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. 2025 Feb 21;8:115. doi: 10.1038/s41746-025-01510-8

A systematic review of consumers’ and healthcare professionals’ trust in digital healthcare

Soraia de Camargo Catapan 1,2,✉, Hannah Sazon 3, Sophie Zheng 4, Victor Gallegos-Rejas 1,2,5, Roshni Mendis 1,2, Pedro H R Santiago 6, Jaimon T Kelly 1,2
PMCID: PMC11845731  PMID: 39984678

Abstract

Despite the well-documented importance of trust in digital healthcare, its domains are not well-understood, preventing theoretically robust instruments for standardised measurements. We identified instruments measuring trust in digital healthcare, explored definitions, associated factors, and outcomes. We systematically reviewed the literature using tailored searches and 49 studies measuring trust in digital healthcare from either consumers’, healthcare professionals’, or both perspectives were included. Trust in digital healthcare is complex and, from a consumers’ perspective, can influence digital healthcare use, adoption, acceptance, and usefulness. Consumers’ trust can be affected by the degree of human interaction in automated interventions, perceived risks, privacy concerns, data accuracy, digital literacy, quality of the digital healthcare intervention, satisfaction, education, and income. Healthcare professionals’ trust is enhanced by education and observing good digital health performance. While studies can benefit from rigorous trust measurements, future efforts should address the need for a theoretical framework for trust in digital healthcare.

Subject terms: Health services, Translational research

Introduction

Digital health has effectively offered an alternative and additive means to healthcare delivery by facilitating person-centred care, streamlining multidisciplinary team management, and increasing the volume of healthcare interactions1,2. Digital health broadly encompasses all technologies used in the health sector, but the focus of this paper is on digital healthcare, which includes technology and products to support the delivery and administration of healthcare services3,4. Examples of digital healthcare include but are not limited to mobile health, electronic health records, wearable devices, telehealth, online health, and medical artificial intelligence (AI)4,5. Despite evidence for the efficacy of digital healthcare, sustainable implementation remains limited6. This indicates a need to identify and address potential barriers experienced by end-users to encourage sustainable adoption in the digital health landscape7.

Trust is a complex relational construct that implies one side of the interpersonal relationship accepts vulnerability and decreased control8. In healthcare, individual trust has been widely explored as a key feature of patient-clinician relationship9,10. More broadly, public trust in the healthcare system is considered vital for its effective functioning11. Trust can vary according to personal factors (e.g., experiences, expectations, perceptions, demographics), interpersonal factors (e.g., new or established patient-clinician relationships, type of provider), and geographical or contextual factors12. For instance, trust in government-provided digital health services, such as electronic health records, is different from trust in privately developed services, such as mobile health apps12.

Trust may also vary depending on the end-user, be they consumers or healthcare professionals. Consumers are often more vulnerable than healthcare professionals in their interactions due to the knowledge and power imbalance. Consumers also rely on beneficence, a key principle of biomedical ethics, and trust the healthcare professional will act in their best interest to support their health and well-being13. This dynamic, whether real or perceived, may manifest as a lack of trust towards digital healthcare and its’ usefulness, accessibility, privacy, data protection, information quality, and efficacy4,14. In contrast, healthcare professionals’ trust in digital health is more often related to interoperability, functionality, communication, service reputation, and the sufficiency of training14.

These myriads of potential influences have disrupted trust in healthcare dynamics since the increased uptake of digital health, affecting not only interpersonal relationships but also the technologies employed8. On an individual level, trust has a fundamental role in predicting the use and adoption of digital healthcare15. But despite its well-documented importance16,17, the domains of trust in digital healthcare are not well-understood, preventing the development of theoretically robust instruments that can provide standardised measurements. Such instruments could guide the improvement of existing digital tools and optimise the development of new ones. Measuring trust in digital healthcare precisely can inform the development of guidelines and recommendations to promote trust, such as fostering positive experiences with digital healthcare and enhancing digital literacy, which are both trust-builder factors that can increase individuals’ self-efficacy with digital technology18,19.

