JMIR MEDICAL INFORMATICS

Lussier et al

Original Paper

Using Ambient Assisted Living to Monitor Older Adults With
Alzheimer Disease: Single-Case Study to Validate the Monitoring
ReportMaxime Lussier1,2, PsyD, PhD; Aline Aboujaoudé1, MSc; Mélanie Couture3,4, PhD; Maxim Moreau5, PhD; Catherine
Laliberté6, MSc; Sylvain Giroux6, PhD; Hélène Pigot6, PhD; Sébastien Gaboury7, PhD; Kévin Bouchard7, PhD; Patricia
Belchior1,8, PhD; Carolina Bottari2, PhD; Guy Paré5, PhD; Charles Consel9, PhD; Nathalie Bier1,2, PhD
1

Research Center of Institut universitaire de gériatrie de Montréal, Integrated Health and Social Services University Network for South-Central Montreal,Montreal, QC, Canada
2

School of Rehabilitation, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada

3

Integrated Health and Social Services University Network for West-Central Montreal, Université de Sherbrooke, Sherbrooke, QC, Canada

4

Department of Psychology, Université de Sherbrooke, Sherbrooke, QC, Canada

5

Research Chair in Digital Health, High Commercial Studies of Montreal, Montreal, QC, Canada

6

Faculty of Sciences and Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada

7

Department of Mathematics and Computer Science, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada

8

School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada

9

Bordeaux Institute of Technology & Inria, Bordeaux, France

Corresponding Author:
Maxime Lussier, PsyD, PhD
Research Center of Institut universitaire de gériatrie de Montréal
Integrated Health and Social Services University Network for South-Central Montreal
4545 chemin Queen-MaryMontreal, QC, H3W 1W6
CanadaPhone: 1 514 340 3540
Email: lussier.maxime@gmail.com

Abstract
Background: Many older adults choose to live independently in their homes for as long as possible, despite psychosocial andmedical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges inallocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted
living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevantchanges to support decision making at an individual level.
Objective: The aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered bycare professionals using triangulation.
Methods: This longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer diseasereceiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times
during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities,
periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring
reports were compared with information gathered by the care professional to explore concurrent validity.
Results: Over time, the monitoring reports showed evolving trends in the care recipient’s daily activities. Significant activity
changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observedsome trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in themonitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detectedchanges in daily trends following an intervention specific to meal preparation.
Conclusions: Overall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse’s
questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential
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Lussier et alof AAL technologies to support health care services and aging in place by providing valid and clinically relevant information
over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision
making.
(JMIR Med Inform 2020;8(11):e20215) doi: 10.2196/20215
KEYWORDS
activities of daily living; aging; Alzheimer disease; ambient assisted living; health care; technology assessment; health; remotesensing technology

Introduction
Background
Neurocognitive disorders affect 50 million individuals globally,
with nearly 10 million new cases diagnosed each year [1].
Alzheimer disease (AD), the most common cause of dementia,
is a disabling condition that has detrimental effects on memory,
thinking abilities, behavior, and the ability to perform daily
activities. By 2050, it is expected that one new case of AD willdevelop every 33 seconds, resulting in nearly 1 million newcases per year [2]. These numbers translate into an important
global economic burden. Although the cost of providing medical
and social care to individuals with dementia differs by country,
the annual global cost of dementia in 2019 was estimated at US$1 trillion and is expected to double by 2030 [1]. As such, theWorld Health Organization recognizes it as a public healthpriority [3].
From the perspective of individual functional status, the speed
and magnitude of decline varies, but the functional loss
continuum begins with difficulties in completing complex
instrumental tasks and continues until there is a complete lossof the ability to perform basic activities of daily living [4].
Instrumental tasks, including cooking, housekeeping, taking
public transportation, and managing medication and finances,
start declining in mild to moderate stages of the disease [4].
Basic activities of daily living—self-care activities such as
eating, hygiene, grooming, and dressing—start declining in themoderate to severe stages of AD [4]. As such, throughout the
progression of AD, older adults can become vulnerable toself-neglect.
Self-neglect is a behavioral condition that is characterized by
the inability to sustain primary personal needs, including
sufficient intake of food, personal hygiene, taking medication,and living safely [5]. Self-neglect predisposes older adults todevastating consequences such as multiple emergency
department visits, maltreatment, nutritional deficiencies,
nonadherence to medical treatment, and higher rates ofmorbidity, mortality, and nursing home placement [6-11]. The
prevalence of self-neglect in older adults is high, ranging from
5% to 21%, on the basis of several factors [9], with the presenceof cognitive impairments being the most important factor [12,13]
even when the decline is mild [14-17]. The resulting decrease
in independence in daily living has a significant impact on theindividual’s ability to stay at home, which causes an increased
risk of institutionalization [18,19]. However, research has shownthat older adults with neurological impairments prefer to live
independently in their own homes even if they may be dependent
on others for managing their daily lives [20]. Moreover, recent
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research has found that, even with cognitive impairment, living
at home is more beneficial to older adults than relocating to anursing home. For example, it was shown that older adults living
at home experience a better quality of life, have better cognitive
function, are less depressed, and are more socially active, witheffects remaining even after stratifying for severity of dementia[21-23].

