This randomized clinical trial assesses the effect of an electronic chatbot-based cancer risk communication tool on knowledge of cancer risk among adolescents and young adults (AYAs) with a hereditary cancer syndrome.
Key Points
Question
Can an electronic chatbot-based cancer risk communication tool support understanding of cancer risk and screening without increasing psychological distress among adolescents and young adults (AYAs) with hereditary cancer predisposition?
Findings
In this randomized clinical trial of 106 AYAs with a hereditary cancer syndrome, AYAs who received the chatbot intervention had statistically significant greater improvements in knowledge of cancer risk by age 30 years compared with those who had a standard clinic visit.
Meaning
These findings indicate that combining human interaction in the clinic with personalized, accurate electronic information can support AYAs with cancer predisposition and other serious conditions.
Abstract
Importance
Adolescents and young adults (AYAs) with hereditary cancer predisposition face high rates of lifetime cancer risk. Models to support their understanding of cancer risk have not been developed.
Objective
To assess the effect of an electronic chatbot-based cancer risk communication tool on AYAs’ understanding of cancer risk.
Design, Setting, and Participants
This randomized clinical trial was conducted at the Dana-Farber Cancer Institute, Emory University/Children’s Healthcare of Atlanta, University of Chicago, and University of Utah Huntsman Cancer Institute between December 20, 2022, and May 30, 2025 and compared cancer predisposition visits alone vs visits with an electronic chatbot-based cancer risk communication tool, AYA Risk Information and Screening Education, using baseline and follow-up surveys to assess changes in knowledge of cancer risk by age 30 years and over their lifetime, knowledge of recommended screening, and level of psychological distress. Eligible participants were English speakers aged 12 to 24 years, who had 1 of 9 hereditary cancer predisposition syndromes. The primary analysis was conducted according to the intention-to-treat principle. Data were analyzed from July 21 to September 3, 2025.
Intervention
Chatbot-based intervention with cancer risk and recommended screening information tailored to each syndrome. Additional links to resource pages with chat summary content (learn), recommended age-based screening (plan), guidance about talking with peers (share), links to support organizations (connect), and coping and bereavement support (grow).
Main Outcomes and Measures
The primary outcome was knowledge of cancer risk. The secondary outcomes were knowledge of recommended screening and level of psychological distress.
Results
Of 798 AYAs assessed, 106 AYAs were randomized and enrolled (54 to the intervention group and 52 to the control group). Participants’ mean (SD) age was 18.3 (SD, 3.9) years, and 64% (68 of 106) were girls or women. At baseline, 38% of AYAs (20 of 52) in the control group and 52% (28 of 54) of AYAs in the intervention group had accurate knowledge of cancer risk by age 30 years. Following the clinic visit, 37% (19 of 52) of AYAs in the control group and 78% (42 of 54) in the chatbot-based intervention group had accurate knowledge, reflecting greater improvement in knowledge of cancer risk by age 30 years (OR, 3.50; 95% CI, 1.45-9.19; P = .005). AYAs in the intervention group had non–statistically significant improvements in knowledge of recommended screening (OR, 2.17; 95% CI, 0.85-5.59; P = .11) and reductions in distress compared with AYAs in the control group (distress thermometer, β = −0.50; 95% CI, −1.09 to 0.08; P = .09).
Conclusions and Relevance
In this randomized clinical trial, use of a chatbot-based intervention combined with a cancer predisposition clinic visit improved knowledge of cancer risk by age 30 years over a clinic visit alone, without increasing distress. Combining human interaction in the clinic with personalized, accurate electronic information can support AYAs with cancer predisposition and other serious conditions.
Trial Registration
ClinicalTrials.gov Identifier: NCT04323774
Introduction
More than 10% of adolescents and young adults (AYAs) with cancer have a hereditary cancer predisposition, resulting in heightened lifetime risk of new cancers.1,2,3,4,5,6,7,8 Screening allows for lifesaving early detection when cancer is more readily treatable.7,9,10,11,12,13,14,15,16,17,18 Genetic counseling and testing are therefore increasingly recommended for AYAs with new cancer diagnoses.19,20,21,22,23 Yet AYAs are a vulnerable group navigating complex personal transitions.24,25,26,27,28,29,30,31 AYA-specific models for cancer risk communication and care are still emerging; new models are needed to help AYAs understand risk and embrace lifesaving screening interventions.
