Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Mar 4.
Published in final edited form as: Drug Alcohol Depend. 2026 Jan 3;279:113022. doi: 10.1016/j.drugalcdep.2026.113022

Leveraging a digital health intervention to improve recovery outcomes among people with substance misuse experiencing housing insecurity

Vidya Eswaran a,*, Fanghong Dong b, Xiao Li b, Hannah S Szlyk b, Nathaniel A Dell b, Erin Kasson b, Jessica Williams b, Patricia A Cavazos-Rehg b
PMCID: PMC12955705  NIHMSID: NIHMS2146827  PMID: 41512653

Abstract

Background:

People with substance use disorders (PwSUD) who experience housing insecurity have disproportionate overdose deaths. Digital interventions may improve care among PwSUD, but evidence specifically for housing-insecure PwSUD, who face unique barriers, remains limited.

Methods:

We examined the efficacy of a mHealth tool (uMAT-R) among people who have misused substances and reported current housing insecurity. Substance use, cravings, access to basic needs, social disconnection, and digital literacy were assessed at baseline and 1 month and compared by uMAT-R use. Generalized estimating equations were employed to assess the efficacy of the intervention.

Results:

Participants who logged into uMAT-R were less likely to report other non-opioid illicit drug use (aOR (95 %CI): 0.49(0.29, 0.85)). Similarly, those who messaged the e-coach were less likely to report using opioids (aOR(95 %CI): 0.39(0.21, 0.73)) or other illicit drugs (aOR (95 % CI): 0.53(0.30, 0.92)) during past 30-days. Significant interaction effects were found in uMAT-R use by time in cravings (Coef (95 %CI): − 2.11(−4.07, − 0.17)) and perceived burdensomeness (Coef(95 %CI): − 2.62 (−4.91, − 0.36)). Messaging an e-coach by time was significantly associated with improved health literacy (Coef (95 %CI): 0.61(0.05, 1.18)) and decreased thwarted belongingness (Coef(95 %CI): − 0.36 (−0.71, − 0.02).

Conclusion:

Our preliminary findings suggest that people who have misused substances and experience housing insecurity may benefit from uMAT-R, which was associated with improved recovery outcomes. Future research is needed to examine the unique barriers experienced by this population and how mHealth tools can be used to provide tailored, equitable access to supportive resources to reduce barriers and promote long-term recovery.

Keywords: Housing insecurity, Substance Use, MHealth, Digital Intervention

1. Introduction

Despite progress in reducing overdose death rates in the United States (US), overdose continues to represent a public health emergency (USDHHS, 2025) and remains a leading cause of death in Missouri (Ahmad et al., 2022; MIMH, 2023). Recent data show that minoritized and low-resource groups, such as those facing housing insecurity, are disproportionately affected (NIH, 2023).

Housing insecurity refers to “the loss of, threat to, or uncertainty of a safe, stable, and affordable home environment” (DeLuca and Rosen, 2022). From 2022–2023, unsheltered homelessness in Missouri increased by 26 % (HUD, 2022, 2023) while emergency shelter beds increased by only 4 % (HUD, 2023). Funding and staffing cuts to the US Department of Housing and Urban Development are projected to negatively impact efforts to promote access to affordable housing and support people exiting homelessness (Bedayn, 2025; Fischer, 2025).

Housing insecurity is pervasive among people with a substance use disorder (PwSUD). Over 16 % of people experiencing homelessness (PEH) in the US also experience SUD (HUD, 2024), and 39 % of PwSUD in Missouri experience housing insecurity (HUD, 2023; Institute, 2024). SUDs are the most common mental health disorder among PEH (Barry et al., 2024; Lowry, 2024). PwSUD disproportionally experience health implications often associated with housing insecurity (i.e., exacerbated mental health symptoms, poor nutrition, exposure to infectious diseases) which contribute to elevated overdose and morbidity (Fazel et al., 2014; Tong et al., 2019). Policies limiting access to housing supports for those with substance use exacerbate these challenges. PwSUD experiencing housing insecurity report barriers to accessing SUD treatment (Assaf et al., 2025), such as cost, wait times and clinic locations (Upshur et al., 2018). Interventions that address these barriers may engage more patients with treatment.

Having a stable, secure place to live with access to basic resources is vital to supporting SUD recovery (Committee on National Statistics, 2016; Dell et al., 2021). Recovery housing like treatment facilities and sober living homes provide stable housing (often temporary) and treatment, improving recovery outcomes by reducing substance use (Austin et al., 2021; Komaromy et al., 2023; Pollack et al., 2021; Pro et al., 2022; Reif et al., 2014). However, the transition to independent housing can increase the risk of overdose, return to use, or homelessness (Morgan et al., 2020; Walley et al., 2020).

PwSUD who experience housing insecurity are more likely to have co-morbid psychiatric conditions than their housed counterparts (Chikwava et al., 2022; Krupski et al., 2015; Upshur et al., 2018) and have high risk of suicide (Nilsson et al., 2025). PwSUD have higher odds of contemplating and attempting suicide (Jones et al., 2023). PwSUD experiencing homelessness have an elevated risk of suicidal ideation (Lee et al., 2017). Perceived burdensomeness (PB), the belief that one is a liability on others (Van Orden et al., 2010), and thwarted belonginess (TB), the perception that one does not belong to positive and meaningful relationships (Silva et al., 2023), are both markers of social disconnection and are mediators between substance use and suicidal ideation (Marvin et al., 2025). PEH have high rates of PB (Baer et al., 2022; Chu et al., 2019; Semborski et al., 2022) and PB is associated with suicidal ideation (Baer et al., 2022) and attempt among people with SUD (Conner et al., 2007). Interventions have been shown to decrease social disconnection (Marchetti et al., 2023; Short et al., 2019; Law et al., 2023), though they are underexamined in PwSUD.

Health literacy (HL), “the ability to find, understand, and use information and services to inform health-related decisions and actions” (NIH, 2025), can improve self-rated health among PEH (Odoh et al., 2019). Individuals with high HL are less likely to have a SUD (Farrell et al., 2020), and among those in treatment for SUD, low HL is associated with lower quality of life, and higher mental unwellness (Degan et al., 2019). HL is an important determinant of mental health, especially for those with major depression (Guo et al., 2023). It is possible this is due, in some part, to the role that literacy plays in enabling individuals to participate in daily activities and apply new information (Wolf et al., 2005).

Given their elevated risk for adverse outcomes and barriers to care associated with homelessness, innovative strategies to support the recovery of PwSUD experiencing housing insecurity are needed. Digital interventions can be used to engage PEH due to lowered physical barriers to care (Heaslip et al., 2021; Polillo et al., 2021). Mobile apps have shown promising results in supporting SUD recovery (Boumparis and Schaub, 2022; Colombo et al., 2019; Mouchabac et al., 2021; Rhinesmith, 2024; Schueller et al., 2017; Waselewski et al., 2021) and in supporting PEH (Burrows et al., 2022). Few studies, however, have focused on digital interventions for PwSUD who are also experiencing housing insecurity (Glover et al., 2019; Lal et al., 2023; Schueller et al., 2019) despite their increased risk of continued substance use and overdose. We address this gap by studying the impact of a digital intervention to support people who have misused substances and are experiencing housing insecurity. We hypothesized that in this population, use of a digital intervention with integrated health coaching would lead to decreased substance use, cravings, and social disconnection while improving HL and perceptions of ability to meet basic needs. We chose to include an assessment of social disconnection due to the high prevalence of depression among PwSUD, prior data describing PB and TB as modifiable (Allan et al., 2018; Law et al., 2023), and the recommendation to address co-occurring major depression in this population (Iqbal et al., 2019). We similarly chose to include HL as it has been shown to be a modifiable factor with important implications on mental health and substance use related outcomes (Guo et al., 2023).

