Development and preliminary effectiveness of a smartphone‐based, just‐in‐time adaptive intervention for adults with alcohol misuse who are experiencing homelessness

Walters, Scott T.; Mun, Eun Young; Tan, Zhengqi; Luningham, Justin M.; Hébert, Emily T.; Oliver, Jason A.; Businelle, Michael S.

doi:10.1111/acer.14908

Cited by 12 publications

(3 citation statements)

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“…Thus, motivation may appropriately decrease over a follow‐up period, or an individual may experience an unsuccessful change attempt and a concomitant reduction in motivation due to discouragement. For example, a recent effectiveness trial among adults experiencing homelessness showed that a motivation‐guided, just‐in‐time intervention delivered via ecological momentary assessment reduced drinking (Walters et al, 2022), suggesting how changing motivation could be utilized to provide a tailored, repeated intervention. Assessing and responding to motivation as a dynamic variable rather than a static indicator of individual differences may help our understanding of how interventions change motivation and, ultimately, drinking behaviors.…”

Section: Discussionmentioning

confidence: 99%

Do brief motivational interventions increase motivation for change in drinking among college students? A two‐step meta‐analysis of individual participant data

Tan

Tanner‐Smith

Walters

et al. 2023

Alcohol: Clinical and Experimental Research

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BackgroundBrief motivational interventions (BMIs) are one of the most effective individually focused alcohol intervention strategies for college students. Despite the central theoretical role of motivation for change in BMIs, it is unclear whether BMIs increase motivation to change drinking behavior. We conducted a two‐step meta‐analysis of individual participant data (IPD) to examine whether BMIs increase motivation for change. N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually delivered motivational interviewing with personalized feedback (MI + PF), stand‐alone personalized feedback (PF), and group‐based motivational interviewing (GMI).MethodsWe included 15 trials of BMI (N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually‐delivered motivational interviewing with personalized feedback (MI + PF), stand‐alone personalized feedback (PF), and group‐based motivational interviewing (GMI). Different measures and responses used in the original trials were harmonized. Effect size estimates were derived from a model that adjusted for baseline motivation and demographic variables for each trial (step 1) and subsequently combined in a random‐effects meta‐analysis (step 2).ResultsThe overall intervention effect of BMIs on motivation for change was not statistically significant (standard mean difference [SMD]: 0.026, 95% CI: [−0.001, 0.053], p = 0.06, k = 19 comparisons). Of the three subtypes of BMIs, GMI, which tended to provide motivation‐targeted content, had a statistically significant intervention effect on motivation, compared with controls (SMD: 0.055, 95% CI: [0.007, 0.103], p = 0.025, k = 5). By contrast, there was no evidence that MI + PF (SMD = 0.04, 95% CI: [−0.02, 0.10], k = 6, p = 0.20) nor PF increased motivation (SMD = 0.005, 95% CI: [−0.028, 0.039], k = 8, p = 0.75), compared with controls. Post hoc meta‐regression analysis suggested that motivation sharply decreased each month within the first 3 months postintervention (b = −0.050, z = −2.80, p = 0.005 for k = 14).ConclusionsAlthough BMIs provide motivational content and normative feedback and are assumed to motivate behavior change, the results do not wholly support the hypothesis that BMIs improve motivation for change. Changing motivation is difficult to assess during and following interventions, but it is still a theoretically important clinical endpoint. Further, the evidence cautiously suggests that changing motivation may be achievable, especially if motivation‐targeted content components are provided.

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Section: Discussionmentioning

confidence: 99%

Do brief motivational interventions increase motivation for change in drinking among college students? A two‐step meta‐analysis of individual participant data

Tan

Tanner‐Smith

Walters

et al. 2023

Alcohol: Clinical and Experimental Research

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“…To the best of our knowledge, this work is the first attempt to go beyond the detection of BDEs and predict imminent BDEs using the combination of smartphone sensor data and ML coupled with XAI for enhancing the explainability of the algorithms. The prediction model of BDEs combines ML and XAI toward the ultimate goal of supporting JITAIs to prevent BDEs [2,[32][33][34].…”