To meet this need, our team developed a chatbot-based intervention to teach AYAs with hereditary cancer predisposition about their cancer risk and recommended screening.32 Developed with input from AYAs and families, this intervention was designed to be delivered following a cancer predisposition clinic visit, allowing AYAs to access information independently and explore topics of interest. The intervention uses an interactive chat to educate AYAs about cancer risk, with resource pages focused on issues of importance to AYAs, such as talking with peers and coping with uncertainty. This trial was designed to assess the effect of a stand-alone cancer predisposition clinic visit vs a clinic visit plus the chatbot-based intervention in improving AYAs’ knowledge of cancer risk and recommended screening. We also assessed the effect of the intervention on AYA’s level of psychological distress.
Methods
The Dana-Farber Institutional Review Board and the institutional review boards at Emory University/Children’s Healthcare of Atlanta, the University of Chicago, and the University of Utah Huntsman Cancer Institute approved the protocol for this randomized clinical trial (Supplement 1). Signed informed consent (from AYAs aged >18 years), or parent or guardian consent and patient assent (from AYAs aged 12-17 years), was obtained. We followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline for RCTs.
Intervention
Development and feasibility and acceptability testing have been described separately.32 AYA-Risk Information and Screening Education (AYA-RISE; hereafter chatbot-based intervention) is an electronic chatbot-based intervention accessed via a Health Insurance Portability and Accountability Act of 1996–compliant hyperlink via computer, tablet, or phone. The home page included hyperlinks to 6 options: chat, learn, plan, share, connect, and grow. The “chat” hyperlink gave participants access to a chatbot that provided information on cancer risk, recommended screening, and cancer prevention. Information specific to included cancer predisposition genes was developed using literature and screening guidelines.7,12,13,15,33,34,35 The research team developed all material; no material was derived from generative artificial intelligence. The chat format allowed AYAs to obtain information in their preferred level of detail. For example, information about cancer risk was offered in general terms (“high risk of cancer in the future”) and in numbers (“more than 5 people out of 10 will develop cancer in the future”); a branching pattern allowed patients to choose their preferred level of detail. Resource web pages provided additional content including summarized information from the chat (learn), recommended screening by age (plan), guidance about talking with peers about cancer risk (share), hyperlinks to support organizations (connect), coping and bereavement support (grow), and optional information about prophylactic surgical procedures and family planning. The interface was developed by Clear Genetics (now Invitae/Labcorp).
Recruitment
We enrolled patients at 4 sites where they were to assigned to receive a clinic visit alone (control group) or a clinic visit plus the chatbot-based intervention (intervention group). AYAs were eligible if they were aged 12 to 24 years, were diagnosed with a cancer predisposition syndrome for which the intervention was created (Li-Fraumeni, DICER1 tumor predisposition; hereditary paraganglioma-pheochromocytoma; hereditary retinoblastoma; familial adenomatous polyposis; BRCA1 and BRCA2–associated hereditary breast and ovarian cancer; Lynch syndrome; Von Hippel Lindau; and neurofibromatosis type 1), spoke and read English, were receiving care at a study site, and had adequate cognitive function (ie, Pediatric NeuroQOL Cognitive Function Short Form score ≥30, [score ranging from 8 to 40, with higher scores indicating better cognitive function]).36,37 The age range of 12 to 24 years was selected to support clinical discussions with youths emerging from adolescence into adulthood.32 AYAs were excluded if they were receiving active cancer therapy or had participated in intervention development. One AYA per family was eligible for randomization; siblings with the same cancer predisposition could directly enroll to the same group but were excluded from the primary analysis.
Eligible AYAs were approached by email or in person before an appointment between December 20, 2022, and May 30, 2025. Participants were randomized 1:1 to intervention or control, stratified by age group (12-17 and 18-24 years) and study site, using random permuted blocks. The random allocation sequence was prepared by the study statistician (H. U.) using R version 4.4.1 (R Project for Statistical Computing).38 Enrolling personnel (S.F., D-L.J., S.M.R., A.K., E.G., J.V.) were blinded to the allocation sequence.
Study Procedures
Following enrollment and randomization, participants completed a previsit survey on personal characteristics, knowledge of cancer risk and screening, and distress. They then had their cancer predisposition clinic visit; processes for the visit were guided by the standard of care at each site and clinician discretion. Clinicians and participants were blinded to study group at the time of the visit. Following the visit, AYAs assigned to the control group completed a follow-up survey. AYAs assigned to the intervention group were given time to interact with the intervention and then completed the follow-up survey in the clinic or at home. Except where specified, questions on baseline and follow-up surveys matched.
