Sage Journals: Discover world-class research

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.

References

Tangalos

Petersen

. Mild cognitive impairment in geriatrics. Clin Geriatr Med 2018; 34: 563–589.

Sanford

. Mild cognitive impairment. Clin Geriatr Med 2017; 33: 325–337.

Petersen

Smith

Waring

, et al. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999; 56: 303–308.

Ismail

Elbayoumi

Fischer

, et al. Prevalence of depression in patients with mild cognitive impairment: a systematic review and meta-analysis. JAMA psychiatry 2017; 74: 58–67.

Chen

, et al. Prevalence of anxiety in patients with mild cognitive impairment: a systematic review and meta-analysis. J Affect Disord 2018; 236: 211–221.

Zhang

, et al. Prevalence of neuropsychiatric symptoms across the declining memory continuum: an observational study in a memory clinic setting. Dement Geriatr Cogn Dis Extra 2012; 2: 200–208.

Pieruccini-Faria

Muir-Hunter

Montero-Odasso

. Do depressive symptoms affect balance in older adults with mild cognitive impairment? Results from the "gait and brain study". Exp Gerontol 2018; 108: 106–111.

Beaulieu-Bonneau

Hudon

. Sleep disturbances in older adults with mild cognitive impairment. Int Psychogeriatr 2009; 21: 654–666.

Guarnieri

Adorni

Musicco

, et al. Prevalence of sleep disturbances in mild cognitive impairment and dementing disorders: a multicenter Italian clinical cross-sectional study on 431 patients. Dement Geriatr Cogn Disord 2012; 33: 50–58.

10.

Shi

Chen

, et al. Sleep disturbances increase the risk of dementia: a systematic review and meta-analysis. Sleep Med Rev 2018; 40: 4–16.

11.

Rawtaer

Fam

, et al. Sleep correlates of depression and anxiety in an elderly Asian population. Psychogeriatrics 2016; 16: 191–195.

12.

Livingston

Huntley

Sommerlad

, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 2020; 396: 413–446.

13.

Mourao

Mansur

Malloy-Diniz

, et al. Depressive symptoms increase the risk of progression to dementia in subjects with mild cognitive impairment: systematic review and meta-analysis. Int J Geriatr Psychiatry 2016; 31: 905–911.

14.

. The impact of anxiety on the progression of mild cognitive impairment to dementia in Chinese and English data bases: a systematic review and meta-analysis. Int J Geriatr Psychiatry 2018; 33: 131–140.

15.

Richard

Schmand

Eikelenboom

, et al. Symptoms of apathy are associated with progression from mild cognitive impairment to Alzheimer’s disease in non-depressed subjects. Dement Geriatr Cogn Disord 2012; 33: 204–209.

16.

. Depression, anxiety, and apathy in mild cognitive impairment: current perspectives. Front Aging Neurosci 2020; 12: 9.

17.

Lee

Hua

, et al. Depressive symptoms in mild cognitive impairment predict greater atrophy in Alzheimer's disease-related regions. Biol Psychiatry 2012; 71: 814–821.

18.

Delrieu

Desmidt

Camus

, et al. Apathy as a feature of prodromal Alzheimer's disease: an FDG-PET ADNI study. Int J Geriatr Psychiatry 2015; 30: 470–477.

19.

Palmer

Di Iulio

Varsi

, et al. Neuropsychiatric predictors of progression from amnestic-mild cognitive impairment to Alzheimer’s disease: the role of depression and apathy. J Alzheimer's Dis 2010; 20: 175–183.

20.

Black

Johnston

Leoutsakos

, et al. Unmet needs in community-living persons with dementia are common, often non-medical and related to patient and caregiver characteristics. Int Psychogeriatr 2019; 31: 1643–1654.

21.

Kickbusch

Piselli

Agrawal

, et al. The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world. Lancet 2021; 398: 1727–1776.

22.

Wong

BLH

Maaß

Vodden

, et al. European Public Health Association (EUPHA) Digital Health Section. The dawn of digital public health in Europe: Implications for public health policy and practice. Lancet Reg Health Eur 2022; 14: 100316.

23.

Fatehi

Samadbeik

Kazemi

. What is digital health? Review of definitions. Stud Health Technol Inform 2020; 275: 67–71.

24.

WHO. Global strategy on digital health 2020–2025. Geneva: World Health Organization, 2021.

25.

