SleepFit: A Persuasive Mobile App for Improving Sleep Habits in Young Adults

Oyebode, Oladapo; Alhasani, Mona; Mulchandani, Dinesh; Olagunju, Tolulope; Orji, Rita

doi:10.1109/segah52098.2021.9551907

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Mobile alcohol treatment apps providing personalized feedback may be especially engaging 17 . Further, recent research on mobile sleep apps 16,[18][19] highlights increased engagement and satisfaction with personalized feedback and communication with health professionals. Our study adds ndings that individually tailored coaching from a health professional signi cantly enhanced participants' experience interpreting digital health data, which likely supported their understanding.…”

Section: The Importance Of Personalized and Interactive Feedback And ...mentioning

confidence: 99%

“…Over 95% of users stop using most mobile mental health apps after 30 days 14 and adherence and engagement are generally low 15 . In the case of sleep and alcohol apps, user engagement may increase with features like personalized feedback, interaction and support from providers, self-monitoring, and user-friendliness [16][17][18][19] . Further, gami cation may signi cantly reduce attrition in mobile mental health app use in general 20 .…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Natural Language Processing to Evaluate Young Adults’ User Experiences with a Digital Sleep Intervention for Alcohol Use

Griffith,

Ash,

Augustine

et al. 2024

Preprint

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Evaluating user experiences with digital interventions is critical to increase uptake and adherence, but traditional methods have limitations. We incorporated natural language processing (NLP) with convergent mixed methods to evaluate a personalized feedback and coaching digital sleep intervention for alcohol risk reduction: ‘Call it a Night’ (CIAN; N = 120). In this randomized clinical trial with young adults with heavy drinking, control conditions were A + SM: web-based advice + active and passive monitoring; and A: advice + passive monitoring. Findings converged to show that the CIAN treatment condition group found feedback and coaching most helpful, whereas participants across conditions generally found advice helpful. Further, most participants across groups were interested in varied whole-health sleep-related factors besides alcohol use (e.g., physical activity), and many appreciated increased awareness through monitoring with digital tools. All groups had high adherence, satisfaction, and reported feasibility, but participants in CIAN and A + SM reported significantly higher effectiveness than those in A. NLP corroborated positive sentiments across groups and added critical insight that sleep, not alcohol use, was a main participant motivator. Digital sleep interventions are an acceptable, novel alcohol treatment strategy, and improving sleep and overall wellness may be important motivations for young adults. Further, NLP provides an efficient convergent method for evaluating experiences with digital interventions.

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Section: The Importance Of Personalized and Interactive Feedback And ...mentioning

confidence: 99%