Ambient Assisted Living
Considering the growth of the aging population, theunprecedented economic pressures on the health and social caresystem this entails, and the benefits of staying at home for older
adults, there is an urgent need to provide sufficient home care
support to individuals with cognitive disorders.
In recent years, the use of ambient assisted living (AAL) systems
has emerged as a way of promoting and extending aging inplace. Implementing AAL systems involves deploying
technologies (eg, sensors and actuators) in a home environment
for the purpose of collecting continuous and real-timemonitoring information about the environment (eg, temperature,humidity, and smoke in the home), the occupants (activities ofdaily living routines), and their health (eg, heart rate, bodytemperature, blood pressure, and blood oxygen level) [24]. AAL
monitoring has many uses, such as detecting the occurrence of
unusual or hazardous events (eg, a fall, cardiac arrest, or
bradycardia) and alerting a dedicated resource person to provide
immediate support and prevent dangerous situations. Anotherapproach uses rich and reliable data from AAL monitoring tosupport clinical decision making regarding a care recipient’s
state [25-27]. For example, home care professionals have usedhome monitoring to adjust their care plan on the basis of clinicaland home monitoring data [27]. For instance, they could add
interventions if through monitoring they observe that a carerecipient is sleeping too much or skipping meals. In addition,in the context of applying technological solutions in the care ofseniors with AD, Kaye [28] proposed that AAL monitoring may
be used to capture meaningful real-time changes in individuals’
dementia trajectories and therefore plan preventive strategies.
Considering the current limitations of functional assessments
and the challenges associated with AD, Kaye suggested thatAAL monitoring be used to improve health maintenance byoffering “the opportunity to not only observe change in the
person’s usual environment but also to more frequently, and insome cases continuously, monitor a subject for salient change”[28]. Indeed, this could facilitate the development of timely
strategies to maintain independence through later life or predict
progression to disability.