2. Methods

2.1. Study sample and procedures

This study is a secondary analysis of a digital intervention called “uMAT-R” (Cavazos-Rehg et al., 2020; Szlyk et al., 2025) (pronounced as “you matter”), a mobile Health application created to provide support for patients with reported misuse of substances (including opioids, stimulants, and hallucinogens). These substances were chosen due to their role in contributing to the overdose crisis (Missouri Department of Health and Senior Services, 2025; Ahmad et al., 2025). The app provides resources on SUD treatment options, educational content on preventing return to use, withdrawal management and mental health, as well as information on community resources. Participants can communicate with a trained coach (e-coach) to help with goal setting and managing cravings, triggers and treatment options. E-coaches receive training in motivational interviewing and crisis management.

This study includes data from a subset of uMAT-R participants who reported housing insecurity between January 2020 and August 2024. Housing insecurity was assessed by asking: “What is your current living arrangement?” Participants were classified as housing insecure if they reported living in one of three mutually exclusive settings: were homeless (e.g., shelter, street), living in a treatment facility or medical center, or someone else’s home or apartment. Participants were recruited from treatment facilities within Missouri, as well as recovery homes, justice settings, and emergency rooms (Appendix). Participants approached the research team to participate either after referral from providers or expressing interest after a presentation at their facility or from another participant (i.e., snowball procedure). The team followed up with each potential participant to determine if they met inclusion criteria: 1) a self-reported lifetime history of opioid, stimulant, or hallucinogen misuse (individuals with only alcohol or cannabis misuse were not eligible unless accompanied by misuse of one or more of these substances), 2) age 18 or older, 3) residing within the United States, 4) proficient in English, and 5) owning an iOS or Android smartphone. Misuse was defined as use in any way not directed by a doctor, consistent with the National Survey on Drug Use and Health (Han et al., 2024).

2.2. Data collection

Trained research staff reviewed the informed consent document with eligible participants and participants provided consent to participate the study. Once consent was obtained, all participants were invited to complete a baseline survey and were granted access to the uMAT-R app. One month later, participants were invited to complete a follow-up survey via email through the Research Electronic Data Capture (REDcap) system. All survey data were self-reported using REDCap, accessible on either a computer or a mobile device. The Washington University in St. Louis Institutional Review Board (IRB #201910161) reviewed and approved the protocol.

3. Measures

3.1. Primary independent variables

3.1.1. Intervention use

App usage was assessed using engagement metrics. The first binary (yes/no) variable captured whether participants had ever logged into the app, and the second measured whether participants sent at least one message to the e-coach. We defined high app use as having at least one conversation with the e-coach and engaging with multiple app features, including the resource directory and educational courses.

3.2. Dependent variables

3.2.1. Past 30-day substance use

Participants’ drug use was evaluated by asking: “When did you last use substances to get high?”. Responses were dichotomized: within the past 30 days vs. more than 30 days ago. Cannabis (i.e., marijuana), opioid, stimulant, and other (non-opioid) illicit drug (e.g., inhalants) use was assessed among all participants at baseline and 1-month follow-up. We chose this metric as the standard public-health definition of current use (NSDUH, 2024). We coded substance use over past-30-day as a binary variable (yes/no). As shown in Supplementary Table 1, several higher-frequency categories were sparse, creating modeling instability supporting this choice.

3.2.2. Substance use cravings

Craving of substances was measured using three items adapted from the Cocaine Craving Scale (Maeda et al., 2015) related to current craving, strength of desire to use, and likelihood to use in their usual environment (Supplementary Table 2). Each item was scored from 0 to 10, resulting in a total possible score ranging of 0–30. Higher scores indicated a stronger level of craving.

3.2.3. Unmet basic needs

The measure of unmet basic needs was adapted from the Wellbeing and Basic Needs Survey (Karpman et al., 2018). The nine-item measure assesses participants’ perceived confidence in accessing resources across various domains (Supplementary Table 2). Participants were asked to rate their confidence in meeting their basic needs on a Likert scale ranging from “very confident” (1) to “not confident at all” (8). To examine changes in scores continuous variables were created to capture the baseline and 1-month follow-up scores, respectively. Internal reliability was similar for the baseline and 1-month assessments (0.90 and 0.92, respectively).

Perceived burdensomeness (PB) and thwarted belongingness (TB) were assessed using the 15-item Interpersonal Needs Questionnaire (INQ), which measures self-perceived burden on others and feelings of social connectedness (Silva et al., 2023; Van Orden et al., 2012). Examples include “These days the people in my life would be better off if I were gone”, and “These days, I feel like I belong” (Supplementary Table 2). Participants rate each item based on their recent feelings using a 7-point Likert scale, ranging from “Not at all true for me (1) ” to “Very true for me (7)”. As suggested, six items were reverse-coded, and items within each domain were averaged so that higher scores reflected greater self-perceived burden or thwarted belonging (Van Orden et al., 2010). To assess the change in scores, continuous PB and TB scores were generated at baseline and at the 1-month follow-up.

3.2.4. Health literacy

HL was assessed using three screening questions (Parker et al., 1995), as noted in Supplementary Table 2. The third item was reverse coded, and the scores for all three items were summed, with higher scores indicating greater HL. To assess changes across study period, continuous variables were created to capture HL scores at both baseline and the 1-month follow-up.

4. Covariates

4.1. Socio-demographic characteristics

All data were self-reported by participants in the baseline survey and included age, gender, race, education completion, and employment status.

5. Statistical data analysis

To evaluate whether the missing data were Missing Completely at Random (MCAR), Little’s MCAR test was conducted. If test results indicated that the missingness was unlikely to be entirely random (i.e., p < 0.05), a retention analysis was conducted to examine differences between participants by 1-month follow-up survey completion status.

Descriptive statistics of socio-demographic characteristics were presented for the entire sample and stratified by whether a participant logged into the app and messaged the e-coach. Pearson’s chi-square tests were performed to compare socio-demographic characteristics by app login and e-coach messaging.

To assess changes in outcomes related to whether a participant logged into uMAT-R or messaged the e-coach, generalized estimating equations (GEE) models were applied to complete cases analysis, accounting for within subject correlations from repeated measures. All models included an interaction term between app usage (e.g., logging into the app, messaging the e-coach) and time (baseline vs. 1-month) (Liang., (1986), Hanley et al., 2003). An exchangeable working correlation structure was applied, along with distribution and link functions appropriate for each outcome type. For continuous outcome variables, the models were specified with a normal distribution and an identity link function to estimate the effect of app usage on scores. For categorical outcome variables, the models included terms with a logit link function and utilized a binomial distribution. We also tested three-way interactions (i.e., housing arrangements by app usage by time); however, none of these interactions reached statistical significance and were therefore excluded from the final models. Sensitivity analyses using inverse probability weighting (IPW) were conducted, with weights derived from logistic regression predicting follow-up completion based on baseline sociodemographic and substance use. The inverse of these probabilities was applied as weights in the GEE models to account for differential attrition.

