Background

It is unclear whether subgroups of patients may benefit from remote monitoring systems (RMS) and what user characteristics and contextual factors determine effective use of RMS in patients with heart failure (HF).

Objective

The study was conducted to determine whether certain user characteristics (i.e. personal and clinical variables) predict use of RMS using advanced machine learning software algorithms in patients with HF.

Methods

This pilot study was a single-arm experimental study with a pre- (baseline) and post- (3 months) design; data from the baseline measures were used for the current data analyses. Sixteen patients provided consent; only 7 patients (mean age 65.8 ± 6.1, range 58-83) accessed the RMS and transmitted daily data (e.g. weight, blood pressure) as instructed during the 12 week study duration.

Results

Baseline demographic and clinical characteristics of users and non-users were comparable for a majority of factors. However, users were more likely to have no HF specialty based care or an automatic internal cardioverter defibrillator. The precision accuracy of decision tree, multilayer perceptron (MLP) and k-Nearest Neighbor (k-NN) classifiers for predicting access to RMS was 87.5%, 90.3%, and 94.5% respectively.

Conclusion

Our preliminary data show that a small set of baseline attributes is sufficient to predict subgroups of patients who had a higher likelihood of using RMS. While our findings shed light on potential end-users more likely to benefit from RMS-based interventions, additional research in a larger sample is warranted to explicate the impact of user characteristics on actual use of these technologies.

Background

The use of remote monitoring systems (RMSs) in healthcare has grown exponentially and has improved the accessibility to and ability of patients to engage in treatment intensification. However, research describing the effects of RMSs on activation, self-care, and quality of life (QOL) in older patients with heart failure (HF) is limited.

Objective

The aim of this study was to compare the effects of a 3-month RMS intervention on activation, self-care, and QOL of older patients versus a reference group matched on age, gender, race, and functional status (ie, New York Heart Association classification) who received standard discharge instructions after an acute episode of HF exacerbation requiring hospitalization.

Methods

A total of 21 patients (mean age, 72.7 ± 8.9 years; range, 58-83 years; 52.4% women) provided consent and were trained to measure their weight, blood pressure, and heart rate at home with an RMS device and transmit this information every day for 3 months to a centralized information system. The system gathered all data and dispatched alerts when certain clinical conditions were met.

Results

The baseline sociodemographic and clinical characteristics of the 2 groups were comparable. Over time, participants in the RMS group showed greater improvements in activation, self-care, and QOL compared with their counterparts. Data showed moderately strong associations between increased activation, self-care, and QOL.

Conclusion

Our preliminary data show that the use of an RMS is feasible and effective in promoting activation, self-care, and QOL. A larger-scale randomized clinical trial is warranted to show that the RMS is a new and effective method for improving clinical management of older adults with chronic HF.

Background

The use of remote monitoring systems (RMSs) in healthcare has grown exponentially and has improved the accessibility to and ability of patients to engage in treatment intensification. However, research describing the effects of RMSs on activation, self-care, and quality of life (QOL) in older patients with heart failure (HF) is limited.

Objective

The aim of this study was to compare the effects of a 3-month RMS intervention on activation, self-care, and QOL of older patients versus a reference group matched on age, gender, race, and functional status (ie, New York Heart Association classification) who received standard discharge instructions after an acute episode of HF exacerbation requiring hospitalization.

Methods

A total of 21 patients (mean age, 72.7 ± 8.9 years; range, 58-83 years; 52.4% women) provided consent and were trained to measure their weight, blood pressure, and heart rate at home with an RMS device and transmit this information every day for 3 months to a centralized information system. The system gathered all data and dispatched alerts when certain clinical conditions were met.

Results

The baseline sociodemographic and clinical characteristics of the 2 groups were comparable. Over time, participants in the RMS group showed greater improvements in activation, self-care, and QOL compared with their counterparts. Data showed moderately strong associations between increased activation, self-care, and QOL.

Conclusion

Our preliminary data show that the use of an RMS is feasible and effective in promoting activation, self-care, and QOL. A larger-scale randomized clinical trial is warranted to show that the RMS is a new and effective method for improving clinical management of older adults with chronic HF.

Objective: This study was conducted to evaluate the feasibility, usability, and acceptability of using remote monitoring systems (RMS) in monitoring health status (e.g., vital signs, symptom distress) in older adults (≥55) with chronic heart failure (HF). Method: Twenty-one patients (52.4% women, mean age 73.1 ± 9.3) were trained to measure and transmit health data with an RMS. Data transmissions were tracked for 12 weeks. Results: All participants initiated use of RMS within 1 week; 71%, 14%, and 14% of patients transmitted daily health data 100%, ≥75%, and <75% of the time, respectively, for 12 weeks. Overall usability and acceptability of the RMS were 4.08 ± 0.634 and 4.10 ± 0.563, respectively (when scored on a range of 1-5, where 1 = strongly disagree and 5 = strongly agree). Discussion: Findings show that an RMS-based intervention can be successfully implemented in a group of older patients with chronic HF.

Background

Medication nonadherence is a critical problem with severe implications in individuals at risk for atherosclerotic cardiovascular disease. Many studies have attempted to predict medication adherence in this population, but few, if any, have been effective in prediction, sug-gesting that essential risk factors remain unidentified.

Objective

This study's objective was to (1) establish an accurate prediction model of medi-cation adherence in individuals at risk for atherosclerotic cardiovascular disease and (2) identify significant contributing factors to the predictive accuracy of medication adherence. In particular, we aimed to use only the baseline questionnaire data to assess medication adherence prediction feasibility.

Methods

A sample of 40 individuals at risk for atherosclerotic cardiovascular disease was recruited for an eight-week feasibility study. After collecting baseline data, we recorded data from a pillbox that sent events to a cloud-based server. Health measures and medication use events were analyzed using machine learning algorithms to identify variables that best predict medication adherence.

Results

Our adherence prediction model, based on only the ten most relevant variables, achieved an average error rate of 12.9%. Medication adherence was closely correlated with being encouraged to play an active role in their treatment, having confidence about what to do in an emergency, knowledge about their medications, and having a special person in their life.

Conclusions

Our results showed the significance of clinical and psychosocial factors for predicting medication adherence in people at risk for atherosclerotic cardiovascular diseases. Clini-cians and researchers can use these factors to stratify individuals to make evidence-based decisions to reduce the risks.