Agitation Monitoring and Prevention System for Dementia Caregiver Empowerment

Homdee, Nutta; Alam, Ridwan; Hayes, James; Hamid, Tamanna; Park, John; Wolfe, Sean; Goins, Hilda; Fyffe, Nykesha; Newbold, T.; Smith‐Jackson, Tonya L.; Bankole, Azziza; Anderson, Martha; Lach, John

doi:10.1109/mc.2019.2933192

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…In-home monitoring of persons living with dementia was studied for unobtrusive preferences in both formal and informal caregivers. Both felt potential benefits in more proactive responses to the needs of persons living with dementia [ 7 ]. The COVID-19 pandemic brought increased urgency in the development of remote monitoring systems, which are applicable to our focus on agitation with dementia.…”

Section: Introductionmentioning

confidence: 99%

Dementia Caregiver Experiences and Recommendations for Using the Behavioral and Environmental Sensing and Intervention System at Home: Usability and Acceptability Study

Anderson¹,

Bankole²,

Homdee³

et al. 2021

JMIR Aging

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Background Caregiver burden associated with dementia-related agitation is one of the most common reasons for a community-dwelling person living with dementia to transition to a care facility. The Behavioral and Environmental Sensing and Intervention (BESI) for the Dementia Caregiver Empowerment system uses sensing technology, smartwatches, tablets, and data analytics to detect and predict agitation in persons living with dementia and to provide just-in-time notifications and dyad-specific intervention recommendations to caregivers. The BESI system has shown that there is a valid relationship between dementia-related agitation and environmental factors and that caregivers prefer a home-based monitoring system. Objective The aim of this study is to obtain input from caregivers of persons living with dementia on the value, usability, and acceptability of the BESI system in the home setting and obtain their insights and recommendations for the next stage of system development. Methods A descriptive qualitative design with thematic analysis was used to analyze 10 semistructured interviews with caregivers. The interviews comprised 16 questions, with an 80% (128/160) response rate. Results Postdeployment caregiver feedback about the BESI system and the overall experience were generally positive. Caregivers acknowledged the acceptability of the system by noting the ease of use and saw the system as a fit for them. Functionality issues such as timeliness in agitation notification and simplicity in the selection of agitation descriptors on the tablet interface were identified, and caregivers indicated a desire for more word options to describe agitation behaviors. Agitation intervention suggestions were well received by the caregivers, and the resulting decrease in the number and severity of agitation events helped confirm that the BESI system has good value and acceptability. Thematic analysis suggested several subjective experiences and yielded the themes of usefulness and helpfulness. Conclusions This study determined preferences for assessing caregiver strain and burden, explored caregiver acceptance of the technology system (in-home sensors, actigraph or smart watch technology, and tablet devices), discerned caregiver insights on the burden and stress of caring for persons living with dementia experiencing agitation in dementia, and solicited caregiver input and recommendations for system changes. The themes of usefulness and helpfulness support the use of caregiver knowledge and experience to inform further development of the technology.

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Section: Introductionmentioning

confidence: 99%

Dementia Caregiver Experiences and Recommendations for Using the Behavioral and Environmental Sensing and Intervention System at Home: Usability and Acceptability Study

Anderson¹,

Bankole²,

Homdee³

et al. 2021

JMIR Aging

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“…Part 2 consisted of a list of variables that may influence cancer pain in the home setting that participants were asked to rank regarding impact. The list of variables was created based on their known relationship with cancer pain (such as the connection between sleep and pain) [ 40 , 41 ], current technological capabilities of the parent BESI system [ 42 - 44 ], and our hypotheses that certain environmental variables (eg, light and noise) that have received scant attention in the literature [ 26 ] can influence cancer pain. Overall, 14 variables were included in the final list and grouped into 4 categories: medication, wellness, interaction, and environmental.…”

Section: Methodsmentioning

confidence: 99%

Understanding the Experience of Cancer Pain From the Perspective of Patients and Family Caregivers to Inform Design of an In-Home Smart Health System: Multimethod Approach

