than the non-randomised studies due to the act of randomisation which lowers selection bias by distributing confounders thus increasing comparability (Kunz, Vist, & Oxman., 2008). The participants with diabetes and co-morbid depression who signed up for the research study, for example, were probably motivated for a change and believed in the effectiveness of psychosocial interventions. These characteristics in the participants could serve as confounders as motivation is vital for treatment effectiveness in psychotherapy (Ryan, Lynch, Vansteenkiste, & Deci, 2011), and could make them unrepresentative of the target population (Mein et al., 2012). Many randomised studies, however, failed to report on blinding of the outcome assessment. Neglecting to report on blinding is common in many research studies but insufficient information regarding the 30 Page 30 of 53
blinding process or whether it was conducted can be misleading (Schulz & Grimes, 2002). If it was the case that blinding was not carried out, differential treatment of the participants by the outcome assessors could occur and influence the psychological response of participants
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(Supino, 2012). Some randomised studies were quite poorly conducted and/or neglectful in their reporting such as that of the RCTs by Pibernik-Okanovic et al (2009), Simson et al
cr
(2008), and van Bastelaar et al (2011). The many high risks of bias in the included RCTs
challenges the notion that RCTs are the gold standard for research evidence and can provide
us
an inaccurate sense of security with regards to their results (Peinemann et al., 2013). One
an
NRS(de Groot et al., 2012), albeit high in selection bias, was fairly well-conducted detailing the attrition data and providing most of the vital information, following their protocol, while
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the other two NRSs were not as well conducted and had multiple high bias risks. As most of the studies were carried out within the same healthcare site and system,
d
contamination of the intervention and control group could have occurred. Participants in the
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control group received their care as usual from their GPs who had the opportunity to come into contact with the intervention group participants attending the same clinic, resulting in the
Ac ce p
diffusion of treatment, and causing the results to be biased. This is with the exception of van Bastelaar et al (2011) who advertised their intervention to the public instead of relying on a particular health site to advertise their study. The results of this review need to be taken with caution due to the biases previously
mentioned. Some of the study findings may well be equivocal but are still worth reporting as they contribute to the knowledge base on this important topic. Ultimately, health professionals need to tap into their clinical expertise and judgement, and use best practice evidence, regarding what works for each of their individual, taking into account their condition, predicament, and preferences to determine the intervention’s appropriateness (Evans, 2003). 31 Page 31 of 53
Potential Biases in the Review Process
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Other Reviews and Ongoing Studies The other reviews identified in the introduction had similar conclusions to this review,
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stating that psychosocial interventions have a positive impact on depression in people with
diabetes, but no observable impact on glycaemic control. In addition, more primary research
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studies are needed to inform the evidence. This review included the most number of
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psychosocial studies, having at most three similar studies to the previous reviews, and therefore adds to the knowledge base. The two relevant study protocols discovered in the
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search are yet to be published and will contribute to our understanding of interventions to
Conclusions
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support the target population.
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While the conclusions of the included studies and previous reviews, and the effect
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sizes calculated in this review reported the effectiveness of psychosocial interventions for people with diabetes and co-morbid depression, the high risks of bias found in the included studies in this review made it difficult to agree with this standpoint. The effect of the interventions on glycaemic control, remained inconclusive. Group therapy stands out in its effectiveness and practicality in terms of limited
mental health resources. In spite of the effectiveness of the group therapy modality, however, comparative methods research study would need to be conducted to confirm its superiority over other delivery methods (Hanneman, 2008). Telephone and web-based interventions, albeit associated with high attrition, are nevertheless convenient and may be considered as an option for people living in remote areas or who have mobility difficulties. After all, the 32 Page 32 of 53
clients’ modality preference is worth considering when choosing a psychosocial intervention to ensure better outcomes and lower attrition rates (Swift, Callahan, Ivanovic, & Kominiak, 2013). Health professionals, especially nurses who already have developed a rapport with
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clients, can be trained in psychotherapeutic techniques and effectively carry out these psychosocial treatments.
