PROTOCOL: Barriers and Facilitators to the Adoption of Speech-Based Cognitive Screening Tools: A Protocol for Systematic Review of Qualitative Evidence

Abstract

Cognitive impairment, including conditions like dementia, is a growing public health concern as the global population ages. Early detection and diagnosis are critical for optimal management and support. Speech-based cognitive screening tools have emerged as a promising alternative to traditional methods, leveraging advances in natural language processing and machine learning to analyze speech samples for signs of cognitive decline. Despite their potential, the adoption of speech-based screening tools in clinical practice has been slow. This systematic review protocol outlines a plan to synthesize qualitative evidence on the barriers and facilitators to the adoption of speech-based cognitive screening tools. The review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement. Eleven electronic databases will be searched: PubMed, Web of Science, Embase, CINAHL, MEDLINE (via Ovid), The Cochrane Library, Campbell Collaboration Library, PsycINFO, Scopus, IEEE Xplore, and ACM Digital Library. Two reviewers will independently screen studies using Covidence software, extract data, assess methodological quality using the Critical Appraisal Skills Programme (CASP) qualitative checklist, and evaluate confidence in the evidence using the Grading of Recommendations Assessment, Development, and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach. Thematic synthesis will be used to analyze the evidence. The results will provide insights to inform the development, evaluation, and implementation of speech-based cognitive screening tools, ultimately improving early detection and care for individuals with cognitive impairment.

Keywords

speech-based cognitive screening,barriers and facilitators,qualitative evidence synthesis,dementia,mild cognitive impairment,digital health,technology adoption,implementation science

Background

The Problem

Cognitive impairment, including conditions like mild cognitive impairment (MCI), Alzheimer’s disease, and other types of dementia, poses a significant and growing challenge to global health as the population ages (Prince et al., 2015). Early detection and timely diagnosis of cognitive decline are crucial for optimal clinical management, treatment planning, resource allocation, and support for patients and their families (Dubois et al., 2016). However, there are substantial barriers to widespread screening and early diagnosis in current healthcare systems.

Traditional cognitive assessment tools, such as pen-and-paper tests and face-to-face neuropsychological evaluations, have several limitations that hinder their utility for large-scale screening efforts. These assessments often require administration by trained professionals in specialized clinical settings, which can limit their accessibility and scalability, particularly in resource-limited or underserved areas (Breton et al., 2019). Many standard cognitive tests are time-consuming to administer and score, placing a burden on healthcare staff and budgets (Cordell et al., 2013). Additionally, performance on these tests can be influenced by cultural, educational, and linguistic factors, leading to potential biases and challenges in interpreting results across diverse populations (Tsoi et al., 2015).

Furthermore, there is often a significant delay between the onset of cognitive symptoms and a formal diagnostic evaluation (Bradford et al., 2009). This may be due to a lack of awareness about early signs of cognitive decline, limited access to specialist services, or the stigma associated with dementia that can discourage people from seeking help (Aminzadeh et al., 2007). Delayed diagnosis means missed opportunities for early intervention, treatment optimization, advance care planning, and connection to support services, which can negatively impact patient outcomes and quality of life (Dubois et al., 2014).

To address these challenges, there is a pressing need for cognitive screening tools that are efficient, cost-effective, easy to administer, and applicable to diverse populations. Speech-based screening tools have emerged as a promising solution in recent years (de la Fuente Garcia et al., 2020). These digital tools leverage advances in artificial intelligence, natural language processing, and machine learning to analyze samples of speech and language for subtle indicators of cognitive impairment (Ding et al., 2024). By enabling automated, remote, and scalable screening, speech-based tools have the potential to improve access, reduce costs, and facilitate earlier detection of cognitive decline (König et al., 2015).

The rationale for using speech as a biomarker for cognitive health is based on the close relationship between language and cognition. Producing coherent speech requires the coordination of multiple cognitive domains, including attention, memory, executive function, and semantic knowledge (Szatloczki et al., 2015). Neurodegeneration in conditions like Alzheimer’s disease can manifest as changes in linguistic complexity, information content, fluency, and other quantifiable aspects of speech long before the onset of overt clinical symptoms (Ahmed et al., 2013). Studies have shown that automated analysis of speech samples can differentiate between cognitively healthy individuals and those with MCI or dementia with high accuracy, in some cases comparable to standard neuropsychological tests (Fraser et al., 2016; Gosztolya et al., 2018).

