Towards Enhancing Health Coaching Dialogue in Low-Resource Settings (2022)

Abstract: Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy. However, building such a system is challenging since real-world health coaching datasets are limited and empathy is subtle. Thus, we propose a modularized health coaching dialogue with simplified NLU and NLG frameworks combined with mechanism-conditioned empathetic response generation. Through automatic and human evaluation, we show that our system generates more empathetic, fluent, and coherent responses and outperforms the state-of-the-art in NLU tasks while requiring less annotation. We view our approach as a key step towards building automated and more accessible health coaching systems.

## introduction health coaching is a patient-centered, motivational interviewing-based clinical practice that focuses on helping patients identify and accomplish personalized, lifestyle-related goals to improve health behaviors. it has been effective in improving the control of chronic conditions such as diabetes and cardiovascular disease and mitigating mental health conditions such as anxiety and depression @xcite @xcite . health coaching can be particularly beneficial to low-socioeconomic status (ses) populations who disproportionately suffer physical and mental disease burdens @xcite . yet, it is invariably cost-prohibitive for these populations due to its highly personalized and labor-intensive nature. i want to do 3000 steps. virtual assistant hi [name], what would you like your goal to be this week? great! what days do you want to reach 3000 steps? maybe monday and wednesday for right now… okay, can you think of anything that might make it hard to reach your goal? goal-setting stage patient empathetic generation i was taken to the er by ambulance… you were walking to the er yesterday. do you want to do the same today? i'm sorry to hear that. i hope everything is okay. feel better! hi how was your goal going yesterday? patient ## health coaching dataset we first briefly describe our dataset, that motivates the approach that we are proposing; full details can be found in @xcite . we collected two datasets of health coaching dialogues between patients and coaches via text messages. dataset 1 and 2 contain 28 and 30 patients, with each patient coached for four and eight weeks, respectively. each week the health coach converses with the patient to create a physical activity s.m.a.r.t. goal and then follows up on the patient's progress. the two datasets contain 336 weeks of dialogues with 21.4 average turns per week. we defined ten slots for the goal's attributes (types of activity, amount, time, days, location, duration, and the confidence score for the activity) (the appendix contains examples of all slots). we used a stage-phase schema for additional turnlevel annotation describing how the health coaching dialogue unfolds. we defined two stages: the goal-setting stage and the goal-implementation stage. each stage includes a set of phases, such as goal identification, negotiation, and follow-up. each turn can belong to a certain stage-phase combination. later, we added dialogue act annotations @xcite , consisting of 12 domain-independent dialogue acts, following the iso-standard by @xcite . since dataset 1 was collected before dataset 2, the manual annotation was developed on dataset 1, and used to develop our nlu component, that was then tested on dataset 2. hence, dataset 1 is fully annotated for slot-value spans, goals, stages and phases. in dataset 2, only three patients are annotated for slot-value spans and phases, and 15 weeks for goals; the whole dataset is annotated for stages. as far as dialogue acts are concerned, they are only available for 15 weeks of dialogues in dataset 1 foot_0 . ## methods in this section, we begin by first providing a brief workflow of our proposed health coaching dialogue system and then describing the model architecture in details. ## experiments this section includes datasets description, evaluation metrics, experimental results, and qualitative analysis. ## conclusions and future work we built an efficient health coaching dialogue system that helps patients create and accomplish specific goals, with a simplified nlu and nlg framework combined with mechanism-conditioned empathetic response generation. the experiments show that the system can generate more coherent and empathetic responses, supporting health coaches and improving coaching efficiency. in addition, our system outperforms the state-of-the-art in nlu tasks while requiring fewer annotations. we view our approach as a key step towards building automated and more accessible health coaching systems in low-resource settings and believe our approach may also generalize to building dialogue systems in similar scenarios, such as patient education at discharge or consulting on behavior change problems. in the future, we will explore the following directions: (1) modelling empathetic understanding and generation with the goal response generator as one integrated end-to-end system while providing explainability. (2) a more comprehensive human evaluation from both the coach's and the patient's perspectives, including but not subject to goal completion and activity engagement rate.