Background Encouraging individuals to report daily information such as unpleasant disease symptoms, daily activities and behaviors, or aspects of their physical and emotional state is difficult but necessary for many studies and clinical trials that rely on patient-reported data as primary outcomes. Use of paper diaries is the traditional method of completing daily diaries, but digital surveys are becoming the new standard because of their increased compliance; however, they still fall short of desired compliance levels. Objective Mobile games using in-game rewards offer the opportunity to increase compliance above the rates of digital diaries and paper diaries. We conducted a 5-week randomized control trial to compare the completion rates of a daily diary across 3 conditions: a paper-based participant-reported outcome diary (Paper PRO), an electronic-based participant-reported outcome diary (ePRO), and a novel ePRO diary with in-game rewards (Game-Motivated ePRO). Methods We developed a novel mobile game that is a combination of the idle and pet collection genres to reward individuals who complete a daily diary with an in-game reward. Overall, 197 individuals aged 6 to 24 years (male: 100 and female: 97) were enrolled in a 5-week study after being randomized into 1 of the 3 methods of daily diary completion. Moreover, 157 participants (male: 84 and female: 69) completed at least one diary and were subsequently included in analysis of compliance rates. Results We observed a significant difference ( F 2,124 =6.341; P =.002) in compliance to filling out daily diaries, with the Game-Motivated ePRO group having the highest compliance (mean completion 86.4%, SD 19.6%), followed by the ePRO group (mean completion 77.7%, SD 24.1%), and finally, the Paper PRO group (mean completion 70.6%, SD 23.4%). The Game-Motivated ePRO ( P =.002) significantly improved compliance rates above the Paper PRO. In addition, the Game-Motivated ePRO resulted in higher compliance rates than the rates of ePRO alone ( P =.09). Equally important, even though we observed significant differences in completion of daily diaries between groups, we did not observe any statistically significant differences in association between the responses to a daily mood question and study group, the average diary completion time ( P= .52), or the System Usability Scale score ( P =.88). Conclusions The Game-Motivated ePRO system encouraged individuals to complete the daily diaries above the compliance rates of the Paper PRO and ePRO without altering the participants’ responses. Trial Registration ClinicalTrials.gov NCT03738254; http://clinicaltrials.gov/ct2/show/NCT03738254 (Archived by WebCite at http://www.webcitation.org/74T1p8u5...
How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL). We identify implicit conversational cues including language similarity, elicitation of laughter, sentiment, and more, which indicate positive human feedback, and embed these in multiple reward functions. A wellknown challenge is that learning an RL policy in an offline setting usually fails due to the lack of ability to explore and the tendency to make over-optimistic estimates of future reward. These problems become even harder when using RL for language models, which can easily have a 20,000 action vocabulary and many possible reward functions. We solve the challenge by developing a novel class of offline RL algorithms. These algorithms use KL-control to penalize divergence from a pretrained prior language model, and use a new strategy to make the algorithm pessimistic, instead of optimistic, in the face of uncertainty. We test the resulting dialog model with ratings from 80 users in an open-domain setting and find it achieves significant improvements over existing deep offline RL approaches. The novel offline RL method is viable for improving any existing generative dialog model using a static dataset of human feedback.
This work introduces The Guardians: Unite the Realms, a novel free-to-play and publicly released mobile game that encourages the adoption of healthy real-world behaviours in exchange for rewards that enrich the gaming experience. We describe the game, its grounding in a mental health therapy known as behavioural activation, and how we designed it to keep players engaged over time. Instead of using traditional digital health gamification techniques such as badges or leaderboards, The Guardians creates a motivational pull by embedding the therapy into a complete mobile game. In-game items earned via the therapy have an immediate purpose in the game and, thus, they are considered intrinsically valuable by players. Analysis of game interaction data from 7,782 real-world users suggests 15day and 30-day retention rates of 10.0% and 6.6%, respectively, which is more than double the average retention levels of most digital mental health interventions. Furthermore, players reported completion of a healthy real-world task on 69.0% of days played (37,574 completed tasks in 54,461 total days). We also report interaction metrics with game features and the effectiveness of the players' chosen real-world activities.Index Terms-digital mental health interventions, mobile game design, serious games, real-world user data, well-being, behavioural activation.
Recommender systems have the potential to improve the user experience of digital mental health apps. Personalised recommendations can help users to identify therapy tasks that they fnd most enjoyable or helpful, thus boosting their engagement with the service and optimising the extent to which it helps them to feel better. Using a dataset containing 23,476 ratings collected from 973 players of a mental health therapy game, this work demonstrates how collaborative fltering algorithms can predict how much a user will beneft from a new therapy task with greater accuracy than a simpler baseline algorithm that predicts the average rating for a task, adjusted for the biases of the specifc user and specifc task. Collaborative fltering algorithms (matrix factorisation and k-nearest neighbour) outperform this baseline with a 6.5-8.3% improvement in mean absolute error (MAE) and context-aware collaborative fltering algorithms (factorisation machines) outperform with a 7.8-8.8% improvement in MAE. These results suggest that recommender systems could be a useful tool for tailoring recommendations of new therapy tasks to a user based on a combination of their past preferences, the ratings of similar users, and their current context. This scalable approach to personalisation -which does not require a human therapist to always be in-the-loop -could play an important role in improving engagement and outcomes in digital mental health therapies.
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