The potential applications of speech-based cognitive screening are wide-ranging. These tools could be used for routine screening in primary care settings, allowing for earlier detection and referral to specialist services (König et al., 2015). They could enable remote monitoring of cognitive function over time, which is particularly valuable for tracking disease progression or response to treatment (Martínez-Nicolás et al., 2021). Speech-based screeners could also be integrated into clinical trials as efficient and sensitive outcome measures (Barragan Pulido et al., 2020). Additionally, these tools could help to democratize access to cognitive screening, reaching underserved populations and regions with limited access to specialist care (Toth et al., 2018).

Despite the promising potential of speech-based cognitive screening tools, their uptake and implementation in real-world clinical practice has been limited to date (Petti et al., 2020). While a growing number of studies have demonstrated the technical feasibility and diagnostic accuracy of these approaches (de la Fuente Garcia et al., 2020), there is a recognition that technological innovations in healthcare often face significant non-technical barriers to adoption (Bajwa et al., 2021). Implementing speech-based screening tools in actual clinical workflows requires buy-in from various stakeholders, alignment with existing care pathways, integration with health IT systems, and consideration of ethical, legal, and social implications (Moyle et al., 2021).

To bridge the gap between the potential and actual use of speech-based cognitive screening tools, it is crucial to understand the factors that influence their adoption in healthcare contexts. This requires going beyond technical validation studies and examining the perceptions, experiences, and behaviors of stakeholders involved in the development, deployment, and use of these technologies (Bajwa et al., 2021). Qualitative research methods are particularly suited to exploring the complex, context-dependent, and often tacit factors that shape the implementation of healthcare innovations (Hamilton & Finley, 2019).

Several qualitative studies have investigated attitudes towards and experiences with speech-based cognitive screening tools among different stakeholder groups, including patients, caregivers, healthcare providers, and researchers (Broome et al., 2023; Colombo et al., 2024; Henderson et al., 2022; Krohne et al., 2011; Wong & Jacova, 2018). These studies have yielded valuable insights into the perceived benefits, risks, challenges, and facilitators of adopting speech-based tools in various settings. For example, some studies have highlighted concerns about data privacy and security (Martin et al., 2015), the need for clear communication of test results (Martin et al., 2015), and the importance of involving end-users in the design process (Werner et al., 2023). However, the qualitative evidence on this topic is currently fragmented and has not been systematically synthesized.

A systematic review of qualitative studies on the barriers and facilitators to adopting speech-based cognitive screening tools would help to consolidate the available evidence, identify key themes and knowledge gaps, and inform future research and implementation efforts. By integrating the perspectives of diverse stakeholders across different studies, a qualitative synthesis can generate a more comprehensive and nuanced understanding of the factors that influence the real-world translation of these promising technologies (Wolski et al., 2019). This understanding is crucial for developing strategies to overcome adoption barriers, leverage facilitators, and ultimately realize the potential of speech-based tools to improve the early detection and care of people with cognitive impairment.

The Intervention and How the Intervention Might Work

The intervention of interest in this systematic review is speech-based cognitive screening tools. We define these as digital technologies or applications that use automated analysis of speech or language samples to detect signs of cognitive impairment, such as those associated with mild cognitive impairment (MCI), Alzheimer’s disease, and other types of dementia. These tools aim to provide an efficient, accessible, and minimally invasive way to screen for cognitive decline, complementing or potentially replacing traditional pen-and-paper tests or face-to-face neuropsychological assessments.

Speech-based screening tools leverage the close relationship between language and cognition, using changes in speech as a proxy for underlying cognitive function. Producing fluent and coherent speech requires the coordination of multiple cognitive domains, such as attention, working memory, executive function, and semantic knowledge (Martínez-Nicolás et al., 2021). Neurodegeneration associated with conditions like Alzheimer’s disease can lead to subtle but detectable changes in various aspects of speech, including linguistic complexity, information content, fluency, prosody, and semantic coherence (Toth et al., 2018). These changes may manifest in speech before the onset of overt clinical symptoms, making speech a promising early biomarker for cognitive decline (Syed et al., 2020).