Moreover, sustaining the adoption of safe and effective digital healthcare can bridge the gap to clinical integration and potentially improve health outcomes. Therefore, identifying the domains of trust that have been described in the literature and used to inform instrument development, as well as evaluate available instruments and what results they present, is a necessary step to understanding consumers' and healthcare professionals’ trust towards digital healthcare. This review aims to identify and evaluate the psychometric properties (e.g., dimensionality, reliability) of existing instruments measuring trust in digital health from consumers’ and healthcare professionals’ perspectives and to explore concepts, factors, and outcomes associated with trust in digital healthcare.

Results

The initial search retrieved 1944 studies from five databases. After screening and full-text review, 49 studies met the inclusion criteria and were included in the evidence synthesis. Three of these were identified manually from citation searching. Figure 1 shows the identification and screening stages, detailing the reasons for exclusion at full-text screening stage.

Fig. 1. PRISMA flow diagram82.

Fig. 1

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PRISMA diagram including the four steps of identification, screening and final inclusion of studies, with the number of studies included and removed at each stage generated in Covidence.

Study quality

Assessment of the 49 included articles using SQUIRE 2.020 revealed a mean score of 16.6 (out of 18) indicating that studies were high quality overall. Fourteen articles (28.6%) scored 100%, six (12.2%) scored below 80.9%, while the remainder scored between 81.0 and 99.9%. The most common reasons for lower quality scores were lack of description for the approach used to assess the impact of the intervention (n = 18, 36.7%), unclear articulation of ethical aspects (n = 16, 32.6%), absence of a concise summary of key findings (n = 16, 32.6%), and a failure to explicitly disclose study fundings (n = 6, 12.2%).

Studies characteristics

Included studies were published from 2010 to 2023, with approximately a quarter (n = 12, 24.5%) published in 2021. Countries with the highest number of publications were China (n = 12, 24.5%) and the United States (n = 8, 16.3%), followed by Australia, Canada, Italy, Spain, Switzerland and the United Kingdom with two studies (2.1%) each. Study designs comprise cross-sectional survey studies (n = 45, 91.8%), randomised controlled trials (n = 1, 2.5%), field experiments (n = 2, 4.1%), and retrospective observational studies (n = 1, 2.5%). In the cross-sectional studies, 26 (53.1%) used structural equation modelling in their methods.

Participants characteristics and data collection methods

Across all study populations, a total of 26,165 people were surveyed, including patients (n = 20,023; 36 studies), patients and their families (n = 60; 1 study), patients and healthcare professionals (n = 316; 1 study), healthcare professionals (n = 647; 2 studies), digital intervention users (n = 9799; 4 studies) or the general population (n = 2635; 5 studies). Survey data was collected predominantly online (n = 28, 57.1%), on paper (n = 9, 18.4%), or by a combination of data collection methods (n = 7, 14.3%). Five studies (10.2%) did not report the data collection method.

Digital healthcare modalities and interventions

Interventions included various modalities of digital healthcare (chatbots, wearables, sensors, virtual reality, electronic medical records, medical artificial intelligence) (n = 13, 26.5%)21–33; telehealth (teleconsultation, remote patient monitoring, eConsults) (n = 22, 44.9%)19,34–55; and mobile health (mobile health apps, mobile management systems, mobile teledermatoscopy) (n = 14, 28.6%)56–68. A table in Supplementary Information (Supplementary Table 1) details the digital healthcare interventions and presents the terminology adopted by the authors in the included studies.

Definitions of trust

While 21 studies (42.9%) did not define trust, the remaining 28 papers (57.1%) presented a broad variety of definitions. These included several definitions based on trust-related processes measured in the study such as concerns about the accuracy of the data collected (n = 3, 6.1%)29,32,57; the willingness to rely or depend on something or someone (n = 5, 10.2%)40,51,55,65,68; the acceptance of uncertainty and vulnerability based on positive expectations (n = 4, 8.1%)43,49,63,67; the explanation of trust’s role in supporting interpersonal relationships (n = 4, 8.1%)22,30,35,69, data sharing (n = 2, 4.1%)38,46, intention to use (n = 3, 6.1%)37,58,62 or adopt (n = 1, 2%)24 digital healthcare. Other explanations differentiated cognitive trust (developed by observing others performance objectively) (n = 2, 4.1%)28,56, from affective trust (involves emotional and irrational feelings beyond performance observation) (n = 1, 2%)28, initial trust (trust from the first interaction) (n = 2, 4.1%)61,66 and online trust (expectation that ones’ vulnerability will not be attacked online) (n = 1, 2%)43.

