Efficacy of problem-solving therapy in treating late-life depression with potential cognitive impairment: a systematic review and meta-analysis

Abstract

Introduction

Late-life depression (LLD) is a global public health issue, often accompanied by cognitive impairments that can exacerbate the severity of depression and impair social functioning. Despite being a well-established treatment for LLD, the suitability of problem-solving therapy (PST) for individuals with LLD and varying degrees of cognitive impairments warrants further investigation. This paper presents the protocol for a systematic review and meta-analysis of randomised controlled trials (RCTs) aimed at evaluating the effectiveness and acceptability of PST for this specific demographic.

Methods/analysis

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we will conduct a systematic review to synthesise existing research on PST for individuals in this demographic. We will comprehensively search multiple databases and sources, including PubMed, EMBASE, the Cochrane Database and APA PsycNET from inception to October 2023, without language, publication year or type restrictions. Relevant studies will be manually screened from the references. Only RCTs involving PST for LLD will be included. The primary efficacy outcome will be the standardised mean difference in total scores on continuous depression severity scales across different comparison arms. Data extraction will be conducted independently by two reviewers (CH and J-JW), and methodological rigour will be assessed using the Cochrane Risk of Bias assessment tool. Subgroup and sensitivity analyses will be performed to investigate the impact of concomitant cognitive impairments and to evaluate the robustness of the findings.

Ethics and dissemination

The meta-analysis project is expected to be ethically unproblematic and does not require approval from a research ethics committee. The results of this study will be shared through articles in scholarly peer-reviewed journals and presentations in various formats, both print and digital.

PROSPERO registration number

CRD42023473782

CH and YF are joint first authors.

STRENGTHS AND LIMITATIONS OF THIS STUDY

• This meta-analysis presents the most comprehensive review to date of randomised controlled trials on problem-solving therapy (PST) for late-life depression with potential cognitive impairment.

• We will conduct a thorough evaluation of the effectiveness and acceptability of PST, incorporating subgroup and sensitivity analyses to address pertinent clinical inquiries within this particular demographic.

• The potential limitations include the variability in study designs and the quality of the included trials, which may impact the overall findings.

Introduction

Depression is a leading cause of global disability, particularly among the elderly, where the prevalence and incidence of severe depression increase twofold after the age of 70–85 years. Clinically significant depression in the elderly exacerbates various physical illnesses and contributes to the onset of disabilities. Research indicates that 9.3% to 23.0% of individuals with one or more chronic physical illnesses also experience depression, a significantly higher proportion compared with those without chronic diseases. When accounting for socioeconomic factors and overall health status, depression has a more pronounced negative impact on average health scores than other chronic diseases.

Late-life depression (LLD) refers to depressive episodes in individuals aged 60 years and above. The internationally accepted classifications include early-onset LLD (EOD), where depressive episodes first occur before the age of 60 years but continue or relapse in later life; late-onset depression (LOD), where depressive episodes first occur at the age of 60 years or older and very late-onset depression (VLOD), where depressive episodes first occur at the age of 75 years or older.¹ With ageing, physical illnesses and specific psychological attributes prevalent in this group, increasing evidence suggests that LLD often accompanies cognitive impairment, a condition that may persist post-depression treatment.^2–4 It is hypothesised that depression featuring prominent negative emotional symptoms is frequently associated with cognitive deficits, potentially due to dysfunction of the prefrontal cortex in emotional processing.⁵ An estimated one-third of depression patients experience some level of cognitive decline, which may worsen dramatically in the elderly.^{6 7} Consequently, the rising incidence of LLD has led to a current research focus on developing new therapies that can address both depression and cognitive comorbidities simultaneously.^{8 9}

Antidepressants, specifically selective serotonin reuptake inhibitors,¹⁰ are pivotal in treating LLD. Despite their proven efficacy over placebos in LLD patients, these medications are consistently accompanied by intolerable side effects and a reduced response rate in the elderly.¹¹ Additionally, cognitive complaints in LLD patients often indicate resistance to conventional antidepressant drugs. Conventional psychotherapies, including cognitive-behavioural therapy (CBT),^12–14 interpersonal psychotherapy^{15 16} or behavioural activation treatments,^{17 18} have demonstrated promising outcomes for individuals with depression. Nonetheless, there is inconsistent evidence regarding their efficacy for patients with LLD. The successful implementation of psychotherapies is known to be significantly influenced by cultural factors and education level, particularly by the presence of comorbid cognitive impairments. Given the high prevalence of cognitive impairments in LLD, providing psychotherapy to these specific demographics is always challenging.

