Review
Abstract
Background: The online nature of decision aids (DAs) and related e-tools supporting women’s decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs.
Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy.
Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023. We included studies reporting on populations at average risk of breast cancer, which utilized DAs or related e-tools, and assessed women’s participation in BCS by mammography or other key cognitive determinants of decision-making as primary or secondary outcomes. We conducted meta-analyses on the identified randomized controlled trials, which were assessed using the revised Cochrane Risk of Bias 2 (RoB 2) tool. We further explored intermediate and high heterogeneity between studies to enhance the validity of our results.
Results: In total, 22 different e-tools were identified across 31 papers. The degree of tailoring in the e-tools, specifically whether the tool was fully tailored or featured with tailoring, was the most influential factor in women’s decision-making regarding BCS. Compared with control groups, tailored e-tools significantly increased women’s long-term participation in BCS (risk ratio 1.14, 95% CI 1.07-1.23, P<.001, I2=0%). Tailored-to-breast-cancer-risk e-tools increased women’s level of worry (mean difference 0.31, 95% CI 0.13-0.48, P<.001, I2=0%). E-tools also improved women’s adequate knowledge of BCS, with features-with-tailoring e-tools designed and tested with the general population being more effective than tailored e-tools designed for or tested with non-BCS participants (χ21=5.1, P=.02). Features-with-tailoring e-tools increased both the rate of women who intended not to undergo BCS (risk ratio 1.88, 95% CI 1.43-2.48, P<.001, I2=0%) and the rate of women who had made an informed choice regarding their intention to undergo BCS (risk ratio 1.60, 95% CI 1.09-2.33, P=.02, I2=91%). Additionally, these tools decreased the proportion of women with decision conflict (risk ratio 0.77, 95% CI 0.65-0.91, P=.002, I2=0%). Shared decision-making was not formally evaluated. This review is limited by small sample sizes, including only a few studies in the meta-analysis, some with a high risk of bias, and high heterogeneity between the studies and e-tools.
Conclusions: Features-with-tailoring e-tools could potentially negatively impact BCS programs by fostering negative intentions and attitudes toward BCS participation. Conversely, tailored e-tools may increase women’s participation in BCS but, when tailored to risk, they may elevate their levels of worry. To maximize the effectiveness of e-tools while minimizing potential negative impacts, we advocate for an “on-demand” layered approach to their design.
doi:10.2196/65974
Keywords
Introduction
Breast cancer constitutes 11.6% (2.309 million new cases) of global cancer incidence and 6.9% (665,684 deaths) of global cancer mortality [
].Mammography is the most widely used screening method in breast cancer screening (BCS) programs for detecting breast cancer. These programs primarily target women at average risk of the disease. A woman is considered at average risk if she lacks risk factors associated with a significantly increased likelihood of breast cancer, such as a personal history of the disease, a strong family history, a genetic mutation known to elevate risk (eg, a BRCA mutation), or prior high-dose radiation therapy to the chest at a young age [
- ]. Conversely, a woman is considered at high or very high risk of breast cancer if she has 1 or more of these risk factors. Such high-risk women do not participate in standard BCS programs; instead, they undergo more personalized screening options [ , ].Despite the proven efficacy of mammography-based BCS and national health policies promoting regular screening for women at average risk, participation in BCS remains suboptimal in many countries, falling well below the recommended rates set by the European Union and other organizations [
- ].Numerous barriers to women undergoing BCS have been identified in the literature, such as fears of a breast cancer diagnosis or the mammography procedure itself [
- ]. For some women, participation in a BCS program may also be perceived as a dilemma due to ongoing debates surrounding BCS. Indeed, scientific and medical discussions continue about specific aspects of BCS, including the optimal screening interval [ , ]. Furthermore, public discourse often highlights controversies and potential risks associated with BCS, such as overdiagnosis and overtreatment [ , ].The use of decision aids (DAs), either independently or as part of a shared decision-making (SDM) process, appears particularly suitable and important for both women and BCS programs. These tools help women make an “informed choice” about their participation in BCS, enabling them to make a reasoned and justifiable decision after receiving reliable and sufficient information about the procedure or examination and its associated risks [
]. DAs are evidence-based cognitive tools designed to elucidate the decision-making process and help patients clarify their values and preferences [ - ]. Similarly, SDM is described as an interactive, balanced, step-by-step discussion between health professionals (HPs) and patients, often incorporating DAs to support the dialogue [ , ].Patients faced with decisions about cancer screening (including BCS) who are exposed to DAs tend to be better informed and more aligned with their values [
, - ]. Although promising, evidence regarding the effectiveness of SDM remains limited, particularly in real-world settings where its implementation is often hindered by the busy and demanding schedules of HPs [ - ]. Additionally, there is insufficient evidence on when it is more beneficial for patients to engage in an SDM discussion with an HP rather than relying solely on a DA [ , - ].Global interest in digital health and eHealth tools, including telehealth, has surged in recent years, largely driven by the COVID-19 pandemic [
- ]. Online delivery offers the potential to enhance access to DAs for patients and HPs, particularly for those in underserved communities, while also enabling more integrated implementation of SDM. As a result, incorporating online DAs and related tools into BCS programs could support women in making informed decisions. A review of web-based DAs found these tools to be effective in increasing women’s knowledge and ability to make informed choices while reducing decisional conflict or dilemmas about BCS [ ].However, to better inform future BCS programs about the utility and implementation of online tools for women’s decision-making, it is necessary to go beyond Yu et al’s review [
] and provide additional evidence. First, it is important to evaluate not just web-based DAs but also all types of online interactive tools (e-tools) to fully explore their potential for supporting women’s decision-making about BCS [ - ]. An overall assessment of the effect of these e-tools, including web-based DAs, on women’s participation in BCS and important cognitive determinants in decision-making (eg, women’s attitudes or perceptions of risk) was lacking [ , , , , ]. Additionally, there was a need for a comprehensive evaluation of factors associated with the greater utility and effectiveness of these e-tools to support more informed BCS programs. This includes exploring aspects related to information delivery (eg, mobile vs computer-based e-tools, degree of message tailoring), user characteristics (eg, nonadherent to BCS vs the general population), and the methods used to evaluate these e-tools (ie, the outcomes assessed to measure their impact on decision-making). Finally, evidence of the effect of these e-tools on the SDM process was also missing.Through a combined systematic review and meta-analysis, our research aimed to address these evidence gaps and synthesize the scientific literature on e-tools designed to support decision-making in the context of BCS by mammography. The primary objective of this review was to inform future BCS programs about the utility and implementation of such e-tools.
Methods
Review Design
This review was registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42020164479) [
]. The main deviation from the original protocol was the inclusion of studies without comparator arms. This change was made for 2 reasons: (1) a preliminary review of the databases indicated that the total number of studies might be limited if only those with comparators were included, and (2) to provide the most comprehensive assessment of the effects of e-tools, including web-based DAs, on women’s participation in BCS and important cognitive determinants in decision-making. The type of design for each study included in this review was considered when assessing the overall certainty of the evidence using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) methodology (outlined below). A workshop held in December 2019, organized by PV, on developing and implementing e-tools as DAs to support women’s decision-making about BCS, played a significant role in shaping the development of this review. We conducted our systematic review in accordance with the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ ] (see Table S1 in ).Study Search, Selection, and Data Extraction
We systematically searched the MEDLINE (via PubMed), PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to August 2020, with an update in April 2023 (see Appendix S1 in
). We limited the search to articles published from 2010 onward to minimize the risk of including e-tools that may have become technologically obsolete—those using outdated technology and no longer considered useful, efficient, or functioning well compared with newer alternatives [ ]. Additional websites (eg, the International Patient Decision Aid Standards [IPDAS] website) were also searched nonsystematically. The following inclusion criteria were applied: (1) women considered to be at average risk of breast cancer, that is, those without risk factors associated with a significantly increased risk of the disease [ - ]; (2) the tool, whether identified as a DA or not, was used for women’s decision-making about BCS, aimed to inform users at least about BCS by mammography, and was an e-tool defined as one that runs on the internet, computer, phone/tablet, or other electronic device and is interactive (ie, allowing users to “participate in modifying the form and content of a mediated environment in real time”) [ , ]; and (3) the study, of any design, reported as primary or secondary outcomes any of the following: women’s participation in BCS by mammography (behavior), intention to participate, knowledge, attitudes, self-efficacy, worry, perceptions of risk, regrets, decisional conflict, and informed choice—measures shown to reflect decision-making and behavior and to be important in decision-making [ , , , ]. Exclusion criteria were as follows: (1) participants at high risk of breast cancer, that is, those with 1 or more risk factors associated with a significantly increased risk of the disease [ - ]; (2) screening limited to breast examination by an HP or self-examination; (3) non–peer-reviewed publications; and (4) studies focused on BCS cessation.The search yielded 16,061 records, which were managed using Covidence software [
]. Three independent reviewers (PV, ALB, and CB) assessed titles and abstracts, followed by full-text evaluation against the study inclusion criteria. A total of 227 papers were selected for full-text eligibility assessment, and 196 were subsequently excluded. A data extraction form was used (see Appendix S2 in ); it was completed by a main reviewer (PV or ALB) and independently assessed by 2 additional reviewers (PV, LD, ALB, or CB).Qualitative and Quantitative Syntheses
Extracted data were synthesized both qualitatively and quantitatively, as detailed later.
