Evaluating the Acceptability, Feasibility and Usability of Various Conversational Data Collection Software

Condition(s) studied

Investigational drug(s) / intervention(s)

Digital Biopsychosocial Conversational Data Collection Software: This intervention involves participants interacting with digital conversational software that uses artificial intelligence and natural language processing to collect biopsychosocial health information. The software engages participants in dialogue-based interactions, asking questions about their chronic pain experience, physical functioning, emotional well-being, and social circumstances. The conversational interface adapts to participant responses, can ask follow-up questions for clarification, and creates an interactive experience that mimics clinical interviews. Some implementations may utilize virtual reality (VR) environments to create immersive data collection experiences at the MIT.nano Immersion Lab. Participants interact with the software either at home using provided devices or at the MIT.nano Immersion Lab. The AI-driven platform standardizes data collection while maintaining conversational flexibility, collecting comprehensive biopsychosocial data through natural language

Standard Digital Questionnaire Data Collection: This intervention involves participants completing standard digital questionnaires or surveys to provide biopsychosocial health information. Participants respond to predetermined, fixed-format questions about their chronic pain experience, physical functioning, emotional well-being, and social circumstances. The data collection uses conventional electronic forms or surveys with static question sets that do not adapt based on participant responses. Questions are presented in a standard sequence without conversational elements or follow-up prompts. This represents traditional electronic data collection methods commonly used in clinical and research settings. Participants complete the questionnaires either at home using their own devices or provided equipment, or at designated study locations. This method provides a comparison baseline against which to evaluate newer digital data collection approaches, representing current standard practice for gathering patient-reported health informat

Study summary

This research study is being conducted to evaluate digital software tools that collect health information through conversations. The study will test how acceptable, feasible, and usable these conversational software programs are for patients with chronic pain.

Purpose of the Study:

The goal is to understand whether digital conversational tools can effectively gather biopsychosocial information (biological, psychological, and social factors related to health) from patients experiencing chronic pain. Biopsychosocial information includes details about your physical symptoms, emotional well-being, social circumstances, and how these factors interact to affect your health. These digital tools are designed to have conversation-like interactions with patients to collect this comprehensive health data in a more natural and engaging way.

What Participation Involves:

If you join this study, you will interact with digital conversational software that will ask you questions about your health, pain experience, and related factors. The software is designed to communicate with you in a conversational manner, similar to talking with a healthcare provider, to gather information about your condition. You will be asked about various aspects of your chronic pain experience, including how it affects your daily life, your emotional state, and your social interactions.

Who Can Participate:

This study is looking for participants who have chronic pain conditions. Medical providers who treat patients with chronic pain are encouraged to refer eligible patients to this study. Healthcare providers interested in referring patients or learning more about the study can contact the study sponsor, AugMend Health Inc.

Why This Research Matters:

Chronic pain affects millions of people and understanding the full scope of how it impacts patients requires collecting detailed information about physical, psychological, and social factors. Traditional methods of collecting this information can be time-consuming and may not capture the complete picture. Digital conversational tools offer a potential new approach to gathering comprehensive health information in a more efficient and patient-friendly manner. This research will evaluate whether patients find these tools acceptable to use, whether they work practically in real-world clinical settings, and whether they are user-friendly and accessible for people with chronic pain.

Study Location:

The study is being conducted at AugMend Health in Cambridge, Massachusetts.

Study Sponsor:

This study is sponsored by AugMend Health Inc. Healthcare providers or potential participants who would like more information about the study or wish to make a referral can contact AugMend Health Inc.

Participation in this study is voluntary, and you can choose to stop participating at any time without affecting your regular medical care.

Eligibility

Primary outcome measure(s)

• Acceptability — Assessed immediately after completion of data collection procedures (typically within 1-2 weeks of enrollment)
  Acceptability will be measured using the Acceptability, Feasibility and Usability Survey (AFUS) - Acceptability Subscale, a researcher-developed instrument. This subscale includes items from Part 1 (Core Assessment Experience), Part 2 (culture), Part 3 (Future Use \& Preference), Part 4 (Privacy \& Trust), and Part 5 (Medical Disclosure Comfort). Items are rated on a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree). Subscale scores will be calculated as the mean of all acceptability items. Higher scores indicate greater acceptability. Subscale breakdown will be accessible in supplemental figures of publications.
• Usability — Assessed immediately after completion of data collection procedures (typically within 1-2 weeks of enrollment)
  Usability will be measured using the Acceptability, Feasibility and Usability Survey (AFUS) - Usability Subscale (Part 6), a researcher-developed instrument adapted from established usability frameworks. The subscale consists of 10 items rated on a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree). Negatively-worded items are reverse-scored. The total score is calculated as the mean of all items. Higher scores indicate better usability.
• Technical Feasibility — Assessed throughout study participation (typically within 1-2 weeks of enrollment)
  Technical feasibility will be measured by the proportion of data collection sessions completed without significant technical failures. A significant technical failure is defined as any system error, software malfunction, connectivity interruption, hardware issue, or other technical problem that results in early termination of the session. Technical success rate will be calculated as the number of sessions without significant technical failures divided by total sessions attempted, expressed as a percentage. Higher percentages indicate greater technical feasibility.
• Implementation Feasibility — Assessed immediately after completion of data collection procedures (typically within 1-2 weeks of enrollment)
  Implementation feasibility will be measured using the Acceptability, Feasibility and Usability Survey (AFUS) - Future Use \& Preference Subscale (Part 3). Items assess participant willingness to use the data collection method again, likelihood of recommending it to others, and trust in the method for ongoing health evaluation. Items are rated on a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree). The subscale score is calculated as the mean of all items. Higher scores indicate greater implementation feasibility.
• Data Collection Feasibility — Assessed at completion of all data collection procedures (typically within 1-2 weeks of enrollment)
  Feasibility of comprehensive data collection will be measured by the proportion of biopsychosocial domains for which data was successfully collected across all sessions. Domain coverage will be calculated as the number of domains with complete data divided by total possible domains, expressed as a percentage. Domains include biological (pain characteristics, sleep, physical function, nutrition), psychological (mood, cognition, coping, trauma, pain catastrophizing), and social (relationships, support, work, identity). Higher percentages indicate more comprehensive data collection.

Trial sites (1)

Other trials for the same condition