The effect sizes of estimates, along with their 95 % confidence intervals (CIs), controlling for socio-demographic variables (e.g., age, gender, race, high school completion, employment status, insurance coverage, and housing arrangement), were reported. All analyses were performed using SAS Version 9.4. All statistical tests were two-sided, with the significance level (α) set at 0.05.

6. Results

In total, 972 individuals were recruited in the parent study between January 2020 and August 2024. Of these, 927 (95 %) completed the baseline survey. Among them, 671(72 %) self-identified as living in insecure housing and were included in the present study. All participants self-reported a history of opioid or stimulant misuse. This group included 102 (15.2 %) individuals living on streets or in shelters, 294 (43.8 %) living in treatment facilities or medical centers, and 275 (41.0 %) living in someone else’s home. At one month, 292 participants (43 %) were lost to follow-up, resulting in a final sample size of 378 participants. The majority (214, 56 %) were recruited from 2020 to 2022, during the COVID-19 public health emergency. No significant difference was found between housing arrangements and 1-month survey completion (Supplementary Table 3). The Little’s MCAR test indicated that loss to follow-up was unlikely to be entirely random with p < .001. Retention analysis suggested that those who were younger than 50 years old and males were more likely to be lost during follow-up (p = 0.02 and 0.03).

We explored housing insecurity across the three disparate housing insecure groups using items from the Basic Needs Scale (see Supplement for details, lower numbers on a scale of 1–8 indicate higher confidence in finding housing/shelter). At baseline, mean confidence was suboptimal across the three groups (p = 0.01). Those living in treatment facilities or medical centers had a mean score of 4.0 (SD 2.6), those living with someone had a mean score of 4.6 (SD 2.6) and those who were homeless had a mean score of 5.1 (SD 2.8).

6.1. Sample characteristics

Nearly half of the participants were over 50 years old (n = 180, 48.3 %, in Table 1); majority were female (n = 232, 62.4 %) and White (n = 277, 73.7 %). White participants were more likely to log into the app (p = 0.06) and send messages to the e-coach (p = 0.02) compared to Participants of Color. No significant differences in the number of messages sent were seen by housing arrangements (Fig. 1).

Table 1.

Socio-demographic characteristics by app usage among the study sample (N = 378).

 
  
 Logged into the app
  
 Messaged to e-coach
  
Overall
N (%)		 No (n = 61, 16.4 %) Yes (n = 317, 83.6 %) p value No (n = 117, 31.3 %) N (%) Yes (n = 261, 68.9 %) p value
Age
50 years old or below 193 (51.7) 32 (52.5) 161 (51.6) 0.91 68 (58.1) 125 (48.8) 0.09
Above 50 years old 180 (48.3) 29 (47.5) 151 (48.4) 49 (41.9) 131 (51.2)
Gendera
Male 142 (37.6) 20 (32.3) 122 (38.6) 0.59 46 (39.0) 96 (36.9) 0.65
Female 232 (62.4) 41 (66.7) 191 (61.4) 70 (61.0) 162 (63.1)
Race & Ethnicity
People of colorb 99 (26.3) 22 (36.1) 77 (24.4) 0.06 40 (34.2) 59 (22.8) 0.02
White 277 (73.7) 39 (63.9) 238 (75.6) 77 (65.8) 200 (77.2)
Education
Below high school 85 (22.6) 16 (25.8) 69 (21.9) 0.51 24 (20.3) 61 (23.6) 0.49
High school equivalent & above 292 (77.5) 46 (74.2) 246 (78.1) 94 (79.7) 198 (76.5)
Employment status
Unemployed 258 (68.8) 48 (77.4) 210 (67.1) 0.11 81 (69.8) 177 (68.3) 0.77
Employed (including part-time) 117 (31.2) 14 (22.6) 103 (32.9) 35 (30.2) 82 (31.7)
Insurance coverage
Uninsured 101 (26.7) 14 (22.6) 87 (27.4) 0.72 31 (26.3) 70 (26.8) 0.93
Medicaid 253 (66.8) 44 (71.0) 209 (65.9) 80 (67.8) 173 (66.3)
Private insurance 25 (6.6) 4 (6.5) 21 (6.6) 7 (5.9) 18 (6.9)
Housing arrangement
Homeless 51 (13.5) 12 (19.4) 39 (12.3) 0.09 22 (18.6) 29 (11.2) 0.15
Treatment facilities or medical centers 182 (47.9) 34 (53.2) 148 (46.8) 54 (44.9) 128 (49.2)
Living with someone else 146 (38.6) 17 (27.4) 129 (40.8) 43 (36.4) 106 (39.6)
Open in a new tab

Bold indicates statistically significant.

a

A total of four participants were identified as having a gender identity other than female or male, including two participants who identified as transgender (50 %), one participant who identified as genderqueer (25 %), and one participant who identified with other gender identity (25 %).

b

People of color (n = 99) includes 81 African Americans (81.8 %), 3 American Indians (3.3 %), 3 Hispanics (3.3 %), 1 Asian (1.1 %), and 11 other races (11.1 %).

Fig. 1.

Fig. 1.

Open in a new tab

Number of messages sent from study participants to the e-coach by housing type during the study period. *No significant difference was found across groups (p-value =0.47).

6.2. The efficacy of the use of uMAT-R

6.2.1. Housing arrangement

Table 2 presents baseline and 1-month assessments by housing arrangement. At baseline, participants staying in treatment facilities had significantly lower proportion of stimulant and other illicit drug use and lower cravings than other housing insecure individuals (adjusted p < 0.05; Supplementary Table 1). Homeless individuals had higher prevalence of opioid use (35.3 % vs. 16.2–19.9 % in other two groups, p < 0.01) and non-medical drug use (31.4 % vs. 8.8 – 25.3 %, p < 0.001). By 1 month, between-group differences in stimulant and other illicit drug use and social disconnection had largely diminished. HL was low across all groups and did not differ significantly by housing status at either time point.

Table 2.

Baseline and 1-month assessments by housing arrangement among individuals with substance misuse and housing insecurity (N = 378).