Survey Measures
Knowledge of Cancer Risk
Participants were asked, “If you had to choose a number, what is the average risk of getting a cancer before age 30 years for someone with your cancer risk syndrome?” Response options included “1 in 10 or less (meaning that out of 10 people like you, 1 or less will get cancer before the age of 30 years),” with analogous options for 2 in 10 people, 3 in 10 people, 4 in 10 people, or 5 or more in 10 people.39,40 A corresponding question asked about lifetime cancer risk.
Knowledge of Cancer Screening
Screening was defined as “a way of using tests such as radiology studies to find cancers early, when they are easier to treat.” Participants were asked “at your age, is there any cancer screening that is recommended for your cancer risk syndrome?” Those who indicated yes were asked to choose all types of screening recommended (a radiology study such as magnetic resonance imaging or ultrasound, mammogram, endoscopy or colonoscopy, laboratory tests of blood or urine, physical examination, or something else [please specify]). For radiology studies, participants were asked to specify included areas of the body (whole body, head and brain, neck, chest and/or breast, abdomen, pelvic area, arms, legs).41
Psychological Challenges
The Psychosocial Aspects of Hereditary Cancer (PAHC) questionnaire includes 24 questions in 4 problem domains (emotional distress about hereditary cancer, practical aspects, social aspects, and family aspects) and an overall score.42 Participants also completed the distress thermometer, a visual analog scale that ranges from 0 (“no distress”) to 10 (“extreme distress”),43 to indicate how much psychological distress they had experienced in the last week, including today.
Electronic Health Literacy and Demographic Characteristics
At baseline only, participants completed the eHealth Literacy Scale44 questionnaire to assess their comfort using the internet to access health information. The questionnaire consists of 8 questions, and the total score ranges from 8 to 10, with higher scores indicating greater self-perceived confidence in using digital technology for health.
The baseline survey assessed self-reported gender (boys or men, girls or women, or other), race and ethnicity (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, White, or Other [please specify]); age and cancer predisposition syndrome were collected from medical records. Self-reported race and ethnicity were included in the analysis because prior work in pediatric communication has identified racial and ethnic differences in communication processes and outcomes.
Statistical Analysis
At study initiation, our target sample size was 300 AYAs to detect 20% or higher improvement in the primary outcome (knowledge of cancer risk at age 30 years) at 2-sided P < .05. Enrollment rates were lower than anticipated, and the sample size was revised to allow for completion within the funding time frame, for a final sample of 106 AYAs. Details of power and sample size considerations are in eTable 1 in Supplement 2. This study did not have planned interim analyses or stopping guidelines.
Data were analyzed from July 21 to September 3, 2025. We used summary statistics (mean, SD, minimum, maximum, and IQR) to describe data, assess data quality, and examine data distribution and randomization success. The primary analysis included all participants and was conducted according to the intention-to-treat principle. For patients who did not complete an after-visit survey (n = 4), changes from baseline to postvisit were set to 0 (carrying last observation forward, and assuming no change in outcomes). The same analyses were performed excluding these 4 patients. In all analyses, cases with missing baseline values were excluded.
Primary analyses used generalized linear regression models, in which the response variable was change in outcome from baseline to postvisit. Independent variables were intervention group and age category (12-17 years vs 18-24 years). For binary outcomes (knowledge of cancer risk [primary outcome], knowledge of recommended screening [secondary outcome], and PAHC [secondary outcome]), change from baseline was an ordered categorical response coded as (−1, 0, or 1) using ordinal categorical logistic regression with the logit-link, implemented using the polr function from the MASS library in R.38 For outcomes with numerous modalities and/or domains (knowledge of recommended screening and PAHC), a single model was fit, and robust SEs were derived using sandwich estimators to account for within-patient clustering and implemented using the vcovCL function from the sandwich library in R.45 For knowledge of recommended screening, analyses were repeated, limiting the dataset to modalities or body areas where screening was recommended. For continuous outcomes (distress thermometer, uncertainty), we used the identity link.
Statistical significance was assessed at 2-sided P < .05. No adjustments were made for multiple testing. Point estimates and 95% CIs of intervention effects were reported.
