Introduction
Anxiety and depression are elevated in people with inflammatory bowel disease (IBD) and are linked to increased healthcare utilization and poor health-related quality of life.1 Although a significant number of adults with IBD in the United States have psychological distress accompanied by functional impairment, mental health care is underutilized with cost cited as a barrier.2 The emergence of digital behavioral therapeutics into clinical practice presents an opportunity to address this barrier. Digital resources may be especially useful for delivering interventions to patients who require less intensive psychological support.
However, despite the recent uptake of digital technology in delivering clinical care, questions remain regarding equitable implementation specifically among low income and marginalized communities.3,4 Participants in recent research testing digital behavioral interventions (DBIs) among people with IBD have been mostly women with high educational attainment who have full-time employment and do not receive social service benefits.5 Racial and ethnic minority groups often have different socioecological factors, such as healthcare access and mental health stigma, that may influence obtaining and using psychological interventions compared with non-Hispanic White groups.3,6 We sought to evaluate the feasibility of using DBIs via smartphone-based applications (apps) among a population of low-income, Black and/or Hispanic IBD patients with symptoms of anxiety and depression.
Methods
At the time of a scheduled gastroenterology visit (November 2021 to March 2023), we approached consecutive patients at an urban, tertiary academic medical center who were 18 years of age or older and self-identified as Black and/or Hispanic. After completing informed consent, participants completed an online screening questionnaire via Heath Information Portability and Accountability Act compliant software (REDCap [Research Electronic Data Capture]) that consisted of sociodemographic data, IBD characteristics, digital access and Internet connectivity, mental health, and health-related quality-of-life questions. Eligible participants had anxiety or depression scores on the National Institutes of Health PROMIS-29 (Patient Reported Outcomes Measurement Information System) within 2 SDs above the mean (ie, T score 50-70). Patients who did not self-identify as Black and/or Hispanic, who had current/past suicidality, or who had more severe psychological symptoms (ie, T score >70) were excluded.
Participants were randomly assigned to 1 of 2 commercially available DBI apps: structured cognitive behavioral activities (intervention) or a mood-tracking app without specific activities (attention control). Allocation was concealed using opaque consecutive envelopes. The intervention app consisted of cognitive behavioral skills training using a clinically validated program (Sanvello)7 in which participants completed cognitive behavioral therapy (CBT) skills and activities at their own pace. The attention control app consisted of daily mood tracking (Pixels) in which participants reflected daily on their mood, selecting a representative emoji and with space to write notes. The first half of participants were instructed to use the apps for 8 weeks and the second half were instructed to use the apps for 4 weeks. After the specified study time frame, we emailed participants an online postintervention questionnaire to be completed remotely, asking participants to quantify app use and experience (eg, ease, enjoyment, usefulness). Participants not responding after 2 reminders were considered lost to follow-up. All screening questionnaires and DBI apps were available in both English and Spanish.
We summarized patient demographics, digital access and Internet connectivity, mental health, and health-related quality of life using frequency and mean ± SD. We compared demographic characteristics among patients excluded with those who were randomized and those lost to follow-up using chi-square tests for categorical and t test or Mann-Whitney U test for continuous variables. We analyzed self-reported app use and the proportion of participants assigned to a DBI app who completed a postintervention questionnaire as measures of engagement. Because we used commercially available apps for which participant app usage data were not available, this was not analyzed. Finally, we analyzed participant attitudes toward using a DBI app as a measure of app acceptability. P < .05 was considered statistically significant. Statistical analyses were performed with Stata 16.1 (StataCorp).
Ethical Considerations
This study was approved by the Einstein-Montefiore Institutional Review Board.
Results
Participant recruitment, screening, and follow-up are described in the CONSORT diagram (Figure 1). Of the 113 unique patients approached for study recruitment, 37 (32.4%) declined to participate and 61 (53.5%) completed screening. Among those screened, 30 met inclusion and were randomized (self-guided CBT, n = 18; mood tracking, n = 12), 21 completed the postintervention questionnaire, and 19 were included in the feasibility and acceptability analysis.
Figure 1.
Study flow diagram of patients randomly assigned to a digital behavioral intervention application. CBT, cognitive behavioral therapy.