 
 Housing arrangement
  
  
  
  
Group 1: Living with someone (N = 146, 38.6 %) Group 2: Treatment facilities or medical centers (N = 181, 47.9 %) Group 3: Homeless (N = 51, 13.5 %) Groups 1 vs 2 Groups 1 vs 3 Groups 2 vs 3
N (%) p-value a Adjusted p-valueb
Current substance use, yes
Opioids
Baseline 29 (19.9) 29 (16.2) 18 (35.3) 0.01 0.39 0.03 0.003
1-month 21 (14.7) 17 (9.4) 12 (23.5) 0.03 0.15 0.15 0.007
Stimulants
Baseline 21 (15.4) 13 (7.7) 13 (26.5) 0.002 0.03 0.09 < .001
1-month 18 (13.0) 15 (8.7) 6 (12.0) 0.46
Other illicit drugs
Baseline 33 (22.8) 21 (11.9) 13 (25.5) 0.01 0.009 0.69 0.02
1-month 22 (15.5) 14 (8.1) 8 (16.0) 0.09
Non-medical drug use
Baseline 37 (25.3) 16 (8.8) 16 (31.4) < .001 < .001 0.41
1-month 29 (24.0) 10 (6.1) 6 (13.3) < .001 < .001 0.14 0.11
Craving to illicit substance, Mean (SD)
Baseline 12.4 (8.3) 9.7 (6.3) 13 (9) 0.001 0.004 0.99 0.02
1-month 9.1 (8.3) 6.9 (7.1) 8.6 (7.5) 0.04 0.04 0.99 0.55
Unmet basic needs
Baseline 35.3 (18) 30.9 (17.1) 36.5 (17.5) 0.04 0.08 0.99 0.15
1-month 31.5 (17.3) 26.4 (16.9) 30.6 (18) 0.04 0.05 0.99 0.49
Perceived Burdensomeness of INQ
Baseline 1.6 (1.9) 0.8 (1.4) 1.7 (1.8) < .001 < .001 0.99 0.003
1-month 1.1 (1.7) 0.6 (1.4) 1.6 (1.8) < .001 0.02 0.25 0.001
Thwarted Belongingness of INQ
Baseline 3.2 (1.8) 2.7 (1.7) 3.3 (1.7) < .001 0.04 0.99 0.06
1-month 3.2 (1.7) 2.6 (1.5) 3.6 (1.7) < .001 0.01 0.45 0.001
Health literacy
Baseline 9.9 (1.6) 10 (1.9) 9.3 (2.2) 0.13
1-month 10 (1.9) 9.8 (1.9) 9.9 (2.1) 0.64
Open in a new tab

Bold indicates statistically significant.

a

The p values were calculated using Cochran-Mantel-Haenszel tests or ANOVA, depending on the type of variables.

b

The adjusted p values were calculated using Bonferroni method.

Table 3 indicates that app login and e-coach messaging were associated with reductions in past 30-day use of opioids and other illicit drugs. Participants who logged into the app had lower odds of reporting non-opioid illicit drugs use (e.g., inhalants; aOR (95 % CI): 0.49 (0.22, 0.81), p = 0.01). Participants who messaged the e-coach had lower odds of reporting opioid use (aOR (95 % CI): 0.51 (0.30, 0.89), p = 0.02). Findings that did not meet statistical significance included a lower likelihood of opioid use among those who logged into the app (aOR (95 % CI): 0.55 (0.29, 1.03), p = 0.06) and a lower likelihood of stimulant use among those who messaged the e-coach (aOR (95 % CI): 0.57 (0.29, 1.10), p = 0.09).

Table 3.

Change in the past 30-day substance use status from baseline to 1-month follow-up among individuals with substance misuse and housing insecurity (N = 378).

Past 30-day Substance Use
Opioids1 (Ref: No use)
 Stimulants2 (Ref: No use)
 Other illicit drugs3 (Ref: No use)
 Non-medical drug use (Ref: Over past 30-day)
aOR (95 % CI)4, P-value
App use status
Logged into the app
No (N = 62, 16.4 %) Ref Ref Ref Ref
Yes (N = 317, 83.6 %) 0.55 (0.29, 1.03) 0.06 0.75 (0.33, 1.70) 0.49 0.42 (0.22, 0.81) 0.01 0.99 (0.47, 2.06) 0.97
Time (Ref: Baseline) 0.70 (0.37, 1.35) 0.29 1.14 (0.57, 2.28) 0.7 0.90 (0.49, 1.63) 0.72 1.05 (0.60, 1.81) 0.88
Logged into the app by Time 0.78 (0.37, 1.64) 0.51 0.64 (0.28, 1.44) 0.28 0.58 (0.28, 1.21) 0.15 0.62 (0.32, 1.22) 0.17
Messaged to e-coach
No (N = 118, 31.3 %) Ref Ref Ref Ref
Yes (N = 261, 68.9 %) 0.51 (0.30, 0.89) 0.02 0.57 (0.29, 1.10) 0.09 0.59 (0.33, 1.04) 0.07 0.64 (0.36, 1.14) 0.13
Time 0.67 (0.41, 1.08) 0.1 0.81 (0.47, 1.4) 0.46 0.87 (0.54, 1.43) 0.59 0.81 (0.51, 1.30) 0.39
Messaged to e-coach by Time 0.78 (0.41, 1.47) 0.45 0.98 (0.47, 2.04) 0.95 0.52 (0.26, 1.04) 0.07 0.81 (0.42, 1.55) 0.52
Open in a new tab

Bold indicates statistically significant.

aOR, adjusted Odds Ratio; CI, Confidence Interval; Ref, Reference.

1

The assessment of opioids includes heroin, methadone, morphine, OxyContin, codeine, oxycodone, fentanyl, hydrocodone, Dilaudid, and other opioids.

2

The assessment of stimulants includes cocaine (injected and snorted, mixed included), crack, rock, and amphetamines.

3

The assessment of other illicit drugs includes benzodiazepines, hallucinogens, and other illicit drugs.

4

aORs (95 % CIs) were obtained by controlling socio-demographic factors, including age, gender, race, high school completion, current employment status, and housing type.

High levels of app use were associated with greater reductions in illicit drug use (aOR = 0.48, 95 % CI: 0.22–0.92) and a significant interaction effect was observed for reduced opioid use over time (high app use × time: aOR = 0.35, 95 % CI: 0.15–0.82) (Supplementary Table 4).

Table 4 shows the effects of logging into the app and messaging the e-coach on substance use cravings, unmet basic needs, and HL. Between baseline to follow-up, logging into the app was associated with reductions in both unmet basic needs (Coef (95 % CI): −5.40 (−10.64, −0.16), p = 0.04). A significant interaction between logging in and time was found for PB, suggesting that app use may decrease PB from baseline to follow-up (Coef (95% CI): −2.62 (−4.91, −0.36), p = 0.02). Significant interactions between messaging the e-coach by time were observed for TB and HL, suggesting that messaging may lead to decreased TB and improved HL from baseline to follow-up (Coef (95 % CI): −0.36 (−0.71, −0.02), p = 0.04 and 0.72 (0.15, 1.29), p = 0.01, respectively). Sensitivity analysis found estimates from the weighted GEE models were consistent in both direction and magnitude with those from the primary analyses (in Supplementary Table 5 and 6).

Table 4.

Past 30-day change in behavioral health outcome from baseline to 1-month follow-up among individuals with substance misuse and housing insecurity (N = 378).