Instruments to measure trust in digital healthcare

In 36 studies (73.5%), trust in digital healthcare was measured with a unidimensional instrument, while the remaining instruments (26.5%) had two to five dimensions to measure the construct of trust. Trust in digital healthcare was often measured together with other constructs (e.g., behavioural intention, satisfaction, privacy and security concerns, perceived use and risk, technology anxiety, etc.) (n = 45, 91.8%), and only four studies (8.2%) measured trust in digital healthcare as the unique construct.

Extensions of existing theoretical frameworks such as Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT and UTAUT2), diffusion of innovation or a combination of those were used in 17 studies (34.7%), while six (12.2%) used the Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB), Social Cognitive Theory, the Extended Valence Framework and/or other references to develop their research models. Previously published surveys were adopted in 37 studies (75.5%), including the Patient Trust Assessment Tool (PATAT) questionnaire34,51 and the Trust in Physician Scale19,36. Twelve studies (24.5%) adapted items from non-health-related references, particularly trust measures for e-commerce30,33,45,68,69. Six studies (12.2%) developed their own trust items without mentioning any reference.

Trust domains and items

A table summarising the trust domains and items to measure trust in digital healthcare described in the included studies, identifying those that went through any qualitative and/or quantitative validation processes is presented in Supplementary Information (Supplementary Table 1). Figure 2 presents trust domains identified more than once in the included studies.

Fig. 2. Trust in digital healthcare domains is commonly identified.

Fig. 2

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This figure outlines and groups the major domains associated with trust. The first group refers to attributes associated with trust, the second group has different types of trust, while the third includes the entities to which trust is attributed.

Response options

Survey responses predominantly used metrics such as Likert scales with 5-points (n = 30, 61.2%), 7-points (n = 12, 24.5%) and 4-points (n = 3, 6.1%). Other options included binary responses (n = 2, 4.1%) and Likert scales with 6-points (n = 1, 2%) or without stating the number of response categories (n = 1, 2%).

Validation

Twelve (28.5%) studies adopted a qualitative validation stage, using either experts’ feedback or a pilot study with a small sample to evaluate face and content validity, followed by changes to the survey items before data collection. Thirty-two studies (65.3%) conducted quantitative validation, including exploratory and confirmatory factor analysis to evaluate structural validity and other methods to evaluate criterion validity, such as convergent and discriminant validity or Structural Equation Modelling (SEM). In total, eight studies (16.3%) did not report any statistical validation method, claiming the survey used was already validated in other populations or contexts. Four studies (8.2%) did not report any validation method.

Internal consistency

In 29 studies (59.2%), Cronbach’s alpha was used to measure internal consistency reliability of trust scales or subscales, and in 22 studies (44.9%) composite reliability was calculated. Sixteen studies conducted both Cronbach’s alpha and composite reliability (31.6%) and 14 studies (28.6%) did not conduct any internal consistency analysis of trust scales.

Factors and outcome measures associated with trust

Factors associated with trust in digital healthcare included perceived risk43,49,55, privacy concerns with data sharing19,23,38,68, and previous experience54,64. The impact of human presence on trust in digital healthcare was also evaluated in three (6.1%) studies28,31,60. Trust was associated also with digital literacy, education levels, and learning (n = 3, 6.1%)22,39,54. Some studies associated sociodemographic variables with trust, such as income (n = 1, 2%)19, or simply quantified the level of trust in the digital healthcare intervention (n = 4, 8.2%)23,34,39,70.

From the included studies, 20 (40.8%) associated trust with intention to use or to continue to use digital healthcare21,26,32,35,37,40,45,53,62,63,65,67–69, five (10.2%) associated trust with adoption, intention to adopt or continued adoption27,43,55,61,66, while two studies (4.1%) associated trust with either acceptance30,33 or usefulness31,49 of digital healthcare interventions as their main outcome.

Factors positively associated with consumers’ trust in digital healthcare

Several factors are associated with consumers’ trust in digital healthcare. The accuracy of data obtained from the digital healthcare intervention has been shown to influence trust29, and trust in health providers, as an information source, optimised consumers’ willingness to share wearable data23. Consumers’ levels of digital literacy and level of education have been identified as predictors of, or have been significantly associated with trust39,54. Similarly, one study found a significant association between previous experience with digital healthcare and trust54, while the same association did not change in a pre and post study64. Consumers’ trust is positively influenced by digital healthcare quality46, while also significantly correlated to satisfaction and learning22. Sociodemographic factors such as consumer income had a significantly positive association with the level of trust in physicians using telemedicine19. Consumers also value personalised or customised digital healthcare services, with both aspects increasing their trust68.

