"Mango Mango, How to Let The Lettuce Dry Without A Spinner?'': Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner

The rapid advancement of the Large Language Model (LLM) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a complex daily task, as a scenario to investigate people's successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system's ability to provide extensive information beyond the recipe, offer customized instructions based on context, and assist them in dynamically planning the task. However, they expect the system to be more adaptive to oral conversation and provide more suggestive responses to keep users actively involved. Recognizing that users began treating our LLM-CA as a personal assistant or even a partner rather than just a recipe-reading tool, we propose several design considerations for future development.Comment: Under submission to CHI202

Web‐based Weight Management Programs in an Integrated Health Care Setting: A Randomized, Controlled Trial

Objective : To assess the efficacy of a Web‐based tailored behavioral weight management program compared with Web‐based information‐only weight management materials. Research Methods and Procedures : Participants, 2862 eligible overweight and obese (BMI = 27 to 40 kg/m 2 ) members from four regions of Kaiser Permanente's integrated health care delivery system, were randomized to receive either a tailored expert system or information‐only Web‐based weight management materials. Weight change and program satisfaction were assessed by self‐report through an Internet‐based survey at 3‐ and 6‐month follow‐up periods. Results : Significantly greater weight loss at follow‐up was found among participants assigned to the tailored expert system than among those assigned to the information‐only condition. Subjects in the tailored expert system lost a mean of 3 ± 0.3% of their baseline weight, whereas subjects in the information‐only condition lost a mean of 1.2 ± 0.4% ( p < 0.0004). Participants were also more likely to report that the tailored expert system was personally relevant, helpful, and easy to understand. Notably, 36% of enrollees were African‐American, with enrollment rates higher than the general proportion of African Americans in any of the study regions. Discussion : The results of this large, randomized control trial show the potential benefit of the Web‐based tailored expert system for weight management compared with a Web‐based information‐only weight management program.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93691/1/oby.2006.34.pd

Adaptive Health Coaching Technology for Tailored Interventions

Recent advances in sensor and communications technology have enabled scalable methods for providing continuity of care to the home for patients with chronic conditions and older adults wanting to age in place. In this article we describe our framework for a health coaching platform with a dynamic user model that enables tailored health coaching messages. We have shown that this can improve coach efficiency without a loss of message quality. We also discovered many lessons for coaching technology, most demonstrating the need for more coach input on sample message content, perhaps even requiring that individual coaches be able to modify the message database directly. Overall, coaches felt that the structure of the automated message generation was useful in remembering what to say, easy to edit if necessary and especially helpful for training new health coaches