No significant demographic differences were detected between those excluded and those randomized except for insurance coverage (P = .02) (Table 1). More patients excluded had Medicare and private insurance than did those randomized: 18.1% vs 10.0% and 32.5% vs 16.7%, respectively. Those excluded also tended to be older than those randomized (39 years of age vs 33 years of age; P = 0.4).
Table 1.
IBD patients in digital behavioral intervention pilot
| Excluded (n = 83) | Randomized (n = 30) | Self-guided CBT (n = 18) | Mood tracking (n = 12) | Lost to follow-up (n = 9) | |
|---|---|---|---|---|---|
| Demographics | |||||
| IBD type | |||||
| Crohn’s disease | 46 (55.4) | 17 (56.7) | 11 (61.1) | 6 (50.0) | 5 (55.6) |
| Ulcerative colitis | 37 (44.6) | 13 (43.3) | 7 (38.9) | 6 (50.0) | 4 (44.4) |
| On IBD biologic or small molecule | 58 (69.9) | 19 (63.3) | 14 (77.8) | 5 (41.7) | 5 (55.6) |
| Age, y | 39 (25-59) | 33 (25-59) | 27 (24-45) | 40 (29-59) | 41 (24-59) |
| Sex | |||||
| Female | 42 (50.6) | 19 (63.3) | 11 (61.1) | 8 (66.7) | 8 (88.9) |
| Male | 41 (49.4) | 11 (36.7) | 7 (38.9) | 4 (33.3) | 1 (11.1) |
| Race and ethnicity | |||||
| Black or African American | 37 (69.8) | 18 (60.0) | 12 (66.7) | 6 (50.0) | 4 (44.4) |
| Hispanic | 43 (65.1) | 18 (60.0) | 10 (55.6) | 8 (66.7) | 6 (66.7) |
| Insurance | |||||
| Medicaid | 38 (45.8) | 16 (53.3) | 10 (55.6) | 6 (50.0) | 4 (44.4) |
| Medicare | 15 (18.1) | 3 (10.0) | 1 (5.6) | 2 (16.7) | 1 (11.1) |
| Private | 27 (32.5) | 5 (16.7) | 2 (11.1) | 3 (25.0) | 2 (22.2) |
| Other | 3 (3.6) | 6 (20.0) | 5 (27.8) | 1 (8.3) | 2 (22.2) |
| Digital technology | |||||
| Regular Internet access | — | 30 (100) | 18 (100) | 12 (100) | 9 (100) |
| Mode of Internet access | |||||
| Phone | — | 28 (93.3) | 17 (94.4) | 11 (91.7) | 8 (88.9) |
| Tablet | — | — | — | — | — |
| Home computer | — | 2 (6.7) | 1 (5.6) | 1 (8.3) | 1 (11.1) |
| Internet use frequency | |||||
| Daily | — | 22 (73.3) | 13 (72.2) | 9 (75.0) | 5 (55.6) |
| Few times a week | — | 6 (20.0) | 4 (22.2) | 2 (16.7) | 2 (22.2) |
| Less than weekly | — | 2 (6.7) | 1 (5.6) | 1 (8.3) | 2 (22.2) |
| Comfortable using apps | — | 28 (93.3) | 16 (88.9) | 12 (100) | 8 (88.9) |
| Mental health and quality of life | |||||
| Current mental health provider | — | 3 (10.0) | 3 (16.7) | — | 1 (11.1) |
| Mental illness diagnosis, past or current | — | 8 (26.7) | 4 (22.2) | 4 (33.3) | 2 (22.2) |
| Physical functiona | — | 47.8 ± 9.5 | 46.7 ± 10.1 | 49.6 ± 8.4 | 45.1 ± 9.7 |
| Anxietya | — | 57.8 ± 6.7 | 57.8 ± 5.1 | 57.9 ± 8.9 | 58.9 ± 4.6 |
| Depressiona | — | 54.1 ± 7.3 | 55.0 ± 6.7 | 52.7 ± 8.2 | 54.4 ± 6.7 |
| Fatiguea | — | 53.5 ± 8.6 | 53.1 ± 8.4 | 54.1 ± 9.4 | 52.7 ± 8.3 |
| Sleep disturbancea | — | 54.1 ± 7.6 | 54.3 ± 7.9 | 53.8 ± 7.4 | 57.9 ± 7.8 |
| Social roles and activitiesa | — | 49.9 ± 7.1 | 49.5 ± 7.3 | 50.7 ± 7.1 | 50.3 ± 9.4 |
| Pain interferencea | — | 56.9 ± 7.5 | 57.5 ± 7.3 | 56.0 ± 8.3 | 58.6 ± 9.9 |
| App assignment | |||||
| Self-guided CBT | — | — | — | — | 6 (33.3) |
| Mood tracking | — | — | — | — | 3 (25.0) |
Values are n (%), median (interquartile range), or mean ± SD. All patients self-identified as either Hispanic and/or Black or African American.