The qualitative synthesis aimed to provide an overview of the characteristics of the e-tools and a description of the reported outcomes and the instruments used to measure them. These are reported in the Results section. Additionally, the characteristics of the studies included in this review and the study populations are reported in Tables S2 and S3, respectively, in
.Quantitative synthesis was conducted to report the effect of the e-tools on women’s decision-making about BCS. Main results are reported in the Results section, with additional results reported in Appendix S4 and Figures S1-S8 in
.Description of the e-Tools
The main descriptive variables were identified through discussions and consensus among the authors. These variables were considered important as they could potentially influence the effects of e-tools on women’s decision-making about BCS. The e-tools identified in this review were classified as either “tailored”—when the e-tools provided individualized or personalized notifications, messages, or information based on an individual’s assessment [
, , ]—or “features with tailoring”—when the e-tools presented some degree of tailored information but not based on an individual assessment [ ].Effect of the e-Tools on Women’s Decision-Making About BCS
Overview
The effect of the e-tools was assessed through the following outcomes: women’s participation in BCS by mammography (behavior), intention to participate, knowledge, attitudes, self-efficacy, worry, perceptions of risk, regrets, decisional conflict, and informed choice. These outcomes have been shown to reflect decision-making and behavior and are particularly important in the context of BCS [
, - , ]. The effects of the e-tools on SDM or communication with HPs were also evaluated.For each outcome, quantitative synthesis was conducted and reported through a 2-step process. First, where possible, we performed meta-analyses with the randomized controlled trials (RCTs) identified in this review, following Cochrane guidance [
]. Additionally, the synthesis without meta-analysis (SWiM) guideline was used to synthesize evidence from RCTs not included in the meta-analyses or other study designs (see Table S2 in ) [ ]. Second and finally, for each outcome, the overall certainty of the evidence collected through the meta-analyses and SWiM exercise was independently evaluated by PV and LD using the GRADE methodology [ - ]. Results from the risk-of-bias assessment (discussed later) were used to downgrade the overall certainty of the evidence, if necessary. Because of the low total number of studies included in this review, we did not downgrade the overall certainty of the evidence, assessed with GRADE, based on the imprecision (number of studies, SD) criteria, except when only 2 studies (meta-analyses) or 1 study were available. This applied to the “intention not to participate,” “worry,” “perception of risk,” and “decisional conflict” outcomes assessed through meta-analyses, as well as the “self-efficacy,” “regret,” and “discussion/SDM” outcomes. Any discrepancies between the 2 independent reviewers were resolved through the intervention of a third reviewer (RS).Meta-Analyses and Risk of Bias
Meta-analyses were conducted with RCTs following Cochrane guidance [
] (see also Part A in Appendix S3 in ).Data were extracted from the original publications, and additional information was obtained by directly contacting the authors when necessary. Only data related to BCS by mammography, as reported by the authors for “women,” were extracted.
PV and LD, along with ALB or CB, independently performed the risk-of-bias assessment for all RCTs included in this review using the revised Cochrane Risk-of-Bias 2 (RoB 2) tool for RCTs [
]. Any discrepancies between the 2 independent reviewers were resolved through the intervention of a third reviewer. As a result of the low number of studies, none were excluded based on RoB 2 results. The level of risk for each RoB 2 domain (A: randomization process; B: deviations from the intended protocol; C: missing data; D: outcome measurement; E: reporting results) and overall risk (F) were evaluated for each RCT and reported on the forest plots (discussed below) as low (green), moderate (yellow), or high (red).To minimize sources of heterogeneity between studies, we chose to include only RCTs that reported the most similar outcomes based on their definitions and the instruments used to measure them in the meta-analyses described in this review. Therefore, contrary to Yu et al’s review [
], we reported the decisional conflict outcome only for RCTs using the “SURE” scale and did not report the pooled effect for the “regret” outcome.We used Review Manager software (RevMan, version 5.4.1; Cochrane) [
] to conduct meta-analyses, perform subgroup analyses, and display forest plots, including risk-of-bias assessments.We reported pooled estimates as mean difference (MD) for continuous variables (adequate knowledge, worry, and accurate perception of risk) and relative risk/risk ratio (RR) for dichotomous variables (other outcomes). Heterogeneity or inconsistency between studies was assessed using the I2 statistic. A fixed-effect (FE) model was applied when low heterogeneity was found (I2≤30), while a random-effects (RE) model was used in other cases. Results from meta-analyses with intermediate or high heterogeneity between studies (I2≥35) were further explored to strengthen the validity of our findings.
Exploration of Heterogeneity and Subgrouping Analysis
Where meta-analyses showed intermediate or high heterogeneity between studies (I2≥35), further analyses were conducted. The main results are reported in the text (see also Figures S1-S8 and Appendix S4 in
). Two approaches were used to explore the causes of intermediate/high heterogeneity [ ]: (1) exclusion of 1 study when it differed from the others in the meta-analysis based on either the type of e-tool used (eg, web application or mobile app) or the control used (eg, a website or a video). We also explored the exclusion of studies with a high risk of bias(es). Exclusion of Lee et al’s [ ] study, which used a mobile app downloaded on a mobile phone, or Roberto et al’s study [ ], which used a website as a control, in the meta-analysis assessing short-term participation or adequate knowledge, respectively, was effective in reducing heterogeneity (reported); (2) additionally, subgroup analyses were conducted based on various variables that could have explained the source of heterogeneity, for example, characteristics of the e-tools such as the type or degree of tailoring, the type of outcomes or instruments, or characteristics of the study populations such as age, ethnicity/rurality, and nonparticipation in previous BCS by mammography (see also Table S3 in ).Besides exploring heterogeneity, subgroup analysis was also conducted to examine the effect of different variables (described above) on the reported outcomes.
Results
Overview of Included Studies and e-Tools
We identified 31 published papers/studies (
; also see Table S2 in ) [ - ], representing 22 different e-tools [ - ]. The majority of these were designed and evaluated in the United States (n=16) and tested through RCTs (n=14). Study populations are detailed in Table S3 in .
Description of the e-Tools
Among the 22 e-tools, the majority (n=16) were web applications, either standalone or integrated into patient portals [
, , , , , , , - ] ( ; see also Table S4 in ).Two e-tools used artificial intelligence (AI)–based virtual doctors [
, ].Most of the e-tools (16/22;
) were tailored to the individual’s (1) breast cancer risk (n=7) [ , , - ]; (2) barriers, beliefs, or screening status identified at baseline (n=8) [ , , , , , - ]; or (3) preferences regarding the content, number, and timing of daily messages [ ]. Of these 16 tailored e-tools, 8 targeted nonparticipants in BCS (see Table S3 in ) [ , , , , , - ]; 11 out of the 16 tailored e-tools included specific features designed to facilitate or enhance opportunities and the quality of discussions about BCS between women and HPs ( ) [ , , , - , , - ].The remaining 6 e-tools (out of 22) were not tailored to individual users and were classified as “features-with-tailoring” e-tools (
) [ , , , , , ]. None of these tools were evaluated specifically with BCS nonattenders (see Table S3 in ). The tailoring features in these tools were designed to either (1) address BCS screening barriers for specific subpopulations (n=2) [ , ] or adapt the communication style of an AI-based virtual HP to meet the needs of specific user groups [ ], or (2) prompt users to identify their personal beliefs and values regarding BCS ( ) [ , , ].Descriptive variables | Characteristics of e-tool |
Theoretical modelb |
|
Decision aidsc |
|
Typed |
|
Information provided |
|
Degree of tailoringf |
|
Access to health professionalsi |
|
aThe e-tools were categorized into groups and subgroups based on the descriptive variables as indicated (see also Table S4 in
).bAny psycho-theoretical model reported to be used to develop the e-tool.
cWe classified the e-tools as “identified as decision aids” when the authors explicitly used “DA” or “decision-making” to describe the e-tool, or when the e-tool contained a decision aid/decision-making module or a link to decision aids. The remaining tools were classified as “not identified as decision aids.” A rapid assessment of these tools against the 5 previously described decision aids criteria [
], that is, (1) information about options, decisions, and outcomes, including benefits and harms; (2) evidence-based information on options; (3) probabilities; (4) value clarification; and (5) decision guidance, did not provide sufficient information to determine whether or not these tools could be classified as decision aids.dThe tools were classified as previously described [
, ].eIn this group of e-tools, the study population was either only women [
, , ] or both men and women [ , , , ].fTools were classified as either “tailored” or “features-with-tailoring” e-tools, as previously defined [
, , ]. Tailored tools provided individualized or personalized notifications based on an individual’s assessment. This assessment could be conducted through participants’ responses to questionnaires, which were either external to the e-tool and completed before its use or integrated into the e-tool itself, addressing factors such as the individual’s breast cancer risk or other characteristics. By contrast, “features-with-tailoring” e-tools offered some degree of personalization or tailoring, but it is not based on individual assessments.gRisk was estimated using 2 validated instruments: the Breast Cancer Risk Assessment Tool for personal risk estimates and the Breast Cancer Genetics Referral Screening Tool for assessing familial breast cancer risk. In 1 study, a different instrument was used, and it was unclear whether the risk estimation was communicated to the women involved [
]. Among the 7 tailored-to-risk tools, 4 utilized risk assessment to exclude women with above-average breast cancer risk from accessing the decision aid, instead directing them to receive messages for appropriate follow-up based on best practices [ - ]. For the remaining 3 tools, risk assessment was conducted online to personalize the features of the tools according to each woman’s individual risk [ , , ]. One tool provided 6 levels of tailored-to-risk messages, and its effects were evaluated [ ].hThe tools aimed to encourage users to identify their personal beliefs and values regarding breast cancer screening. The methods used were all explicit, meaning they required active user participation—such as moving sliders, assigning weights to scales, or inputting numbers—to express their values, preferences, and concerns [
].iSpecific features aimed to facilitate access to or communication with health professionals.