P30D change from baseline to 1-month follow-up
Craving to Illicit Substance Unmet Basic Needs Health Literacy Perceived Burdensomeness of INQ Thwarted Belongingness of INQ
adjusted Coef (95 % CI)1, P-value
App use status
Logged into the app
No (N = 62, 16.4 %) Ref Ref Ref Ref Ref
Yes (N = 317, 83.6 %) 0.30 (−1.43, 2.02) 0.74 1.55 (−2.87, 5.96) 0.49 −0.04 (−0.64, 0.56) 0.89 1.20 (−1.41, 3.82) 0.37 −0.36 (−0.83, 0.11) 0.13
Time (Ref: Baseline) −1.52 (−3.34, 0.30) 0.10 −0.87 (−5.52, 3.78) 0.71 −0.39 (−1.14, 0.36) 0.31 0.50 (−1.52, 2.52) 0.63 −0.02 (−0.31, 0.28) 0.92
Logged into the app by Time −2.11 (−4.07, −0.17) 0.04 −4.52 (−9.60, 0.56) 0.08 0.56 (−0.23, 1.35) 0.16 −2.62 (−4.91, −0.36) 0.02 0.04 (−0.34, 0.45) 0.98
Messaged to e-coach
No (N = 118, 31.3 %) Ref Ref Ref Ref Ref
Yes (N = 261, 68.9 %) −0.19 (1.71, 1.34) 0.81 1.19 (−2.47, 4.87) 0.52 −0.10 (−0.55, 0.35) 0.65 −0.06 (1.23, −2.47) 0.96 0.03 (−0.37, 0.43) 0.88
Time −2.40 (−3.84, −0.96) 0.001 −3.18 (−6.89, 0.53) 0.09 −0.34 (−0.85, 0.15) 0.17 −0.77 (−2.71, 1.17) 0.44 0.24 (−0.06, 0.54) 0.12
Messaged to e-coach by Time −1.25 (−2.92, 0.42) 0.14 −2.09 (−6.40, 2.23) 0.34 0.61 (−0.05, 1.18) 0.03 −1.30 (−3.53, 0.92) 0.25 −0.36 (−0.71, −0.02) 0.04
Open in a new tab

Bold indicates statistically significant.

INQ, Interpersonal Needs Questionnaire, Coef, Coefficient; CI, Confidence Interval; Ref, Reference.

1

aORs (95 % CIs) were obtained by controlling socio-demographic factors, including age, gender, race, high school completion, and current employment status.

7. Discussion

Most uMAT-R participants, 70 %, were eligible for the study based on experiencing housing insecurity. There were no significant demographic differences in those who did and did not log into uMAT-R, and only race and ethnicity were associated with messaging the e-coach. These data differ from studies on the general population of digital intervention participants (Bol et al., 2018) as well as prior uMAT-R data (Filiatreau et al., 2025), where younger individuals, females and emp were more likely to log in and use the application. This suggests that factors beyond typical demographic characteristics related to digital literacy impact app login and e-coach messaging among PwSUD experiencing housing insecurity.

Across the study period, participants reported lower past 30-day substance use. Those who logged into the app had lower odds of opioid use, although this association was not statistically significant. App login was associated with lower likelihood of other non-opioid illicit substances, however. Although uMAT-R engagement happened only during the brief study period, it was associated with lower substance use; further research is needed to confirm these associations. From baseline to follow-up, app login and e-coach messaging were associated with reductions in cravings to use substances. Equipping individuals with housing insecurity with digital tools, resources and a trained e-coach may ease the burdens associated with other sources of support which may be less accessible due to location, costs or timing.

A significant interaction between messaging the e-coach by time was observed for HL. This finding is promising, as increased HL has been associated with improved health outcomes among people undergoing addiction treatment (Rolova et al., 2021). Among unhoused individuals, low HL has been associated with poor self-rating of health (Odoh et al., 2019). The uMAT-R application’s inclusion of e-coaching may be a way to improve HL and thereby clients’ health.

The Recovery Capital Model suggests that access to financial, social, and community capital resources supports successful recovery (Hennessy, 2017). Decreases in these resources during the transition to independent housing can make it harder to meet recovery goals. uMAT-R may be a useful tool to enhance recovery capital among PwSUD during this transition by providing motivational coaching and access to community-specific resources (Acevedo et al., 2018; Doran et al., 2022).

8. Limitations

This study has several limitations. Restricting participation to individuals with access to a smartphone may potentially limit generalizability to the broader unhoused population. However, data suggest that most unhoused individuals do have access to a mobile phone, reducing the scope of this concern (Rhoades et al., 2019). Over 70 % of participants identified as White. Given systemic racism and resultant inequities in substance use treatment access and disparate levels of social risk, further work to recruit individuals of diverse backgrounds is necessary.

Our attrition rate was high, 43 %. Although sensitivity analyses yielded consistent results, younger people and males were more likely to be lost to follow up, potentially limiting generalizability. Selection bias may exist, as individuals more motivated or comfortable with mHealth may have been more likely to participate.

Our sample comprised people in recovery, with low current use, resulting in few events. Future work should collect finer-grained measures (e.g., days of use or continuous frequency) to improve precision. Housing insecurity included a heterogenous group residing in treatment facilities or medical centers, reflecting the practices of our community partners. Because these groups frequently overlapped, we could not separate them for more detailed analyses. Additionally, the survey did not capture whether individuals residing in these facilities had permanent housing available upon discharge, which may have led to some misclassification of housing insecurity among those receiving short-term residential care. We have analyzed results excluding those coming from a clinical treatment setting which also provides inpatient and residential care (n = 32; in Supplementary Table 7 & 8). The results stayed consistent with the original findings. Further work to understand this subpopulation can clarify their unique needs.

App data are provided by the hosting platform, limiting our ability to assess usage frequency, duration, or temporality with substance use. Messaging with e-coaches was emphasized as a core feature, but richer data are needed to clarify engagement patterns. Further we are unable to capture temporality between substance use and actual log in or interface with the app. Future analyses could query participants’ use in relation to app engagement. Noteworthy, most participants reported entering their current recovery within the past six months (Supplementary Table 9).

The second HL item queries how often respondents have someone help read hospital materials, which may capture lack of support or reluctance to seek help rather than literacy. This is mitigated, however, by the other questions in the validated scale.

Other limitations include the impacts of the COVID-19 pandemic, the likelihood that those in inpatient facilities had reduced substance access, the possibility of residual confounding in this non-randomized study, and the inability to infer causality given the observational design. Differences in baseline motivation, recovery stage, or substance use severity between app users and non-users may explain the observed associations. App engagement may reflect individual or contextual factors rather than directly influencing outcomes. Future research should integrate qualitative and contextual data to better understand barriers to sustained app use.

9. Conclusions

Our findings suggest uMAT-R may be a promising intervention among PwSUD experiencing housing insecurity to build and sustain recovery capital for long-term recovery success. Further research is needed to examine the unique barriers this population faces in addressing substance use and how a digital tool could best address them. As more people experience housing insecurity, and overdose continues as a major health risk, there is a need for greater research on the utility of digital interventions in addressing SUDs and comorbid issues.

Supplementary Material

Supplementary material

Financial disclosure

Funding for this study was provided by the Substance Abuse and Mental Health Services Administration (1H79TIO80271), the National Institute on Drug Abuse (R34DA050453, R44DA055161) and the National Institute of Nursing Research (T90NR021683).

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.drugalcdep.2026.113022.

