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Pervasive Computing Technologies to Continuously Assess Alzheimer's Disease Progression and Intervention Efficacy.

Lyons BE, Austin D, Seelye A, Petersen J, Yeargers J, Riley T, Sharma N, Mattek N, Wild K, Dodge H, Kaye JA - Front Aging Neurosci (2015)

Bottom Line: Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function.These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline.Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

View Article: PubMed Central - PubMed

Affiliation: Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Neurology, Oregon Health and Science University , Portland, OR , USA.

ABSTRACT
Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals' health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients' and caregivers' ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

No MeSH data available.


Related in: MedlinePlus

ROC curve for a subset of 1,000,000 randomly selected data points comparing the trade-off between sensitivity and specificity of the logistic regression model’s performance for predicting transitions to a higher level of care.
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Figure 3: ROC curve for a subset of 1,000,000 randomly selected data points comparing the trade-off between sensitivity and specificity of the logistic regression model’s performance for predicting transitions to a higher level of care.

Mentions: The results indicated that transitions to an advanced level of care were highly predictable and the model was a good fit for the data (McFadden’s R2 = 0.71; p < 0.00001). There was a clear pattern of in-home measured activity and behavior – a behavioral signature – associated with increased risk of transition to higher care. Figure 2 illustrates this graphically with a spider plot and Figure 3 shows the receiver operating curve (ROC). From Figure 2, it can be seen that in-home measured computer use and sleep latency are especially important for predicting whether an individual is likely to need additional care. In a sensitivity analysis, the area under the curve of the ROC was 0.947. These results are somewhat optimistic because no unseen data were used to test out-of-sample model predictions. However, the expected modest reduction in performance due to out-of-sample generalization would still allow precise predictions of who is likely to need additional care. Overall, these results suggest that fusing data collected in-home with more traditional assessments has the potential to improve diagnostic precision and efficiency. Future work will compare the predictive accuracy of different measurement domains (e.g., in-home assessment, clinical assessment, self-report, etc.) and to identify the optimally fused model that has the fewest and most predictive variables.


Pervasive Computing Technologies to Continuously Assess Alzheimer's Disease Progression and Intervention Efficacy.

Lyons BE, Austin D, Seelye A, Petersen J, Yeargers J, Riley T, Sharma N, Mattek N, Wild K, Dodge H, Kaye JA - Front Aging Neurosci (2015)

ROC curve for a subset of 1,000,000 randomly selected data points comparing the trade-off between sensitivity and specificity of the logistic regression model’s performance for predicting transitions to a higher level of care.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4462097&req=5

Figure 3: ROC curve for a subset of 1,000,000 randomly selected data points comparing the trade-off between sensitivity and specificity of the logistic regression model’s performance for predicting transitions to a higher level of care.
Mentions: The results indicated that transitions to an advanced level of care were highly predictable and the model was a good fit for the data (McFadden’s R2 = 0.71; p < 0.00001). There was a clear pattern of in-home measured activity and behavior – a behavioral signature – associated with increased risk of transition to higher care. Figure 2 illustrates this graphically with a spider plot and Figure 3 shows the receiver operating curve (ROC). From Figure 2, it can be seen that in-home measured computer use and sleep latency are especially important for predicting whether an individual is likely to need additional care. In a sensitivity analysis, the area under the curve of the ROC was 0.947. These results are somewhat optimistic because no unseen data were used to test out-of-sample model predictions. However, the expected modest reduction in performance due to out-of-sample generalization would still allow precise predictions of who is likely to need additional care. Overall, these results suggest that fusing data collected in-home with more traditional assessments has the potential to improve diagnostic precision and efficiency. Future work will compare the predictive accuracy of different measurement domains (e.g., in-home assessment, clinical assessment, self-report, etc.) and to identify the optimally fused model that has the fewest and most predictive variables.

Bottom Line: Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function.These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline.Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

View Article: PubMed Central - PubMed

Affiliation: Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Neurology, Oregon Health and Science University , Portland, OR , USA.

ABSTRACT
Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals' health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients' and caregivers' ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

No MeSH data available.


Related in: MedlinePlus