Effectiveness of digital health interventions in improving mental health of older adults with mild cognitive impairment: A systematic review and meta-analysis protocol

Abstract

Background

Mental health issues are highly prevalent in older adults with mild cognitive impairment (MCI). Digital Health Interventions (DHIs) for individuals with MCI have proven effective in improving cognitive function, but their impact on mental health has received less attention.

Objective

Reporting a protocol for a systematic review and meta-analysis to evaluate the effectiveness of DHIs for older adults with MCI, focusing on mental health outcomes both overall and within homogeneous subgroups.

Methods

A systematic review and meta-analysis of randomized controlled trials will be conducted, with searches performed in the Cochrane Library, PubMed, Embase, Web of Science, CINAHL, CNKI, and WanFang date from inception to March 2024. Mental health outcomes are the primary outcome. Risk of bias will be assessed with the Cochrane Collaboration tool, and evidence quality will be evaluated with Grading of Recommendations Assessment, Development, and Evaluation. If sufficient studies are available, subgroup analysis will assess variations based on population and intervention characteristics, including types and modalities of DHIs, intervention settings, session frequency, and duration, focusing on specific mental health outcomes and both short- and long-term effects. Additionally, sensitivity analysis and meta-regression analysis will identify sources of other potential heterogeneity.

Conclusion

The protocol will guide a systematic review and meta-analysis to determine the effectiveness of DHIs in improving mental health of older adults with MCI. This effort will enhance understanding and optimization of DHIs, vital for reducing mental health disparities and improving psychotherapy access for older adults with MCI.

PROSPERO registration number

CRD42024522342

Keywords

Mild cognitive impairment digital health intervention mental health smart aging protocol

Introduction

Dementia, recognized globally as a major public health issue, along with its preclinical stage known as mild cognitive impairment (MCI), is widely acknowledged as a critical focus of dementia prevention.^1,2 MCI represents a transitional phase between normal cognitive aging and dementia.³ Similar to dementia, an increasing number of studies indicate a high prevalence of mental health issues in older adults with MCI. Depression, anxiety, and apathy are the most common in older adults with MCI population, with prevalence rates of 42.0%, 31.2%, and 39.5%,^4–6 respectively. Severe depressive moods may affect homeostasis in older adults with MCI, which may increase the risk for falls.⁷ Meanwhile, there is about 14%–60% prevalence of sleep disorders among older adults with MCI,^8,9 with increasing evidence suggesting that negative emotions may contribute to these disorders and exacerbate cognitive decline.^10–12 Negative emotions, such as depression, anxiety, and apathy, are recognized as potential contributors to cognitive decline and progression to dementia, with OR of 1.28, 1.18, and 1.8,^13–15 respectively. These symptoms profoundly affect the quality of life and prognostic trajectories of individual.^12,16 Furthermore, older adults with MCI and depression had a higher rate of dementia development, with approximately 31% conversion, compared to 13.5% for those without depression,¹⁶ indicating the synergistic effect of mental symptoms and MCI on cognitive performance and dementia development, as shown in previous studies.^17–19