Problem-solving therapy (PST)¹⁹ is a practical approach focusing on resolving issues and taking action. It has demonstrated efficacy in treating LLD in the elderly, especially for patients with insight and normal cognitive functioning. However, there is a rising concern regarding the convergence of depression and cognitive impairment in older individuals, which is referred to as depression-executive dysfunction syndrome.^{20 21} Moreover, research has shown that PST not only significantly improves depressive symptoms but also produces positive therapeutic effects by enhancing skill developments, such as generating alternative solutions and making decisions.^22–24 Therefore, as a practice-oriented approach, PST has the potential to improve depression and depression-related cognitive impairments, including executive function. Thus, further research is needed to examine the potential impact of PST and its interaction with cognition in the treatment of LLD.

The objective of this meta-analysis is to assess the efficacy and acceptability of PST for LLD, whether or not cognitive impairment is present. Subgroup and sensitive analyses will be performed to explore heterogeneity and identify factors contributing to variations in outcomes. The successful implementation of this meta-analysis will present the most comprehensive evidence and is expected to offer valuable insights to guide clinical decision-making regarding the use of PST in treating LLD with potential cognitive complaints.

Methods

Criteria for included studies

Types of studies

We will include pertinent randomised controlled trials (RCTs) and cluster-randomised trials. For studies employing a crossover design, only data from the initial randomisation phase will be analysed. However, quasi-randomised trials that allocate participants based on criteria like alternating days of the week²⁵ will be excluded.

Types of participants

The original studies should encompass all subgroups of LLD participants, including those with early-onset LLD, late-onset LLD and very late-onset LLD of any gender. These participants must exhibit clinically significant depression meeting either of two criteria: (1) current diagnosis of major depression according to the International Classification of Diseases or the Diagnostic and Statistical Manual of Mental Disorders criteria or (2) clinically significant depression as indicated by depression rating scale threshold values. Participants with comorbid organic diseases are not excluded, while those diagnosed with schizophrenia or bipolar disorder are not included.

Types of interventions

The study will incorporate RCTs that compare interventions involving PST or its adaptively modified forms for LOD. The interventions will not exceed 12 weeks in duration and can be administered in person or online, irrespective of whether antidepressants are used as the primary treatment. The primary objective is to improve the patients’ problem-solving skills, enhance their ability to cope with stress and challenges and ultimately elevate their quality of life and decision-making capabilities.

Types of control groups

The eligible control group treatments encompass waiting lists, standard care, placebos and medication, as well as other psychotherapies like CBT, supportive therapy and reminiscence therapy.

Primary outcome

The primary efficacy outcome will be the standardised mean difference (SMD) in total scores on continuous depression severity scales across all comparison arms. This will first be assessed by the Hamilton Depression Rating Scale (HDRS),²⁶ and if those data are not available, we will use the Beck Depression Inventory (BDI)²⁷ or the Montgomery-Asberg Depression Rating Scale,²⁸ followed by other depressive rating scales.

Secondary outcomes

• The response rate refers to the percentage of patients showing significant improvement or a positive response to treatment, typically indicated by a 50% reduction in depression scores at the end of treatment.

• The remission rate represents the percentage of patients whose symptoms either disappear or decrease to a clinically asymptomatic level, such as an HDRS score of 7 or less, a BDI score of 10 or less.

• Improvement in cognitive function is related to the overall change in scores on continuous cognitive function assessment scales, such as the Mini-Mental State Examination²⁹ or the WHO Disability Assessment Schedule II.³⁰

• The acceptability rate is defined by the all-cause discontinuation rate, indicating the percentage of patients discontinuing treatment for any reason and serving as an indicator of treatment acceptability.