Effect of the e-Tools on Women’s Decision-Making About BCS
Overview of the Outcomes
The effect of the e-tools on women’s decision-making about BCS was assessed through various outcomes in the studies included in this review. A comprehensive overview of these outcomes and the instruments used to measure them are provided in
.The synthesized results regarding the effects of the e-tools are presented in the following paragraphs, with the outcomes listed in the same order as in
. We encourage readers to refer to this table for the definitions of each outcome.Outcomesa | Definitionsb and instrumentsc | |
Participation (behavior) [ | - , , , - , ]
| |
Intention to undergo breast cancer screening [ | , , , - , ]
| |
Intention not to undergo breast cancer screening (negative intention) [ | , ]
| |
Knowledge [ | , , - , , , , , ]
| |
Attitudes about breast cancer screening [ | , , , ]
| |
Self-efficacy [ | , ]
| |
Worry [ | , , , , , ]
| |
Risk perceptions [ | , , , ]
| |
Quality of decision | ||
Regret [ | , ]
| |
Decisional conflict [ | , , , , , ]
| |
Informed choice [ | , , ]
| |
Shared decisions and communications with health professionals [ | , , , ]
| |
Other outcomes |
|
aThe outcomes are reported in the following order: first, we present participation in breast cancer screening (behavior), followed by related determinants/factors based on the main theories, models, and concepts used to explain or understand cancer screening behavior [
, ]. Among those, intention, identified as the most proximal determinant of behavior in the Theory of Planned Behavior [ ], is reported first. Next, we include outcomes related to the “quality of decision,” such as “regret” and the composite outcomes “decisional conflict” and “informed choice.” The outcomes reported last are those that were less frequently assessed in the included studies.bAlthough reported by the authors, we did not include some studies; the reasons are detailed in Part B in Appendix S3 in
.c“Instruments” were any item (eg, scale, questionnaire, or medical records) used to measure the outcomes.
dSURE: Sure of myself (Uncertainty), Understand information, Risk-benefit ratio (value clarity), and Encouragement.
Participation in BCS (Behavior)
Our meta-analysis found that e-tools, including a mix of tailored-not-to-risk e-tools (ie, tailored e-tools but not to the individual’s breast cancer risk; n=4) and features-with-tailoring tools (n=2), did not significantly increase women’s participation in BCS up to 6 months after using the e-tools (RR 1.09, 95% CI 0.97-1.23, P=.16, I2=78%, RE model) compared with the control group, which included either usual care [
, , , , ] or a control website [ ] (see Figure S1A in ). Excluding Lee et al’s study [ ] significantly reduced heterogeneity (see the “Methods” section) without altering the overall pooled result (RR 1.03, 95% CI 0.98-1.08, P=.28, I2=14%, FE model; see Figure S1B and S1C in ). No subgroup differences were observed between tailored and features-with-tailoring e-tools (χ21=1.2, P=.28; see Figure S1D in ). Two interventional studies without concurrent controls that assessed tailored-not-to-risk e-tools reported an increase in BCS participation among users compared with nonusers [ , ].Our meta-analysis further demonstrated that e-tools (n=5), all tailored to either risk (n=2) or other variables (n=3), increased women’s participation in BCS assessed at long-term follow-up (12-16 months after tool use; RR 1.14, 95% CI 1.07-1.23, P<.001, I2=0%, FE model) compared with the control group, which included usual care [
, , , ] or a control video [ ] ( A). Subgroup analysis indicated a significant difference in effect between tailored-to-risk e-tools and other tailored e-tools, while highlighting a high heterogeneity within one subgroup (χ21=23.54, P<.001, risk-based group: I2=0%, non-risk–based group: I2=98%; see Figure S2 in ).
Intention
Our meta-analysis, which incorporated 6 RCTs, showed that a mix of tailored (n=3, with 2 not based on risk) and features-with-tailoring (n=3) e-tools did not affect the rate of women intending to undergo BCS (RR 1.02, 95% CI 0.99-1.05, P=.24, I2=31%, FE model) compared with the control group, which included either usual care [
, , ] or a control website [ , , ] ( B; also see Appendix S4 in ). No subgroup differences were observed between tailored and features-with-tailoring e-tools (χ21=0.6, P=.43; see Figure S3 in ). Similarly, no significant difference in intention was reported in 2 pre-post studies evaluating tailored-to-risk e-tools [ , , ]. In 2 other studies with no control and small sample sizes (N=8 or 49), participants either expressed an intent to undergo BCS [ ] or showed no change in intention [ ].Moreover, in 2 RCTs [
, ], the effect of 2 features-with-tailoring e-tools on the rate of women with negative intention (willingness not to undergo BCS) was assessed. Our meta-analysis showed that these tools increased the rate compared with usual care (RR 1.88, 95% CI 1.43-2.48, P<.001, I2=0%, FE model; C).Knowledge
One RCT evaluating a tailored e-tool reported a positive correlation between increased knowledge and participation in BCS [
]. Our meta-analysis, conducted with 5 RCTs assessing a mix of tailored (n=3) and features-with-tailoring (n=2) e-tools, demonstrated that the e-tools increased women’s adequate knowledge about BCS (MD 0.75, 95% CI 0.31-1.19, P<.001, I2=89%, RE model; see Figure S4A in ) compared with the control group, which consisted of either usual care [ , , , ] or a control website [ ]. We conducted subanalyses to explore the sources of heterogeneity, with all analyses not altering the overall result (see Figure S4B and S4C in ). Excluding Roberto et al’s [ ] study (see the “Methods” section) and subgroup analysis based on the degree of tailoring of the e-tools successfully reduced heterogeneity. Our findings indicated that adequate knowledge was higher with the features-with-tailoring e-tools, which were designed and tested with the general population, compared with the tailored e-tools, which were designed for and tested with women who were nonparticipants in BCS (χ21=5.1, P=.02; D) [ , , , ]. In a pre-post study [ ], a significant increase in adequate knowledge was reported with 1 features-with-tailoring e-tool (P=.001). For 2 tailored e-tools, with no reported control group, 48%-64% of users indicated increased knowledge or reported receiving new information, while approximately 15% either disagreed or had some level of disagreement with that statement [ , , ].Attitudes
Our meta-analysis showed that features-with-tailoring e-tools, compared with the control group (either usual care [
, ] or a control website [ ]), had no significant effect on the rate of women with positive attitudes toward undergoing BCS (ie, being favorable to undergo BCS; RR 0.98, 95% CI 0.95-1.01, P=.19, I2=0%, FE model; see Figure S5 in ). In another RCT with a 2×2 design that tested a features-with-tailoring e-tool featuring a virtual AI physician, the communication style of the physician and whether the women’s needs were made salient were found to influence women’s attitudes toward undergoing BCS [ ].Self-Efficacy
The impact of e-tools on women’s level of self-efficacy regarding BCS was reported for 2 tailored e-tools. One study, an RCT, found no significant difference in self-efficacy compared with a brochure [
]. By contrast, a pre-post study [ ] demonstrated a significant increase in self-efficacy.Worry
Emotional changes, including anxiety, worry, or fear, were assessed either in general [
, , ] or specifically related to breast cancer [ , , ] ( ). Our meta-analysis, conducted with 2 RCTs evaluating tailored-to-risk tools, showed that compared with the control group (usual care [ ] or a control website [ ]), the e-tools increased women’s level of worry (MD 0.31, 95% CI 0.13-0.48, P<.001, I2=0%, FE model; A), irrespective of the complexity of the tailored messages (see Figure S6 in ). In a prospective single-arm study, a tailored-to-risk tool that predicted breast cancer risk for women also increased their frequency of worry, which correlated with their level of risk (P<.01 by analysis of variance) [ ]. In 2 other studies not comparing with controls [ , ], women reported not being particularly worried about breast cancer after using a features-with-tailoring tool [ ]. Additionally, 80.9% of users of a tailored-not-to-risk tool indicated that the clinician helped reduce their fears and worries [ ].
