We used natural language processing (NLP) in convergent mixed methods to evaluate young adults’ experiences with Call it a Night (CIAN), a digital personalized feedback and coaching sleep-alcohol intervention. Young adults with heavy drinking (N = 120) were randomized to CIAN or controls (A + SM: web-based advice + self-monitoring or A: advice; clinicaltrials.gov, 8/31/18, #NCT03658954). Most CIAN participants (72.0%) preferred coaching to control interventions. Control participants found advice more helpful than CIAN participants (X² = 27.34, p < 0.001). Most participants were interested in sleep factors besides alcohol and appreciated increased awareness through monitoring. NLP corroborated generally positive sentiments (M = 15.07(10.54)) and added critical insight that sleep (40%), not alcohol use (12%), was a main participant motivator. All groups had high adherence, satisfaction, and feasibility. CIAN (Δ = 0.48, p = 0.008) and A + SM (Δ = 0.55, p < 0.001) had higher reported effectiveness than A (F(2, 115) = 8.45, p < 0.001). Digital sleep-alcohol interventions are acceptable, and improving sleep and wellness may be important motivations for young adults.

我们在收敛混合方法中使用自然语言处理 （NLP） 来评估年轻人对 Call it a Night （CIAN） 的体验，这是一种数字个性化反馈和指导睡眠酒精干预。酗酒的年轻人 （N = 120） 被随机分配到 CIAN 或对照组 （A + SM：基于网络的建议 + 自我监测或 A：建议;clinicaltrials.gov，8/31/18，#NCT03658954）。大多数 CIAN 参与者 （72.0%） 更喜欢教练来控制干预措施。对照组参与者发现建议比 CIAN 参与者更有帮助 （X² = 27.34，p < 0.001）。大多数参与者对酒精以外的睡眠因素感兴趣，并赞赏通过监测提高意识。NLP 证实了普遍的积极情绪 （M = 15.07（10.54）），并增加了关键见解，即睡眠 （40%），而不是饮酒 （12%），是参与者的主要动机。所有组均具有较高的依从性、满意度和可行性。CIAN （Δ = 0.48，p = 0.008） 和 A + SM （Δ = 0.55，p < 0.001） 报告的有效性高于 A （F（2， 115） = 8.45，p < 0.001）。 数字睡眠酒精干预是可以接受的，改善睡眠和健康可能是年轻人的重要动机。