How Early Tracking of Sundowning Improves Care, Staffing and Billing

The signs are all there. What happens next — that depends.

Dementia care is a major priority in senior living and continues to grow, as the ballooning Boomer aging population increases the raw numbers of seniors affected by dementia and other cognitive declines. A 2018 paper showed that by age 80, 16% of adults in 2015 had probable dementia, rising to 31% by age 90. The State of Seniors Housing Report for 2021 captured data from 1,800 properties; 7% of respondents were freestanding memory care providers while close to 24% offered a combination of AL and memory care.

And in the 2023 Senior Housing News Outlook Survey and Report, 79% of respondents predicted national occupancy rates in memory care to rise over the next year, the highest percentage of any care setting.


This increase in residents with dementia means an increase in sundowning, the term for late-day confusion among people with Alzheimer’s and other dementias. Sundowning can lead to increased feelings of agitation, at times escalating to wandering, hallucinations and, in rare cases, violence. Senior living operators, even those without standalone memory care, benefit from the ability to proactively track resident behaviors in order to detect early patterns associated with sundowning.

In its continued promotion of “Health Sustainability,” senior living health care platform Fynn is helping care teams identify resident changes earlier for proactive, personalized care. Since August 2022, Fynn has deployed its care model in four combined IL/AL/MC communities, and have been able to draw early findings, showing that they have a positive impact on both residents and staff by identifying earlier indications of patterns that may be related to sundowning.

This is a look at how one operator found those early indicators and improved life for their residents.


The Big Idea: A Proactive Approach to Dementia Care

“Health Sustainability” is the conceptual and actionable approach to transforming the availability, efficiency, and quality of senior living options and operations. While the Health Sustainability movement seeks to bring together the larger senior living ecosystem, Fynn’s novel data collection and analysis of behavioral data recorded in the platform allows operators to detect early patterns of behaviors associated with sundowning.

These early findings are the first step of many toward improving the resident care experience and, ultimately, creating Health Sustainability.

“As our data collection becomes more robust, we anticipate Fynn’s behavioral analysis will become even more precise in its identification of ‘residents at-risk,’ which will be a major game-changer for resident well-being and cost-saver for communities,” says John Thomas, Chief Technology Officer at Fynn.

Fynn leverages behavioral analysis to continually record resident behaviors as well as physical health data collected by staff upon completion of tasks and activities of daily living (ADLs). By examining both behavioral and physiological data, Fynn provides a more complete picture of a resident’s health across frequently recorded data points rather than limiting data to a few formal assessments per year.

Recognizing that sundowning often has negative impacts on both resident and employee well-being, Fynn began analyzing behavioral trends among residents compared to the time of day to see if negative behavioral patterns increased in the afternoon and evening hours.

How one operator tracked early indicators to improve a resident’s care plan

In Fynn’s 2022 study, one memory care operator was able to help a resident by using data to adjust ADL task scheduling and discuss possible medication and/or programming changes with the resident’s physician and family. The starting point was identifying an increase in negative behaviors for that resident. Staff members observed that this resident was stable throughout most of the morning and sharply declined in the afternoon.

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For another memory care resident, the demonstrated increase in negative behaviors triggered a change in caregivers, as the data helped identify that the resident responded better to male caregivers. It also prompted an appointment with the resident’s neurologist to discuss adjusting his medications as well as a shift in his daily routine.

Going beyond residents of memory care, the operator credited Fynn with helping to identify other at-risk residents in IL and AL. Fynn detected declining behavioral patterns that may indicate changing acuity and recommended staff investigate the causality of the negative behavior. Based on the findings, the operator implemented interventions in collaboration with residents’ families and clinicians.

Fynn’s findings in resident care, staffing and billing

These early findings are part of Fynn’s larger ongoing study on how using continuous behavioral analysis with physiological data can impact larger challenges such as:

  • Improving the resident care experience with the appropriate level of care
  • Improving billing accuracy
  • Improving workload balancing/appropriate staffing for staff retention
  • Reducing incidents through proactive care

To improve both the resident and employee experience, Fynn’s behavioral analysis capabilities enable staff to:

  1. transform observations and valuable tacit knowledge into measurable insights
  2. …recognize shifts in resident behavior as a potential early indicator of changes in acuity
  3. …take data-informed preventative measures to promote resident well-being

In all, the community believes Fynn’s behavioral analysis will help them better understand when and how to intervene earlier, thus improving resident care and potentially reducing incidents. Furthermore, community ownership and managers hope to use the correlation between sundowners and adverse events to improve root-cause analysis of the major challenges affecting NOI.

“As communities’ volume of data increases, Fynn’s intelligent system will expand from demonstrating trends to recognizing specific risks and offering recommended actions based on identified trends,” Thomas says. “Using behavioral data to generate automated alerts and system-directed tasks based on identified patterns is the next step as Fynn works to help communities provide proactive care.”

This article is sponsored by Fynn and is adapted from its initial early findings report “Behavioral Analysis to Detect Early Indicators of Changing Acuity of Seniors.” To read the entire report, visit

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