Search strategy

Relevant studies will be systematically retrieved via the following electronic databases: PubMed, EMBASE, the Cochrane database and APA PsycNET. The deadline for the search is from the date of establishment to October 2023. There are no language limitations, and the study will encompass all published RCTs regarding PST treatment for LLD. Information regarding the search strategy is available in online supplemental etable 1 of the Supplementary materials. Additionally, we will reach out to corresponding authors to complete information for studies with incomplete data.

Study selection

Two independent reviewers (CH and J-JW) will employ the designated search strategy to screen all retrieved studies according to predetermined inclusion criteria. Studies considered potentially suitable by reviewers will undergo full-text assessment and verification against the inclusion criteria. If there is a disagreement between the two reviewers on the selection of studies, a third reviewer (XL) will be consulted to resolve the disagreement.

Quality assessment

The included studies will be independently assessed by two reviewers using the Cochrane Risk of Bias assessment tool,³¹ categorising the risk of bias as ‘low’, ‘unclear’ or ‘high’. The assessment criteria encompass random sequence generation, allocation concealment, blinding of participants, treatment providers or outcome assessors, incomplete outcome data, selective reporting of results and other biases. Disagreements during this process will be resolved through discussion or consultation with a third reviewer if necessary. Study quality will be visualised using the risk of bias graphs generated by the RevMan software.³²

Data management

The literature search results will be uploaded to EndNote, a reference management software that facilitates collaboration among reviewers during the study selection process. The team will screen and tag the literature based on inclusion and exclusion criteria. The EndNote’s duplicate detection feature will help identify and eliminate duplicate publications, reducing the risk of bias. Before the formal screening process, a calibration exercise will be conducted to pilot and refine the screening questions. Additionally, training will be provided to review team members who are not familiar with the EndNote software and the content area before the review begins.

Data extraction

Data will be extracted from each study by two reviewers using a predesigned standardised data extraction form. This includes basic information on the study, study design, participant characteristics (such as patient type, age, recruitment method and diagnostic criteria for depression), intervention measures (intervention course, duration and mode of intervention) and outcome measurements (changes in depression scale scores, cognitive measurement outcomes and follow-up results). To ensure accuracy and consistency, two researchers will independently perform data extraction and then compare their results to ascertain consistency. If there are discrepancies in the data extraction results of the two researchers, discussions will be held to resolve the differences, and if necessary the authors of the original study may be contacted to get the data clarified or obtain missing information.

Statistical analysis

Data analysis in this study will be executed using Review Manager (V.5.2).³² To evaluate the statistical heterogeneity among studies, I² statistics will be computed. Depending on the I² values, different models will be employed: a random-effects model for I²>50% and a fixed-effects model for I²<50%.³³ The primary focus will be on the SMD for continuous outcomes across diverse comparison groups. In instances where studies report data from multiple control groups, we will integrate these datasets using established statistical methods. This integration approach will also be applied to multiple experimental groups compared to a single control group.³⁴ The occurrence of positive effect sizes will indicate that the group treated with PST shows improved outcomes in LLD measures related to the control group. To assess publication bias, a funnel plot analysis will be conducted.³⁵ Additionally, a meta-regression analysis will be performed to scrutinise the impact of factors like sponsorship or publication year on the outcome estimates.

Subgroup analysis

We anticipate conducting the following subgroup analyses: (1) participants subgroups (eg, early-onset LLD, LOD and VLOD); economic status of countries (patients from high-income and low-income countries); (2) method of recruitment; (3) form of treatment and (4) duration of treatment.

Sensitivity analyses

The following sensitivity analyses will be conducted: (1) restricting the study to those with higher quality; (2) limiting the research to studies employing standardised criteria for major depression and (3) excluding studies involving participants diagnosed with schizophrenia or bipolar affective disorder.

