OBJECTIVE: This review aims to identify the contribution of user experience features and underlying technical features to sustained engagement in unguided chatbots for improving health-related behaviors. MATERIALS AND METHODS: Following PRISMA-2020 guidelines, we conducted a systematic review, searching PubMed, ACM, APA PsycINFO, Cochrane, Web of Science, and IEEE Xplore from June to September 2022 and updated in April 2025. Data was analyzed via Synthesis without Meta-Analysis (SWiM), to understand the relationship between user engagement overall and individual experience metrics. RESULTS: Customizable avatars and flexible input interactions may enhance overall user engagement. Conversely, pre-scripted content that lacks personalization and emotional support negatively impacts user satisfaction and adherence to health interventions. Other features contributing to sustained engagement are in-app technical assistance, user learning features, and crisis support systems. A strong positive correlation (r = 0.808, n = 16) was observed between user satisfaction and engagement, specifically for satisfaction dimensions including need fulfillment (r = 0.872, n = 6), willingness to recommend chatbot (r = 0.817, n = 4) and user enjoyment (r = 0.971, n = 3) in SWiM analysis. The limited application of large language models and retrieval augmented generation techniques may constrain the quality of support available to users and overall sustained engagement. CONCLUSION: Effective unguided chatbot design requires an emphasis on interactive educational elements, in-app technical assistance and crisis support, and personalized content. This can be achieved with high context awareness, input understanding, and quality content generation. Our findings suggest that user satisfaction is a primary driver of sustained engagement, though further research is needed to validate individual user satisfaction features for sustained engagement.

Accurate estimation of aerodynamic forces is essential for advancing the control, modeling, and design of flapping-wing aerial robots with dynamic morphing capabilities. In this paper, we investigate two distinct methodologies for force estimation on Aerobat, a bio-inspired flapping-wing platform designed to emulate the inertial and aerodynamic behaviors observed in bat flight. Our goal is to quantify aerodynamic force contributions during tethered flight, a crucial step toward closed-loop flight control. The first method is a physics-based observer derived from Hamiltonian mechanics that leverages the concept of conjugate momentum to infer external aerodynamic forces acting on the robot. This observer builds on the system’s reduced-order dynamic model and utilizes real-time sensor data to estimate forces without requiring training data. The second method employs a neural network-based regression model, specifically a multi-layer perceptron (MLP), to learn a mapping from joint kinematics, flapping frequency, and environmental parameters to aerodynamic force outputs. We evaluate both estimators using a 6-axis load cell in a high-frequency data acquisition setup that enables fine-grained force measurements during periodic wingbeats. The conjugate momentum observer and the regression model demonstrate strong agreement across three force components (Fx, Fy, Fz).

Over 1.1 million tweets were collected using the four hashtags (# health, # fitness, # diet, # weightloss) from August 2018 to January 2019 for the purpose of understanding obesity-related image sharing practice in Twitter. Of these tweets, more than 200,000 (17.76%) had images, which were shared by more than 60,000 unique twitter users (22.13%). Over 130,000 unique images were computationally processed by applying a very deep VGG16 model (convolutional neural network) and 967 unique labels were used to describe objects within the images extracted from obesity related tweets. Informational content was most frequently re-shared, followed by images of individuals posing, comical content, commercial content, physical activities, and motivational quotes. Although a general deep learning model provided high level insights into the image sharing activity on Twitter, manual analysis revealed that a tailored model is needed to learn detailed information.

OBJECTIVE: Online health communities offer a diverse peer support base, yet users can struggle to identify suitable peer mentors as these communities grow. To facilitate mentoring connections, we designed a peer-matching system that automatically profiles and recommends peer mentors to mentees based on person-generated health data (PGHD). This study examined the profile characteristics that mentees value when choosing a peer mentor. MATERIALS AND METHODS: Through a mixed-methods user study, in which cancer patients and caregivers evaluated peer mentor recommendations, we examined the relative importance of four possible profile elements: health interests, language style, demographics, and sample posts. Playing the role of mentees, the study participants ranked mentors, then rated both the likelihood that they would hypothetically contact each mentor and the helpfulness of each profile element in helping the make that decision. We analyzed the participants' ratings with linear regression and qualitatively analyzed participants' feedback for emerging themes about choosing mentors and improving profile design. RESULTS: Of the four profile elements, only sample posts were a significant predictor for the likelihood of a mentee contacting a mentor. Communication cues embedded in posts were critical for helping the participants choose a compatible mentor. Qualitative themes offer insight into the interpersonal characteristics that mentees sought in peer mentors, including being knowledgeable, sociable, and articulate. Additionally, the participants emphasized the need for streamlined profiles that minimize the time required to choose a mentor. CONCLUSION: Peer-matching systems in online health communities offer a promising approach for leveraging PGHD to connect patients. Our findings point to interpersonal communication cues embedded in PGHD that could prove critical for building mentoring relationships among the growing membership of online health communities.

Online health communities provide a rich source of expertise from experienced patients, but uncovering “peer mentors” with shared circumstances is like finding a needle in a haystack—a problem that will escalate as these communities grow and diversify. We investigated interactive health interest profiles (HIPs) that summarize health-related terms extracted from users’ community posts. Through iterative design, we explored practical designs that accommodate differences in users’ community participation in three HIP prototypes: Text , Word Cloud, and Timeline . By comparing prototype usability with patients and design experts, we found that patients accurately used each prototype but completed some tasks faster with the Timeline HIP . Despite this advantage, patients preferred the Text HIP . Design experts and patients agreed that simple data overviews and granular details with salient cues that invite interactivity are key design considerations for HIPs. Findings offer key design considerations for HIPs that patients find most useful when forging critical connections.