As a sensitivity analysis, we calculated the proportion of patients with accurate knowledge at the postvisit survey among those with inaccurate knowledge at baseline; unadjusted differences in proportions between intervention and control groups were reported with 95% CI. Similar analyses were conducted for PAHC, calculating the proportion of patients with responses that did not meet the “problem” threshold at the postvisit survey among those who met the threshold at baseline. We also conducted sensitivity analyses for change in knowledge of cancer risk, adjusting for baseline eHealth literacy.
Results
Across sites, 798 AYAs were assessed for eligibility, 549 were eligible, and 430 were approached for participation. Of 324 who did not participate, 6 did not meet cognitive screening criteria, 146 declined participation, and 172 did not respond after at least 1 approach. In total, 106 of 549 AYAs were enrolled (25% of those approached and eligible); 52 were randomized to the control group and 54 to the intervention group (Figure and Table 1). The mean (SD) age of participants was 18.3 (3.9) years; 68 (64%) were girls or women, 37 were boys or men (35%). The sample included 3 individuals (3%) who identified as American Indian or Alaska Native, 7 (7%) as Black or African American, 7 (7%) as Hispanic or Latino, and 99 (93%) as White. eTable 5 in Supplement 2 shows the demographic and clinical characteristics of participants, excluding those who did not complete the postvisit survey. All 9 cancer predisposition syndromes were represented, with Li-Fraumeni (19 of 106 [18%]) and DICER1 (20 of 106 [19%]) as the most common.
Figure. CONSORT Flow Diagram.
AYAs indicate adolescents and young adults.
Table 1. Demographic and Clinical Characteristics of Study Participants by Study Group.
| Characteristic | Participants, No. (%) | |
|---|---|---|
| Control group (n = 52) | Intervention group (n = 54) | |
| Age at registration, y | ||
| Mean (SD) | 18.3 (4.1) | 18.4 (3.7) |
| Median (IQR) | 18.5 (15.0-21.7) | 18.2 (14.8-21.7) |
| Range | 12.1-24.8 | 12.4-24.6 |
| Age group, y | ||
| 12-17 | 24 (46) | 25 (46) |
| 18-24 | 28 (54) | 29 (54) |
| Gender | ||
| Girls and women | 31 (60) | 37 (69) |
| Boys and men | 21 (40) | 16 (30) |
| Othera | 0 | 1 (2) |
| Race and ethnicity | ||
| American Indian or Alaska Native | 1 (2) | 2 (4) |
| Asian | 1 (2) | 1 (2) |
| Black or African American | 3 (6) | 4 (7) |
| Hispanic or Latino | 4 (8) | 3 (6) |
| Native Hawaiian or Other Pacific Islander | 1 (2) | 0 |
| White | 50 (96) | 49 (91) |
| Otherb | 0 | 1 (2) |
| Diagnosis | ||
| Li-Fraumeni syndrome | 9 (17) | 10 (19) |
| DICER1 | 11 (21) | 9 (17) |
| Hereditary paraganglioma-pheochromocytoma | 7 (13) | 7 (13) |
| Retinoblastoma | 1 (2) | 3 (6) |
| Familial adenomatous polyposis | 9 (17) | 5 (9) |
| BRCA1 hereditary breast and ovarian cancer syndrome | 5 (10) | 3 (6) |
| BRCA2 hereditary breast and ovarian cancer syndrome | 4 (8) | 2 (4) |
| Lynch syndrome | 1 (2) | 3 (6) |
| Von Hippel Lindau syndrome | 0 | 3 (6) |
| Neurofibromatosis type 1 | 5 (10) | 9 (17) |
| eHealth literacy score (8-40) | ||
| Mean (SD) | 30 (7) | 27 (7) |
| Median (IQR) | 30 (25-36) | 28 (24-32) |
| Range | 8-40 | 8-40 |
| Site | ||
| Emory University/Children’s Healthcare of Atlanta | 6 (12) | 8 (15) |
| University of Utah Huntsman Cancer Institute | 17 (33) | 13 (24) |
| University of Chicago | 2 (4) | 2 (4) |
| Dana-Farber Cancer Institute | 27 (52) | 31 (57) |
One participant identified as other; no further details were provided.
One participant selected other and specified Puerto Rican.
At baseline, 38% of AYAs (20 of 52) in the control group and 52% (28 of 54) in the intervention group had accurate knowledge of cancer risk before age 30 years (Table 2). Following the clinic visit, 37% (19 of 52) in the control group compared with 78% (42 of 54) in the intervention group had accurate knowledge, reflecting greater improvement in knowledge of cancer risk by age 30 years (OR, 3.50; 95% CI, 1.45-9.19; P = .005) among those assigned to the intervention group.
