Smart Selection of Useful Insights from Wearables

Susaiyah, Allmin; Härmä, Aki; Balloccu, Simone; Reiter, Ehud; Petković, Milan

doi:10.1109/icasspw59220.2023.10193140

Cited by 1 publication

(3 citation statements)

References 7 publications

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“…These associative insights could help in understanding the behaviour of the user. Härmä and Helaoui [52] introduced a probabilistic method to discover and recommend insights in textual format which led to several adaptations to health interventions [53,54]. Message delivery frameworks not only focus on how to generate them but also on which messages to deliver.…”

Section: Behavioral Interventions Provide Lessons To Successfully Man...mentioning

confidence: 99%

“…These types of insight can be used in various domains such as digital marketing, organizational resource utilization analysis, personal health intervention, etc. [54].…”

Section: Behavioral Interventions Provide Lessons To Successfully Man...mentioning

confidence: 99%

“…For self-health management, we aim such insights to be readable, relatable, actionable, and quantifiable. For this, we adopted the generalized insight generation framework [54] that allows us to structure insight generation in a controllable and scalable manner. The framework shown in Figure 1 The what-to-look component defines the overall scope of the insights.…”

Section: Behavioral Interventions Provide Lessons To Successfully Man...mentioning

confidence: 99%

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Feedback-Driven Insight Generation and Recommendation for Health Self-Management

Susaiyah,

Härmä,

Reiter

et al. 2024

Preprint

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Purpose: This study aims to investigate the impact of personalized health insights generated from wearable device data on users' health behaviors. The primary objective is to assess whether user feedback-driven algorithms enhance the relevance and effectiveness of health insights, ultimately influencing positive changes in users' daily activities. Methods: A two-month field study was conducted with 25 healthy volunteers using Mi Band 6 wearable devices. Participants were divided into test and control groups, and the test group received personalized insights recommended by a neural network-based algorithm fine-tuned by user feedback. The data collected included various health parameters such as calories burned, step count, heart rate, heart minutes, active minutes, sleep duration, sleep time and sleep segments. Insights were provided through a Telegram chatbot and user feedback was collected through a rating system. Results: The study revealed that the test group, which considered user feedback for insight recommendations, showed a significant improvement in daily activity compared to the control group. The relevance of the insights over time, as evidenced by feedback regression trends, showed a notable increase in the test group. Additional analyzes explored the relationship between insight delivery timing, user feedback, and delays, providing insights into user engagement patterns. Conclusion: This research highlights the effectiveness of personalized health insights generated from wearable data in positively influencing user health behaviors. Incorporating feedback from users into recommendation algorithms greatly enhances the relevance and effectiveness of insights, encouraging behavioral improvements. The results emphasize the significance of timing when providing insights and propose potential areas for future investigation, such as utilizing Graph Neural Networks to improve recommendation systems. In general, personalized insights from wearables have the potential to empower individuals to manage their health and well-being effectively.

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Section: Behavioral Interventions Provide Lessons To Successfully Man...mentioning

confidence: 99%

“…These types of insight can be used in various domains such as digital marketing, organizational resource utilization analysis, personal health intervention, etc. [54].…”

Section: Behavioral Interventions Provide Lessons To Successfully Man...mentioning

confidence: 99%

Section: Behavioral Interventions Provide Lessons To Successfully Man...mentioning

confidence: 99%

See 1 more Smart Citation

Feedback-Driven Insight Generation and Recommendation for Health Self-Management

Susaiyah,

Härmä,

Reiter

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Smart Selection of Useful Insights from Wearables

Cited by 1 publication

References 7 publications

Feedback-Driven Insight Generation and Recommendation for Health Self-Management

Feedback-Driven Insight Generation and Recommendation for Health Self-Management

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