Meta-bias

To assess the presence of meta-biases such as publication bias and selective reporting within studies, we will implement several strategies. First, we will check whether the protocols of RCTs were published before the recruitment of patients commenced. For studies published after 1 July 2005, we will screen the Clinical Trial Register at the International Clinical Trials Registry Platform of the WHO (http://apps.who.int/trialssearch). We will evaluate the presence of outcome reporting bias by comparing outcomes reported in the trial protocols with those in the published reports. Sensitivity analyses will be conducted to evaluate the impact of potential selective reporting on the meta-analytic results. These analyses will help ensure that the findings of our systematic review are robust and not unduly influenced by reporting biases.

Levels of evidence

The Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) instrument,³⁶ as recommended by Cochrane, was used to evaluate the quality of evidence and the strength of recommendations in the studies included in the quantitative synthesis meta-analysis.^{37 38} This evaluation considered factors such as study design, consistency, directness, heterogeneity, precision, publication bias and other relevant aspects reported in the included studies of this systematic review. The quality of evidence was categorised as high, moderate, low or very low. The assessment of GRADE was conducted using tools available on the website: http://gradepro.org

Patient and public involvement statement

Research design: patients and the public provided valuable input on the research questions and objectives during the design phase, ensuring that the study addresses their needs and concerns.

Interpretation of results: patients and the public will participate in discussions to help researchers understand the clinical and practical implications of the findings, providing important perspectives.

Dissemination of results: we plan to communicate the findings to patients and the public through clear and concise reports and community meetings, ensuring that they fully understand the study’s discoveries and significance.

Amendments

When protocol amendments occur, the date of each amendment will be specified along with a description of the change and the reasoning behind it.

Ethics statements

Patient consent for publication

Not applicable.

Footnote

CH and YF contributed equally.

Contributors XL is the guarantor. CH and JW made equal contributions during the meta-analysis protocol’s development, collaboratively shaping the study’s overarching framework and leading the drafting of the protocol. YF, FY, DC and XL were instrumental in the protocol design phase, offering crucial support and expert guidance to ensure that the research design was both comprehensive and practical. TW and ZZ, through their expertise, significantly influenced the formulation of the search strategy, guaranteeing the research’s extensive coverage of relevant literature. All authors involved meticulously reviewed and endorsed the final manuscript, upholding the study’s high standards of quality and accuracy.

Funding This study was funded by the National Key R&D Program of China (grant numbers 2023YFC3603200 and 2017YFC1310500); the STI2030-Major Projects (grant number 2022ZD0213100); the National Natural Science Foundation of China (grant number 82004284), the Collaborative Innovation Center for Clinical and Translational Science by the Ministry of Education & Shanghai (CCTS-202203), Sanming Project of Medicine in Shenzhen (Grant number. SZZYSM202111011), and Shenzhen Medical Research Fund (Grant number. A2403052).

Competing interests Our team affirms the absence of any conflicts of interest. This research protocol's drafting and execution were carried out autonomously, uninfluenced by any external peer review commissions or interventions.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

References

¹ Kazmi H, Walker Z, Booij J, et al. Late onset depression: dopaminergic deficit and clinical features of prodromal Parkinson’s disease: a cross-sectional study. J Neurol Neurosurg Psychiatry 2021; 92: 158–64. doi:10.1136/jnnp-2020-324266

² Alexopoulos GS. Depression in the elderly. Lancet 2005; 365: 1961–70. doi:10.1016/S0140-6736(05)66665-2

³ Moussavi S, Chatterji S, Verdes E, et al. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 2007; 370: 851–8. doi:10.1016/S0140-6736(07)61415-9

⁴ Rhee TG, Shim SR, Manning KJ, et al. Neuropsychological Assessments of Cognitive Impairment in Major Depressive Disorder: A Systematic Review and Meta-Analysis with Meta-Regression. Psychother Psychosom 2024; 93: 8–23. doi:10.1159/000535665

⁵ Liu X, Sun G, Zhang X, et al. Relationship between the prefrontal function and the severity of the emotional symptoms during a verbal fluency task in patients with major depressive disorder: A multi-channel NIRS study. Prog Neuro-Psychopharmacol Biol Psychiatry 2014; 54: 114–21. doi:10.1016/j.pnpbp.2014.05.005