Table 2. Analysis of Knowledge of Cancer Risk and Recommended Screening.
| Knowledge category | No. of observationsa | No. (%) | Change, No. (%)b | Between-group difference | ||||
|---|---|---|---|---|---|---|---|---|
| Baseline | After visit | −1 | 0 | 1 | OR (95% CI)c | P value | ||
| Cancer risk | ||||||||
| Accurate knowledge at age 30 y | ||||||||
| Control | 52 | 20 (38) | 19 (37) | 6 (12) | 41 (79) | 5 (10) | 1 [Reference] | NA |
| Intervention | 54 | 28 (52) | 42 (78) | 3 (6) | 34 (63) | 17 (31) | 3.50 (1.45-9.19) | .005 |
| Accurate knowledge of lifetime risk | ||||||||
| Control | 52 | 23 (44) | 26 (50) | 3 (6) | 43 (83) | 6 (12) | 1 [Reference] | NA |
| Intervention | 54 | 18 (33) | 28 (52) | 1 (2) | 42 (78) | 11 (20) | 2.15 (0.81-6.15) | .13 |
| Recommended screening | ||||||||
| Accurate knowledge of recommended screening | ||||||||
| Control | 208 | 176 (85) | 174 (84) | 5 (2) | 200 (96) | 3 (1) | 1 [Reference] | NA |
| Intervention | 216 | 195 (90) | 201 (93) | 4 (2) | 202 (94) | 10 (5) | 2.17 (0.85-5.59) | .11 |
| Accurate knowledge of recommended screening: subset analysis of modalities for which screening was recommended | ||||||||
| Control | 51 | 39 (76) | 41 (80) | 1 (2) | 47 (92) | 3 (6) | 1 [Reference] | NA |
| Intervention | 51 | 39 (76) | 43 (84) | 2 (4) | 43 (84) | 6 (12) | 1.43 (0.38-5.41) | .60 |
| Accurate knowledge of recommended body parts for radiology studies | ||||||||
| Control | 364 | 296 (81) | 288 (79) | 18 (5) | 336 (92) | 10 (3) | 1 [Reference] | NA |
| Intervention | 378 | 311 (82) | 301 (80) | 33 (9) | 322 (85) | 23 (6) | 0.95 (0.49-1.84) | .88 |
| Accurate knowledge of recommended body parts for radiology studies (subset analysis of body parts for which screening was recommended) | ||||||||
| Control | 78 | 39 (50) | 45 (58) | 1 (1) | 70 (90) | 7 (9) | 1 [Reference] | NA |
| Intervention | 89 | 52 (58) | 56 (63) | 6 (7) | 73 (82) | 10 (11) | 0.78 (0.26-2.30) | .65 |
Abbreviations: NA, not applicable; OR, odds ratio.
For analyses of knowledge of cancer risk, patients contributed 1 observation. For analyses of recommended screening, patients contributed 4 observations for accurate knowledge of recommended screening and 7 observations for accurate knowledge of recommended body parts for radiology studies; for the respective subset analyses, patients contributed up to 4 and up to 7 observations.
A change of 1 represents an increase in accuracy, 0 represents no change in accuracy, and −1 represents a decrease in accuracy.
Adjusted for age group. ORs are from ordinal logistic regression models; values greater than 1 correspond to enhanced knowledge for the intervention group vs control group. For analyses of recommended screening, CIs and P values were calculated using robust SEs (sandwich estimator) to account for within-patient clustering due to multiple screening modalities/body parts.
Findings for lifetime cancer risk were not statistically different between groups, with baseline accuracy in 44% of AYAs (23) in the control group and 33% of AYAs (18) in the intervention group, and after-visit accuracy in 50% of AYAs (26) in the control and 52% of AYAs (28) in the intervention group. Odds of enhanced knowledge among AYAs in the intervention group compared with AYAs in the control group directionally favored the intervention group but were not statistically significant (OR, 2.15; 95% CI, 0.81-6.15; P = .13).
Knowledge of recommended screening similarly favored the intervention group but did not meet the threshold for statistical significance (OR, 2.17; 95% CI, 0.85-5.59; P = .11). Most AYAs (85% [176] in the control and 90% [195] in the intervention groups) had accurate knowledge of recommended screening at baseline. Change in knowledge did not significantly differ between groups when analyses were limited to modalities for which screening was recommended, or for analyses focused on knowledge of body areas requiring screening.