Footnotes

CRediT authorship contribution statement

Dell Nathaniel A: Writing – review & editing. Erin Kasson: Writing – review & editing, Writing – original draft. Jessica Williams: Writing – review & editing. Cavazos Patricia A: Writing – original draft, Supervision, Conceptualization. Vidya Eswaran: Writing – review & editing, Writing – original draft. Fanghong Dong: Writing – review & editing, Writing – original draft, Formal analysis. Xiao Li: Writing – review & editing, Writing – original draft, Formal analysis, Data curation. Szlyk Hannah S: Writing – review & editing.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Patricia Cavazos-Rehg is a consultant for Rissana LLC, Woebot, and PredictView. Dr. Hannah S. Szlyk is a paid consultant for Google Health.

References

  1. Acevedo A, Panas L, Garnick D, Acevedo-Garcia D, Miles J, Ritter G, Campbell K, 2018. Disparities in the treatment of substance use disorders: does where you live matter? J. Behav. Health Serv. Res 45 (4), 533–549. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ahmad F, Cisewski J, Rossen L, Sutton P, 2022. Provisional drug overdose death counts. National Center for Health Statistics 2022. https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm (accessed December 14, 2022).
  3. Allan NP, Boffa JW, Raines AM, Schmidt NB, 2018. Intervention related reductions in perceived burdensomeness mediates incidence of suicidal thoughts. J. Affect. Disord 234, 282–288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Assaf R,D, Morris MD, Straus ER, Martinez P, Philbin MM, Kushel M, 2025. Illicit substance use and treatment access among adults experiencing homelessness. J. Am. Med. Assoc 333 (14), 1222–1231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Austin AE, Shiue KY, Naumann RB, Figgatt MC, Gest C, Shanahan ME, 2021. Associations of housing stress with later substance use outcomes: a systematic review. Addict. Behav 123, 107076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Baer MM, Tull MT, Gratz KL, 2022. Substance use frequency relates to suicidal ideation through perceived burdensomeness and to suicide attempts through capability for suicide. Arch. Suicide Res 26 (3), 1520–1540. [DOI] [PubMed] [Google Scholar]
  7. Barry R, Anderson J, Tran L, Bahji A, Dimitropoulos G, Ghosh SM, Kirkham J, Messier G, Patten SB, Rittenbach K, 2024. Prevalence of mental health disorders among individuals experiencing homelessness: a systematic review and meta-analysis. JAMA Psychiatry. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bedayn J, 2025. Trump Adm. throws Hundreds Afford. Hous. Proj. into limbo Contract cuts 〈https://apnews.com/article/affordable-housing-trump-doge-hud-funding-af0cadf5238f1654d723350cc2e8e0f7〉. [Google Scholar]
  9. Bol N, Helberger N, Weert JC, 2018. Differences in mobile health app use: a source of new digital inequalities? Inf. Soc 34 (3), 183–193. [Google Scholar]
  10. Boumparis N, Schaub MP, 2022. Recent advances in digital health interventions for substance use disorders. Curr. Opin. Psychiatry 35 (4). 〈https://journals.lww.com/co-psychiatry/fulltext/2022/07000/recent_advances_in_digital_health_interventions.4.aspx〉. [DOI] [PubMed] [Google Scholar]
  11. Burrows R, Mendoza A, Pedell S, Sterling L, Miller T, Lopez-Lorca A, 2022. Technology for societal change: evaluating a mobile app addressing the emotional needs of people experiencing homelessness. Health Inform. J 28 (4), 14604582221146720. [DOI] [PubMed] [Google Scholar]
  12. Cavazos-Rehg PA, Krauss MJ, Costello SJ, Ramsey AT, Petkas D, Gunderson S, Bierut L, Marsch L, 2020. Delivering information about medication assisted treatment to individuals who misuse opioids through a mobile app: a pilot study. J Public Health (Oxf) 42, 149–154. 10.1093/pubmed/fdy207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chikwava F, O’Donnell M, Ferrante A, Pakpahan E, Cordier R, 2022. Patterns of homelessness and housing instability and the relationship with mental health disorders among young people transitioning from out-of-home care: retrospective cohort study using linked administrative data. PLoS One 17 (9), e0274196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Chu C, Hom MA, Hirsch JK, Joiner TE, 2019. Thwarted belongingness and perceived burdensomeness explain the relationship between sleep problems and suicide risk among adults identifying as sexual and/or gender minorities. Psychol. Sex. Orientat. Gend. Divers 6 (1), 22. [Google Scholar]
  15. Colombo D, Fernández-Álvarez J, Patané A, Semonella M, Kwiatkowska M, García-Palacios A, Cipresso P, Riva G, Botella C, 2019. Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review. J. Clin. Med 8 (4). [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Committee on National Statistics, 2016. Board on behavioral, cognitive, and sensory sciences; Division of behavioral and social sciences and education; board on health sciences policy; health and medicine division; national academies of sciences, engineering, and medicine. Measuring Recovery from Substance Use or Mental Disorders: Workshop Summary. National Academies Press (US), Washington (DC). Sep 19. 3, SAMHSA’s Definition of Recovery. Available from: 〈https://www.ncbi.nlm.nih.gov/books/NBK390393/〉. [PubMed] [Google Scholar]
  17. Conner KR, Britton PC, Sworts LM, Joiner TE, 2007. Suicide attempts among individuals with opiate dependence: The critical role of belonging. Addict. Behav 32 (7), 1395–1404. [DOI] [PubMed] [Google Scholar]
  18. Degan TJ, Kelly PJ, Robinson LD, Deane FP, Wolstencroft K, Turut S, Meldrum R, 2019. Health literacy in people living with mental illness: a latent profile analysis. Psychiatry Res. 280, 112499. [DOI] [PubMed] [Google Scholar]
  19. Dell NA, Long C, Mancini MA, 2021. Models of mental health recovery: an overview of systematic reviews and qualitative meta-syntheses. Psychiatr. Rehabil. J 44 (3), 238. [DOI] [PubMed] [Google Scholar]
  20. DeLuca S, Rosen E, 2022. Housing insecurity among the poor today. Annu. Rev. Sociol 48 (1), 343–371. [Google Scholar]
  21. Doran KM, Fockele CE, Maguire M, 2022. Overdose and homelessness—why we need to talk about housing. JAMA Netw. Open 5 (1), e2142685. [DOI] [PubMed] [Google Scholar]
  22. Farrell SJ, Dunn M, Huff J, Team PO, Group ROHC, 2020. Examining health literacy levels in homeless persons and vulnerably housed persons with mental health disorders. Community Ment. Health J 56 (4), 645–651. [DOI] [PubMed] [Google Scholar]
  23. Fazel S, Geddes JR, Kushel M, 2014. The health of homeless people in high-income countries: descriptive epidemiology, health consequences, and clinical and policy recommendations. Lancet 384 (9953), 1529–1540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Filiatreau L, Li X, Szlyk H, Kasson E, Zhang Z, Ramsey A, Cavazos-Rehg P, 2025. Sociodemographic differences in logins and engagement with the electronic health coach messaging feature of a mobile application to support opioid and stimulant use recovery. JMIR mHealth uHealth. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Fischer W, 2025. DOGE-driven HUD cuts will make it harder for people to afford housing, exit homelessness. Cent. Budg. Policy Priorities 〈https://www.cbpp.org/blog/doge-driven-hud-cuts-will-make-it-harder-for-people-to-afford-housing-exit-homelessness〉. [Google Scholar]