Factors negatively associated with consumers’ trust in digital healthcare

Perceived risk and privacy concerns were found to be negatively associated with trust in digital healthcare43,49,55,68. Mistrust negatively impacts the frequency and willingness to share data online38, whereas digital natives (i.e., people born after 1980) are more worried about data protection, compared to digital immigrants (i.e., people born before 1980)50.

Outcomes positively associated with consumers’ trust in digital healthcare

Over half of the included studies in this review (N = 49) found that consumers’ trust is positively associated with some aspects of digital healthcare. Of the 46 studies measuring trust in digital healthcare from consumers’ perspectives, trust positively affected their intention to use or to continue using digital healthcare in 13 studies (28.2%)32,35,37,40,45,47,48,53,57,58,62,65,69. In three studies (6.5%), trust predicted the intention to use21,63,67, and in one (2.2%) trust did not predict the intention to use digital health49. Another study reported that trust could mediate the paradoxical effect of beneficial and desired personalisation in mobile health versus privacy concerns on the intentions to use this modality of digital healthcare68. Initial trust, online trust, and trust in health providers positively affected consumers’ adoption of digital healthcare43,55,61,66, and in one study, consumers’ trust was a predictor for the adoption of medical AI27. Trust significantly impacted the perceived usefulness of AI-assisted living technology25 and of online fertility consultations and self-collection tests49. Trust was also a strong predictor of electronic medical records acceptance.

Figure 3 summarises on the left-hand side the factors positively and negatively associated to consumers’ trust in digital healthcare and on the right-hand side the outcomes positively associated to consumers’ trust in digital healthcare found in the included studies.

Fig. 3. Factors and outcomes associated with consumers’ trust in digital healthcare, with the number of studies that reported this result.

Fig. 3

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The bubbles on the left-hand side of the figure denote factors associated with trust, while the bubbles on the right-hand side represent outcomes associated with having trust. Each bubble contains the number of identified studies exploring that factor. The arrows explain the direction of the association.

Consumers trust human interactions more than machines

Consumers are less likely to trust AI-goal setting features28, telediagnosis in dermatology60, and virtual reality mindfulness training22, compared to in-person interactions. Consumers also tend to have more trust in AI-enabled digital healthcare if they perceive that there is interaction with a human rather than a virtual agent only31.

Levels of trust in different aspects of digital healthcare

The studies that analysed levels of trust in different aspects of digital healthcare found: consumers tend to trust telemedicine services34, trust providers as a source of health information23,39, and trust a mobile virtual AI-supported virtual agent to perform medical interviews70. Also, trust in the technology, healthcare professionals, and the treatment affects trust in the telemedicine service to manage anticoagulation treatment, whereas trust in the healthcare organisation does not seem to affect this treatment51.

Healthcare professionals’ trust in digital healthcare (n = 3)

The three studies (6.1%) reporting healthcare professionals’ trust in digital healthcare were published in Germany, Spain, and China, and included results from cross-sectional surveys. Altogether, these papers analysed 697 health professionals’ trust in digital healthcare interventions, including mobile health apps56, electronic healthcare records30, and eHealthMonitor (a personalised eHealth platform to accumulate online health data to support decisions on disease treatment or prevention)26. The results indicate that cognitive trust (i.e., developed by observing others performance objectively) can significantly influence the use behaviours of mobile health apps56. Also, provider’s perceptions of the favourable conditions that lead to the success of the digital healthcare resource strongly predict acceptance of electronic record systems30. Educational interventions can improve medical professionals’ trust in data privacy, considered the main hindrance to eHealthMonitor use26.