LeBaron¹,

Bennett²,

Alam³

et al. 2020

JMIR Form Res

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Background Inadequately managed pain is a serious problem for patients with cancer and those who care for them. Smart health systems can help with remote symptom monitoring and management, but they must be designed with meaningful end-user input. Objective This study aims to understand the experience of managing cancer pain at home from the perspective of both patients and family caregivers to inform design of the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) smart health system. Methods This was a descriptive pilot study using a multimethod approach. Dyads of patients with cancer and difficult pain and their primary family caregivers were recruited from an outpatient oncology clinic. The participant interviews consisted of (1) open-ended questions to explore the overall experience of cancer pain at home, (2) ranking of variables on a Likert-type scale (0, no impact; 5, most impact) that may influence cancer pain at home, and (3) feedback regarding BESI-C system prototypes. Qualitative data were analyzed using a descriptive approach to identity patterns and key themes. Quantitative data were analyzed using SPSS; basic descriptive statistics and independent sample t tests were run. Results Our sample (n=22; 10 patient-caregiver dyads and 2 patients) uniformly described the experience of managing cancer pain at home as stressful and difficult. Key themes included (1) unpredictability of pain episodes; (2) impact of pain on daily life, especially the negative impact on sleep, activity, and social interactions; and (3) concerns regarding medications. Overall, taking pain medication was rated as the category with the highest impact on a patient’s pain (=4.79), followed by the categories of wellness (=3.60; sleep quality and quantity, physical activity, mood and oral intake) and interaction (=2.69; busyness of home, social or interpersonal interactions, physical closeness or proximity to others, and emotional closeness and connection to others). The category related to environmental factors (temperature, humidity, noise, and light) was rated with the lowest overall impact (=2.51). Patients and family caregivers expressed receptivity to the concept of BESI-C and reported a preference for using a wearable sensor (smart watch) to capture data related to the abrupt onset of difficult cancer pain. Conclusions Smart health systems to support cancer pain management should (1) account for the experience of both the patient and the caregiver, (2) prioritize passive monitoring of physiological and environmental variables to reduce burden, and (3) include functionality that can monitor and track medication intake and efficacy; wellness variables, such as sleep quality and quantity, physical activity, mood, and oral intake; and levels of social interaction and engagement. Systems must consider privacy and data sharing concerns and incorporate feasible strategies to capture and characterize rapid-onset symptoms.

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“…Data availability Behavioral and Environmental Sensing and Intervention for Caregiver Empowerment [9][10][11][12].…”

Section: Appendixmentioning

confidence: 99%

“…In this paper, a data-driven deep learning methodology is presented which, utilizing data collected within the home of a dementia dyad, distinguishes between agitation and non-agitation time segments using data collected from a single dyad of the Behavioral and Environmental Sensing and Intervention for Caregiver Empowerment (BESI) study [9][10][11][12]. Here, five environmental factors (i.e., light, audio, temperature, humidity, and air pressure) were collected and validated against the ground truth record of agitation events as reported by the caregivers.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Forecasting of Agitation for Persons with Dementia: A Deep Learning-Based Approach

et al. 2021

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The World Health Organization estimates that approximately 10 million people are newly diagnosed with dementia each year and a global prevalence of nearly 50 million persons with dementia (PwD). The vast majority of PwD living at home receive the majority of their care from informal familial caregivers. The quality of life (QOL) of familial caregivers may be significantly impacted by their caregiving responsibilities and resultant caregiver burden. A major contributor to caregiver burden is the random occurrence of agitation in PwD and familial caregivers' lack of preparedness to manage these episodes. Caregiver burden may be reduced if it is possible to forecast impending agitation episodes. In this study, we leverage datadriven deep learning models to predict agitation episodes in PwD. We used Long Short-Term Memory (LSTM), a deep learning class of algorithms, to forecast agitations up to 30 min before actual agitation events. In particular, we managed the missing data by estimating the missing values and compensated for the class imbalance challenge by down-sampling the majority class. The simulations were based on real-world data from Alzheimer's disease (AD) caregivers and PwD dyads home environments, including ambient noise level, illumination, room temperature, atmospheric pressure (Pa), and relative humidity. Our results show the efficacy of data-driven deep learning models in predicting agitation episodes in communitydwelling AD dyads with accuracy of 98.6% and recall (sensitivity) of 84.8%.

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Agitation Monitoring and Prevention System for Dementia Caregiver Empowerment

Cited by 16 publications

References 17 publications

Dementia Caregiver Experiences and Recommendations for Using the Behavioral and Environmental Sensing and Intervention System at Home: Usability and Acceptability Study

Dementia Caregiver Experiences and Recommendations for Using the Behavioral and Environmental Sensing and Intervention System at Home: Usability and Acceptability Study

Understanding the Experience of Cancer Pain From the Perspective of Patients and Family Caregivers to Inform Design of an In-Home Smart Health System: Multimethod Approach

Data-Driven Forecasting of Agitation for Persons with Dementia: A Deep Learning-Based Approach

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