  26. Glover AC, Schueller SM, Winiarski DA, Smith DL, Karnik NS, Zalta AK, 2019. Automated mobile phone–based mental health resource for homeless youth: pilot study assessing feasibility and acceptability. JMIR Ment. Health 6 (10), e15144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Guo C, Cui Y, Xia Z, Hu J, Xue Y, Huang X, Wan Y, Fang J, Zhang S, 2023. Association between health literacy, depressive symptoms, and suicide-related outcomes in adolescents: a longitudinal study. J. Affect. Disord 327, 15–22. [DOI] [PubMed] [Google Scholar]
  28. Han B, Jones C, Einstein E, Dowell D, Compton W, 2024. Prescription opioid use disorder among adults reporting prescription opioid use with or without misuse in the United States. J. Clin. Psychiatry 85 (3), 24m15258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hanley JA, Negassa A, Edwards MD, Forrester JE, 2003. Statistical analysis of correlated data using generalized estimating equations: an orientation, 15 February Am. J. Epidemiol 157 (4), 364–375. 10.1093/aje/kwf215. [DOI] [PubMed] [Google Scholar]
  30. Heaslip V, Richer S, Simkhada B, Dogan H, Green S, 2021. Use of technology to promote health and wellbeing of people who are homeless: a systematic review. Int. J. Environ. Res. Public Health 18 (13), 6845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hennessy EA, 2017. Recovery capital: a systematic review of the literature. Addict. Res. Theory 25 (5), 349–360. 10.1080/16066359.2017.1297990. [DOI] [Google Scholar]
  32. HUD, 2022. HUD 2022 Contin. Care Homeless Assist. Prog. Hous. Inventory Count Rep 〈https://files.hudexchange.info/reports/published/CoC_HIC_State_MO_2022.pdf〉. [Google Scholar]
  33. HUD, 2023. HUD 2023 Contin. Care Homeless Assist. Prog. Hous. Inventory Count Rep 〈https://files.hudexchange.info/reports/published/CoC_HIC_State_MO_2023.pdf〉. [Google Scholar]
  34. HUD, 2024. HUD 2023 Continuum of Care Homeless Assistance Programs Homeless Populations and Subpopulations. US Department of Housing and Urban Development. 〈https://files.hudexchange.info/reports/published/CoC_PopSub_NatlTerrDC_2023.pdf〉. [Google Scholar]
  35. Institute, C. 2024. Homelessness in Missouri: An Evolving Crisis. Retrieved September 13 from 〈https://ciceroinstitute.org/wp-content/uploads/2024/03/MO-Homelessness-Landscape-2023-3-13-2024.pdf〉
  36. Iqbal MN, Levin CJ, Levin FR, 2019. Treatment for substance use disorder with co-ocurring mental illness. Focus 17 (2). [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Jones AA, Hard G, Gray J, Apsley HB, Santos-Lozada AR, 2023. The role of substance use disorders on suicidal ideation, planning, and attempts: a nationally representative study of adolescents and adults in the United States, 2020. Subst. Abus. Res. Treat 17, 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Karpman M, Zuckerman S, Gonzalez D, 2018. The Well-Being and. Basic Needs Survey. Urban Institute, Washington, DC. [Google Scholar]
  39. Komaromy M, Stone A, Peterson A, Gott J, Koenig R, Taylor JL, 2023. Facilitating exit from encampments: combining low-barrier transitional housing with stabilizing treatment for substance related problems. Addict. Sci. Clin. Pract 18 (1), 66. 10.1186/s13722-023-00420-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Krupski A, Graves MC, Bumgardner K, Roy-Byrne P, 2015. Comparison of homeless and non-homeless problem drug users recruited from primary care safety-net clinics. J. Subst. Abus. Treat 58, 84–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lal S, Elias S, Sieu V, Peredo R, 2023. The use of technology to provide mental health services to youth experiencing homelessness: scoping review. J. Med. Internet Res 25, e41939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Law YW, Lok RHT, Chiang B, Lai CCS, Tsui SHM, Chung PYJ, Leung SC, 2023. Effects of community-based caring contact in reducing thwarted belongingness among postdischarge young adults with self-harm: randomized controlled trial. JMIR Form. Res 7, e43526. 10.2196/43526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lee KH, Jun JS, Kim YJ, Roh S, Moon SS, Bukonda N, Hines L, 2017. Mental health, substance abuse, and suicide among homeless adults. J. Evid. Inf. Soc. Work 14 (4), 229–242. [DOI] [PubMed] [Google Scholar]
  44. Lowry F, 2024. Most Homeless People Have Ment. Health Disord. 〈https://www.mdedge.com/psychiatry/article/269014/schizophrenia-other-psychotic-disorders/most-homeless-people-have-mental?ecd=WNLPD_PSYCH_241112_mdedge&uac=352350AT&sso=true〉 Retrieved November 6 from.
  45. Maeda A, Bateman BT, Clancy CR, Creanga AA, Leffert LR, 2015. Opioid abuse and dependence during pregnancy: temporal trends and obstetrical outcomes. Obstet. Anesth. Dig 35 (4), 191–192. [DOI] [PubMed] [Google Scholar]
  46. Marchetti I, Alloy LB, Koster EHW, 2023. Breaking the vise of hopelessness: targeting its components, antecedents, and context. Int J. Cogn. Ther 16 (3), 285–319. 10.1007/s41811-023-00165-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Marvin C, LeDuc M, Mitchell S, Mills D, 2025. The indirect association between alcohol use and suicide ideation through perceived burdensomeness and thwarted belonging in a high-risk USA sample. Crisis J. Crisis Interv. Suicide Prev 46 (6), 345–348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. MIMH. 2023. Missouri Statewide Overdose Mortality Information. M. D. o. M. Health. 〈https://mimhaddisci.org/missouri-statewide-reports#:~:text=In%20Missouri%20in%202022%2C%202%2C178,the%20Kansas%20City%20Metro%20Area〉. [Google Scholar]
  49. Missouri Department of Health & Senior Services, 2025. Drug Overdose Dashboard - Fatal Overdoses. Retrieved October 20, 2025 from. 〈https://health.mo.gov/data/opioids/index.php〉.
  50. Morgan JR, Wang J, Barocas JA, Jaeger JL, Durham NN, Babakhanlou-Chase H, Bharel M, Walley AY, Linas BP, 2020. Opioid overdose and inpatient care for substance use disorder care in Massachusetts. J. Subst. Abus. Treat 112, 42–48. 10.1016/j.jsat.2020.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Mouchabac S, Maatoug R, Conejero I, Adrien V, Bonnot O, Millet B, Ferreri F, Bourla A, 2021. In search of digital dopamine: how apps can motivate depressed patients, a review and conceptual analysis. Brain Sci. 11 (11). [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. NIH, 2023. Drug overdose death rates. National Institutes of Health; Retrieved March 8 from. 〈https://nida.nih.gov/research-topics/trends-statistics/overdose-death-rates#:~:text=Opioid%2Dinvolved%20overdose%20deaths%20rose,with%2080%2C411%20reported%20overdose%20deaths〉. [Google Scholar]