The automated analysis of speech for cognitive screening relies on advances in artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) (Ding et al., 2024). These technologies enable the rapid, objective, and quantitative assessment of large volumes of speech data, detecting patterns and abnormalities that may be difficult for human listeners to perceive (Fritsch et al., 2019). Typical components of a speech-based cognitive screening tool include:

(1) Speech elicitation: The tool prompts the user to provide a speech sample, either through open-ended tasks (e.g., describing a picture, recounting a memory) or constrained tasks (e.g., naming objects, repeating phrases) (Chen et al., 2021).

(2) Audio recording: The user’s speech is recorded using a microphone on a computer, smartphone, or other digital device (Qiao et al., 2020).

(3) Acoustic feature extraction: The tool preprocesses the audio signal to extract relevant acoustic features, such as measures of fluency, prosody, voice quality, and spectral properties (Kumar et al., 2022).

(4) Linguistic feature extraction: Using automatic speech recognition (ASR) and NLP techniques, the tool transcribes the speech sample and extracts linguistic features, such as word counts, type-token ratios, syntactic complexity, semantic content, and coherence markers (Dodge et al., 2015).

(5) Machine learning classification: The extracted acoustic and linguistic features are fed into a machine learning model, which has been trained on labelled data from cognitively healthy and impaired individuals. The model outputs a classification (e.g., cognitively normal, MCI, dementia) or a probability score indicating the likelihood of impairment (Kumar et al., 2022).

(6) Reporting and interpretation: The tool generates a report with the screening results, which may include the overall classification, relevant feature values, and normative comparisons. The report is intended to support clinical decision-making, but the final diagnosis requires integration with other medical data and clinical judgment (Asgari et al., 2017).

Speech-based screening tools can be delivered through various platforms, such as standalone software applications, web-based interfaces, mobile apps, or voice assistants (Barragan Pulido et al., 2020). They may be designed for use in clinical settings (e.g., primary care, memory clinics), research contexts (e.g., population studies, clinical trials), or home environments for self-assessment or remote monitoring (Latif et al., 2021). Some tools are fully automated, while others may involve human-in-the-loop components, such as manual transcription or review of flagged cases (Martínez-Nicolás et al., 2021).

The scope of speech-based screening tools in this review encompasses a range of technologies and approaches, including but not limited to:

• Natural language processing (NLP) tools that analyze transcribed speech for linguistic markers of cognitive impairment (Beltrami et al., 2018)

• Acoustic analysis tools that detect changes in voice quality, prosody, and fluency associated with cognitive decline (Liu et al., 2022)

• Dialogue systems that engage users in spoken interactions and assess their responses for signs of impairment (Asgari et al., 2017)

• Mobile apps that prompt users to record speech samples and provide feedback on their cognitive health (Piau et al., 2019)

• Virtual agents or avatars that guide users through speech-based cognitive assessments (Mirheidari et al., 2024)

• Ambient monitoring systems that analyze speech during daily activities for signs of cognitive change (Ambrosini et al., 2024)

We will include studies of speech-based tools at various stages of development and validation, from early proof-of-concept studies to commercially available products. We will include tools designed for the general adult population as well as those targeting specific populations (e.g., older adults, individuals with subjective cognitive complaints) or adapted for particular languages or cultures. Tools targeting specific populations will be included in addition to, not instead of, general population tools. If sufficient data are available, we will conduct subgroup analyses to explore whether barriers and facilitators differ by target population characteristics, including age groups, clinical populations (e.g., those with subjective cognitive complaints versus clinical referrals), and linguistic or cultural adaptations. However, the primary focus will be on tools intended for cognitive screening, rather than those designed for other purposes (e.g., speech therapy, language learning) that may have secondary applications in detecting cognitive impairment.

The context for adoption includes current clinical practice environments where speech-based tools may be introduced, either alongside or as alternatives to traditional cognitive assessment methods.

The potential advantages of speech-based screening tools over traditional methods include:

• Improved accessibility and scalability, as speech samples can be collected remotely and analyzed automatically (Zhao et al., 2024)

• Reduced costs and resource requirements compared to in-person assessments by trained professionals (Zhao et al., 2024)

• Increased frequency and ecological validity of cognitive monitoring, as speech can be sampled in naturalistic settings over time (Liu et al., 2022)

• Enhanced patient acceptability and engagement, as speech tasks may be perceived as less threatening or burdensome than formal testing (Broome et al., 2023)

• Potential for earlier detection of cognitive decline, as subtle changes in speech may precede overt clinical symptoms (Mirheidari et al., 2024)