Perceptions of Risk of Breast Cancer
Two tailored-to-risk e-tools were evaluated for their effect on women’s accuracy in perceiving their breast cancer risk. Accuracy was determined by measuring the difference between the risk perception assessed by the woman herself and her objective risk estimates provided by the e-tool. Our meta-analysis showed a significant decrease in this difference, indicating that women’s accurate estimation of breast cancer risk improved with the use of the e-tools compared with the control group, which consisted of usual care [
] or a control website [ ]. A significant improvement in the accuracy of breast cancer risk perception was observed when extended information with untailored examples was used in the e-tool (MD –5.10, 95% CI –7.85 to 2.35, P<.001, I2=0%, FE model; B), but not with other types of tailored messages (see Figure S7 in ). In another study using a tailored-to-risk tool without a control group, 70% of users reported having an “accurate perception” of their breast cancer risk, correctly identifying themselves as at low or average risk [ ]. Additionally, the perceived susceptibility of breast cancer was assessed using a tailored-not-to-risk e-tool, which showed no significant difference compared with a control brochure (F1,118=0.73, P=.01, effect size = 0.01) [ ].Regret
One RCT [
] assessed women’s regret about their decision to undergo BCS. The study found no significant difference in regret between the group using the features-with-tailoring e-tool and the usual care group. Additionally, anticipated regret for decisions to delay or initiate mammography was evaluated using a tailored-to-risk e-tool in another RCT [ ], which also showed no significant difference compared with usual care.Decisional Conflict
Our meta-analysis, which included 2 RCTs comparing 2 features-with-tailoring e-tools with either usual care [
] or a control website [ ], found that the e-tools significantly reduced the rate of women experiencing decisional conflict (ie, uncertainty regarding the choice to be made or the decision to undergo BCS). The results showed an RR of 0.77 (95% CI 0.65-0.91, P=.002, I2=0%, FE model; C).Other results obtained with tailored-to-risk tools were more mixed [
, , , ]. One RCT reported no change in the average decisional conflict (RR –0.34, 95% CI –0.71 to 0.03, P=.07) [ ]. For 2 other e-tools tested through pre-post study designs [ , ], Yu et al [ ] reported a significant decrease in decisional conflict. In a prospective single-arm study, women were found not to have difficulties in implementing decisions, as indicated by their average decisional conflict score [ ].Informed Choice
Our meta-analysis showed that, compared with the control group (either usual care [
, ] or a control website [ ]), 3 features-with-tailoring tools increased the rate of women who made an informed choice regarding their intention to undergo BCS by mammography (RR 1.60, 95% CI 1.09-2.33, P=.02, I2=91%; RE model; D). Subanalyses to reduce heterogeneity were conducted but did not yield a significant decrease (data not shown).Shared Decision-Making and Communications With Health Professionals
The effect of the tools on discussions about BCS during appointments between HPs and women was evaluated with 4 tailored tools [
, , , ]. One study reported that an additional 17.2% of women discussed mammography with their HPs compared with a control video group (P<.01) [ ]. Another study found that women felt well-prepared to make shared decisions with clinical providers [ ]. In 2 other studies comparing e-tool users with nonusers, HPs reported no significant difference in the rates of mammography discussions (90% vs 92%) [ ], and the rates of medical “wellness” appointments did not increase (16.3% vs 21.5%) [ ].Grading of the Available Evidence
We summarized and graded the evidence detailed above in
.With a moderate to high level of certainty, the evidence supports the findings that e-tools (1) increased women’s long-term participation in BCS, intention not to participate, adequate knowledge, worry, and informed choice, while decreasing decisional conflict, and (2) had no effect on short-term participation in BCS, intention, and positive attitudes toward undergoing BCS (
). Additionally, evidence indicates that the tailoring nature of the e-tools is the most influential factor in driving their effectiveness ( ).Outcome and e-tool effect (compared with control) | Main source of evidence (number of e-tools and type of control) | Certainty of evidence (GRADEa)b (reasons for downgrading) | E-tool or population main characteristics | |
Participation at short term in BCSc | ||||
No effect |
| ⊗⊗⊗○ Moderatee (downgrading due to the presence of 2 high-risk of bias studies in the meta-analysis) | Mix of tailored-not-to-risk and features-with-tailoring e-tools, no differences between the 2 types of e-tools | |
Participation at long term in BCSc | ||||
Increased |
| ⊗⊗⊗○ Moderatee (downgrading due to 3 high-risk of bias RCTs) | All tailored tools, but not only based on risk. Results suggest a difference between tailored-to-risk and other tailored e-tools | |
Intention to undergo BCSc | ||||
No effect |
| ⊗⊗⊗⊗ Highf | Mix of tailored and features-with-tailoring e-tools, no differences between the 2 types of tools, further suggested by Yu et al’s results with 2 tailored-to-risk e-tools | |
Intention not to participate (negative intention) in BCSc | ||||
Increased |
| ⊗⊗⊗○ Moderatee (downgrading due to very low number of RCTs [2 studies]) | Features-with-tailoring e-tools | |
Adequate knowledge | ||||
Increased |
| ⊗⊗⊗⊗ Highf | The increase is independent of whether the e-tool is tailored or featured-with-tailoring; however, adequate knowledge is higher with features-with-tailoring e-tools assessed with the general population compared with tailored tools assessed with the nonscreened population | |
Positive attitude | ||||
No effect |
| ⊗⊗⊗⊗ Highf | Features-with-tailoring e-tools | |
Self-efficacy | ||||
No effect |
| ⊗○○○ Very lowg (downgrading due to very low number of studies, contradictory results obtained with 1 RCT and 1 pre-post study) | Tailored e-tools | |
Worry | ||||
Increased |
| ⊗⊗⊗○ Moderatee (downgrading due to low number of RCTs [2 studies] including 1 with high bias; upgrading due to correlation result) | Tailored-to-risk e-tools | |
Accurate risk perception | ||||
Increased |
| ⊗⊗○○ Lowh (downgrading due to low number of RCTs [2 studies], including 1 with a high risk of bias) | Tailored-to-risk e-tools but with specific types of tool messages. The increase seems to be specific to tailored-to-risk e-tools as no effect on perceived susceptibility was obtained with tailored-not-to-risk e-tool | |
Regret (decision regret or anticipated regret) | ||||
No effect |
| ⊗○○○ Very lowg (downgrading due to very low number of RCTs [n=1] for each type of regret) | 1 features-with-tailoring and 1 tailored e-tool | |
Decisional conflict | ||||
Decreased |
| ⊗⊗⊗○ Moderatee (downgrading due to 2 RCTs only) | Features-with-tailoring e-tools | |
No effect |
| ⊗○○○ Very lowg (downgrading due to the very low number of RCTs [1 study] and contradictory results with Yu et al’s [ ] meta-analysis with pre-post studies) | Tailored-to-risk e-tools | |
Informed choice about the intention to undergo BCSc | ||||
Increased |
| ⊗⊗⊗○ Moderatee (downgrading due to inconsistency, ie, high I2) | Features-with-tailoring e-tools | |
Discussion/SDMi | ||||
Increase women’s discussion about mammography |
| ⊗○○○ Very lowg (Downgrading due to the very low number of RCTd [1 study]) | 1 tailored e-tool | |
No evidence about SDM |
| ○○○○ No formal evidence | Not applicable |
aGRADE: Grading of Recommendations, Assessment, Development, and Evaluation.
bWe adopted the GRADE Working Group methodology of grading evidence [
- ]. Our baseline statement was high for meta-analyses with RCTs (our results), moderate for RCTs without meta-analysis, and low for non-RCTs. Where indicated, we decreased the certainty in the evidence to at least one level based on the assessment of the following domains: risk of bias, imprecision (number of studies, SD), inconsistency (inconsistent effect between several studies, I2), indirectness, and publication bias; additionally, downgrading and reasons why it was performed are indicated. As a result of the low total number of studies selected in this review, we did not downgrade based on imprecision (number of studies, SD) criteria except where only 2 studies were available and as detailed in the table. We graded evidence as in footnotes e, f, g, and h.cBCS: breast cancer screening
dRCT: randomized controlled trial.
eModerate certainty: we are moderately confident in the effect estimate: the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.
fHigh certainty: we are very confident that the true effect lies close to that of the estimate of the effect.
gVery low certainty: we have very little confidence in the effect estimate; the true effect is likely to be substantially different from the estimate of effect.
hLow certainty: our confidence in the effect estimate is limited; the true effect may be substantially different from the estimate of the effect.
iSDM: shared decision-making.
