This study tested several pragmatic digital medicine support models that systematically varied the level and type of human support used to facilitate implementation of an evidence-based digital medicine intervention for AUD. The primary aim was to detect the effectiveness of each support model on self-reported risky drinking days (RDD: more than 4 standard drinks on any day for men under 65, more than 3 standard drinks for women and men over 65) and quality of life (QOL). We conducted a 12-month randomized controlled trial using a hybrid type 1 effectiveness-implementation study design to test the effectiveness of an intervention while exploring implementation aspects associated with varying digital medicine support models. Our rationale for this design is that a low-touch model is the least costly and easiest to implement. However, if active involvement of either peer support specialists or health coaches increases the intervention’s effectiveness, the additional cost may represent a worthwhile investment. Our null hypothesis was that adding human support in the form of peer mentoring and health coaching to self-guided use would have no additional effect on RDD and QOL (other secondary outcomes are reported separately).31 Our alternative hypotheses were that (1) adding group-oriented peer mentorship would increase effectiveness compared to the self-monitoring group and (2) adding 1:1 health coaching would increase effectiveness compared to the self-monitoring group.
Theoretical and Empirical Foundations
This RCT used a digital medicine app called Tula (Sanskrit for “balance”). Tula was adapted from the Addiction-Comprehensive Health Enhancement Support System (A-CHESS)—one of the first digital medicine apps proven effective in an RCT of patients recovering from severe AUD after leaving a 90-day in-patient rehabilitation facility.24 These results were replicated when A-CHESS was shown to be effective in reducing heavy drinking in a 2022 study comparing telephone- and smartphone-based continuing care for AUD.32 A-CHESS has also been tested across a range of other populations.33–37
The theoretical framework supporting the A-CHESS app is self-determination theory, which asserts that meeting three fundamental psychological needs (competency, relatedness, and autonomous motivation) produces intrinsic motivation for behavior change.38
Eligibility Criteria
Eligibility criteria are listed in Table 1. To maintain anonymity, all participants were identified by usernames they created while setting up their accounts.
Table 1
Patient inclusion and exclusion criteria
Inclusion criteria | Exclusion criteria |
21 years old or older | Reports symptoms consistent with severe AUD during screen (6 or more of 11 DSM-5 criteria)** |
Wants to reduce drinking | Active psychotic disorder diagnosis |
Owns a smartphone and willing to download and use the Tula app | Acute medical problem requiring immediate hospitalization |
Lives within the healthcare system service area | Terminal illness |
Meets at least one of the following criteria: ● AUDIT55 screening score 8+, OR ● Responds “yes” to at least 2 questions on the AUD DSM-5+,41 OR ● Reports moderate- to high-risk drinking patterns* o more than 3 drinks on any single day and more than 7 drinks per week (women) o more than 4 drinks on any single day and more than 14 drinks per week (men) | |
[Table 1]
Setting
Study participants were recruited within the geographic catchment area of an integrated health system serving more than 700,000 patients each year in Wisconsin and the Upper Midwest. Individuals interested in the study used a QR code or a link from the study website to complete a secure, web-based screening survey. IP addresses and location verification were used to confirm that research participants lived within the health system’s catchment area.
Intervention and Support Models
Tula is designed to address a broad spectrum of issues related to alcohol misuse likely to be found in the general population. Unlike A-CHESS, which focuses on abstinence as part of recovery from severe AUD, Tula focuses on reducing alcohol consumption for individuals with mild to moderate AUD. Tula is built upon the Whole Health Model, which focuses on “what matters to me” as a whole person (and not just the disease- “what’s the matter with me?”).31,35 Tula places alcohol use within the context of integrative medicine, an adaptation made based on input from partnering healthcare stakeholders.31 Tools and services included in Tula are listed in Table 2.