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The generalisability of the results was found to be limited and the methodological
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quality was lacking in most studies. Therefore, replication of the interventions needs to be taken with caution. More well-conducted RCTs addressing the effectiveness of psychosocial
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interventions for people with diabetes and co-morbid depression, with measures taken to lower biases and utilise ITT analysis, need to be conducted. If possible, studies should
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routinely offer the control group the intervention after the study is completed to make them feel valued, and also match baseline characteristics to increase equivalence between the
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groups. It is recommended that these RCTs examine the effectiveness of group therapy, and if
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possible, compare this method to other delivery methods and/or psychosocial techniques.
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Over the last 15 years, only ten studies that researched this topic were identified and ironically, there were a number of reviews in recent years that identified this problem area and similarly concluded that more original research studies needs to be conducted. What is promising, however, is that eight of these ten studies were conducted in the last five years and two more ongoing studies were also identified, highlighting the research priority of this problem area. Ultimately, health professionals need to draw on their own experiences when managing people living with diabetes and co-morbid depression, and take into account their individual characteristics and preferences before making an informed decision regarding treatment options (Jenicek, 2006) as there is no panacea for the management of diabetes and co-morbid depression.
33 Page 33 of 53
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World Federation for Mental Health. (2013). Diabetes and depression: why treating depression and maintaining positive mental health matters when you have diabetes. Retrieved from http://wfmh.com/wp-
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content/uploads/2013/11/WFMH_GIAS_DiabetesAndDepression.pdf World Health Organization. (2008). The global burden of disease: 2004 update. Retrieved
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from
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http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.
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World Health Organization. (2013b). International Classification of Diseases (ICD).
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Retrieved from http://www.who.int/classifications/icd/en/
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World Health Organization. (2013c). Investing in mental health: evidence for action.
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http://apps.who.int/iris/bitstream/10665/87232/1/9789241564618_eng.pdf
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Appendix 1: Database Search Results CINAHL Search term entered
Hits
1.
Diabet* (Search as Keyword) OR Diabetes Mellitus (MeSH;
111,003
Explode) OR Diabetes Mellitus, Type 1 (MeSH; Explode) OR Co-morbid Depression (Search as Keyword) OR Comorbid
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Diabetes Mellitus, Type 2 (MeSH) OR Diabetic Patients (MeSH) 2.
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No
59,012
us
Depression (Search as Keyword) OR Depression, Reactive (MeSH) OR Depression (MeSH; Explode)
Psychosocial Interventions (Search as Keyword) OR Support,
an
3.
83, 351
Psychosocial (MeSH; Explode) OR Psychosocial Care (MeSH; Explode) OR Rehabilitation, Psychosocial (MeSH, Explode) OR
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Psychological interventions OR Behaviour Therapy (MeSH; Explode) OR Counseling (MeSH; Explode) OR Peer Counseling Psychoeducation (MeSH)
Glyc*mic control (Search as Keyword) OR Hemoglobin A,
te
4.
d
(MeSH) OR Teaching, Guidance, and Counseling (MeSH) OR 17, 599
Glycosylated (MeSH) OR Depress* symptom (Search as Keyword)
6.
1. AND 2. AND 3. AND 4.
51
Limiter: Published from 1998-2013
49
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5.
Cochrane Library No 1.
Search term entered
Hits
Diabet* (Search as Keyword) OR Diabetes Mellitus (MeSH;
31, 087
Explode) OR Diabetes Mellitus, Type 1 (MeSH; Explode) OR Diabetes Mellitus, Type 2 (MeSH) OR Diabetic Patients (MeSH)
2.
Co-morbid Depression (Search as Keyword) OR Comorbid
11, 539
Depression (Search as Keyword) OR Depression (MeSH; Explode) OR Depressive Disorder (MeSH; Explode) OR Depressive Disorder, Major (MeSH; Explode)
50 Page 50 of 53
3.
Psychosocial Interventions (Search as Keyword) OR Psychological
23, 605
Techniques (MeSH; Explode) OR Social Support (MeSH; Explode) OR Behaviour Therapy (MeSH; Explode) OR Nondirective Therapy (MeSH; Explode) OR Cognitive Therapy (MeSH; Explode) OR Explode) OR Psychotherapy, Group (MeSH; Explode) OR Explode) OR Peer Group (MeSH; Explode) 4.
