JMIR Publications

ABSTRACT

Background:

Telehealth is performing as an effective means to assist the existing health care systems, in particular for the current aging society. However, most extant telehealth systems employ individual data sources by offline-data-processing, which may not recognize health deterioration in a timely way.

Objective:

The objective of our study was to design and implement an integrated, personalized telehealth system on a community-based level and to evaluate user acceptance of the system.

Methods:

The system was designed to capture and record older adults’ health-related information (e.g., continuous vital signs and gait behaviors) through various measuring tools. State-to-art data mining techniques were integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members and their caregivers for appropriate interventions to prevent further health deterioration. In the implementation phase, a total of 45 older adults were recruited from three Hong Kong elderly care centers and participated in our study. They were instructed to use the system for three months. Exploratory data analysis was conducted to summarize the collected datasets. For the user acceptance, we used a questionnaire survey to evaluate users’ attitude, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention towards the system.

Results:

The findings of our pilot study showed that there were significant differences for the step data (P<0.001), body temperature data (P <0.001), and systolic blood pressure data (P =0.018) among the older adults in the three centers. The findings emphasized the importance of integrating personalized characteristics into systems so that the analyzed results could meet individual demands for the quality of healthcare services. A high level of system acceptance was obtained from the participants.

Conclusions:

The present study enriched the literature of health monitoring systems by providing quantitative data such as vital signs and gait behavior data that can help develop reliable and accurate predictive analytics. The findings related to user acceptance showed the stakeholders (e.g., policymakers, elderly care centers, and healthcare providers) that community-dwelling older adults are ready to accept and intend to continuously use such a telehealth system to manage their healthcare.