However, mental health issues of MCI have often been disregarded and access to psychotherapy is also limited.^6,20 Currently, the rapid evolution and widespread integration of internet technology are facilitating its application in all facets of healthcare, and cognitive and psychological interventions for older adults have entered a new phase characterized by digital therapy.^21,22 Digital Health Interventions (DHIs) are defined as the delivery of cognitive and psychological interventions through various technological or digital platforms (apps, email, wearable devices, virtual reality, etc.) to provide information, treatment, and support for physical or mental health disorders.^22,23 DHIs are widely acknowledged internationally for their potential to address diverse mental health service needs comprehensively.^24,25 Studies have shown that this novel digital-based health treatment technology can provide more flexible, effective, and personalized intervention strategies to improve cognitive function and quality of life for individuals with cognitive impairment.^26–28 A meta-analysis study found that cognitive interventions delivered through information and communication technology (ICT) significantly enhanced cognitive function in individuals with MCI.²⁹ These interventions often include computer-based exercises and games designed to challenge cognitive processes. Liu et al. utilized a virtual scenario-based interactive rehabilitation training system for older adults with MCI and other chronic conditions, highlighting the effectiveness of group-based DHIs in enhancing medication adherence, daily living skills, and cognitive function.³⁰ Another significant benefit of DHIs is the enhancement of social interaction among individuals with MCI. A recent study that provided up to one year of personalized teleconversational interactions for older adults with MCI, finding that this flexible DHI could effectively reduce the risk of dementia by preventing social isolation and cognitive decline.³¹ Additionally, the acceptability and feasibility of DHIs among older adults with MCI have been positive, with high levels of satisfaction and usability reported by MCI individuals and their caregivers when using digital health platforms.³² Katherine analyzed a pilot of a smartphone reminder application intervention for older adults with MCI, demonstrating its feasibility, effectiveness, and subjective usability in reducing functional impairment within a domestic setting.²⁶ However, the effectiveness of DHIs in ameliorating negative emotions, such as anxiety and depression, in individuals with cognitive impairments remains uncertain. The findings from a systematic review and meta-analysis showed that various digital interactive devices effectively addressed depression and anxiety in older adults, including those neurocognitive disorders.³³ However, other studies have not consistently verified these results,^34–36 suggesting that the effectiveness of DHIs may be influenced by the type of intervention, individual differences, and distinctions between short-term and long-term effects. More notably, existing studies on DHIs in the context of MCI have primarily concentrated on improving participants’ cognitive function, with assessments of mental health outcomes receiving less attention. Hence, there is a critical necessity to apply a comprehensive approach to examine the holistic impact of DHIs on mental function in individuals with MCI.

This study seeks to examine the effectiveness of DHIs on mental health in older adults with MCI. Specifically, (1) investigate the overall effectiveness of DHIs on mental health outcomes compared to non-DHIs; (2) explore the impacts of population and intervention characteristics (types and modalities of DHIs, intervention settings, session frequency, and duration) on specific mental health outcomes through subgroup analysis; (3) evaluate the evidence quality of DHIs and summarize the advantages of DHIs in ameliorating mental health, aiming to enrich health management strategies for older adults with MCI.

Methods

This study has been registered in the PROSPERO (CRD42024522342) and followed the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist³⁷ (seen as Supplementary file 1). The review is undergoing literature screening and is expected to be completed in June 2024.

Inclusion criteria

The inclusion criteria will be determined according to the principle of the Population, Intervention, Comparison, Outcome, Study design (PICOS) framework, as outlined in Table 1.

Table 1.

Selection criteria of studies in PICOS format.

PICOS	Inclusion	Exclusion
Population (P)	Aged 60 years or older who met any one of the following formal diagnostic criteria for MCI: Petersen's MCI criteria³; Revised Petersen's MCI criteria³⁸; MCI criteria established by the National Institute on Aging and the Alzheimer's Association (NIA-AA)³⁹; International Working Group (IWG)-MCI criteria⁴⁰; MCI criteria constructed by the Diagnostic and the Statistical Manual of Mental Disorder (DSM-IV & V)	Dementia and healthy adults, as well as individuals with other diseases not related to MCI.
Intervention (I)	At least one type of DHIs, such as mHealth, mobile apps, eHealth, wearable, or virtual reality, etc.	Non-DHIs.
Comparison (C)	Where applicable, placebo group, usual care group, or any other alternative intervention devoid of DHIs.	No exclusion criteria applied.
Outcome (O)	Any quantitative outcome that measuring mental health and assessing changes in these mental health outcomes by comparing post-intervention data with baseline measurements.	Qualitative assessment of mental health outcomes.
Study design (S)	Randomized controlled trials (RCTs) that assess DHIs applied in older adults with MCI.	Non-RCTs such as qualitative studies, protocols, conference abstracts, narrative reviews, cohort studies, and so on.

