Effects of Text4Hope-Addiction Support Program on Cravings and Mental Health Symptoms: Results of a Longitudinal Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Drug misuse is complex, and various treatment modalities are emerging. Providing supportive text messages to individuals with substance use disorder offers the prospect of managing and improving symptoms of drug misuse and associated comorbidities. OBJECTIVE: This study evaluated the impact of the daily supportive text message program (Text4Hope-Addiction Support) in mitigating cravings and mental health symptoms in subscribers and quantify user satisfaction with the Text4Hope-Addiction Support program. METHODS: Subscribers to the Text4Hope-Addiction Support program received daily supportive text messages for 3 months; the messages were crafted based on addiction counseling and cognitive behavioral therapy principles. Participants completed an anonymous web-based questionnaire to assess cravings, anxiety, and depressive symptoms using the Brief Substance Craving Scale (BSCS), Generalized Anxiety Disorder-7 (GAD-7) scale, and Patient Health Questionnaire-9 (PHQ-9) scale at enrollment (baseline), after 6 weeks, and after 3 months. Likert scale satisfaction responses were used to assess various aspects of the Text4Hope-Addiction program. RESULTS: In total, 408 people subscribed to the program, and 110 of 408 (26.9%) subscribers completed the surveys at least at one time point. There were significant differences between the mean baseline and 3-month BSCS scores P=.01 (-2.17, 95% CI -0.62 to 3.72), PHQ-9 scores, P=.004 (-5.08, 95% CI -1.65 to -8.51), and GAD-7 scores, P=.02 (-3.02, 95% CI -0.48 to -5.56). Participants who received the supportive text messages reported a reduced desire to use drugs and a longer time interval between substance use, which are reflected in 41.1% and 32.5% decrease, respectively, from baseline score. Approximately 89% (23/26) of the participants agreed that Text4Hope-Addiction program helped them cope with addiction-related stress, and 81% (21/25) of the participants reported that the messages assisted them in dealing with anxiety. Overall, 69% (18/26) of the participants agreed that it helped them cope with depression related to addiction; 85% (22/26) of the participants felt connected to a support system; 77% (20/26) of the participants were hopeful of their ability to manage addiction issues; and 73% (19/26) of the participants felt that their overall mental well-being was improved. Most of the participants agreed that the interventions were always positive and affirmative (19/26, 73%), and succinct (17/26, 65%). Furthermore, 88% (21/24) of the participants always read the messages; 83% (20/24) of the participants took positive or beneficial actions after reading; and no participant took a negative action after reading the messages. In addition, most participants agreed to recommend other diverse technology-based services as an adjunctive treatment for their mental and physical health disorders. CONCLUSIONS: Subscribers of Text4Hope-Addiction Support program experienced improved mental health and addiction symptoms. Addiction care practitioners and policy makers can implement supportive text-based strategies to complement conventional treatments for addiction, given that mobile devices are widely used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".