Abbreviations: app, application; CBT, cognitive behavioral therapy; IBD, inflammatory bowel disease.
aNational Institutes of Health PROMIS-29 (Patient Reported Outcomes Measurement Information System) scores. Higher numbers indicate a greater level of the measured trait. Raw scores are converted to a T score that centers at a mean of 50 with a standard deviation of 10 in the general population.
No significant baseline characteristics were detected between treatment groups. All randomized participants (n = 30) reported regular Internet access, which they predominately logged onto on their phone (93.3%) and used daily (73.3%). Anxiety was the most elevated reported symptom (mean T score 57.8 ± 6.7), followed by pain interference (mean T score 56.9 ± 7.5). The depression mean T score was 54.1 ± 7.3.
Participants lost to follow-up (n = 9) tended to be older (41 years of age vs 33 years of age) with less comfort using apps (88.9% vs 95.2%) than those who completed. Those who were lost to follow-up also tended to report more baseline symptoms of anxiety (58.9 ± 4.6 vs 57.4 ± 7.5), depression (54.5 ± 6.7 vs 53.9 ± 7.7), and pain interference (58.6 ± 9.9 vs 56.2 ± 6.3), as well as lower physical function (45.1 ± 9.7 vs 49.0 ± 9.3), than those who completed. More participants in the 8 weeks of follow-up (35.3%) than the 4 weeks of follow-up (23.0%) and in the self-guided CBT group (33.3%) than the mood-tracking group (25.0%) were lost to follow-up (P > .05).
Participants who completed the postintervention questionnaire reported using the app less than daily (self-guided CBT: 66.7%; mood tracking: 57.1%) or daily (self-guided CBT: 33.3%; mood tracking: 42.9%). More participants using self-guided CBT than mood tracking reported that they enjoyed using the app (80.0% vs 55.6%). Nearly three-quarters (73.7%) of participants in both groups would recommend the app to someone else. Regardless of which DBI app they used, many participants felt that it would be good to talk to a mental health professional in addition to (57.9%) and/or instead of using an app (52.6%).
Discussion
In this pilot study examining the feasibility and acceptability of brief digital behavioral interventions among a predominately low-income racial and ethnic minority IBD population, roughly a third of patients declined participation, a third were ineligible after screening, and a third met inclusion for randomization. Almost half of those screened had elevated anxiety/depression symptoms, and ineligibility was more often due to low rather than high anxiety/depression symptoms. Among participants with elevated anxiety/depression symptoms who were randomly assigned to use either a CBT or mood-tracking app, there was similar reported app use and postintervention follow-up. That the mood-tracking app was designed as an attention control but enjoyed similar uptake to the structured CBT app may point to the value of mood tracking (self-reflective activity) in IBD in and of itself.
We estimated that 85% of our patients would have Internet access, based on active patient portal registration in the electronic medical record as a rough proxy for Internet connectivity. This is consistent with U.S. Census Bureau data of households with a broadband internet in our patients’ geographic region.8 Our patients’ poverty rate is well above that of its greater metropolitan area, and half our study population was Medicaid insured. Nonetheless, near universal Internet access via daily phone use in our study population is encouraging toward the feasibility of using DBIs.
Participant attrition over time is a recognized issue with digital behavioral interventions.9 Modest levels of engagement observed in this study may be due to the brief intervention time frame. Alternatively, because app use was suggested by a gastroenterology physician, patients may have entered the study with a high level of expectancy and subsequent follow through. That patients with poorer baseline mental health and quality of life tended to be lost to follow-up indicates a need for optimizing the efficacy and/or implementation of available tools. It is important to note that neither app was specifically designed with IBD patients as the end users. Future studies should explore optimizing patient engagement via user-centered and community-based participatory research design.10
Although most patients enjoyed using the DBI apps, many also desired support from a mental health professional in addition to or instead of app use. Advances in digital technology are rapidly emerging and are poised to be a useful tool to assist in integrated mental healthcare in IBD. However, the role of digital behavioral interventions is most likely best viewed as complementary to the work of mental health professionals in underserved populations.