Across 5 domains of PAHC (secondary outcome), AYAs in the control group reported problems in 37% of domains (97), and AYAs in the intervention group reported problems in 36% (96) at baseline (Table 3 and Table 4). Both groups reported fewer problems at follow-up, including 30% (77) in the control group and 25% (67) in the intervention group, without differences in reduction of problems by group (OR, 0.82; 95% CI, 0.47-1.43; P = .48). The most prevalent problem domain was psychological distress about hereditary predisposition (worry about getting cancer; preventive options like screening or surgery; fulfilling plans for having children); 52% of AYAs (27) in the control group and 54% of AYAs (29) in the intervention group reported problems at baseline. Both groups experienced absolute reductions in problems at follow-up, with 44% of AYAs (23) in the control group and 31% (17) in the intervention group reporting problems following the clinic visit with or without the chatbot intervention. Differences in change in problems by group did not reach statistical significance, although directionally favored greater reduction in the intervention group (OR, 0.43; 95% CI, 0.15-1.14; P = .09). Findings were similar for change in distress thermometer ratings, with a non–statistically significant reduction in distress in intervention relative to control (β = −0.50; 95% CI, −1.09 to 0.08; P = .09). Change in illness uncertainty did not differ by group.
Table 3. Analysis of Psychological Aspects of Hereditary Cancer.
| PAHC problem domain | No. of observationsa | No. (%) | Change, No. (%)b | Between-group differencec | ||||
|---|---|---|---|---|---|---|---|---|
| Baseline | After visit | −1 | 0 | 1 | OR (95% CI) | P value | ||
| Aggregate analysis for 5 domainsd | ||||||||
| Control | 260 | 97 (37) | 77 (30) | 31 (12) | 218 (84) | 11 (4) | 1 [Reference] | NA |
| Intervention | 270 | 96 (36) | 67 (25) | 42 (16) | 215 (80) | 13 (5) | 0.82 (0.47-1.43) | .48 |
| Hereditary disposition | ||||||||
| Control | 52 | 27 (52) | 23 (44) | 7 (13) | 42 (81) | 3 (6) | 1 [Reference] | NA |
| Intervention | 54 | 29 (54) | 17 (31) | 12 (22) | 42 (78) | 0 | 0.43 (0.15-1.14) | .09 |
| Practical issues | ||||||||
| Control | 52 | 14 (27) | 8 (15) | 8 (15) | 42 (81) | 2 (4) | 1 [Reference] | NA |
| Intervention | 54 | 14 (26) | 10 (19) | 7 (13) | 44 (81) | 3 (6) | 1.24 (0.46-3.42) | .67 |
| Problems with family | ||||||||
| Control | 52 | 14 (27) | 10 (19) | 5 (10) | 46 (88) | 1 (2) | 1 [Reference] | NA |
| Intervention | 54 | 16 (30) | 14 (26) | 7 (13) | 42 (78) | 5 (9) | 1.27 (0.46-3.61) | .64 |
| Emotions | ||||||||
| Control | 52 | 21 (40) | 20 (38) | 4 (8) | 45 (87) | 3 (6) | 1 [Reference] | NA |
| Intervention | 54 | 24 (44) | 19 (35) | 7 (13) | 45 (83) | 2 (4) | 0.57 (0.18-1.68) | .31 |
| Living with cancer | ||||||||
| Control | 52 | 21 (40) | 16 (31) | 7 (13) | 43 (83) | 2 (4) | 1 [Reference] | NA |
| Intervention | 54 | 13 (24) | 7 (13) | 9 (17) | 42 (78) | 3 (6) | 0.89 (0.34-2.33) | .81 |
Abbreviations: NA, not applicable; OR, odds ratio.
Patients contributed 5 observations for the aggregate analysis (1 for each domain).
A change of 1 represents an increase in problems, 0 represents no change in problems, and −1 represents a decrease in problems.
Adjusted for age group. ORs are from ordinal logistic regression models; values less than 1 correspond to decreases in problems for the intervention vs control. Mean differences are from linear regression models; values less than 0 correspond to better outcomes for the intervention vs control.
For aggregate analysis, 95% CIs and P values were calculated using robust SEs (sandwich estimator) to account for within-patient clustering.