⁶ Knight MJ, Baune BT. Cognitive dysfunction in major depressive disorder. Curr Opin Psychiatry 2018; 31: 26–31. doi:10.1097/YCO.0000000000000378

⁷ Rock PL, Roiser JP, Riedel WJ, et al. Cognitive impairment in depression: a systematic review and meta-analysis. Psychol Med 2014; 44: 2029–40. doi:10.1017/S0033291713002535

⁸ Siddarth P, Funes CM, Laird KT, et al. Predictors of Cognitive Improvement Following Treatment for Late-Life Depression. J Geriatr Psychiatry Neurol 2021; 34: 162–8. doi:10.1177/0891988720915515

⁹ WHO. WH: depression and other common mental disorders. 2017.

¹⁰ Furukawa TA, Cipriani A, Cowen PJ, et al. Optimal dose of selective serotonin reuptake inhibitors, venlafaxine, and mirtazapine in major depression: a systematic review and dose-response meta-analysis. Lancet Psychiatry 2019; 6: 601–9. doi:10.1016/S2215-0366(19)30217-2

¹¹ Alexopoulos GS. Mechanisms and treatment of late-life depression. Transl Psychiatry 2019; 9: 188. doi:10.1038/s41398-019-0514-6

¹² Karyotaki E, Efthimiou O, Miguel C, et al. Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis. JAMA Psychiatry 2021; 78: 361–71. doi:10.1001/jamapsychiatry.2020.4364

¹³ Etzelmueller A, Vis C, Karyotaki E, et al. Effects of Internet-Based Cognitive Behavioral Therapy in Routine Care for Adults in Treatment for Depression and Anxiety: Systematic Review and Meta-Analysisy: Systematic Review and Meta-Analysis. J Med Internet Res 2020; 22: e18100. doi:10.2196/18100

¹⁴ Hrynyschyn R, Dockweiler C. Effectiveness of Smartphone-Based Cognitive Behavioral Therapy Among Patients With Major Depression: Systematic Review of Health Implications. JMIR Mhealth Uhealth 2021; 9: e24703. doi:10.2196/24703

¹⁵ Bian C, Zhao W-W, Yan S-R, et al. Effect of interpersonal psychotherapy on social functioning, overall functioning and negative emotions for depression: A meta-analysis. J Affect Disord 2023; 320: 230–40. doi:10.1016/j.jad.2022.09.119

¹⁶ van Bronswijk S, Moopen N, Beijers L, et al. Effectiveness of psychotherapy for treatment-resistant depression: a meta-analysis and meta-regression. Psychol Med 2019; 49: 366–79. doi:10.1017/S003329171800199X

¹⁷ Alber CS, Krämer LV, Rosar SM, et al. Internet-Based Behavioral Activation for Depression: Systematic Review and Meta-Analysis. J Med Internet Res 2023; 25: e41643. doi:10.2196/41643

¹⁸ Ciharova M, Furukawa TA, Efthimiou O, et al. Cognitive restructuring, behavioral activation and cognitive-behavioral therapy in the treatment of adult depression: A network meta-analysis. J Consult Clin Psychol 2021; 89: 563–74. doi:10.1037/ccp0000654

¹⁹ Shang P, Cao X, You S, et al. Problem-solving therapy for major depressive disorders in older adults: an updated systematic review and meta-analysis of randomized controlled trials. Aging Clin Exp Res 2021; 33: 1465–75. doi:10.1007/s40520-020-01672-3

²⁰ Patience J, Lai KSP, Russell E, et al. Relationship Between Mood, Thinking, and Walking: A Systematic Review Examining Depressive Symptoms, Executive Function, and Gait. Am J Geriatr Psychiatry 2019; 27: 1375–83. doi:10.1016/j.jagp.2019.07.007

²¹ Gansler DA, Suvak M, Arean P, et al. Role of Executive Dysfunction and Dysexecutive Behavior in Late-Life Depression and Disability. Am J Geriatr Psychiatry 2015; 23: 1038–45. doi:10.1016/j.jagp.2015.05.003