Regarding diagnostic accuracy, systematic reviews and meta-analyses have reported promising performance metrics for speech-based cognitive screening tools. De la Fuente Garcia et al. (2020) found that automated speech and language processing approaches achieved classification accuracies ranging from 75% to over 90% for distinguishing between cognitively healthy individuals and those with Alzheimer’s disease or MCI, with some studies reporting sensitivity and specificity values comparable to established neuropsychological tests. For example, Fraser et al. (2016) demonstrated that linguistic features extracted from connected speech could differentiate Alzheimer’s disease from healthy controls with 81% accuracy. More recently, Zhao et al. (2024) reported that a voice-recognition-based digital cognitive screener achieved an area under the receiver operating characteristic curve (AUC) of 0.86 for detecting dementia and 0.78 for MCI in a large community-based implementation study. However, it should be noted that accuracy metrics vary considerably across studies depending on the specific tools, linguistic features, machine learning algorithms, and populations examined.

However, the successful implementation of speech-based screening tools in real-world settings depends on various factors beyond their technical performance, such as stakeholder acceptance, integration with existing workflows, and ethical and regulatory considerations (Zhao et al., 2024). Understanding these contextual factors is crucial for designing tools that are not only clinically valid but also feasible, appropriate, and sustainable in practice.

Why It Is Important to do This Review

Conducting a systematic review of qualitative evidence on the barriers and facilitators to adopting speech-based cognitive screening tools is important for several reasons:

(1) To synthesize the available knowledge on the factors influencing the real-world uptake and use of these tools, beyond their technical validity and diagnostic accuracy.

(2) To identify common themes and patterns across different stakeholder perspectives, healthcare settings, and cultural contexts, which can inform the design and implementation of more user-centered and context-sensitive screening solutions.

(3) To highlight knowledge gaps and areas where further research is needed to advance the development and translation of speech-based screening tools.

(4) To generate actionable recommendations for researchers, technology developers, healthcare professionals, and policymakers to guide the evaluation and deployment of speech-based screening programs.

(5) To ultimately contribute to the broader goal of improving the early detection, diagnosis, and care of individuals with cognitive impairment, by leveraging the potential of digital health technologies while attending to their social and ethical implications.

Objectives

The objective of this systematic review is to identify, appraise, and synthesize qualitative research evidence on the barriers and facilitators to the adoption of speech-based cognitive screening tools. The review aims to answer the following question:

What are the barriers and facilitators to the adoption of speech-based cognitive screening tools among relevant stakeholders, including patients, caregivers, healthcare professionals, researchers, and policymakers, as reported in qualitative studies?

Specifically, we aim to:

(1) Identify qualitative studies that explore the perceptions, attitudes, experiences, and behaviors of stakeholders regarding the adoption of speech-based cognitive screening tools in various healthcare and research settings.

(2) Appraise the methodological quality of the included studies using established criteria for qualitative research.

(3) Synthesize the findings of the included studies using thematic analysis to identify key barriers and facilitators to the adoption of speech-based screening tools, considering the perspectives of different stakeholder groups and contexts.

(4) Interpret the synthesized findings in light of relevant theoretical frameworks and models of technology adoption and implementation in healthcare, such as the Consolidated Framework for Implementation Research (CFIR) (Damschroder et al., 2009) or the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework (Greenhalgh et al., 2017).

(5) Generate actionable recommendations for researchers, technology developers, healthcare professionals, and policymakers to guide the design, evaluation, and implementation of speech-based cognitive screening tools in a way that is responsive to the needs, values, and contexts of end-users and stakeholders.

(6) Identify gaps in the existing qualitative evidence base and suggest directions for future research to advance the understanding and adoption of speech-based screening tools.

By synthesizing qualitative evidence on the adoption of speech-based cognitive screening tools, this review aims to provide a nuanced and contextualized understanding of the factors that shape their real-world implementation and impact. The review findings can inform the development of tools that are not only technically robust but also aligned with the preferences, workflows, and constraints of end-users and healthcare systems. By identifying strategies to overcome adoption barriers and leverage facilitators, the review can contribute to the broader goal of improving the early detection, diagnosis, and care of individuals with cognitive impairment through the use of innovative digital technologies.

Methods

This systematic review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Page et al., 2021) and the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement (Tong et al., 2012). The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) (registration number: CRD42024600244).