Table 4. Analysis of Distress and Uncertainty in Illness.
| Measure | No. of patients | Baseline score, mean (SD) | Postvisit score, mean (SD) | Score change, mean (SD) | Adjusted mean difference (95% CI) | P value |
|---|---|---|---|---|---|---|
| Distress thermometer (0-10)a | ||||||
| Control | 52 | 3.1 (2.0) | 3.2 (2.3) | 0.08 (1.37) | 0 [Reference] | NA |
| Intervention | 54 | 3.1 (2.5) | 2.7 (2.1) | −0.43 (1.68) | −0.50 (−1.09 to 0.08) | .09 |
| Uncertainty in illness–ambiguity (7-35)b | ||||||
| Control | 52 | 25.1 (5.7) | 25.4 (5.5) | 0.33 (4.79) | 0 [Reference] | NA |
| Intervention | 54 | 25.4 (4.3) | 24.7 (5.2) | −0.72 (4.11) | −1.05 (−2.75 to 0.65) | .23 |
| Uncertainty in illness–comprehension (2-10)c | ||||||
| Control | 52 | 8.7 (1.5) | 8.6 (1.2) | −0.06 (1.88) | 0 [Reference] | NA |
| Intervention | 54 | 8.1 (1.4) | 8.4 (1.5) | 0.22 (1.31) | 0.28 (−0.33 to 0.89) | .37 |
Abbreviation: NA, not applicable.
Scores range from 0 to 10, with higher scores indicating higher distress.
Scores range from 7 to 35, with higher scores indicating a higher level of uncertainty.
Scores range from 2 to 10, with higher scores indicating a higher level of illness uncertainty.
Sensitivity analyses explored change in knowledge among those with inaccurate baseline knowledge, and change in psychological problems among those with problems at baseline (eTable 2 in Supplement 2). Findings identified improvement in knowledge of cancer risk before age 30 years (difference: 49.8%; 95% CI, 24.1%-75.4%; P < .001), recommended screening (difference: 38.2%; 95% CI, 10.7%-65.8%; P = .005), and identification of body areas for which screening was indicated (difference: 19.6%; 95% CI, 4.0%-35.3%; P = .01) in the intervention compared with the control group. Additional sensitivity analyses that were adjusted for baseline ehealth literacy (eTables 3 and 4 in Supplement 2) and excluded 4 participants who did not complete the follow-up survey (eTables 6-8 in Supplement 2), had findings similar to those of the main analyses.
Discussion
AYAs with hereditary cancer predisposition live with the possibility that they may develop cancer at any time. Those in our study had sufficiently high short-term risk that tumor screening was part of ongoing medical care. Many also live with complex family histories, including parents or siblings who have had cancer, some of whom have died.24,25,26,27,28,46,47,48 While ensuring that cancer screening takes place can be lifesaving, it also requires meeting the needs of a psychologically vulnerable population with a higher cancer risk and attendant distress.26,49,50
We found that an electronic intervention, codesigned with AYAs with cancer predisposition to meet informational and support needs, was more effective at improving knowledge of cancer risk before age 30 years than a clinic visit alone. Odds of enhanced knowledge of lifetime cancer risk directionally favored the intervention but did not reach statistical significance. We selected knowledge of risk before age 30 years as a primary outcome given its immediacy to this young group, for whom short-term risk might drive health behavior and impact follow-up for recommended screening, especially across the pediatric-to-adult transition. The relevance of this time point was affirmed by AYAs as we developed the intervention; lifetime risk, while important, was experienced as less immediately relevant to health behavior.
We also assessed psychological experiences, including problems related to cancer predisposition and overall distress, to ensure that enhanced knowledge did not come at the expense of psychological well-being. Fortunately, we did not see increased psychosocial distress after the intervention. Results favored a beneficial effect of the intervention on well-being, but the trial was underpowered to detect a statistically significant effect in this limited sample. This finding supports a possible orientation toward empowerment and favorable self-identity that can result from health knowledge,51 with potential for a range of outcomes of interventions, such as this chatbot-based cancer risk communication tool, for young people facing serious health challenges.
Electronic interventions are impersonal and not suited to all needs; high quality medical care requires human interactions and relationships. We embedded our communication tool into a clinical care model in which AYAs would first see their medical team and then engage with the intervention. The goal was to ensure opportunity for human interaction and flexible conversation in the visit, while using the electronic modality to allow AYAs to explore topics of their choosing on their own time. As guidelines evolve, the intervention will need to be updated to accurately reflect current standard of care recommendations and support evolving practices of clinical care teams.