Table 2
Feature | Description |
Thought of the Day | Daily inspirational quotes intended to motivate and engage participants. |
Whole Health | Information and tools to improve one’s whole health: What is Whole Health; Circle of Health; Self-Care; Mindful Awareness; Whole Health Resources; Personal Health Inventory |
Motivation | Users can record in words and photos their reasons for wanting to work on their drinking and wellness |
Tracker * | The Tracker allows users to set and review goals, track their progress, and record their health and wellness patterns related to their quality of life (e.g., mood, sleep, social support). |
Communication** | Users can send and receive private messages with other Tula members and can access in-app discussion forums with other members of their group. |
Information | A content and resource library organized around the Circle of Health’s eight domains of self-care: Working Your Body, Sleep & Recharge, Food & Drink, Personal Development, Relationships, Mood & Mindset, Surroundings, and the Power of the Mind. |
Relaxation | Relaxation techniques, audio recordings for guided meditation, and binaural beats. |
Strategies | Tips for reducing drinking, cognitive behavioral therapy, and goal setting. |
Gratitude | A daily prompt to reflect on gratitude. |
*Tracker feature accessed by all participants but is also monitored by the health coaches in the clinically integrated group as part of goal setting. |
**Communication features are limited in the self-monitored group. |
[Table 2]
Digital Medicine Support Models: Study Arms
The study was designed to examine the clinical integration of digital medicine, as described by Hermes et al.,39 where we compared a fully automated support model with two human-guided support models, differentiated by graduated levels of external support. While all participants had access to the basic content in Tula, the three study arms varied by the level of human touch (self-monitored “low touch,” peer-supported “medium touch,” and clinically integrated-“high touch”). See Fig. 2.
Self-monitored (SM) Group (low touch). Participants in this group used the app on their own, like most commercially available health apps. They had no access to a discussion forum or private messaging nor any connection to an external support system (i.e., peer support specialist or health coach). Participants could communicate with the study team via email, phone, or a messaging functioning built into Tula. However, communication was limited to questions about the study and receiving tech support.
Peer-supported (PS) Group (medium touch). Participants in this group had access to social support from others in the same study arm via Tula’s discussion forum and to certified peer specialists who worked for a community-based non-profit organization that has historically partnered with the health system to provide peer-mentoring services for patients with substance use disorders. Peer specialists are certified recovery coaches who have lived experience with substance use and/or mental health issues. They were not assigned to individual participants but were available to any participant who reached out via Tula’s private messaging feature. They also facilitated and moderated discussion forums and encouraged participants to utilize Tula’s resources.
Clinically Integrated (CI) Group (high touch). Participants in this group worked one-on-one with health coaches employed within the healthcare system and had access to a discussion forum with other participants in their study arm. Health coaches are trained and certified in behavior change theories, motivational strategies, and health education. They worked with participants 1:1 to set goals for a healthier lifestyle through reduced alcohol consumption. Health coaches engaged with participants via Tula’s private messaging feature. Participants were offered three 1:1 health coaching sessions (via phone).
Participant Recruitment, Retention and Timeline
We used a three-pronged recruitment strategy encompassing clinical settings, community-based organizations, and public media. We enlisted clinical study champions (primary care providers, behavioral health specialists, etc.) to provide information to potentially eligible patients. We also engaged local leaders from underrepresented communities to promote the study in ways that invite inclusion of diverse perspectives. Lastly, we used targeted digital, television, and print media to promote the study broadly.
All participants who completed the eligibility screening survey received a $10 digital gift code. Study incentives were built into the first 12 weekly check-in surveys (as part of the self-monitoring feature of Tula) and four quarterly follow-up surveys at months 3, 6, 9, and 12. The incentives were distributed monthly in the form of digital gift codes sent via the message feature in Tula. By the end of the 12-month study period, participants could earn up to US $250 in digital gift codes for data submission.
Eligible participants were given a 72-hour window to download the app, complete a baseline survey and schedule a phone call with research staff for enrollment. Recruitment lasted from March 2020 – September 2023 with the last participant completing their 12-month intervention in September 2024.
Patient Randomization
A sequence of randomized assignments for each group was generated by the project statistician using the block randomization procedure in the Power Analysis and Statistical Software package.40 Randomization was stratified on participant-reported biological sex (male/female) and alcohol use severity (mild/moderate) based on DSM-5 score (mild = 2–3 and moderate = 4–6),41 calculated from participants’ responses to the screening survey. The randomization list was masked and placed in a protected spreadsheet. The randomization process involved a phone call between a member of the study team and the participant. When the study arm assignment was unmasked, a study ID was entered into the next available placement in the randomization sequence. While still on the call, the study team member configured the participant’s permissions on the app based on their group assignment and reviewed a second informed consent specific to that study arm. Once randomized, participants engaged in a 3-month active intervention period with a 9-month follow-up period.