Patel

Akhtar

Malins

, et al. The acceptability and usability of digital health interventions for adults with depression, anxiety, and somatoform disorders: qualitative systematic review and meta-synthesis. J Med Internet Res 2020; 22: e16228.

26.

Hackett

Lehman

Divers

, et al. Remind me to remember: a pilot study of a novel smartphone reminder application for older adults with dementia and mild cognitive impairment. Neuropsychol Rehabil 2022; 32: 22–50.

27.

Anderson-Hanley

Barcelos

Zimmerman

, et al. The aerobic and cognitive exercise study (ACES) for community-dwelling older adults with or at-risk for mild cognitive impairment (MCI): neuropsychological, neurobiological and neuroimaging outcomes of a randomized clinical trial. Front Aging Neurosci 2018; 10: 76.

28.

Di Lorito

Bosco

Rai

, et al. A systematic literature review and meta-analysis on digital health interventions for people living with dementia and mild cognitive impairment. Int J Geriatr Psychiatry 2022; 37(6). doi:https://doi.org/10.1002/gps.5730.

29.

Chae

Lee

. Effectiveness of online-based cognitive intervention in community-dwelling older adults with cognitive dysfunction: A systematic review and meta-analysis. International Journal of Geriatric Psychiatry 2023; 38(1): e5853.

30.

Liu

Yang

Wang

, et al. Study on adjuvant medication for patients with mild cognitive impairment based on VR technology and health education. Contrast Media Mol Imaging 2021; 2021: 1187704.

31.

Dodge

, et al. Internet-based conversational engagement randomized controlled clinical trial (I-CONECT) among socially isolated adults 75 + years old with normal cognition or mild cognitive impairment: topline results. Gerontologist 2024; 64(4): gnad147.

32.

Alley

Schoeppe

, et al. Engagement, acceptability, usability and satisfaction with active for life, a computer-tailored web-based physical activity intervention using Fitbits in older adults. International Journal of Behavioral Nutrition and Physical Activity 2023; 20: 15.

33.

Shi

Zhang

Wang

, et al. The effectiveness of digital interactive intervention on reducing older adults' depressive and anxiety symptoms: a systematic review and meta-analysis. Gerontology 2024; 70(9): 991–1012.

34.

Zarski

Weisel

Berger

, et al. Efficacy of an internet-and mobile-based intervention for subclinical anxiety and depression (ICare prevent) with two guidance formats: results from a three-armed randomized controlled trial. Psychother Psychosom 2024; 93(3): 155–168.

35.

Farrer

Gulliver

Chan

JKY

, et al. Technology-based interventions for mental health in tertiary students: systematic review. J Med Internet Res 2013; 15: e2639.

36.

Harrer

Adam

Baumeister

, et al. Internet interventions for mental health in university students: a systematic review and meta-analysis. Int J Methods Psychiatr Res 2019; 28: e1759.

37.

Moher

Shamseer

Clarke

, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 2015; 4: 1.

38.

Petersen

. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256: 183–194.

39.

Jack

Albert

Knopman

, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011; 7: 257–262.

40.

Albert

DeKosky

Dickson

, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Focus 2013; 11: 96–106.

41.

Bell

. DSM-IV: diagnostic and statistical manual of mental disorders. Jama 1994; 272: 828–829.

42.

American Psychiatric Association . Diagnostic and statistical manual of mental disorders (DSM-5®): American psychiatric pub; 2013. J Physiother Res Salvador 2019; 9: 155–158.

43.

Van De Schoot

De Bruin

Schram

, et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell 2021; 3: 125–133.

44.

Higgins

JPT

Altman

Gøtzsche

, et al. The Cochrane collaboration’s tool for assessing risk of bias in randomised trials. Br Med J 2011; 343.

45.

Wan

Wang

Liu

, et al. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 2014; 14: 1–13.

46.

Harris

Deeks

Altman

, et al. Metan: fixed-and random-effects meta-analysis. Stata J 2008; 8: 3–28.

47.

Egger

Smith

Schneider

, et al. Bias in meta-analysis detected by a simple, graphical test. Br Med J 1997; 315: 629–634.

48.

Duval

Tweedie

. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000; 56: 455–463.

49.

Guyatt

Oxman

Vist

, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. Br Med J 2008; 336: 924–926.

50.

Huh

Yang

Lee

, et al. Association between executive function and physical performance in older Korean adults: findings from the Korean longitudinal study on health and aging (KLoSHA). Arch Gerontol Geriatr 2011; 52: e156–e161.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

0.03 MB