Previous research suggests that AYAs with hereditary cancer predisposition believe that clinical visits do not meet all their needs,27,28,52,53,54 in part due to a predominance of communication between clinicians and parents.28,53,55,56 Many are waiting for results of screening studies to be disclosed in the visit, making it harder to ask and absorb bigger picture information. While further work is needed to understand meaningful components of interventions, we believe this hybrid model, combining clinical care and opportunity to explore topics electronically, offers potential to support AYAs.
Limitations
This study has some limitations. Only 25% of AYAs approached for this study enrolled, and we revised our enrollment goals during the study to match our funding timeline. Limited enrollment may have affected the results. While we did not systematically collect reasons for nonenrollment or differences between enrolled and nonenrolled AYAs, many AYAs told study team members that they preferred not to add time to a clinic visit. Notably, our enrollment rate was similar to rates from other published behavioral interventions, especially for AYAs.57,58,59,60,61,62 We administered baseline survey measures after randomization for pragmatic reasons but may have introduced expectancy bias. Our primary outcome, knowledge of cancer risk, required comfort with numeracy, which can be challenging; however, it was based on a prior measure developed for AYAs.39,40
Our study was conducted at academic centers with established cancer predisposition clinics, and our population lacked racial and ethnic diversity. These findings offer promise for dissemination beyond academic settings and into the community, where many AYAs receive care and where dedicated resources for rare diseases are often lacking; reducing burdens of study procedures may help to increase uptake.
Conclusions
In this randomized clinical trial, we demonstrated that a chatbot-based communication tool combined with a cancer predisposition clinic visit improved knowledge of cancer risk by age 30 years without increasing distress. AYAs with hereditary cancer predisposition live under a threat of cancer. Many have already experienced cancer once or have parents or siblings who have had cancer; many live with grief over loved ones who died from cancer with the same hereditary predisposition. Returning to the cancer predisposition clinic for screening is an act of faith that active surveillance can make their lives better and longer. The electronic chatbot-based tool provided information to supplement the information AYAs received in the clinic, increasing knowledge of short-term cancer risk without increasing distress. This chatbot-based communication tool offers promise that other electronic interventions, adapted to meet needs of AYAs and embedded in a clinical care model, can provide needed support and empowerment for those living with life-threatening conditions.
AYA-RISE Study Protocol
eTable 1. Sample size and power considerations
eTable 2. Sensitivity analysis for accurate knowledge/absence of problems at after-visit survey, among those with inaccurate knowledge/presence of problems at baseline
eTable 3. Sensitivity analysis for accurate knowledge of cancer risk at age 30, adjusting for baseline eHealth Literacy score
eTable 4. Sensitivity analysis for accurate knowledge of lifetime cancer risk, adjusting for baseline eHealth literacy score
eTable 5. Demographic and clinical characteristics of study participants by study arm, excluding 4 participants who did not complete the after-visit survey
eTable 6. Analysis of knowledge of cancer risk and knowledge of recommended screening, excluding 4 participants who did not complete the after-visit survey
eTable 7. Analysis of PAHC (psychological aspects of hereditary cancer), distress, and uncertainty in illness, excluding 4 participants who did not complete the after-visit survey
eTable 8. Sensitivity analysis for accurate knowledge/absence of problems at after-visit survey, among those with inaccurate knowledge/presence of problems at baseline, excluding 4 participants who did not complete the after-visit survey
Data Sharing Statement
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
AYA-RISE Study Protocol
eTable 1. Sample size and power considerations
eTable 2. Sensitivity analysis for accurate knowledge/absence of problems at after-visit survey, among those with inaccurate knowledge/presence of problems at baseline
eTable 3. Sensitivity analysis for accurate knowledge of cancer risk at age 30, adjusting for baseline eHealth Literacy score
eTable 4. Sensitivity analysis for accurate knowledge of lifetime cancer risk, adjusting for baseline eHealth literacy score
eTable 5. Demographic and clinical characteristics of study participants by study arm, excluding 4 participants who did not complete the after-visit survey
eTable 6. Analysis of knowledge of cancer risk and knowledge of recommended screening, excluding 4 participants who did not complete the after-visit survey
eTable 7. Analysis of PAHC (psychological aspects of hereditary cancer), distress, and uncertainty in illness, excluding 4 participants who did not complete the after-visit survey
eTable 8. Sensitivity analysis for accurate knowledge/absence of problems at after-visit survey, among those with inaccurate knowledge/presence of problems at baseline, excluding 4 participants who did not complete the after-visit survey
Data Sharing Statement