Strengths of this study include a focus on IBD patients traditionally underrepresented in research. A limitation of this study is that granular app usage data were not available, and this level of detail would be important to better understand and improve app engagement. Additionally, the small sample size in this single-center study limits generalizability.
Nonetheless, results from this study provide preliminary insights on leveraging DBI apps into clinical practice among Black and Hispanic IBD patients with elevated anxiety and depression symptoms. Design of larger studies exploring app content, efficacy, engagement, and implementation among patients with IBD from predominately low-income racial and ethnic minority groups is underway.
Acknowledgments
The authors thank Rebecca Almonte and Sheila Benitez for the research coordinator efforts in this study.
Contributor Information
Ruby Greywoode, Division of Gastroenterology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Shadi Nahvi, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Psychiatry and Behavioral Sciences, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Thomas Ullman, Division of Gastroenterology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Laurie Keefer, Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Funding
R.G. is supported by National Institutes of Health/National Center for Advancing Translational Sciences Einstein Montefiore Clinical and Translational Science Award Grant Number UL1TR001073. S.N. is supported by National Institutes of Health/National Institute on Drug Abuse K24DA051807.
Conflicts of Interest
S.N. has grant recipient Pfizer. L.K. has served as a consultant for Pfizer, AbbVie, Eli Lilly, Takeda, Ardelyx, and Trellus Health; and is co-founder of and owns equity in Trellus Health. The other authors disclose no conflicts.
References
- 1. Bisgaard TH, Allin KH, Keefer L, Ananthakrishnan AN, Jess T.. Depression and anxiety in inflammatory bowel disease: epidemiology, mechanisms and treatment. Nat Rev Gastroenterol Hepatol. 2022;19(11):717-726. [DOI] [PubMed] [Google Scholar]
- 2. Greywoode R, Ullman T, Keefer L.. National prevalence of psychological distress and use of mental health care in inflammatory bowel disease. Inflamm Bowel Dis. 2023;29(1):70-75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Wegermann K, Patel Y, Wilder J.. Health equity and telemedicine in gastroenterology and hepatology. Clin Gastroenterol Hepatol. 2021;19(8):1516-1519. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Rodriguez JA, Shachar C, Bates D.. Digital inclusion as health care-supporting health care equity with digital-infrastructure initiatives. N Engl J Med. 2022;386(12):1101-1103. [DOI] [PubMed] [Google Scholar]
- 5. Romano D, Chesterman S, Fuller-Tyszkiewicz M, et al. Feasibility, acceptability, and preliminary efficacy of acceptance commitment therapy for adults living with inflammatory bowel disease and distress. Inflamm Bowel Dis. Published July 21, 2023. doi: https://doi.org/ 10.1093/ibd/izad122 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Misra S, Jackson VW, Chong J, et al. Systematic review of cultural aspects of stigma and mental illness among racial and ethnic minority groups in the united states: implications for interventions. Am J Community Psychol. 2021;68(3-4):486-512. [DOI] [PubMed] [Google Scholar]
- 7. Moberg C, Niles A, Beermann D.. Guided self-help works: randomized waitlist controlled trial of pacifica, a mobile app integrating cognitive behavioral therapy and mindfulness for stress, anxiety, and depression. J Med Internet Res. 2019;21(6):e12556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. U.S. Census Bureau. Quick Facts: Bronx County, New York. Accessed June 16, 2023. https://www.census.gov/quickfacts/bronxcountynewyork
- 9. Nwosu A, Boardman S, Husain MM, Doraiswamy PM.. Digital therapeutics for mental health: is attrition the Achilles heel? Front Psychiatry. 2022;13:900615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Moore Q, Tennant PS, Fortuna LR.. Improving research quality to achieve mental health equity. Psychiatr Clin North Am. 2020;43(3):569-582. [DOI] [PubMed] [Google Scholar]