Criteria for Including and Excluding Studies

Types of Study Designs

We will include primary qualitative studies that use qualitative methods for both data collection and analysis. Eligible data collection methods include, but are not limited to:

• In-depth individual interviews

• Focus group discussions

• Ethnographic observations

• Open-ended survey questions

• Qualitative document analysis (e.g., interpretive analysis of policies, guidelines, or media reports that goes beyond quantitative content counting to explore themes, meanings, and contextual factors)

Eligible data analysis methods include, but are not limited to:

• Thematic analysis

• Content analysis

• Grounded theory

• Phenomenological analysis

• Discourse analysis

We will also include mixed-methods studies that report qualitative findings separately from quantitative results.

We will exclude the following types of studies:

• Quantitative studies with no qualitative component

• Surveys that rely exclusively on closed-ended questions without opportunities for open-ended, explanatory responses. Surveys that include both closed-ended and open-ended questions will be considered for inclusion if the open-ended responses are analyzed qualitatively and provide data relevant to barriers and facilitators.

• Opinion pieces, editorials, or commentaries

• Conference abstracts or proceedings in the absence of a corresponding full-text peer-reviewed publication

• Literature reviews, including systematic reviews and scoping reviews (although their reference lists will be screened for relevant primary studies)

• We will also exclude studies that employ purely quantitative documentary or content analysis methods (i.e., studies that only count frequencies of predefined categories without qualitative interpretation of meanings, themes, or contextual factors).

No restrictions will be placed on the study setting, language of publication, or publication date.

Types of Participants

We will include studies that report qualitative data from any relevant stakeholders involved in the adoption of speech-based cognitive screening tools. This includes, but is not limited to:

• Patients or individuals who have undergone or are eligible for speech-based cognitive screening

• Family members or informal caregivers of individuals with cognitive impairment

• Healthcare professionals involved in the administration or interpretation of speech-based cognitive screening, such as:

• Primary care physicians

• Geriatricians

• Neurologists

• Psychiatrists

• Psychologists

• Nurses

• Speech-language pathologists

• Researchers or technology developers involved in the creation or validation of speech-based screening tools

• Policymakers, administrators, or other decision-makers involved in the implementation or reimbursement of speech-based screening programs

We will include studies with participants from any age group, gender, ethnicity, or geographical location. We will exclude studies that do not report qualitative data from relevant stakeholders, such as those that only include healthy volunteers or technical validation studies without user feedback.

Types of Interventions

We will include studies that discuss speech-based cognitive screening tools, defined as digital technologies or applications that use automated analysis of speech or language samples to detect signs of cognitive impairment. Eligible tools may use various methods for eliciting speech samples (e.g., picture description, story recall, spontaneous speech) and analyzing speech data (e.g., acoustic analysis, natural language processing, machine learning).

We will include studies of tools targeting any type of cognitive impairment, including but not limited to:

• Mild cognitive impairment (MCI)

• Alzheimer’s disease

• Vascular dementia

• Frontotemporal dementia

• Lewy body dementia

• Mixed dementia

• Parkinson’s disease-related cognitive impairment

• Other neurodegenerative conditions associated with cognitive decline

• Unspecified dementia or cognitive impairment

We will exclude studies focusing exclusively on cognitive impairment resulting from acute acquired brain injury (e.g., traumatic brain injury, stroke-induced cognitive impairment in the acute phase) unless the tool is explicitly intended for screening for progressive neurodegenerative conditions. Studies examining speech-based tools for post-stroke cognitive screening in the context of vascular contributions to cognitive impairment and dementia will be included.

We will exclude studies of tools primarily designed for other purposes (e.g., general-purpose voice assistants, speech therapy applications) that are not explicitly intended for cognitive screening. We will also exclude studies of non-speech-based cognitive screening tools (e.g., computerized versions of pen-and-paper tests).

Types of Outcome Measures

The outcomes of interest in this review are the barriers and facilitators to the adoption of speech-based cognitive screening tools, as reported by relevant stakeholders in qualitative studies. We define adoption broadly as the uptake, implementation, and sustained use of speech-based tools in real-world healthcare or research settings.

Barriers are defined as any factors that impede or discourage the adoption of speech-based screening tools, while facilitators are factors that enable or encourage adoption.