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Although novel, AAL monitoring has already shown promisingoutcomes. A recent literature review [26] concluded that themost promising technologies are those that monitor activities
of daily living and detect falls and changes in health status. In
2016, a comprehensive review of the frameworks and sensors
used in various AAL systems also showed that such technologies
are often used to assess immediate safety risks to promote older
adults living independently by alerting someone when aworrying situation arises [29]. However, these authors foundthat AAL systems are not often used for analytics and decision
making, particularly for long-term care. For example,
environmental factors such as temperature, humidity, motion,light, and contact could be analyzed to monitor health declineor the efficacy of an intervention.
Nevertheless, implementing AAL monitoring in real-life clinicalsettings presents several challenges. According to Peetom et al[26], to provide data effectively, it is essential that monitoring
technology be based on algorithms that enable clinically relevantchanges and situations to be detected without an overabundanceof false alarms. Although AAL monitoring can predict cognitive
decline among large cohorts of older adults [30], it remains tobe shown whether it can support clinical decision making at anindividual level (eg, for a care recipient). Clinicians have raised
concerns about technology overloading them with information,
especially irrelevant information [25], and generally clinicians
have little office time to examine lengthy reports. Thus, it isimportant for them to be able to visualize collected data in a
way that is intuitive and relevant for clinical decision making[31]. Other studies have used machine learning approaches topredict conditions such as cognitive decline [32-36]. For
example, Dawadi et al [33] used statistical features (variances,autocorrelation, skewness, kurtosis, and change) of daily activity
behavior (ie, total sensor events, cook duration, sleep duration)
to train machine learning algorithms to predict the clinicalassessment scores. With it, they achieved an accuracy of 72%
in classifying cognitive assessment scores. Although these arepromising and effective approaches, the process to which scores
are calculated based on machine learning are at times described
as a black box, owing to their lack of transparency [37]. By lackof transparency, we mean that it can be difficult for a clinician
to grasp why the system has reached a given conclusion and,afterward, to explain or justify how these scores influenced theirdecision making (ie, understanding why the system has reacheda given conclusion can be unfathomable). Transparency is
critical for commercial, legal, and clinical applications because
professionals must justify their decisions on the basis of tangible
observations [38]. This finding was supported in preliminary
focus groups conducted within the context of this study, as care
professionals explicitly stated that they did not wish to beprovided a conclusion or told what to do; instead, they wished
to receive valid information that could be used to enhance theirdecision making [27].
As such, in this study, we provided a care professional with
AAL monitoring reports that showed transparent informationon their care recipients’ daily routines (ie, time spent per day
performing daily activities and standard deviation in time spent
performing them). Statistical analyses applied to the collected
data highlighted significant trends. This was done in a way thathttps://medinform.jmir.org/2020/11/e20215

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Lussier et al
allowed the care professional to easily distinguish normal
fluctuations from presumed significant changes in daily routinesand to allow the care professional interpret these trends and takethem into account in their decision-making process.

Objectives
This study seeks to provide essential insights into the clinicalrelevance of AAL use in real-life settings to support the deliveryof home care services over time. Specifically, we seek to
examine the concurrent validity of AAL monitoring reports andthe well-established methods used by care professionals togather information, given that any discrepancy between these2 methods would lessen the perceived value of monitoring
reports. To achieve this goal, a single-case study design is used
to compare the information gathered regarding one care recipientwith AD. A single-case approach is recommended when changes
over time need to be assessed repeatedly [39]. Moreover, it
allows for a more in-depth examination of consistency between
events that would be difficult to translate into group means, asis the case with AAL monitoring reports.

Methods
This longitudinal single-case study is part of a larger project
designed to understand how AAL monitoring can be
successfully implemented in home care services and support
the decision making of health and social care professionals in
the public social and health care system [27]. The case in thisstudy comprised one care recipient (Lisette) and the health andsocial care professional responsible for her home care support(referred to here as the care professional). Lisette’s assigned
case manager is a clinical nurse with a Bachelor’s degree innursing. She evaluates client health status and ensures that thenursing care and treatment plan is implemented for patientswith complex health problems and provides care and treatment.
As a case manager, her duties also include coordinating Lisette’s
specific needs for care and services and supervising her home
support [40].

