Methods: This is a pre- and post-measurement intervention study conducted in low-income community housing projects in Kuala Lumpur, Malaysia. A total of 90 participants aged 18 years and above with hypertension received intervention. The participants were divided into small groups and received instructions on the use of home blood pressure measurement. They also attended a series of talks on dietary intake modification and exercise demonstration for the first six months (active phase). In another 6 months (maintenance phase), they received only pamphlet and SMS reminders. Their anthropometry, blood pressure, dietary, and biochemical parameter changes were measured at baseline, 6 months, and 12 months of intervention.
Results: Macronutrients and micronutrients showed a significant improvement at the end of 12-month dietary intervention. The energy, carbohydrate, protein, total fat, sodium, and potassium are showing significant reduction from baseline to end of the 12-month intervention. There is no significant reduction in blood pressure. Fasting blood glucose, renal sodium, triglyceride, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol showed a significant improvement, after controlling for age and reported physical activity.
Conclusion: The intervention improved the nutritional intake and biochemical profiles of the low-income urban population with hypertension. This promising result should be replicated in a larger scale study.
METHODS: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge.
RESULTS: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED.
CONCLUSIONS: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.