Discussion

This review evaluates the prevailing methods of measuring trust in digital healthcare amongst consumers and healthcare professionals, explores trust definitions, and factors and outcomes associated with trust in digital healthcare. Our review found that trust in digital healthcare is often poorly measured, with over a third of studies using trust items from non-health sources or developed without validation. Common definitions of trust in digital healthcare focus on the willingness to rely on digital healthcare and accept a degree of uncertainty in its use. Our results demonstrate that trust in digital healthcare is a complex construct that, from consumers’ perspectives, can be associated with and, in some cases, predict the use, adoption, usefulness, or acceptance of digital healthcare interventions. Further, consumers’ trust in digital healthcare interventions can be affected by factors such as the degree of human interaction experienced in automated interventions, as well as perceived risks, privacy concerns, data accuracy, quality of the intervention, digital literacy, satisfaction, customisation, experience, level of education, and income. From a healthcare professionals’ perspective, trust is developed by observing how well digital healthcare tools perform, and whether its use optimises healthcare services, which can be associated with increased use and acceptance.

Trust is an abstract, complex, and relational construct, and measuring trust in digital healthcare is inheriting a very challenging task. This does make in-depth evaluation and synthesis of trust parameters, methods, and quality measures difficult across the included studies. However, we found that trust in digital healthcare is generally poorly measured. Over a third of studies used trust items either from non-health-related sources or developed without citing references or methods to validate their tools. Further to this, trust was often undefined (42.9%). The quality and robustness of methods applied to the development and validation of surveys are crucial to ensure that results obtained by these instruments are meaningful and trustworthy71.

Further, it is important to clarify that best practice in psychometrics recommends instruments to be validated in specific populations and contexts71,72. These results serve as an audit of the quality of available instruments and as a recommendation to improve the quality of survey design and development by researchers when measuring trust in digital healthcare. Also, it is important to mention that cultural and geographical differences may have impacted not only individuals’ perceptions of trust but also the way it is measured. Nearly a quarter of the studies were conducted in China, where digital privacy and civil liberties pertaining to freedom of the internet are more regulated than in Western countries73.

In addition to the need for rigorous development and validation processes, comprehensive instruments designed to measure trust should be tailored to the specific features of each digital health modality they aim to assess. For example, trust in eConsults should focus on the transferable trust from the primary care provider to the specialist, while trust in remote patient monitoring should also consider the accuracy of the data collected by wearable devices. Further, in the absence of an instrument that identifies the domains comprising this complex construct and comprehensively measures trust in digital healthcare, systematically exploring issues associated with trust in digital healthcare (domains of interest) and the dimensionality of the construct is the first step towards developing a comprehensive and precise instrument74,75.

The trust associations drawn by included studies are bidirectional (factors and outcomes) and not univariate. The inability to quantify how trust influences or is influenced by personal or interpersonal factors may pose a detriment to the quality and rigour of many studies. Trust is included in unidimensional measures in 73.5% of cases. Additionally, trust is almost universally not measured on its own (91.8%), but with other characteristics separate to trust, such as behavioural intention, satisfaction, privacy, digital literacy, and perception. Hence, trust can be hard to separate from any confounders in these results.

It appears that people have increased trust when human interaction prevails or adds to AI-interventions, reinforcing our finding that trust is influenced by interpersonal factors. The positive influence of interpersonal trust on the quality of life and health-promoting behaviours such as medication adherence, lifestyle modifications, or online information seeking has been reported previously76. Our review provides a more comprehensive rendering of trust and its’ associated and predictive factors and describes the effect of trust on the acceptance, use, adoption, and usefulness of digital health.

Trust in health providers can have a positive effect on consumers’ adoption of digital healthcare. More specifically, consumers’ trust in healthcare providers may be extended or transferred to the digital healthcare service endorsed by health providers77. In this sense, a rise in healthcare provider trust would thereby also promote consumers’ trust in digital healthcare, making it the logical target for trust-builder interventions, particularly in the digital healthcare spheres of electronic medical platforms and mobile health. Clinical and service implementation guidelines to support the prescription of digital healthcare modalities and education opportunities are essential to assist healthcare professionals in this emerging practice78–80. In this context, we emphasise the role of medical devices regulatory bodies, such as the European Union’s Medical Device Regulation (MDR), the United States Food and Drug Administration (FDA), and Australia’s Therapeutic Goods Administration (TGA) for example, in establishing safety, efficacy, and quality standards for digital healthcare. By broadening their scope to encompass other digital health modalities not currently considered medical devices (e.g., health and wellbeing mobile apps), these regulatory bodies could help to promote, build, and sustain trust in the digital health ecosystem.