Discussion
Principal Findings
This is the first systematic review to provide a comprehensive overview of the effects of e-tools on women’s decision-making about BCS. The findings indicate that e-tools increase women’s long-term participation in BCS, intention not to participate, adequate knowledge, worry, and informed choice. They also reduce women’s decisional conflict. However, e-tools have no effect on women’s short-term participation in BCS, intention, or positive attitudes toward undergoing BCS. Additionally, the review identifies variables that could influence these effects. Among all the variables explored, the degree of tailoring in e-tools (ie, whether the tools were fully “tailored” or “featured with tailoring”) appeared to be the most influential (
).While all e-tools were shown to increase knowledge about BCS, the extent of the increase depended on the degree of tailoring or the study population. The increase was greater with “featured-with-tailoring” e-tools assessed in the general population than with tailored e-tools assessed in non-BCS participants. However, the observation that 1 tailored e-tool was more effective in less-educated women [
, ] supports the notion that the degree of tailoring plays a key role in driving a greater or lesser effect on knowledge. Our results suggest that complex e-tools (ie, tailored tools) are not necessarily more effective in ensuring that women are adequately informed about BCS than less complex e-tools (ie, features-with-tailoring). This finding aligns with recent studies, particularly those involving women from low socioeconomic backgrounds, which indicate that complex information about cancer screening may not be essential for women to be adequately informed [ , ].Our results indicate that features-with-tailoring e-tools influence women’s decisions about undergoing BCS. These tools reduce decisional conflict regarding BCS as well as increase both the intention not to undergo BCS and informed choice about this intention (
). However, they have no effect on positive attitudes toward BCS. The reduction in decisional conflict concerning BCS aligns with findings from a previous meta-analysis of web-based tools [ ]. By contrast, a meta-analysis that included both e-tools and printed DAs did not report this effect [ ], which may be attributable to the quality of one of the assessed printed DAs [ , ]. An increase in women’s informed choice regarding BCS has been consistently reported in all meta-analyses conducted to date on DAs used for BCS, regardless of whether the tools were web-based or printed [ , , ]. Similar to our findings, these reported effects were associated with features-with-tailoring tools, as defined in our study [ , , ]. However, a more detailed analysis of our results suggests that this increase in informed choice may stem from a rise in both negative intentions and negative attitudes toward BCS. As defined by Marteau et al [ ], individuals are considered to have made an informed choice about BCS if they possess adequate knowledge and hold either positive attitudes and intentions or negative attitudes and intentions. Our results showed that while the e-tools by Reder and Kolip [ ], Mathieu et al [ ], and Roberto et al [ ] increased informed choice ( D) and knowledge (see Figure S8A in ), they did not increase either positive attitudes toward BCS (see Figure S5 in ) or the intention to undergo BCS (see Figure S8B in ). It is likely that the observed increase in informed choice among women using these e-tools was driven by an increase in negative intentions, as evidenced by 2 of the 3 tools ( C), and possibly by negative attitudes toward participation in BCS, as reported in other studies [ ]. Interestingly, these 3 e-tools are among the highest-rated BCS DAs based on the IPDAS criteria. This was formally evaluated for the e-tools by Reder and Kolip [ ] and Mathieu et al [ ] in a recent systematic review [ ]. Although Roberto et al’s [ ] e-tool was not formally assessed in the same review, it is expected to be of similarly high quality due to its strong resemblance to Reder and Kolip’s [ ] tool [ ]. Our results suggest that even high-quality e-tools, as assessed by IPDAS criteria, and their ability to achieve higher levels of adequate knowledge, increase informed choice, and reduce decisional conflict, do not guarantee the promotion of positive intentions and attitudes toward undergoing BCS. On the contrary, these tools may increase negative intentions and attitudes. BCS programs should take this finding into consideration.Our results on participation in BCS (
) strongly suggest that only tailored e-tools have a significant effect on participation. Two factors may explain this finding regarding tailored-to-risk e-tools. First, these e-tools, when appropriately designed, increase women’s accurate perception of their own breast cancer risk (as shown by our results). Second, they may also heighten women’s levels of worry ( ), which is likely a key driver of increased participation in BCS. Indeed, while fear of breast cancer has been reported as a barrier, it has also been identified as a facilitator of BCS participation [ , , ]. BCS programs should address women’s feelings of worry if they aim to implement tailored-to-risk e-tools. However, further studies are needed to assess women’s acceptability of feeling worried in comparison to their perceived benefits of using these e-tools. In addition, it is important to note that the tailored e-tools evaluated in this review incorporate multiple features that allow for their effective integration into clinical pathways or health care services ( ). These features can be seen as opportunities to reduce barriers and increase access to BCS participation, such as by facilitating helpful discussions with health care providers (HPs) or simplifying the appointment process [ , , ]. Additionally, these features could help mitigate the impact of the tools on increasing worry [ ].Among the 22 e-tools investigated in this research, including the 6 identified as preparing for or facilitating SDM [
, , - ], none were evaluated using any validated SDM instruments [ - ]. There was no evidence to indicate whether the e-tools had an effect on SDM. Although this review suggests that e-tools, particularly tailored e-tools, may improve SDM by enhancing the quality of appointments with health care providers (HPs), this is likely due to their multiple features, which enable effective integration into clinical pathways or health care services ( ).Recommendations for Future Developments
While all types of e-tools appeared effective in increasing users’ knowledge about BCS, our results highlight important dilemmas for BCS programs that are using or planning to use e-tools to support women at average risk of breast cancer [
- ] in making decisions about BCS. Although features-with-tailoring e-tools can potentially increase informed choice and reduce decisional conflict, they may also be perceived as negatively impacting BCS programs by fostering negative intentions and attitudes toward undergoing BCS. Conversely, tailored e-tools, despite lacking evidence to support their effects on informed choice and decisional conflict, would increase women’s participation in BCS but, when tailored to risk, these tools may also heighten their levels of worry. One potential approach to minimize risks to both women’s well-being (worry) and BCS programs would be an “on-demand” model. In this model, women would “tailor” their own information needs, deciding on the nature and amount of information they feel is necessary. In this approach, we recommend that (1) all women be provided with a minimum of important, carefully assessed information about BCS in both text and audio formats, with multiple language options; and (2) women who do not wish to participate in BCS be given the option, through the e-tool, to obtain estimates of their breast cancer risk or, at the very least, to have a discussion with health care providers (HPs) about breast cancer risk in general. Some women may be unaware of the average breast cancer risk for women or of their own increased risk [ - ]. We also suggest that e-tools be well integrated into clinical pathways or health care services in various ways to reduce access-related barriers, especially those specific to low socioeconomic groups, such as health literacy. Notably, embedding e-tools in a health portal ( ) would facilitate appointments and communication with health care providers (HPs). AI tools, which have not been fully explored in the context of BCS according to this review, could support the “on-demand” approach. However, both the efficacy of such AI-based tools and potential ethical issues or biases need to be carefully assessed [ - ].Limitations
This study has several limitations that should be considered in future research. Caution is needed when generalizing our results due to (1) the limited number of studies included, particularly in the meta-analyses, with some outcomes assessed by only 2 RCTs (eg, “intention not to undergo BCS,” “level of worry,” “accuracy of perception of individual breast cancer risk,” and “decisional conflict”), some of which were at high risk of bias; (2) the high heterogeneity between the studies and e-tools; (3) the fact that almost all studies were conducted in high-income countries, mostly in the United States, which may limit the applicability of the results to other settings or low- and middle-income countries; and (4) the lack of assessment of implementation factors (eg, comparing users vs nonusers or completers vs noncompleters) and their impact on decision-making outcomes. Additionally, (1) we did not thoroughly review the content of the BCS information provided in the e-tools evaluated in this review, so we cannot draw conclusions about the tangible value of the increase in knowledge [
]; and (2) we did not have enough information to determine whether some of the e-tools could be classified as DAs. However, we applied a robust methodology to minimize some of these limitations, notably by comprehensively exploring the variables that could explain the heterogeneity of the results, using RoB 2 and GRADE assessments. A review is currently underway to explore implementation outcomes.Conclusions
Although no evidence was available to assess the efficacy of the tools in supporting SDM, this review demonstrates that the e-tools designed to assist women’s decision-making regarding BCS do impact this process. The degree of tailoring of the e-tools (ie, whether they are tailored, particularly to risk, or include features with tailoring) appears to be the most significant factor influencing decision-making. This review provides valuable insights for BCS programs when implementing such e-tools and offers directions for future development.
Acknowledgments
The authors would thank Dr. Marie-Anne Durand (Unisanté, Lausanne) for her valuable comments and participation. We are also grateful to all the study authors with whom we communicated to gather additional data for our meta-analyses. We thank our IARC colleague Mrs. Karima Bendeddouche for her support with the submission process. Our thanks also go to Teresa Lee (IARC Knowledge Manager) and Latifa Bouanzi (IARC Information Assistant) for their help in developing the research question and building it across all databases. This review is dedicated to the memory of our kind and highly professional colleague from WHO/Geneva, Tomas John Allen (Librarian, WHO), who helped identify the search strategy for Embase.
Data Availability
, which cites additional references [ - ], contains extra information about the methods used and reports additional meta-analyses and subanalyses. The complete qualitative data sets generated or analyzed during this study are available from the corresponding author upon reasonable request.
Authors' Contributions
PV conceived the review, conducted the analyses, including meta-analyses, and wrote the publication. RS supervised the study and the writing. PV and ALB performed the initial data search and PV updated the search. PV and LD, ALB, or CB extracted the data. Risk-of-bias of the RCTs was assessed by PV and LD, ALB, or CB. Grade assessment was performed by PV and LD. All authors contributed to establishing the data extraction form. All authors critically reviewed the preliminary results and the final report. The main contributors at the 2019 workshop on e-DAs and in further discussions are all listed as co-authors or in the Acknowledgments (Marie-Anne Durand). PV/IARC obtained funding from the Institut National du Cancer, France (INCa, grant DEP18-066). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Conflicts of Interest
None declared.
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and additional methods and analysis.
PDF File (Adobe PDF File), 3280 KBReferences
- Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263. [FREE Full text] [CrossRef] [Medline]
- National Comprehensive Cancer Network (NCCN). NCCN Clinical Practice Guidelines in Oncology: breast cancer (v. 6.2024). NCCN. Oct 23, 2024. URL: https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1419 [accessed 2024-12-18]
- Oeffinger KC, Fontham ETH, Etzioni R, Herzig A, Michaelson JS, Shih YT, et al. American Cancer Society. Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA. Oct 20, 2015;314(15):1599-1614. [FREE Full text] [CrossRef] [Medline]
- US Preventive Services Task Force, Nicholson WK, Silverstein M, Wong JB, Barry MJ, Chelmow D, et al. Screening for breast cancer: US Preventive Services Task Force recommendation statement. JAMA. Jun 11, 2024;331(22):1918-1930. [CrossRef] [Medline]
- Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, et al. American Cancer Society Breast Cancer Advisory Group. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin. 2007;57(2):75-89. [FREE Full text] [CrossRef] [Medline]
- The Council of the European Union. Council recommendation of 9 December 2022 on strengthening prevention through early detection: a new EU approach on cancer screening replacing Council recommendation 2003/878/EC 2022/C 473/01. Publication Office of the European Union. Dec 09, 2022. URL: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32022H1213(01) [accessed 2024-12-18]
- Zielonke N, Kregting LM, Heijnsdijk EAM, Veerus P, Heinävaara S, McKee M, et al. EU-TOPIA collaborators. The potential of breast cancer screening in Europe. Int J Cancer. Jan 15, 2021;148(2):406-418. [FREE Full text] [CrossRef] [Medline]
- IARC Working Group on the Evaluation of Cancer-Preventive Interventions. Breast Cancer Screening: IARC Handbooks of Cancer Prevention (Vol. 15). Lyon, France. International Agency for Research on Cancer; 2016.