Recruitment
Health and social care professionals from the home care servicesof a local community health and social services center wereinvited to identify older adults who could benefit from AAL
monitoring technology. To be included in the study, a carerecipient had to be (1) receiving home care services due to aloss in functional autonomy related to a cognitive decline and(2) living alone. Care recipients were informed that AAL
monitoring technology would be integrated into their home tomonitor their daily routine (eg, sleeping and cooking). Care
professionals were told that they would receive monthly reports
on such activities to better understand the person’s dailyfunctioning. They would also be interviewed to better understandhow they found the experience of using the monitoring datareports. The project was approved by the Aging and
Neuroimaging Ethics Review Board of the South-Central
Montreal Integrated University Health and Social Services
Center (CER VN 16-17-22). All participants signed an informed
consent form before taking part in the data collection process.
Lisette was identified as a fitting care recipient for the study by
her care professional in January 2017.
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Data Collection
The AAL monitoring technology was installed in Lisette’s home
on February 9, 2017. Her participation in the project ended ayear and a half later, after her hospitalization and transfer to along-term care facility, on July 6, 2018. Her data set wascollected through a monitoring period that extended over 490
days, using 25 sensors and comprising approximately 617,000
logs. The sensors and algorithms used to create the monitoring
reports are described later.
The monitoring reports were triangulated with multiple sourcesof data to examine their concurrent validity: medical file,verbatim of interviews with the care professional, emails, andmemos of telephone exchanges (approximately 20) with thecare professional. The care professional was interviewed on
January 24, 2017 (before any monitoring report was sent); June
14, 2017; October 25, 2017; and October 26, 2018 (after
Lisette’s hospitalization). These interviews were conducted bya researcher specializing in qualitative research (MC) andtriangulated with the monitoring report outcomes [27]. The final
interview took place 3 months after Lisette’s hospitalization
due to uncertainty about Lisette’s transfer during that period.

Case Description
Lisette was selected as a representative case for this study forseveral reasons. First, she was an older woman with AD whowished to remain in the apartment in which she had been living
for several years for as long as possible. Second, the extensive
data set collected through a 490-day monitoring period allowed
for a rich analysis. Third, the monitoring began when the care
professional became concerned about Lisette’s ability to live
independently and continued until her transfer due to thedeterioration of her condition. Therefore, we were confident
that documenting this individual during this particular period
would encompass significant changes in behavior, frombeginning to end.
Lisette, a widowed older woman, was 91 years old at the timeof her recruitment and had been living in the same one-bedroom
apartment for the last 14 years. Her apartment was in a residence
for independent seniors. Her son was her main caregiver andthe primary contact for health care providers in case of an
emergency. In 2015, she was diagnosed with AD, but a vascularetiology was also considered. Despite the diagnosis, she wished
to continue living in her apartment, with assistance, anddeveloped a social network with her neighbors. Although her
son was considering whether Lisette should be transferred to afacility with a higher level of care, her daughter did not consider
it necessary; thus, they had conflicting points of view about
their mother’s level of independence. Their difference of opinion
made it difficult for the care professional to gather reliable
information to guide her own clinical decision making.

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Moreover, Lisette seemed to have limited awareness of her
cognitive difficulties and their impact on daily living. Her son
and the care professional mentioned that she was often unreliable
when asked about her everyday routine and recent events.
In this context, Lisette’s care professional wished to acquire
objective and reliable information regarding Lisette’s routine.
This would enable her to better assess the safety risks related
to maintaining Lisette in her home and to confirm or refute her
clinical hypothesis before developing a comprehensive
intervention plan. The main issues of concern identified by thecare professional at the outset of the study were as follows: (1)malnutrition (because of food left untouched in the refrigerator
over long periods of time and low body weight), (2) hazardous
use of the stove (safety concerns expressed by the landlord),and (3) poor personal hygiene (slow shrinkage of the bar ofsoap observed by the care professional).
The monitoring period began on February 9, 2017, and ended
on July 6, 2018. During this period, the care professional
observed that Lisette’s AD symptoms progressed. Duringfollow-up clinical visits, Lisette’s memory and orientation
declined noticeably and continuously from December 2018
onward. Monitoring continued until Lisette experienced aserious episode of confusion and panic, calling her son because
she was looking for her late husband. Following that episode,she was hospitalized on July 6, 2018, and shortly thereafter was
moved by her family to a private home for older adults, ending
the monitoring.