²² Alexopoulos GS, Raue PJ, Kanellopoulos D, et al. Problem solving therapy for the depression-executive dysfunction syndrome of late life. Int J Geriatr Psychiatry 2008; 23: 782–8. doi:10.1002/gps.1988

²³ Kanellopoulos D, Rosenberg P, Ravdin LD, et al. Depression, cognitive, and functional outcomes of Problem Adaptation Therapy (PATH) in older adults with major depression and mild cognitive deficits. Int Psychogeriatr 2020; 32: 485–93. doi:10.1017/S1041610219001716

²⁴ Law LLF, Mok VCT, Yau MKS, et al. Effects of functional task exercise on everyday problem-solving ability and functional status in older adults with mild cognitive impairment-a randomised controlled trial. Age Ageing 2022; 51: afab210. doi:10.1093/ageing/afab210

²⁵ Omori C, Ekuni D, Ohbayashi Y, et al. Quasi-Randomized Trial of Effects of Perioperative Oral Hygiene Instruction on Inpatients with Heart Diseases Using a Behavioral Six-Step Method. Int J Environ Res Public Health 2019; 16: 4252. doi:10.3390/ijerph16214252

²⁶ HAMILTON M. A rating scale for depression. J Neurol Neurosurg Psychiatry 1960; 23: 56–62. doi:10.1136/jnnp.23.1.56

²⁷ Beck at sr: beck depression inventory: manual. 1987.

²⁸ Müller MJ, Szegedi A, Wetzel H, et al. Moderate and severe depression. Gradations for the Montgomery-Asberg Depression Rating Scale. J Affect Disord 2000; 60: 137–40. doi:10.1016/s0165-0327(99)00162-7

²⁹ Bartos A, Raisova M. The Mini-Mental State Examination: Czech Norms and Cutoffs for Mild Dementia and Mild Cognitive Impairment due to Alzheimer’s Disease. Dement Geriatr Cogn Disord 2016; 42: 50–7. doi:10.1159/000446426

³⁰ Epping-Jordan JA UT. The whodas-ii: leveling the play ing field for all disorders. WHO Mental Health Bulletin; 2000.

³¹ Crocker TF, Lam N, Jordão M, et al. Risk-of-bias assessment using Cochrane’s revised tool for randomized trials (RoB 2) was useful but challenging and resource-intensive: observations from a systematic review. J Clin Epidemiol 2023; 161: 39–45. doi:10.1016/j.jclinepi.2023.06.015

³² Li N, Gong YC, Chen J. A meta-analysis of the therapeutic effect of intranasal salmon calcitonin on osteoporosis. Eur J Med Res 2021; 26: 140. doi:10.1186/s40001-021-00610-x

³³ Kanters S. Fixed- and Random-Effects Models. Methods in molecular biology. 2022; 2345: 41–65. doi:10.1007/978-1-0716-1566-9_3

³⁴ de Vries YA, Schoevers RA, Higgins JPT, et al. Statistical power in clinical trials of interventions for mood, anxiety, and psychotic disorders. Psychol Med 2023; 53: 4499–506. doi:10.1017/S0033291722001362

³⁵ Lin L. Graphical augmentations to sample-size-based funnel plot in meta-analysis. Res Synth Methods 2019; 10: 376–88. doi:10.1002/jrsm.1340

³⁶ Balshem H, Helfand M, Schünemann HJ, et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol 2011; 64: 401–6. doi:10.1016/j.jclinepi.2010.07.015

³⁷ Pereira L, Monyror J, Almeida FT, et al. Prevalence of adenoid hypertrophy: A systematic review and meta-analysis. Sleep Med Rev 2018; 38: 101–12. doi:10.1016/j.smrv.2017.06.001

³⁸ Amaral LKB, Souza MB, Campos MGM, et al. Efficacy of conservative therapy in older people with nonspecific low back pain: A systematic review with meta-analysis and GRADE recommendations. Arch Gerontol Geriatr 2020; 90: 104177. doi:10.1016/j.archger.2020.104177