- Mandrik O, Tolma E, Zielonke N, Meheus F, Ordóñez-Reyes C, Severens JL, et al. Systematic reviews as a "lens of evidence": determinants of participation in breast cancer screening. J Med Screen. Jun 2021;28(2):70-79. [FREE Full text] [CrossRef] [Medline]
- Mascara M, Constantinou C. Global perceptions of women on breast cancer and barriers to screening. Curr Oncol Rep. May 03, 2021;23(7):74. [CrossRef] [Medline]
- Özkan İ, Taylan S. Barriers to women's breast cancer screening behaviors in several countries: a meta-synthesis study. Health Care Women Int. Sep 2021;42(7-9):1013-1043. [CrossRef] [Medline]
- Autier P. Screening for breast cancer: worries about its effectiveness. Rev Prat. Dec 2013;63(10):1369-1377. [Medline]
- Pivot X, Viguier J, Touboul C, Morère J-F, Blay J, Coscas Y, et al. Breast cancer screening controversy: too much or not enough? Eur J Cancer Prev. Jun 2015;24 Suppl:S73-S76. [CrossRef] [Medline]
- Bour C. Is the socio-cultural environment favorable to women making an informed decision regarding breast cancer screening in France? BMJ Evidence-Based Medicine. Jun 2022;27(Suppl 1):A2. [FREE Full text] [CrossRef]
- Mandrik O, Zielonke N, Meheus F, Severens JLH, Guha N, Herrero Acosta R, et al. Systematic reviews as a 'lens of evidence': determinants of benefits and harms of breast cancer screening. Int J Cancer. Aug 15, 2019;145(4):994-1006. [FREE Full text] [CrossRef] [Medline]
- Marteau TM, Dormandy E, Michie S. A measure of informed choice. Health Expect. Jun 2001;4(2):99-108. [FREE Full text] [CrossRef] [Medline]
- Elwyn G, O'Connor A, Stacey D, Volk R, Edwards A, Coulter A, et al. International Patient Decision Aids Standards (IPDAS) Collaboration. Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. BMJ. Aug 26, 2006;333(7565):417. [FREE Full text] [CrossRef] [Medline]
- Joseph-Williams N, Newcombe R, Politi M, Durand M, Sivell S, Stacey D, et al. Toward minimum standards for certifying patient decision aids: a modified Delphi consensus process. Med Decis Making. Aug 2014;34(6):699-710. [CrossRef] [Medline]
- Stacey D, Légaré F, Lewis K, Barry MJ, Bennett CL, Eden KB, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. Apr 12, 2017;4(4):CD001431. [FREE Full text] [CrossRef] [Medline]
- Burnside ES, Schrager S, DuBenske L, Keevil J, Little T, Trentham-Dietz A, et al. Team science principles enhance cancer care delivery quality improvement: interdisciplinary implementation of breast cancer screening shared decision making. JCO Oncol Pract. Jan 2023;19(1):e1-e7. [FREE Full text] [CrossRef] [Medline]
- Légaré F, Stacey D, Pouliot S, Gauvin F, Desroches S, Kryworuchko J, et al. Interprofessionalism and shared decision-making in primary care: a stepwise approach towards a new model. J Interprof Care. Jan 2011;25(1):18-25. [FREE Full text] [CrossRef] [Medline]
- McAlpine K, Lewis KB, Trevena LJ, Stacey D. What Is the Effectiveness of Patient Decision Aids for Cancer-Related Decisions? A Systematic Review Subanalysis. JCO Clin Cancer Inform. Dec 2018;2:1-13. [FREE Full text] [CrossRef] [Medline]
- Martínez-Alonso M, Carles-Lavila M, Pérez-Lacasta MJ, Pons-Rodríguez A, Garcia M, Rué M, et al. InforMa Group. Assessment of the effects of decision aids about breast cancer screening: a systematic review and meta-analysis. BMJ Open. Oct 06, 2017;7(10):e016894. [FREE Full text] [CrossRef] [Medline]
- Ivlev I, Hickman EN, McDonagh MS, Eden KB. Use of patient decision aids increased younger women's reluctance to begin screening mammography: a systematic review and meta-analysis. J Gen Intern Med. Jul 2017;32(7):803-812. [FREE Full text] [CrossRef] [Medline]
- Légaré F, Adekpedjou R, Stacey D, Turcotte S, Kryworuchko J, Graham I, et al. Interventions for increasing the use of shared decision making by healthcare professionals. Cochrane Database Syst Rev. Jul 19, 2018;7(7):CD006732. [CrossRef] [Medline]
- Stacey D, Volk RJ. The International Patient Decision Aid Standards (IPDAS) Collaboration: evidence update 2.0. Med Decis Making. Aug 20, 2021;41(7):729-733. [CrossRef]
- Hashem F, Calnan MW, Brown PR. Decision making in NICE single technological appraisals: how does NICE incorporate patient perspectives? Health Expect. Feb 2018;21(1):128-137. [FREE Full text] [CrossRef] [Medline]
- Holmes-Rovner M, Srikanth A, Henry SG, Langford A, Rovner DR, Fagerlin A. Decision aid use during post-biopsy consultations for localized prostate cancer. Health Expect. Feb 2018;21(1):279-287. [FREE Full text] [CrossRef] [Medline]
- Leppin AL, Kunneman M, Hathaway J, Fernandez C, Montori VM, Tilburt JC. Getting on the same page: communication, patient involvement and shared understanding of "decisions" in oncology. Health Expect. Feb 2018;21(1):110-117. [FREE Full text] [CrossRef] [Medline]
- Menear M, Garvelink MM, Adekpedjou R, Perez MMB, Robitaille H, Turcotte S, et al. Factors associated with shared decision making among primary care physicians: findings from a multicentre cross-sectional study. Health Expect. Feb 2018;21(1):212-221. [FREE Full text] [CrossRef] [Medline]
- DuBenske LL, Schrager SB, Hitchcock ME, Kane AK, Little TA, McDowell HE, et al. Key elements of mammography shared decision-making: a scoping review of the literature. J Gen Intern Med. Oct 2018;33(10):1805-1814. [FREE Full text] [CrossRef] [Medline]
- Schrager SB, Phillips G, Burnside E. A simple approach to shared decision making in cancer screening. Fam Pract Manag. 2017;24(3):5-10. [FREE Full text] [Medline]
- Makoul G, Clayman ML. An integrative model of shared decision making in medical encounters. Patient Educ Couns. Mar 2006;60(3):301-312. [CrossRef] [Medline]
- Bomhof-Roordink H, Gärtner FR, Stiggelbout AM, Pieterse AH. Key components of shared decision making models: a systematic review. BMJ Open. Dec 17, 2019;9(12):e031763. [FREE Full text] [CrossRef] [Medline]
- Ruco A, Dossa F, Tinmouth J, Llovet D, Jacobson J, Kishibe T, et al. Social media and mHealth technology for cancer screening: systematic review and meta-analysis. J Med Internet Res. Jul 30, 2021;23(7):e26759. [FREE Full text] [CrossRef] [Medline]
- World Health Organization (WHO). Global strategy on digital health 2020-2025. WHO. Geneva, Switzerland. World Health Organization (WHO); 2021. URL: https://www.who.int/publications/i/item/9789240020924 [accessed 2024-12-18]
- Duffy A, Christie GJ, Moreno S. The challenges toward real-world implementation of digital health design approaches: narrative review. JMIR Hum Factors. Sep 09, 2022;9(3):e35693. [FREE Full text] [CrossRef] [Medline]
- Yu L, Li P, Yang S, Guo P, Zhang X, Liu N, et al. Web-based decision aids to support breast cancer screening decisions: systematic review and meta-analysis. J Comp Eff Res. Oct 2020;9(14):985-1002. [CrossRef] [Medline]
- Stout P, Villegas J, Kim H. Enhancing learning through use of interactive tools on health-related websites. Health Educ Res. Dec 2001;16(6):721-733. [CrossRef] [Medline]
- World Health Organization (WHO). Classification of digital health interventions v1.0. WHO. 2018. URL: https://www.who.int/publications/i/item/WHO-RHR-18.06 [accessed 2024-12-18]
- Le Bonniec A, Bauquier C, Mandrik O, Villain P. Effectiveness of online or interactive tools used as decision aids or shared decision making for women to decide about breast cancer screening: a systematic review. The International Prospective Register of Systematic Reviews (PROSPERO). 2020. URL: https://www.crd.york.ac.uk/prospero/ [accessed 2024-12-18]
- Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [FREE Full text] [CrossRef] [Medline]
- Straw I, Brass I, Mkwashi A, Charles I, Soares A, Steer C. Insights from a clinically orientated workshop on health care cybersecurity and medical technology: observational study and thematic analysis. J Med Internet Res. Jul 11, 2024;26:e50505. [FREE Full text] [CrossRef] [Medline]
- Veritas Health Innovation. Covidence systematic review software. Covidence. Melbourne, Victoria, Australia. Veritas Health Innovation URL: https://www.covidence.org/ [accessed 2024-12-18]
- Briñol P, Petty R. Fundamental processes leading to attitude change: implications for cancer prevention communications. Journal of Communication. 2006;56(suppl_1):S81-S104. [CrossRef]
- Bull FC, Kreuter MW, Scharff DP. Effects of tailored, personalized and general health messages on physical activity. Patient Educ Couns. Feb 1999;36(2):181-192. [CrossRef] [Medline]
- Deeks JJ, Higgins JPT, Altman DG, McKenzie JE, Veroniki AA. Chapter 10: Analysing data and undertaking meta-analyses. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.5. London, UK. Cochrane; 2024.
- Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ. Jan 16, 2020;368:l6890. [FREE Full text] [CrossRef] [Medline]
- Andrews JC, Schünemann HJ, Oxman AD, Pottie K, Meerpohl JJ, Coello PA, et al. GRADE guidelines: 15. Going from evidence to recommendation-determinants of a recommendation's direction and strength. J Clin Epidemiol. Jul 2013;66(7):726-735. [CrossRef] [Medline]
- Siemieniuk R, Guyatt G. What is GRADE? BMJ. 2019. URL: https://bestpractice.bmj.com/info/toolkit/learn-ebm/what-is-grade/ [accessed 2024-12-18]
- Zeng L, Brignardello-Petersen R, Hultcrantz M, Mustafa RA, Murad MH, Iorio A, et al. GRADE Guidance 34: update on rating imprecision using a minimally contextualized approach. J Clin Epidemiol. Oct 2022;150:216-224. [FREE Full text] [CrossRef] [Medline]
- Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. Aug 28, 2019;366:l4898. [FREE Full text] [CrossRef] [Medline]
- The Cochrane Collaboration. Review Manager (RevMan), version 5.4. Cochrane. URL: https://revman.cochrane.org [accessed 2024-12-18]
- Lee H, Ghebre R, Le C, Jang YJ, Sharratt M, Yee D. Mobile phone multilevel and multimedia messaging intervention for breast cancer screening: pilot randomized controlled trial. JMIR Mhealth Uhealth. Nov 07, 2017;5(11):e154. [FREE Full text] [CrossRef] [Medline]
- Roberto A, Colombo C, Candiani G, Satolli R, Giordano L, Jaramillo L, et al. A dynamic web-based decision aid to improve informed choice in organised breast cancer screening. A pragmatic randomised trial in Italy. Br J Cancer. Sep 17, 2020;123(5):714-721. [FREE Full text] [CrossRef] [Medline]
- Champion VL, Christy SM, Rakowski W, Lairson DR, Monahan PO, Gathirua-Mwangi WG, et al. An RCT to increase breast and colorectal cancer screening. Am J Prev Med. Aug 2020;59(2):e69-e78. [FREE Full text] [CrossRef] [Medline]
- Champion VL, Rawl SM, Bourff SA, Champion KM, Smith LG, Buchanan AH, et al. Randomized trial of DVD, telephone, and usual care for increasing mammography adherence. J Health Psychol. Jun 2016;21(6):916-926. [FREE Full text] [CrossRef] [Medline]
- Fissler T, Bientzle M, Cress U, Kimmerle J. The impact of advice seekers' need salience and doctors' communication style on attitude and decision making: a web-based mammography consultation role play. JMIR Cancer. Sep 08, 2015;1(2):e10. [FREE Full text] [CrossRef] [Medline]
- Henry SL, Shen E, Ahuja A, Gould MK, Kanter MH. The online personal action plan: a tool to transform patient-enabled preventive and chronic care. Am J Prev Med. Jul 2016;51(1):71-77. [CrossRef] [Medline]
- Klippert H, Schaper A. Using Facebook to communicate mammography messages to rural audiences. Public Health Nurs. Mar 29, 2019;36(2):164-171. [CrossRef] [Medline]
- Krist AH, Woolf SH, Rothemich SF, Johnson RE, Peele JE, Cunningham TD, et al. Interactive preventive health record to enhance delivery of recommended care: a randomized trial. Ann Fam Med. Jul 09, 2012;10(4):312-319. [FREE Full text] [CrossRef] [Medline]
- Pereira AAC, Destro JR, Picinin Bernuci M, Garcia LF, Rodrigues Lucena TF. Effects of a WhatsApp-delivered education intervention to enhance breast cancer knowledge in women: mixed-methods study. JMIR Mhealth Uhealth. Jul 21, 2020;8(7):e17430. [FREE Full text] [CrossRef] [Medline]
- Schapira MM, Hubbard RA, Seitz HH, Conant EF, Schnall M, Cappella JN, et al. The impact of a risk-based breast cancer screening decision aid on initiation of mammography among younger women: report of a randomized trial. MDM Policy Pract. 2019;4(1):2381468318812889. [FREE Full text] [CrossRef] [Medline]
- Walsh J, Potter M, Salazar R, Ozer E, Gildengorin G, Dass N, et al. PreView: a randomized trial of a multi-site intervention in diverse primary care to increase rates of age-appropriate cancer screening. J Gen Intern Med. Feb 2020;35(2):449-456. [CrossRef] [Medline]
- Bowen DJ, Robbins R, Bush N, Meischke H, Ludwig A, Wooldridge J. Effects of a Web-based intervention on women's breast health behaviors. Transl Behav Med. Mar 2011;1(1):155-164. [FREE Full text] [CrossRef] [Medline]
- Champion VL, Monahan PO, Stump TE, Biederman EB, Vachon E, Katz ML, et al. The effect of two interventions to increase breast cancer screening in rural women. Cancers (Basel). Sep 07, 2022;14(18):4354. [FREE Full text] [CrossRef] [Medline]
- Krist AH, Woolf SH, Hochheimer C, Sabo RT, Kashiri P, Jones RM, et al. Harnessing information technology to inform patients facing routine decisions: cancer screening as a test case. Ann Fam Med. May 08, 2017;15(3):217-224. [FREE Full text] [CrossRef] [Medline]
- Lin Z, Wang S. A tailored web-based intervention to promote women's perceptions of and intentions for mammography. J Nurs Res. Dec 2009;17(4):249-260. [CrossRef] [Medline]
- Mathieu E, Barratt AL, McGeechan K, Davey HM, Howard K, Houssami N. Helping women make choices about mammography screening: an online randomized trial of a decision aid for 40-year-old women. Patient Educ Couns. Oct 2010;81(1):63-72. [CrossRef] [Medline]
- Reder M, Kolip P. Does a decision aid improve informed choice in mammography screening? Results from a randomised controlled trial. PLoS One. 2017;12(12):e0189148. [FREE Full text] [CrossRef] [Medline]
- Seitz HH, Gibson L, Skubisz C, Forquer H, Mello S, Schapira MM, et al. Effects of a risk-based online mammography intervention on accuracy of perceived risk and mammography intentions. Patient Educ Couns. Oct 2016;99(10):1647-1656. [FREE Full text] [CrossRef] [Medline]
- Eden KB, Scariati P, Klein K, Watson L, Remiker M, Hribar M, et al. Mammography decision aid reduces decisional conflict for women in their forties considering screening. J Womens Health (Larchmt). Dec 2015;24(12):1013-1020. [FREE Full text] [CrossRef] [Medline]
- Eden KB, Ivlev I, Bensching KL, Franta G, Hersh AR, Case J, et al. Use of an online breast cancer risk assessment and patient decision aid in primary care practices. J Womens Health (Larchmt). Jun 01, 2020;29(6):763-769. [FREE Full text] [CrossRef] [Medline]
- Scariati P, Nelson L, Watson L, Bedrick S, Eden KB. Impact of a decision aid on reducing uncertainty: pilot study of women in their 40s and screening mammography. BMC Med Inform Decis Mak. Nov 10, 2015;15:89. [FREE Full text] [CrossRef] [Medline]
- Elkin EB, Pocus VH, Mushlin AI, Cigler T, Atoria CL, Polaneczky MM. Facilitating informed decisions about breast cancer screening: development and evaluation of a web-based decision aid for women in their 40s. BMC Med Inform Decis Mak. Mar 21, 2017;17(1):29. [FREE Full text] [CrossRef] [Medline]
- Lin Z, Effken JA. Effects of a tailored web-based educational intervention on women's perceptions of and intentions to obtain mammography. J Clin Nurs. May 08, 2010;19(9-10):1261-1269. [CrossRef] [Medline]
- Arora M, Gerbert B, Potter M, Gildengorin G, Walsh J. PRE-VIEW: development and pilot testing of an interactive video doctor plus provider alert to increase cancer screening. ISRN Prev Med. 2013;2013:935487. [FREE Full text] [CrossRef] [Medline]
- Seitz HH, Schapira MM, Gibson LA, Skubisz C, Mello S, Armstrong K, et al. Explaining the effects of a decision intervention on mammography intentions: the roles of worry, fear and perceived susceptibility to breast cancer. Psychol Health. May 2018;33(5):682-700. [FREE Full text] [CrossRef] [Medline]
- Gathirua-Mwangi WG, Monahan PO, Stump T, Rawl SM, Skinner CS, Champion VL. Mammography adherence in African-American women: results of a randomized controlled trial. Ann Behav Med. Feb 2016;50(1):70-78. [FREE Full text] [CrossRef] [Medline]
- Lin ZC, Effken JA, Li YJ, Kuo CH. Designing a tailored web-based educational mammography program. Comput Inform Nurs. 2011;29(1):16-23. [CrossRef] [Medline]
- Lee HY, Lee MH, Gao Z, Sadak K. Development and evaluation of culturally and linguistically tailored mobile app to promote breast cancer screening. J Clin Med. Jul 24, 2018;7(8):181. [FREE Full text] [CrossRef] [Medline]
- Klein KA, Watson L, Ash JS, Eden KB. Evaluation of risk communication in a mammography patient decision aid. Patient Educ Couns. Jul 2016;99(7):1240-1248. [FREE Full text] [CrossRef] [Medline]
- Skinner CS, Buchanan A, Champion V, Monahan P, Rawl S, Springston J, et al. Process outcomes from a randomized controlled trial comparing tailored mammography interventions delivered via telephone vs. DVD. Patient Educ Couns. Nov 2011;85(2):308-312. [FREE Full text] [CrossRef] [Medline]
- Reder M, Soellner R, Kolip P. Do women with high eHealth literacy profit more from a decision aid on mammography screening? Testing the moderation effect of the eHEALS in a randomized controlled trial. Front Public Health. 2019;7:46. [FREE Full text] [CrossRef] [Medline]
- Hild S, Johanet M, Valenza A, Thabaud M, Laforest F, Ferrat E, et al. DEDICACES Group‚ the French National College of General Practitioners. Quality of decision aids developed for women at average risk of breast cancer eligible for mammographic screening: Systematic review and assessment according to the International Patient Decision Aid Standards instrument. Cancer. Jun 15, 2020;126(12):2765-2774. [FREE Full text] [CrossRef] [Medline]
- Elwyn G, O'Connor AM, Bennett C, Newcombe RG, Politi M, Durand M, et al. Assessing the quality of decision support technologies using the International Patient Decision Aid Standards instrument (IPDASi). PLoS One. 2009;4(3):e4705. [FREE Full text] [CrossRef] [Medline]
- Keary T. Web-based application. Technopedia. URL: https://www.techopedia.com/definition/26002/web-based-application [accessed 2024-12-18]
- Witteman HO, Ndjaboue R, Vaisson G, Dansokho SC, Arnold B, Bridges JFP, et al. Clarifying values: an updated and expanded systematic review and meta-analysis. Med Decis Making. Oct 2021;41(7):801-820. [FREE Full text] [CrossRef] [Medline]
- Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991;50(2):179-211. [CrossRef]
- Dormandy E, Michie S, Hooper R, Marteau TM. Informed choice in antenatal Down syndrome screening: a cluster-randomised trial of combined versus separate visit testing. Patient Educ Couns. Apr 2006;61(1):56-64. [CrossRef] [Medline]
- Rosenstock IM. The health belief model: explaining health behavior through expectancies. In: Glanz K, Lewis FM, Rimer BK, editors. Health Behavior and Health Education: Theory, Research, and Practice. San Francisco, CA. Jossey-Bass/Wiley; 1990:39-62.