Sensors and Algorithms
Three different types of sensors were integrated into the homeenvironment: passive infrared (PIR) sensors (Everspring
HSP02), magnetic contact sensors (Everspring HSM02), andsmart electric switches (Aeotec ZW078 and ZW096). An attempt
to integrate water sensors was unsuccessful due to rapid
corrosion of the sensors when submerged in water anddifficulties mounting them in a humid environment. Wirelesssensors were used because they were easy to install in a real-life
setting and could be moved to a new apartment if needed; thesewere considered important characteristics for decision makers
in the context of a public health care system.
One PIR sensor per room was installed in the kitchen, dining
room, and living room; at the entrance; and in the bathroom.
Two were installed in the bedroom: one aimed toward the bed
and another directed toward the door by which to exit the room.
Electric sensors were connected to a television, a bed lamp, amicrowave, a toaster, and a stove. Contact sensors were installedon the entrance door, on 2 dresser drawers in the bedroom, andon 2 food storage cabinets, the refrigerator, freezer, oven, utensil
drawer, and 4 cupboards in the kitchen. Figure 1 illustrates amap of sensor deployment in Lisette’s apartment.

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Figure 1. Sketch of the placement of sensors in Lisette’s apartment. Conic area: passive infrared motion sensors; door symbol: magnetic contact sensors;
electric plug symbol: smart electric switches.

The selection and placement of wireless sensors were
customized to specifically focus on daily activities that wererelevant to Lisette’s care professional, namely, sleep habits(sleep), exiting the apartment (outing), periods of prolonged
inactivity (low mobility), cooking-related activities (cooking),
and hygiene-related activities performed in the bathroom(hygiene). Algorithms were built around various assumptions
about these different activities, as previously described in astudy by Lussier et al [27]. First, room occupation wasrecognized as follows: the occupant was considered to be in
one room for as long as he or she was not detected in anotherroom or outside the apartment. Sleep was identified if theoccupant spent more than 20 min in the bedroom without
interacting with any sensors other than the PIR directed at thebed. Outing was recognized if the following sequence of eventsoccurred: (1) closing the entrance door, (2) no PIR activity inthe home for more than 5 min, and (3) opening the entrance
door. Low mobility was recognized if no PIR sensors and nocontact sensors were triggered over a 15-min period and theoccupant was not recognized as resting (in the bedroom) or
being out. With respect to cooking activities (ie, meal
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assumption was that the occupant was cooking if several sensors
placed in the kitchen were triggered over a short period. Moreprecisely, for each 15-min period (ie, 3 PM to 3:15 PM, 3:01
PM to 3:16 PM, etc), a cooking score was established on thebasis of the frequency and diversity of sensors triggered in the
kitchen. If the score was 2 SDs higher than the average for thatoccupant, they were then recognized as cooking. This approach
is similar to that used by Rantz et al [41] and was done to report
only on activities that were significant in the context of theoccupant’s daily routine. Finally, given that water sensors could
not be successfully installed on the sink and bathtub faucets,the recognition of hygiene (ie, brushing teeth, showering, goingto the toilet, washing hands) relied exclusively on prolonged
presence in the bathroom. Similar to the cooking activity, ascore was calculated on the basis of the number of minutes spentin the bathroom per 15-min window. The score then had to be
2 SDs higher than the average for that occupant to be recognized
as engaging in a period of bathroom hygiene. It is important tonote that it was impossible to verify whether the occupant orsomeone else (eg, the caregiver) was performing the actions.
The care professional was aware of this limitation. See Textbox
1 for an overview of detailed algorithms.

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Textbox 1. Overview of daily activity algorithms.
##MAIN ALGORITHM
While(TRUE)
Map(RoomName,Presence)=IndoorPIRMotionSensorList ()ROOMOCCUPATION=Map(RoomName,Presence)
Sleeping=Event (Sleep(ROOMOCCUPATION))
Outing=Event(Outings (ROOMOCCUPATION))
LowMobility=Event(LMobility(ROOMOCCUPATION))
Cooking=Event (Cook(ROOMOCCUPATION, standardDeviations(cookingScore)))Hygiene=Event (BathroomAct(ROOMOCCUPATION, standardDeviations(hygieneScore)))End
MIN_OUTING_TIME=5 minutes
MIN_SLEEPING_TIME=20 minutes
MIN_LOWMOBILITY_TIME=20 minutes##SUBFUNCTION ALGORITHM##Sleep
IF(ROOMOCCUPATION in [Bedroom]>, MIN_SLEEPING_TIME)
IndoorPIRMotionSensorLastTrigger([Bedhead] ,Duration))##Outings
IF (ROOMOCCUPATION in [Entrance]]>MIN_OUTING_TIME)
ClosingEntranceDoor(True)
IndoorPIRMotionSensorLastTrigger([],Duration)##LMobility
IF(NOT(sleeping)&
NOT(outing)&
RoomOccupationLastChange>MIN_LOWMOBILITY_TIME)