Section: Study Objectives and Noveltymentioning

confidence: 99%

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study

Bae¹,

Suffoletto²,

Zhang³

et al. 2023

JMIR Form Res

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Background Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering just-in-time support messages in the hours prior to BDEs could improve intervention impact. Objective We aimed to determine the feasibility of developing a machine learning (ML) model to accurately predict future, that is, same-day BDEs 1 to 6 hours prior BDEs, using smartphone sensor data and to identify the most informative phone sensor features associated with BDEs on weekends and weekdays to determine the key features that explain prediction model performance. Methods We collected phone sensor data from 75 young adults (aged 21 to 25 years; mean 22.4, SD 1.9 years) with risky drinking behavior who reported their drinking behavior over 14 weeks. The participants in this secondary analysis were enrolled in a clinical trial. We developed ML models testing different algorithms (eg, extreme gradient boosting [XGBoost] and decision tree) to predict same-day BDEs (vs low-risk drinking events and non-drinking periods) using smartphone sensor data (eg, accelerometer and GPS). We tested various “prediction distance” time windows (more proximal: 1 hour; distant: 6 hours) from drinking onset. We also tested various analysis time windows (ie, the amount of data to be analyzed), ranging from 1 to 12 hours prior to drinking onset, because this determines the amount of data that needs to be stored on the phone to compute the model. Explainable artificial intelligence was used to explore interactions among the most informative phone sensor features contributing to the prediction of BDEs. Results The XGBoost model performed the best in predicting imminent same-day BDEs, with 95% accuracy on weekends and 94.3% accuracy on weekdays (F1-score=0.95 and 0.94, respectively). This XGBoost model needed 12 and 9 hours of phone sensor data at 3- and 6-hour prediction distance from the onset of drinking on weekends and weekdays, respectively, prior to predicting same-day BDEs. The most informative phone sensor features for BDE prediction were time (eg, time of day) and GPS-derived features, such as the radius of gyration (an indicator of travel). Interactions among key features (eg, time of day and GPS-derived features) contributed to the prediction of same-day BDEs. Conclusions We demonstrated the feasibility and potential use of smartphone sensor data and ML for accurately predicting imminent (same-day) BDEs in young adults. The prediction model provides “windows of opportunity,” and with the adoption of explainable artificial intelligence, we identified “key contributing features” to trigger just-in-time adaptive intervention prior to the onset of BDEs, which has the potential to reduce the likelihood of BDEs in young adults. Trial Registration ClinicalTrials.gov NCT02918565; https://clinicaltrials.gov/ct2/show/NCT02918565

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“…The state of being homeless is important because people who experience it can be at increased risk of adverse outcomes compared to their peers who do not, especially those experiencing long-term and repeated homelessness [12,13]. Individuals who experience homelessness may have complex health care needs [14][15][16][17][18]. Homeless individuals are at greater risk of death, hospital admission related to substance use, and poor mental and physical health [14][15][16]19,20].…”

Section: Overviewmentioning

confidence: 99%

Predictors and Consequences of Homelessness: Protocol for a Cohort Study Design Using Linked Routine Data

Mitchell¹,

O’Reilly²,

O’Donovan³

et al. 2023

JMIR Res Protoc

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Background Homelessness is a global burden, estimated to impact more than 100 million people worldwide. Individuals and families experiencing homelessness are more likely to have poorer physical and mental health than the general population. Administrative data is being increasingly used in homelessness research. Objective The objective of this study is to combine administrative health care data and social housing data to better understand the consequences and predictors associated with being homeless. Methods We will be linking health and social care administrative databases from Northern Ireland, United Kingdom. We will conduct descriptive analyses to examine trends in homelessness and investigate risk factors for key outcomes. Results The results of our analyses will be shared with stakeholders, reported at conferences and in academic journals, and summarized in policy briefing notes for policymakers. Conclusions This study will aim to identify predictors and consequences of homelessness in Northern Ireland using linked housing, health, and social care data. The findings of this study will examine trends and outcomes in this vulnerable population using routinely collected health and social care administrative data. International Registered Report Identifier (IRRID) DERR1-10.2196/42404

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Development and preliminary effectiveness of a smartphone‐based, just‐in‐time adaptive intervention for adults with alcohol misuse who are experiencing homelessness

Cited by 12 publications

References 46 publications

Do brief motivational interventions increase motivation for change in drinking among college students? A two‐step meta‐analysis of individual participant data

Do brief motivational interventions increase motivation for change in drinking among college students? A two‐step meta‐analysis of individual participant data

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study

Predictors and Consequences of Homelessness: Protocol for a Cohort Study Design Using Linked Routine Data

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