Specific barriers and facilitators may relate to various domains, including but not limited to:

• Acceptability and perceived value of speech-based tools among patients, caregivers, and healthcare professionals

• Usability and user experience of speech-based tools, including ease of use, learnability, and satisfaction

• Technical feasibility and performance of speech-based tools, including accuracy, reliability, and validity

• Compatibility of speech-based tools with existing clinical workflows, care pathways, and health IT systems

• Resource requirements for implementing speech-based tools, including costs, time, training, and support

• Data privacy and security concerns related to the collection, storage, and sharing of speech data

• Ethical considerations, such as informed consent, equity of access, and potential for bias or misuse

• Regulatory and reimbursement policies that influence the uptake of speech-based tools

• Social and cultural factors, such as language diversity, health literacy, and stigma associated with cognitive impairment

• Individual attitudes, beliefs, and motivations of stakeholders regarding the use of technology in healthcare

We will include studies that report qualitative data on any of these outcomes or other relevant barriers and facilitators to the adoption of speech-based cognitive screening tools. We will exclude studies that do not report stakeholder perspectives on adoption, such as those that only focus on technical aspects of tool development or validation.

Comparator Considerations

This review focuses on barriers and facilitators to adoption rather than comparative effectiveness; therefore, we will not restrict inclusion based on any specific comparator. We will consider both comparative and non-comparative qualitative studies as long as they provide qualitative data on barriers and/or facilitators to the adoption of speech-based cognitive screening tools. Studies may compare speech-based tools to traditional cognitive assessment methods (e.g., pen-and-paper tests, face-to-face neuropsychological evaluations) or may examine speech-based tools in isolation within the context of current clinical practice. We will also include studies that explore how speech-based tools fit within or disrupt existing care pathways, workflows, and health IT infrastructures.

Search Strategy

We will conduct a comprehensive search for qualitative studies on the barriers and facilitators to the adoption of speech-based cognitive screening tools. The search strategy will be developed in consultation with a health sciences librarian and will be adapted for each database using appropriate controlled vocabulary terms (e.g., MeSH terms) and free-text keywords.

The following electronic databases will be searched from inception to the present:

• PubMed

• Web of Science

• Embase

• CINAHL

• MEDLINE (via Ovid)

• The Cochrane Library

• Campbell Collaboration Library

• PsycINFO

• Scopus

• IEEE Xplore

• ACM Digital Library

The search strategy will combine terms related to three key concepts (See Appendix 1):

(1) Speech-based cognitive screening tools (e.g., “automated speech analysis”, “natural language processing”, “machine learning”, “speech biomarkers”)

(2) Cognitive impairment (e.g., “dementia”, “Alzheimer’s disease”, “mild cognitive impairment”, “cognitive decline”)

(3) Qualitative research (e.g., “qualitative”, “interview”, “focus group”, “thematic analysis”, “grounded theory”)

In addition to the electronic database searches, we will hand-search the reference lists of all included studies and relevant review articles to identify any additional eligible studies. We will also conduct a grey literature search to identify any relevant unpublished studies, conference abstracts, or reports, using sources such as:

• OpenGrey

• Grey Literature Report

• Conference Proceedings Citation Index

• Websites of relevant organizations (e.g., Alzheimer’s Association, Dementia Alliance International, speech and language technology companies)

We will contact authors of included studies or relevant conference abstracts to inquire about any unpublished or ongoing studies that may meet our inclusion criteria. No date limits will be applied to the search strategy, allowing the inclusion of all relevant studies published from database inception to the date of the final search. This approach is intended to ensure a comprehensive capture of the existing evidence and to avoid the exclusion of earlier studies that may provide important historical context or foundational insights into the topic.

Screening and Study Selection

The study selection process will consist of two stages: (1) title and abstract screening and (2) full-text review. Both stages will be conducted independently by two reviewers using Covidence, a web-based systematic review management platform (Covidence, 2020).

In the first stage, the titles and abstracts of all records identified through the search will be screened against the eligibility criteria. Records that clearly do not meet the inclusion criteria based on the title and abstract will be excluded. Records that appear potentially eligible or where eligibility is unclear based on the title and abstract will be advanced to full-text review.

In the second stage, the full texts of all records included from the first stage will be retrieved and reviewed against the eligibility criteria. Records that do not meet the inclusion criteria based on the full text will be excluded, with reasons for exclusion documented. Any disagreements between the two reviewers at either stage will be resolved through discussion or consultation with a third reviewer if needed.

