Integrated Telehealth and Telecare for Monitoring Frail Elderly with Chronic Disease

Abstract

Objective:To investigate the potential of an integrated care system that acquires vital clinical signs and habits data to support independent living for elderly people with chronic disease.Materials and Methods:We developed an IEEE 11073 standards-based telemonitoring platform for monitoring vital signs and activity data of elderly living alone in their home. The platform has important features for monitoring the elderly: unobtrusive, simple, elderly-friendly, plug and play interoperable, and self-integration of sensors. Thirty-six (36) patients in a primary care practice in the United Kingdom (mean [standard deviation] age, 82 [10] years) with congestive heart failure (CHF) or chronic obstructive pulmonary disease (COPD) were provided with clinical sensors to measure the vital signs for their disease (blood pressure [BP] and weight for CHF, and oxygen saturation for COPD) and one passive infrared (PIR) motion sensor and/or a chair/bed sensor were installed in a patient's home to obtain their activity data. The patients were asked to take one measurement each day of their vital signs in the morning before breakfast. All data were automatically transmitted wirelessly to the remote server and displayed on a clinical portal for clinicians to monitor each patient. An alert algorithm detected outliers in the data and indicated alerts on the portal. Patient data have been analyzed retrospectively following hospital admission, emergency room visit or death, to determine whether the data could predict the event.Results:Data of patients who were monitored for a long period and had interventions were analyzed to identify useful parameters and develop algorithms to define alert rules. Twenty of the 36 participants had a clinical referral during the time of monitoring; 16 of them received some type of intervention. The most common reason for intervention was due to low oxygen levels for patients with COPD and high BP levels for CHF. Activity data were found to contain information on the well-being of patients, in particular for those with COPD. During exacerbation the activity level from PIR sensors increased slightly, and there was a decrease in bed occupancy. One subject with CHF who felt unwell spent most of the day in the bedroom.Conclusions:Our results suggest that integrated care monitoring technologies have a potential for providing improved care and can have positive impact on well-being of the elderly by enabling timely intervention. Long-term BP and pulse oximetry data could indicate exacerbation and lead to effective intervention; physical activity data provided important information on the well-being of patients. However, there remains a need for better understanding of long-term variations in vital signs and activity data to establish intervention protocols for improved disease management.

Keywords: activities of daily living; ageing; assistive technology; chronic disease; decision making; e-health; elderly care; habits; integrated care; pervasive care; telecare; telehealth; telemedicine; telemetry; well-being.

Fig. 1.

inCASA monitoring platform.

Fig. 2.

Gateway and sensors used: (a) the gateway, (b) pulse oximeter, (c) PIR motion sensor, (d) bed sensor, (e) glucose meter, (f) weight scale, (g) medication dispenser, (h) BP meter. BP, blood pressure; PIR, passive infrared.

Fig. 3.

Mean number of movements detected by motion sensors per hour over the period between day 251 and 270 for living room (white) and bedroom (black).

Fig. 4.

Times of first movement after 5 am and last movement before midnight in the living room.

Fig. 5.

BP readings for patient 1—showing days with clinical concerns and medication change (vertical dotted lines) and nighttime event (dash and dot vertical line): systolic BP (top), diastolic BP (middle), and pulse rate (bottom).

Fig. 6.

Number of movements detected by motion sensor in living room for whole day (top) and for nighttime (bottom).

Fig. 7.

Number of movements detected by motion sensor in the bedroom for whole day (top) and for nighttime (bottom).

Fig. 8.

Quantiles of time-to-next-move for each day with daily move counts greater than 15: the 10th quantile (top), 50th quantile (middle) and 90th quantile (bottom).

Fig. 9.

Number of movements detected by motion sensors per hour for living room (white) and bedroom (black), for day 349 (left) and day 350 (right).

Fig. 10.

BP readings for patient 2: systolic BP (top), diastolic BP (middle), and pulse per second (bottom).

Fig. 11.

Number of movements detected by motion sensor in living room for whole day; clinical intervention marked by circle.

Fig. 12.

Quantiles of time-to-next-move for each day with daily move counts greater than 10: the 10th quantile (top), 50th quantile (middle), and 90th quantile (bottom); clinical intervention marked by circle.

Fig. 13.

SpO₂ readings for patient 3 indicating days of clinical concerns (vertical light dashed lines) and intervention (heavy dashed lines).

Fig. 14.

Number of movements detected by motion sensor in living room for whole day (top), nighttime 22.00–6.00 (middle), and afternoon 12.00–18.00 (bottom).

Fig. 15.

Times of first movement after 4 am and last movement before midnight in the living room.

Fig. 16.

Quantiles of time-to-next-move for each day with daily move counts greater than 30: the 10th quantile (top), 50th quantile (middle), and 90th quantile (bottom).

Fig. 17.

SpO₂ readings for patient 4.

Fig. 18.

Times of bed occupancies as stacked vertical black bars for each day.

Fig. 19.

Ordered occupancies of the five longest occupancies each day for the last 90 days.