  53. NIH, 2025. Health Literacy. National Institutes of Health; Retrieved August 6 from. 〈https://www.nih.gov/institutes-nih/nih-office-director/office-communications-public-liaison/clear-communication/health-literacy〉. [Google Scholar]
  54. Nilsson SF, Laursen TM, Erlangesen A, Hawton K, Nordentoft M, Fazel S, 2025. Homelessness, psychiatric disorders, and risks of suicide and self-harm: a population-based cohort study. Lancet Public Health 10 (7), e559–e567. [DOI] [PubMed] [Google Scholar]
  55. NSDUH, 2024. 2024 National Survey on Drug Use and Health Public Use File Codebook. Substance Abuse and Mental Health Services Administration. 〈https://www.samhsa.gov/data/data-we-collect/nsduh-national-survey-drug-use-and-health/methodology〉. [Google Scholar]
  56. Odoh C, Vidrine JI, Businelle MS, Kendzor DE, Agrawal P, Reitzel LR, 2019. Health literacy and self-rated health among homeless adults. Health Behav. Res 2 (4), 13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Parker RM, Baker DW, Williams MV, Nurss JR, 1995. The test of functional health literacy in adults: a new instrument for measuring patients’ literacy skills. J. Gen. Intern. Med 10, 537–541. [DOI] [PubMed] [Google Scholar]
  58. Polillo A, Gran-Ruaz S, Sylvestre J, Kerman N, 2021. The use of eHealth interventions among persons experiencing homelessness: a systematic review. Digit. Health 7, 2055207620987066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Pollack CE, Saloner B, Lucas S, Warren E, Chaubal M, Kennedy-Hendricks A, 2021. Leveraging the affordable housing sector to respond to the opioid crisis. Public Health Rep. ® 137 (3), 414–419. 10.1177/00333549211007591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Pro G, Liebert M, Remiker M, Sabo S, Montgomery BEE, Zaller N, 2022. Homeless opioid treatment clients transitioning to dependent and independent housing: differential outcomes by race/ethnicity. Subst. Use Misuse 57 (6), 867–875. 10.1080/10826084.2022.2046097. [DOI] [PubMed] [Google Scholar]
  61. Reif S, George P, Braude L, Dougherty RH, Daniels AS, Ghose SS, Delphin-Rittmon ME, 2014. Recovery housing: assessing the evidence. Psychiatr. Serv 65 (3), 295–300. 10.1176/appi.ps.201300243. [DOI] [PubMed] [Google Scholar]
  62. Rhinesmith C, 2024. “It’s one of the most important things we carry for us”: how mobile hotspots support people experiencing homelessness. Mob. Media Commun 12 (2), 274–292. [Google Scholar]
  63. Rhoades H, Wenzel S, Winetrobe H, Ramirez M, Wu S, Carranza A, Dent D, Caraballo Jones M, 2019. A text messaging-based intervention to increase physical activity among persons living in permanent supportive housing: feasibility and acceptability findings from a pilot study. Digit. Health 5, 2055207619832438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Rolova G, Gavurova B, Petruzelka B, 2021. Health literacy, self-perceived health, and substance use behavior among young people with alcohol and substance use disorders. Int. J. Environ. Res. Public Health 18 (8), 4337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Schueller SM, Aguilera A, Mohr DC, 2017. Ecological momentary interventions for depression and anxiety. Depress Anxiety 34 (6), 540–545. 10.1002/da.22649. [DOI] [PubMed] [Google Scholar]
  66. Schueller SM, Glover AC, Rufa AK, Dowdle CL, Gross GD, Karnik NS, Zalta AK, 2019. A mobile phone–based intervention to improve mental health among homeless young adults: pilot feasibility trial. JMIR mHealth uHealth 7 (7), e12347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Semborski S, Srivastava A, Rhoades H, Fulginiti A, Goldbach JT, 2022. Burden, belonging, and homelessness: disclosure and social network differences among LGBTQ youth recruited from a suicide crisis service provider. J. Homosex 69 (5), 894–910. [DOI] [PubMed] [Google Scholar]
  68. Short NA, Stentz L, Raines AM, Boffa JW, Schmidt NB, 2019. Intervening on thwarted belonginess and perceived burdensomeness to reduce suicidality among veterans: subanalyses from a randomized controlled trial. Behav. Ther 50 (5), 886–897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Silva C, Smith PN, Rogers M, Joiner TE, Foote B, Van Orden KA, 2023. Clinically significant scores for thwarted belonging and perceived burden from the Interpersonal Needs Questionnaire (INQ-15). Crisis. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Szlyk HS, Dell NA, Li X, Winograd RP, Cavazos-Rehg P, 2025. Psychosocial outcomes associated with mobile health app use among medicaid recipients who use substances. J. Soc. Work Pract. Addict 25 (3), 269–287. 10.1080/1533256X.2025.2471330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Tong M, Tieu L, Lee C, Ponath C, Guzman D, Kushel M, 2019. Factors associated with food insecurity among older homeless adults: results from the HOPE HOME study. J. Public Health 41 (2), 240–249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Upshur CC, Jenkins D, Weinreb L, Gelberg L, Orvek EA, 2018. Homeless women’s service use, barriers, and motivation for participating in substance use treatment. Am. J. Drug Alcohol Abus 44 (2), 252–262. 10.1080/00952990.2017.1357183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. USDHHS, 2025. Secretary kennedy renews public health emergency declaration to address national opioid crisis. 〈https://www.hhs.gov/press-room/secretary-kennedy-opiod-crisis-emergency-declaration.html〉. [Google Scholar]
  74. Van Orden KA, Cukrowicz KC, Witte TK, Joiner TE Jr, 2012. Thwarted belongingness and perceived burdensomeness: construct validity and psychometric properties of the Interpersonal Needs Questionnaire. Psychol. Assess 24 (1), 197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Van Orden KA, Witte TK, Cukrowicz KC, Braithwaite SR, Selby EA, Joiner TE Jr, 2010. The interpersonal theory of suicide. Psychol. Rev 117 (2), 575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Walley AY, Lodi S, Li Y, Bernson D, Babakhanlou-Chase H, Land T, Larochelle MR, 2020. Association between mortality rates and medication and residential treatment after in-patient medically managed opioid withdrawal: a cohort analysis. Addiction 115 (8), 1496–1508. 10.1111/add.14964. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Waselewski ME, Flickinger TE, Canan C, Harrington W, Franklin T, Otero KN, Huynh J, Waldman ALD, Hilgart M, Ingersoll K, 2021. A mobile health app to support patients receiving medication-assisted treatment for opioid use disorder: development and feasibility study. JMIR Form. Res 5 (2), e24561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Wolf MS, Gazmararian JA, Baker DW, 2005. Health literacy and functional health status among older adults. Arch. Intern. Med 165 (17), 1946–1952. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material
NIHMS2146827-supplement-Supplementary_material.docx (58.2KB, docx)

RESOURCES