- The Ottawa Hospital Patient Decison Aids Reseach Group. Decisional Conflict Scale. The Ottawa Hospital Patient Decison Aids Reseach Group. URL: https://decisionaid.ohri.ca/eval_dcs.html [accessed 2024-12-18]
- Tyldesley-Marshall N, Grove A, Ghosh I, Kudrna L, Ayorinde A, Singh M, et al. Investigating informed choice in screening programmes: a mixed methods analysis. BMC Public Health. Dec 12, 2022;22(1):2319. [FREE Full text] [CrossRef] [Medline]
- Guigon L, Sánchez LXG, Petit A, Bonniec AL, Basu P, Rodrigue CM, et al. Would shared decision-making be useful in breast cancer screening programmes? A qualitative study using focus group discussions to gather evidence from French women with different socioeconomic backgrounds. BMC Public Health. Feb 07, 2024;24(1):404. [FREE Full text] [CrossRef] [Medline]
- Sasieni PD, Smith RA, Duffy SW. Informed decision-making and breast cancer screening. J Med Screen. Dec 2015;22(4):165-167. [FREE Full text] [CrossRef] [Medline]
- Hersch J, Barratt A, Jansen J, Irwig L, McGeechan K, Jacklyn G, et al. Use of a decision aid including information on overdetection to support informed choice about breast cancer screening: a randomised controlled trial. Lancet. Apr 25, 2015;385(9978):1642-1652. [CrossRef] [Medline]
- Lemmo D, Martino ML, Vallone F, Donizzetti AR, Freda MF, Palumbo F, et al. Clinical and psychosocial constructs for breast, cervical, and colorectal cancer screening participation: a systematic review. Int J Clin Health Psychol. 2023;23(2):100354. [FREE Full text] [CrossRef] [Medline]
- Nicolai J, Moshagen M, Eich W, Bieber C. The OPTION scale for the assessment of shared decision making (SDM): methodological issues. Z Evid Fortbild Qual Gesundhwes. 2012;106(4):264-271. [CrossRef] [Medline]
- Ubbink DT, van Asbeck EV, Aarts JW, Stubenrouch FE, Geerts PA, Atsma F, et al. Comparison of the CollaboRATE and SDM-Q-9 questionnaires to appreciate the patient-reported level of shared decision-making. Patient Educ Couns. Jul 2022;105(7):2475-2479. [FREE Full text] [CrossRef] [Medline]
- Kriston L, Scholl I, Hölzel L, Simon D, Loh A, Härter M. The 9-item Shared Decision Making Questionnaire (SDM-Q-9). Development and psychometric properties in a primary care sample. Patient Educ Couns. Jul 2010;80(1):94-99. [CrossRef] [Medline]
- Melbourne E, Sinclair K, Durand M-A, Légaré F, Elwyn G. Developing a dyadic OPTION scale to measure perceptions of shared decision making. Patient Educ Couns. Feb 2010;78(2):177-183. [CrossRef] [Medline]
- Martin LR, DiMatteo MR, Lepper HS. Facilitation of patient involvement in care: development and validation of a scale. Behav Med. 2001;27(3):111-120. [CrossRef] [Medline]
- Lerman CE, Brody DS, Caputo GC, Smith DG, Lazaro CG, Wolfson HG. Patients’ perceived involvement in care scale: relationship to attitudes about illness and medical care. J Gen Intern Med. Jan 1990;5(1):29-33. [CrossRef]
- Degner L, Kristjanson L, Bowman D, Sloan J, Carriere K, O'Neil J, et al. Information needs and decisional preferences in women with breast cancer. JAMA. May 14, 1997;277(18):1485-1492. [Medline]
- Barr PJ, Thompson R, Walsh T, Grande SW, Ozanne EM, Elwyn G. The psychometric properties of CollaboRATE: a fast and frugal patient-reported measure of the shared decision-making process. J Med Internet Res. Jan 03, 2014;16(1):e2. [FREE Full text] [CrossRef] [Medline]
- The National Health Service (NHS). Measuring shared decision making, a review of research evidence. NHS. 2012. URL: https://www.england.nhs.uk/wp-content/uploads/2013/08/7sdm-report.pdf [accessed 2024-12-18]
- Parviainen J, Rantala J. Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care. Med Health Care Philos. Mar 2022;25(1):61-71. [FREE Full text] [CrossRef] [Medline]
- Pallardy C. The chatbot will see you now: 4 ethical concerns of AI in health care. Information Week. 2023. URL: https://www.informationweek.com/machine-learning-ai/the-chatbot-will-see-you-now-4-ethical-concerns-of-ai-in-health-care [accessed 2024-12-18]
- Coghlan S, Leins K, Sheldrick S, Cheong M, Gooding P, D'Alfonso S. To chat or bot to chat: ethical issues with using chatbots in mental health. Digit Health. 2023;9:20552076231183542. [FREE Full text] [CrossRef] [Medline]
- Xu L, Sanders L, Li K, Chow JCL. Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR Cancer. Nov 29, 2021;7(4):e27850. [FREE Full text] [CrossRef] [Medline]
- Reeves BC, Deeks JJ, Higgins JPT, Shea B, Tugwell P, Wells GA. Chapter 24: Including non-randomized studies on intervention effects. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.2. London, UK. Cochrane; 2024.
- Lefebvre C, Glanville J, Briscoe S, Featherstone R, Littlewood A, Metzendorf MI, et al. Chapter 4: Searching for and selecting studies [last updated September 2024]. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.5. London, UK. Cochrane; 2024.
- Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M, et al. Medical Research Council Guidance. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ. Sep 29, 2008;337:a1655. [FREE Full text] [CrossRef] [Medline]
- Champion VL, Christy SM, Rakowski W, Gathirua-Mwangi WG, Tarver WL, Carter-Harris L, et al. A randomized trial to compare a tailored web-based intervention and tailored phone counseling to usual care for increasing colorectal cancer screening. Cancer Epidemiol Biomarkers Prev. Dec 2018;27(12):1433-1441. [FREE Full text] [CrossRef] [Medline]
- Biederman E, Baltic R, Katz ML, Rawl S, Vachon E, Monahan PO, et al. Increasing breast, cervical, and colorectal cancer screening among rural women: baseline characteristics of a randomized control trial. Contemp Clin Trials. Dec 2022;123:106986. [CrossRef] [Medline]
Abbreviations
AI: artificial intelligence |
BCS: breast cancer screening |
DA: decision aid |
FE: fixed effect |
GRADE: Grading of Recommendations, Assessment, Development, and Evaluation |
HP: health professional |
IPDAS: International Patient Decision Aid Standards |
MD: mean difference |
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
PROSPERO: International Prospective Register of Systematic Reviews |
RCT: randomized controlled trial |
RE: random effect |
RoB 2: Risk-of-Bias 2 |
RR: relative risk/risk ratio |
SDM: shared decision-making |
SWiM: synthesis without meta-analysis |
Edited by G Eysenbach, T de Azevedo Cardoso; submitted 30.08.24; peer-reviewed by R Trivedi; comments to author 18.10.24; revised version received 27.10.24; accepted 29.10.24; published 29.01.25.
Copyright©Patricia Villain, Laura Downham, Alice Le Bonniec, Charlotte Bauquier, Olena Mandrik, Tom Nadarzynski, Lorie Donelle, Raúl Murillo, Eleni L Tolma, Sonali Johnson, Patricia Soler-Michel, Robert Smith. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.01.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.