Search strategy

The search process and extraction of information will be guided by the Cochrane Handbook of Evaluation. A comprehensive search strategy will be developed by our multidisciplinary research team, tailored to each electronic database. Relevant literature in English and Chinese will be searched across seven databases, including PubMed, Web of Science, Cochrane Library, Embase, CINAHL, CNKI, and WanFang data, from the database inception to March 7, 2024. Search terms and search strategies will be constructed to identify relevant studies based on population (mild cognitive impairment) and DHIs (mHealth, eHealth, mobile phone, application, wearables, virtual reality, artificial intelligence, tablet, robot and email, etc.) and mental health outcomes (anxiety, apathy, depression, loneliness, stress, psychological distress, and suicidal ideation, etc.), details provided in Supplementary file 2.

Selection of studies

The flowchart will be developed to document the study selection process, showing details of the studies included and excluded at each stage. Subgroup analysis will also be depicted in the flowchart if performed. After completing the search, study citations will be imported to EndNote 20 and two authors will independently screening, duplicate removal, and storage. ASReview software will then automatically screen titles and abstracts using the PICOS format selection criteria. During this process, the software will iteratively rearrange records by relevance, learning from the author's choices based on references and research topics.⁴³ Finally, two authors will further screen the titles, abstracts and full text against the inclusion criteria in the database, with eligible studies to be selected. Discrepancies and uncertainties will be reassessed and decided by the third author. The details of study selection process are presented in in the PRISMA flowchart Figure 1.

Figure 1.

PRISMA flowchart.

Data extraction

A standardized data extracting form will be created. Two authors will independently extract data from the included studies into the data table. Main elements will include the following:

Study characteristics: first author's name, year of publication, country, sources of funding, declared conflict of interest;

Study population characteristics: number of participants, age, gender, MCI subtype;

Characteristics of the DHIs, including types and modalities of DHIs, intervention modalities (face-to-face, fully automated, or remote), intervention settings, session frequency and duration, as well as the follow-up period;

Comparator: the intervention types of comparators used in the RCTs;

Outcomes: mental health outcomes and associated adverse events;

Assessment of risk of bias

Two authors will use the Cochrane Collaboration's tool to independently assess the risk of bias for seven entries, which encompass random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other bias.⁴⁴ Each entry will be categorized as low, high, or unclear risk. Disagreements will be resolved by discussion and consensus.

Missing data

For incomplete, unclear, or missing data, the corresponding authors will be contacted. If the authors decline or do not respond, the missing data will not be analyzed but will be reported in this study.

Assessment of intervention effects and data synthesis

Meta-analysis will be performed using STATA 17.0 software. To address potential inconsistency in outcomes measurement units, the standardized mean difference (SMD) and 95%CI will be applied for continuous data, while the risk ratio and 95%CI will be used for dichotomous data to investigate the impact of DHIs on mental health outcomes in older adults with MCI. Mean difference (MD) and 95%CI were used to pool effects if the units of measurement of the results were consistent. When mean and standard deviation (SD) were unavailable, estimates were derived from the sample size, median, range, and/or interquartile range.⁴⁵ If heterogeneity test results in p > .10, similar studies can be considered homogeneous, and the fixed effects model will be used; if p ≤ .10, sensitivity analysis, subgroup analysis, meta-regression analysis will be conducted to identify sources of heterogeneity, with the random effects model used if heterogeneous persists.⁴⁶ If data from the included studies are not insufficient for meta-analysis, a narrative synthesis will be conducted.

Assessment of publication bias

For 10 or more studies are included, publication bias will be assessed using funnel plot and Egger's test.⁴⁷ A significance level of p < .05 will indicate publication bias and the “trim and fill method” will be used to adjust the combined results while analyzing the influence of bias on the overall analysis.⁴⁸

Grading the quality of evidence

The quality of the generated evidence will be assessed according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework,⁴⁹ which categorizes into four grades: high, medium, low, and very low. A GRADE quality of evidence for the comparisons of DHIs with non-DHIs on mental health will be provided. Any disagreements will be evaluated by a third author.