Glyc*mic control (Search as Keyword) OR Hemoglobin A,
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Psychotherapy, Multiple (MeSH; Explode) OR Counseling (MeSH;
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Psychotherapy (MeSH; Explode) OR Psychotherapy, Brief (MeSH;
18, 102
us
Glycosylated (MeSH; Explode) OR Depress* symptom (Search as 1. AND 2. AND 3. AND 4.
6.
Limiter: Published from 1998-2013
54 54
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5.
an
Keyword)
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Ovid MEDLINE Search term entered
1.
Diabet* (Search as Keyword) OR Diabetes Mellitus (MeSH;
te
No
Hits 448,377
Explode) OR Diabetes Mellitus, Type 1 (MeSH; Explode) OR
2.
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Diabetes Mellitus, Type 2 (MeSH) OR Diabetic Patients (MeSH) Co-morbid Depression (Search as Keyword) OR Comorbid
155,279
Depression (Search as Keyword) OR Depression (MeSH; Explode) OR Depressive Disorder (MeSH; Explode) OR Depressive Disorder, Major (MeSH; Explode)
3.
Psychosocial Interventions (Search as Keyword) OR Psychotherapy
302,831
(MeSH; Explode) OR Social Support (MeSH; Explode) OR Behaviour Therapy (MeSH; Explode) OR Cognitive Therapy (MeSH; Explode) OR Peer Group (MeSH; Explode) OR Counseling (MeSH; Explode) OR Psychotherapy, Group (MeSH; Explode) OR Patient Education as Topic (MeSH; Explode)
4.
Glycemic control (Search as Keyword) OR Hemoglobin A,
148,774
Glycosylated (MeSH; Explode) OR Blood Glucose (MeSH; 51 Page 51 of 53
Explode) OR Depress* symptom (Search as Keyword) 5.
1. AND 2. AND 3. AND 4.
92
6.
Limiter: Published from 1998-Current
84
Search term entered
Hits
1.
Diabet* (Search as Keyword) OR Diabetes Mellitus (MeSH; Explode) Co-morbid Depression (Search as Keyword) OR Comorbid
108,940
us
2.
17,701
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No
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PsycINFO
Depression (Search as Keyword) OR Major Depression (MeSH; Reactive Depression (MeSH; Explode) 3.
an
Explode) OR “Depression (Emotion)” (MeSH; Explode) OR
Psychosocial Interventions (Search as Keyword) OR Psychosocial
258,936
M
Rehabilitation (MeSH; Explode) OR Psychotherapy (MeSH; Explode) OR Cognitive Behaviour Therapy (MeSH; Explode) OR Psychotherapeutic Techniques (MeSH; Explode) OR Counseling
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(MeSH; Explode) OR Counseling Psychology (MeSH; Explode) OR
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Educational Counseling (MeSH; Explode) OR Group Counseling (MeSH; Explode) OR Peer Counseling (MeSH; Explode) OR
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Psychotherapeutic Counselling (MeSH; Explode) OR Group Psychotherapy (MeSH; Explode) OR Social Support (MeSH; Explode) OR Support Groups (MeSH; Explode) OR Psychoeducation (MeSH; Explode)
4.
Glycemic control (Search as Keyword) OR Blood Sugar (MeSH;
3,069
Explode) OR Depress* symptom (Search as Keyword)
5. 6.
1. AND 2. AND 3. AND 4.
11
Limiter: Published from 1998-2013
11
52 Page 52 of 53
Contribution of the Paper What is already known about the topic? - Psychosocial interventions are found to be effective in improving depression in people with
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diabetes and co-morbid depression
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What this paper adds?
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- This paper informs the global population with diabetes and co-morbid depression of the various treatment options for their depression
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to be the most effective in improving depression
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- Different treatment modalities were explored and compared, and group therapy was found
- Health professionals other than psychologists can also carry out these interventions to good
d
effect
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te
- This paper provides a comprehensive summary of all psychosocial interventions, adding to the knowledge base of treatment for people with diabetes and co-morbid depression
53 Page 53 of 53