The Australian Digital Health Blueprint delineates the role of digital health capabilities in fortifying our healthcare system by 2033, with one of its focal areas being the reinforcement of trust81. Our study underscores the importance of fostering trust in digital health to enhance its adoption and utilisation, with over a third of the studies illustrating this correlation. Efforts addressing factors crucial for promoting trust, such as data accuracy, intervention quality, digital literacy, user experience, and customisation, can be readily tackled in the development or enhancement of digital healthcare interventions.

Establishing an evidence-based theoretical framework delineating trust in digital healthcare could further streamline these efforts. The attempts discussed in this paper to measure trust in digital healthcare varied in quality and rigour, and care should be taken in translating these results to clinical practice and guidelines development. Although cultural appropriateness, geographical location, and different contexts should be considered when developing a trust in digital healthcare framework to address the myriads of potential influences of how trust is built and influenced, this review marks an initial stride towards understanding what constructs could be considered in such a framework.

To our knowledge, this is the first study systematically reviewing instruments to measure trust in digital healthcare interventions. While a review on digital health trust exists14, our study benefits from using a thorough, rigorous, and systematic search following PRISMA guidelines and exploring results beyond barriers and enablers. Our findings collated substantial psychometric and descriptive knowledge around trust that may support bridging the digital healthcare sustainability and usage gap. However, limitations remain which should be noted.

Multiple included studies measuring trust in digital healthcare used non-validated tools, tools previously validated in different contexts or populations or items originally developed to measure trust in non-health related interventions. Consideration of such heterogeneity is essential to mitigate the risk of unfair comparisons in interpreting the results, ultimately affecting translation to clinical practice and healthcare service improvement.

Given the wide range of study designs included, our results might have been affected by limitations inherent in each study. Selection bias in the primary studies might be a significant limitation that impacts the generalisability of our results. Our results included data collected online in nearly two-thirds of the included studies. This may have meant that people facing barriers to accessing healthcare or telehealth (or any online) services, such as culturally and linguistically diverse consumers, people living with disabilities, and those from low socioeconomic backgrounds, might not be accurately represented in this study42. Given these population groups continually face challenges in accessing healthcare or digital healthcare services, future studies should consider an equity perspective in developing instruments measuring trust in digital healthcare.

This systematic review found trust in digital healthcare to be a complex, heterogeneous, and relational concept, often challenging to define. Trust in digital healthcare is associated with an interplay of factors related to individual perceptions and experiences, where education can play a positive role. Improving trust can ultimately impact the intention to use, adopt, and accept digital healthcare interventions, as well as impact perceptions of usefulness. While studies can benefit from rigorous approaches to measuring trust in digital healthcare, future research should consider applying an equity lens when developing instruments for digital healthcare and efforts to address the potential need for a framework in this space.

Methods

Study design

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines82. The protocol for this was registered with the Open Science Framework (10.17605/OSF.IO/4UYV8).

Screening

The literature search was performed on electronic databases (PubMed, Embase, Cochrane, CINAHL, PsycINFO, Scopus, and Web of Science) with tailored search strategies (Supplementary Information—Supplementary Note 1). Search strategies were developed using three concepts: trust, digital healthcare (e.g., telehealth, mobile health, remote monitoring, digital medicine, electronic medical record, online health, etc.), and survey (e.g., instrument, questionnaire, scale, etc.). Databases were accessed through The University of Queensland Library website. Search terms were adapted from a preliminary search, including trust in digital health relevant terms from published reviews14,83. The search was conducted from inception until April 2023. Search results were uploaded to Covidence, and duplicates were removed. Studies were split equally between two reviewers (S.C., S.Z.), and the title and abstract were screened separately. Conflicts were resolved by discussion between authors, as necessary. Full-text screening was conducted by one reviewer (S.C.).

Eligibility criteria

Studies that were included either outlined the development of a new instrument measuring trust, the use of an existing instrument, or the alteration of an instrument to include items measuring trust. Instruments measured trust within a modality of digital healthcare, such as online communication tools between patients and healthcare professionals, mobile health applications, electronic medical record platforms, or medical devices. Included studies used measurement instruments consisting of surveys or questionnaires with close-ended questions. Studies assessing either consumers’ or providers’ trust were included. In this paper, patients, users, and the general population are considered consumers.