The study selection process will be reported in a PRISMA flow diagram (Page et al., 2021), which will show the number of records identified, screened, included, and excluded at each stage, along with reasons for exclusions.

Data Extraction

Data will be extracted from the included studies using a standardized form developed for this review. The draft data extraction form will be pilot tested on a sample of included studies and refined as needed before full data extraction begins. Two reviewers will independently extract data from each included study, with disagreements resolved through discussion or consultation with a third reviewer if needed.

The data extraction form will capture the following information:

(1) Study characteristics

• Authors

• Year of publication

• Country of origin

• Study objectives

• Study setting (e.g., primary care, memory clinic, community)

(2) Participant characteristics

• Stakeholder group(s) (e.g., patients, caregivers, healthcare providers)

• Sample size

• Age range

• Gender distribution

• Cognitive status (e.g., cognitively healthy, MCI, dementia)

• Other relevant demographic or clinical characteristics

(3) Speech-based cognitive screening tool characteristics

• Name or description of the tool

• Type of technology or approach (e.g., acoustic analysis, NLP)

• Language(s) supported

• Stage of development or implementation (e.g., prototype, validation study, deployed in practice)

(4) Qualitative methods

• Data collection methods (e.g., interviews, focus groups)

• Data analysis methods (e.g., thematic analysis, grounded theory)

• Theoretical or conceptual frameworks used

(5) Barriers and facilitators to adoption

• Description of identified barriers and facilitators

• Stakeholder group(s) reporting each barrier or facilitator

• Illustrative quotes related to each barrier or facilitator

• Themes or categories used to organize barriers and facilitators

(6) Other relevant findings or conclusions

• Authors’ conclusions regarding the adoption of speech-based tools

• Recommendations for future research, development, or implementation

• Study limitations or strengths

Relationship Between Data Extraction and Synthesis Coding

The data extraction process described above serves to capture study-level characteristics and contextual information needed for describing and comparing included studies. This is distinct from the line-by-line coding conducted during thematic synthesis (described in Section 3.6), which involves detailed coding of the qualitative findings (i.e., the results sections of included studies, including participant quotes and author interpretations) to identify barriers and facilitators. The extracted data on barriers and facilitators (item 5 above) will provide an initial descriptive summary, while the thematic synthesis coding will enable deeper interpretive analysis across studies.

Quality Appraisal

The methodological quality of the included studies will be critically appraised using the Critical Appraisal Skills Programme (CASP) Qualitative Checklist (CASP, 2019). This checklist consists of 10 questions that assess the appropriateness of qualitative methodology, the transparency of research aims and design, the rigor of data collection and analysis, the clarity of findings, and the value of the research.

Two reviewers will independently apply the CASP checklist to each included study, with disagreements resolved through discussion or consultation with a third reviewer if needed. Each study will be assigned an overall quality rating of “high”, “moderate”, or “low” based on the reviewers’ judgments and the guidelines provided by the CASP checklist.

For grey literature sources (e.g., unpublished reports, policy documents, or conference proceedings that meet inclusion criteria), we will apply the same CASP checklist where applicable. We acknowledge that grey literature may not report all methodological details typically found in peer-reviewed publications; in such cases, we will note items as ‘unclear’ or ‘not reported’ rather than automatically assigning negative ratings. The quality assessment of grey literature will be considered in the interpretation of findings and the GRADE-CERQual assessment of confidence in the evidence.

Studies will not be excluded based on the results of the quality appraisal, as even methodologically flawed studies may still provide valuable insights. However, the quality ratings will be used to inform the interpretation and confidence in the review findings. The results of the quality appraisal will be reported in a table and summarized narratively.

Data Synthesis

The extracted qualitative data will be synthesized using a thematic synthesis approach, as described by Thomas and Harden (Thomas & Harden, 2008). Two reviewers will independently conduct all stages of the thematic synthesis. This approach involves three main stages: (1) line-by-line coding of the primary studies; (2) development of descriptive themes; and (3) generation of analytical themes.

In the first stage, two reviewers will independently read and re-read the text of each included study, applying codes to capture key concepts and ideas related to barriers and facilitators to the adoption of speech-based cognitive screening tools. The codes will be inductively derived from the data, rather than using a pre-determined framework. The reviewers will meet regularly to compare and discuss their codes, resolving any discrepancies through consensus and developing a common coding framework.