Discussion

Research has found that older adults with MCI have an annual conversion rate to dementia that is 10 times higher than those without MCI.⁵⁰ Poor mental health have been identified as an important risk factor for the progression from MCI to dementia.^{4–6,13–16} Therefore, the treatment of mental disorders should be an integral part of the treatment strategies of MCI.

Currently, many studies have begun to focus on the effectiveness of DHIs in improving cognitive functioning in older adults with MCI, but there is a lack of systematic review and meta-analysis on the effectiveness of DHIs in improving mental health in older adults with MCI. This systematic review and meta-analysis will to consolidate diverse findings on DHIs, which vary significantly in types of interventions, modalities, and operational settings, frequency and duration of sessions, as well as the follow-up period. These elements play crucial roles in their efficacy and accessibility on mental health promotion, influencing individuals’ engagement and treatment adherence. By identifying which configurations of these variables are most effective, this study will address critical gaps in the existing literature and provide a subtle understanding that can guide future digital health strategies.

Methodologically, this study will utilize rigorous quality assessment tools and a well-defined search strategy to ensure the reliability and relevance of the data. Additionally, sensitivity analysis, subgroup analysis, and meta-regression analysis will be conducted to identify sources of heterogeneity, if permission. This systematic approach is essential to explore the complex interplay between different intervention characteristics and their impact on mental health outcomes, ensuring that findings are robust and applicable across diverse settings and populations. The findings of this study are expected to advance the theoretical framework surrounding DHIs and offer practical insights that can improve their implementation.

Conclusion

This protocol outlines a systematic review and meta-analysis that will synthesize findings from multiple randomized controlled trials. This approach will enhance the accuracy and reliability of results, offering critical insights for developing more effective DHIs for older adults with MCI and optimizing management strategies for individuals with cognitive impairments.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076241288651 - Supplemental material for Effectiveness of digital health interventions in improving mental health of older adults with mild cognitive impairment: A systematic review and meta-analysis protocol

Supplemental material, sj-docx-1-dhj-10.1177_20552076241288651 for Effectiveness of digital health interventions in improving mental health of older adults with mild cognitive impairment: A systematic review and meta-analysis protocol by An Huang, Xueqi Liu, An Gu, Dan Zhao, Cheng Huang and Lina Wang in DIGITAL HEALTH

Supplemental Material

sj-docx-2-dhj-10.1177_20552076241288651 - Supplemental material for Effectiveness of digital health interventions in improving mental health of older adults with mild cognitive impairment: A systematic review and meta-analysis protocol

Supplemental material, sj-docx-2-dhj-10.1177_20552076241288651 for Effectiveness of digital health interventions in improving mental health of older adults with mild cognitive impairment: A systematic review and meta-analysis protocol by An Huang, Xueqi Liu, An Gu, Dan Zhao, Cheng Huang and Lina Wang in DIGITAL HEALTH

Footnotes

Acknowledgements

The authors would like to express our gratitude to the National Natural Science Foundation of China, China Scholarship Council foundation, and The Zhejiang Provincial College Students Scientific and Technological Innovation Activities-Zhejiang Xinmiao Talents Program for their financial support.

Contributorship

An Huang contributed to conceptualization, methodology, and writing–original draft. Xueqi Liu and An Gu contributed to conceptualization, methodology, and data curation. Dan Zhao contributed to supervision and validation. Cheng Huang contributed to writing–review and editing. Lina Wang contributed to conceptualization, methodology, validation, writing–review and editing, and funding acquisition. All authors read and approved the final version of the manuscript.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

This study did not require ethical approval as it did not include any patient information. The results of this systematic review and meta-analysis will be published openly in peer-reviewed journals.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the China Scholarship Council foundation, National Natural Science Foundation of China, Zhejiang Provincial College Students Scientific and Technological Innovation Activities-Zhejiang Xinmiao Talents Program (grant numbers: 202308330251, 71704053, 72174061, and 2023R462028).

Data availability statement

As a protocol there are no publicly available data.

Guarantor

Lina Wang.

Informed consent

This study will comply with ethical standards by including only studies that had obtained informed consent from participant.

ORCID iD

Lina Wang

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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