Blinding
Due to pragmatic and ethical considerations, the randomization assignment was not blind to either implementers or participants.
Outcomes
Demographics and Severity. Participants reported biological sex and their AUD severity (mild or moderate; those with severe AUD were excluded) in the screening survey, which were used as strata for randomization. Participants reported additional demographics, including age, race/ethnicity, education, and income in the baseline survey.
Primary Outcomes. The primary outcomes were RDD and QOL. RDD were measured using the 7-day timeline follow-back survey.42,43 Participants were asked how many days they had more than 4 standard drinks (for men under 65) or more than 3 standard drinks (for women and for men over 65 years) on any single day in the last 7 days. The definition of a standard drink provided in the survey is “One drink = one shot of hard liquor (1.5 oz.), a 12-ounce can or bottle of beer, or a 5-ounce glass of wine.” To allow for comparison with other studies using varying recall periods, we converted the number of RDD to the percentage of RDD (PRDD) by dividing the number of RDD by 7 (days).
Patient-reported QOL was measured using the four global physical health and four global mental health items in the Patient-Reported Outcome Measurement Information System (PROMIS) global health short form (SF10 ver.1.2).44 The raw scores for physical and mental health were converted to T-scores using the PROMIS scoring manual (p.16).44 T-scores are standardized scores that can be compared to the US general population that has the mean QOL score of 50 with a standard deviation of 10.
Participants received automated reminders to complete surveys in the app. Survey data were stored on a secure server at the University of Wisconsin.
Sample Size
The study was designed to detect differences in two primary outcomes (RDD and the QOL) among three study arms across a 12-month period. Sufficient power (1-β = .80, multiple comparison adjusted α = .00833, 2-tailed) to detect a conservative effect size of Cohen’s d = 0.25 with four repeated measurements and a first-order autoregressive covariance structure (correlation ρ = 0.2) required approximately 182 participants per study arm (546 total), assuming 28% attrition.32 An effect size of 0.25 equated to a difference of approximately 0.24 RDD per week, 2.18 points for QOL-Physical Health, and 2.03 points for QOL-Mental Health.44
Statistical Methods
Descriptive Analysis. We conducted descriptive analysis for demographic, utilization, and outcome variables across all three study arms. Group comparisons were conducted using chi-squared tests for categorical variables and t-tests for continuous variables. All statistical tests were two-tailed with α = 0.05 and conducted using SPSS 29.
Primary Analysis. We constructed a longitudinal model of the outcome measures at months 3, 6, 9, and 12 after randomization using the General Linear Mixed Model (GLMM) with a random intercept and an auto-regression covariance structure for repeated measures. The stratification variables and the baseline values of the outcomes were included as covariates, with a separate model for each primary outcome. The primary analysis is the group fixed effects and pairwise comparisons between study arms. Other significant fixed effects and pair-wise comparisons were reported. The Sidak method was used to adjust p-values in pairwise comparisons.45 Because the value of PRDD has a positive skewness, we used the square root transformed PRDD, consistent with prior analyses of A-CHESS.32 Participants who could not be reached for follow-up surveys were analyzed following the intention to treat principle. Missing outcomes data were considered missing at random. Other secondary analyses (mediation, economic, and qualitative)31 will be reported separately.
Ancillary Analyses. Sensitivity analyses are included to examine model assumptions regarding equivalence of groups at baseline and to account for differential attrition (see Technical Appendix).
Important Changes after Trial Commencement
Originally, in-person health coaching was available but due to COVID-19, coaching was only offered via telephone. Coaches had access to participant phone numbers but only knew participants by their usernames.
Ethics
This protocol was approved by the University of Wisconsin Health Sciences Minimal Risk Institutional Review Board and prospectively registered at clinicaltrials.gov (NCT04011644).