In the second stage, the reviewers will work collaboratively to group the codes into descriptive themes and subthemes based on similarities and differences across the coded data. The descriptive themes will stay close to the original findings of the included studies and will be refined through an iterative process of discussion and re-examination of the coded data.

In the third stage, the reviewers will develop higher-order analytical themes that go beyond the content of the original studies to answer the review question. This will involve interpreting the descriptive themes in light of the review objectives, the broader literature on technology adoption in healthcare, and relevant theoretical frameworks such as the CFIR (Damschroder et al., 2009) or NASSS (Greenhalgh et al., 2017). The analytical themes will represent a new understanding or hypothesis about the key factors influencing the adoption of speech-based cognitive screening tools.

The synthesis process will be conducted using NVivo qualitative data analysis software (Dhakal, 2022) to manage the coding and theme development. The reviewers will keep a reflexive journal throughout the synthesis process to document their decisions, insights, and potential biases. The results of the thematic synthesis will be presented narratively, with a summary of the key themes and subthemes, supported by illustrative quotes from the primary studies. The relationships between the themes will be explored and visually represented using tables, concept maps, or other diagrams as appropriate.

Confidence in Cumulative Evidence

The confidence in the review findings will be assessed using the Grading of Recommendations Assessment, Development, and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach (Lewin et al., 2018). This approach provides a systematic and transparent framework for assessing how much confidence to place in individual review findings, based on four components:

(1) Methodological limitations of the primary studies contributing to each review finding

(2) Coherence of the data contributing to each review finding

(3) Adequacy of data supporting each review finding

(4) Relevance of the data from the primary studies to the context of the review question

For each review finding (i.e., each analytical theme or subtheme), two reviewers will independently assess the four components and assign a confidence level of high, moderate, low, or very low. The confidence level represents the extent to which the finding is a reasonable representation of the phenomenon of interest. Disagreements between the reviewers will be resolved through discussion or consultation with a third reviewer if needed. The GRADE-CERQual assessments will be presented in a Summary of Qualitative Findings table, along with an explanation of the judgments for each component.

Ethics and Dissemination

This review does not involve primary data collection with human participants, and all analyses will be conducted on data from published studies. Therefore, no ethical approval or informed consent is required. There are no anticipated ethical concerns in synthesizing the existing literature. To maximize dissemination and impact, the findings of this review will be published in a peer-reviewed journal and shared at conferences and seminars in the fields of geriatrics, neurology, and digital health. We will also prepare a plain-language summary for broader audiences (e.g. clinicians, policymakers, and patient advocacy groups) to facilitate knowledge translation. By identifying barriers and facilitators to adoption, our results will inform stakeholders and contribute to guiding the responsible implementation of speech-based cognitive screening tools in practice.

Protocol Amendments

Any changes or updates to this protocol will be documented transparently. Substantive amendments (e.g. modifications to eligibility criteria, search strategy, or analysis methods) will be recorded with the date, a description of the change, and the rationale. We will update our PROSPERO registration accordingly to reflect major protocol amendments. All deviations from the protocol will be reported in the final review publication, with an explanation for each change, as recommended by PRISMA guidelines. This process ensures transparency and allows readers to understand any differences between the planned methods and those actually used in the review.

Supplemental Material

Supplemental Material - PROTOCOL: Barriers and Facilitators to the Adoption of Speech-Based Cognitive Screening Tools: A Protocol for Systematic Review of Qualitative Evidence

Supplemental Material for PROTOCOL: Barriers and Facilitators to the Adoption of Speech-Based Cognitive Screening Tools: A Protocol for Systematic Review of Qualitative Evidence by Ravi Shankar, Vahul Sundar, Karen Sui Geok Chua in Campbell Systematic Reviews.

Footnotes

Acknowledgements

We thank the health sciences librarians from the National University of Singapore who assisted in developing the search strategy.

Author Contributions

Content: Ravi Shankar. Review methods: Ravi Shankar, Vahul Sundar. Information retrieval: Ravi Shankar, Karen Sui Geok Chua.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Preliminary Timeframe

Approximate date for submission of the systematic review: Within 18 months of protocol approval.

Plans for Updating This Review

Ravi Shankar will be responsible for leading the update, in collaboration with the original review team and any new members as needed. If Ravi Shankar is unable to lead the update, responsibility will be delegated to Vahul Sundar or Karen Sui Geok Chua.

Supplemental Material

Supplemental material for this article is available online.

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