Studies were excluded if the instrument measured attitudes, perceptions, satisfaction, usability, acceptance, opinions, and digital or health literacy, without additional domains or items specifically measuring trust. Studies solely using open-ended questions, single questions with binary response, interviews, or focus groups were excluded. Checklists measuring the trustworthiness of online health information (e.g., social media, websites) or mobile health apps, and not assessed from an end-user perspective were excluded. Protocols, systematic reviews, letters to the editor, posters, conference papers, books or book chapters, and editorial reviews were excluded.

Quality assessment

Quality assessment was conducted by two reviewers independently (S.C., H.S.) according to the guidelines by the Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) checklist20.

Data extraction and synthesis

Data was extracted simultaneously by two independent reviewers (S.C., H.S.) using Microsoft® Excel according to the items described in Table 1. To ensure the extraction was congruent, five studies were initially extracted by each reviewer, and results were compared to align and refine the extraction process. Once all data was extracted, columns were checked for consistency. Quantitative data was synthesised descriptively (e.g., year and country of publication, study design, data collection modality, number of participants, number of trust dimensions and items). Thematic synthesis of qualitative research84 was applied to extracts of the original text in the columns describing trust definitions, domains, and outcome measures in three stages: coding line by line, development of descriptive themes, and summary of results84. The terminology used for the factors and outcomes associated with trust was the same as in the original study (e.g., associated, affected, predicted, etc.).

Table 1.

Data extraction form and item description

Category Data Extracted Description
Study Author and Year Surname of first author and year of publication
Country Country of publication
Study design The design of the study
Admin mode Data collection modality: online, in-person, mixed (online/in-person), paper
Participants characteristics Number of participants Number of participants (N) that respond to the instrument used for the analysis
Demographics Participants demographics (mean age, condition, gender, etc.)
Description Identification of patient, consumer, user, or healthcare provider population
Intervention Digital healthcare service or intervention Description of digital healthcare service or intervention analysed
Trust measurement tool Name of instrument Where the instrument has been named, include the full name and acronym in parentheses. If a validated or published survey was used, indicate the original reference.
Definition of trust Theory, definition, or model of trust used to determine the measurement of trust.
Number of trust dimensions Number of dimensions that the construct of trust was broken down to be measured, if that was the case.
Name of trust dimension(s) Name of the dimensions that compose the trust construct.
Number of items to measure trust Number of trust items in relation to the total number of the items used to survey participants.
List of included trust items A list of all the trust items included in each of the surveys.
Response option Binary, ordinal (e.g., 5-point Likert scale), and continuous (e.g., rating scale from 0 to 10).
Validity Identify whether a validation process was conducted or not. Validation methods evaluate the construct validity of an instrument (face, content, structural, and criterion validity), according to the theoretical/methodological framework used (Classical Test Theory, Factor Analysis, Item Response Theory, Rasch Modelling, or Network Psychometrics). For face and content validity, focus groups, survey pilots, or expert reviews were considered.
Reliability Internal consistency (e.g., Cronbach’s alpha), interrater reliability coefficient (e.g., kappa), test-retest.
Trust results Trust factors Trust predictors, factors associated or correlated to trust in digital health
Trust outcomes Outcomes from optimised trust, where healthcare providers were extracted separately from consumers when possible.
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Supplementary information

Supplementary Information File (272.2KB, pdf)

Acknowledgements

This study received no specific funding. J.K. was supported by a Postdoctoral Fellowship (106081) from the National Heart Foundation of Australia.

Author contributions

The project was conceived by S.C. and J.K. The experimental design was developed by S.C., V.G.R., and S.Z. Data collection was conducted by S.C., S.Z., and H.S., and data analysis by S.C., R.M., and H.S. Writing and editing of the paper were done by S.C., J.K., H.S., V.G.R., P.H.R.S., and S.Z. The submission process was handled by S.C. and R.M.

Data availability

The data that support the findings of this study (data collection spreadsheet with information from included studies) will be made available upon request to the corresponding author.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

The online version contains supplementary material available at 10.1038/s41746-025-01510-8.

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Supplementary Materials

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Data Availability Statement

The data that support the findings of this study (data collection spreadsheet with information from included studies) will be made available upon request to the corresponding author.

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