MagneticContactSensorLastTrigger>MIN_LOWMOBILITY_TIME&

IndoorPIRMotionSensorLastTrigger([] ,Duration))##BathroomAct
IF(ROOMOCCUPATION in [Bathroom])
BathroomSensorsUsed[IndoorPIRMotionSensorLastTrigger([Bathroom], MAX_ACTIVITY_TIME)]
hygieneScore = FrequencyOfUse (BathroomSensorsUsed)##CookIF(ROOMOCCUPATION in [Kitchen, Dining])
KitchenSensorsUsed[IndoorPIRMotionSensorLastTrigger([Kitchen, DiningRoom], MAX_ACTIVITY_TIME)] +KitchenSensorsUsed[MagneticContactSensorLastTrigger ([Kitchen, DiningRoom], MAX_ACTIVITY_TIME)] +KitchenSensorsUsed[ElectricalMeasurementSensor ([Kitchen, DiningRoom], MAX_ACTIVITY_TIME))]cookingScore = FrequencyOfUse(KitchenSensorsUsed)

Monitoring Reports and Statistical Analysis
Each month postbaseline, the care professional was presented
with a monitoring report sent via email. Reports were sentmonthly because this was considered frequent enough by thecare professionals. Monitoring reports were divided into 2
sections. The first section shows features from the previous
month (eg, the time of day when daily activities were most
likely to occur, a pie chart of the different room occupancy
averages within the home; see Lussier et al [27] for an example).
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The second section, which is the main focus of this paper, shows
the monthly evolution of the average time per day spent
performing each of the 5 daily activities. It included theevolution of the frequency and average duration of stove andmicrowave use as well.
To determine which lifestyle changes should be highlighted as
significant to the care professional, linear mixed models for
repeated measures were used for each activity (ie, sleep, outing
activities, periods of low mobility, cooking-related activities,
and hygiene-related activities). Outcomes were expressed as
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the daily duration of time spent performing these activities. In
Lisette’s case, 490 continuous days of monitoring were divided
into 14 months. Although the term month will be used for thesake of simplicity, 35-day periods were used instead of calendar
months. This was done so that each month contained the same
number of days, with 5 occurrences of each day of the week
(ie, Monday to Sunday). This further allowed us to control forany discrepancy in weekly routines (eg, having a dance course
every Sunday afternoon). Fixed factors were defined as months
and weekdays. The covariance structure selected was compound
symmetry for all the tested responses and was determinedaccording to the Akaike information criterion. For outcome, themarginal means of each month were compared with the initialbaseline month. P values were not adjusted for multiple
comparisons as each observation was compared with thebaseline observation. This was also done to preserve thestatistical power of this exploratory experiment, as we did 13
comparisons, adjusting the P value accordingly was considered
too restrictive. All analyses were conducted with an α thresholdof .05, using IBM SPSS Statistics version 25.
For each activity, 2 sets of analyses were performed. First, we
examined statistically significant overall trends, as a trend could
correlate with cognitive or health decline. Second, we compared
the baseline report (ie, the first month of monitoring) with eachfollowing month to determine if, and which, months
significantly differed from the baseline. On the one hand, if ageneral trend was observed, this could be used to help determine
at which month a trend reached significance. On the other hand,
this could be used to identify irregular months that did notcorrespond to any overall trend. This could further be useful topinpoint important but nonpermanent changes in the daily
routine for the care professional to explore. It is also important
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therefore, what could initially appear to be an irregular month
could in fact become part of an ongoing trend as months passedby.

Concurrent Validity
To examine the concurrent validity of AAL monitoring reports
for each of the main activities monitored (sleep, outing, lowactivity, cooking activities, and hygiene), we first used statisticalanalyses and values for changes in daily activities that werehighlighted in the monitoring reports. We then explored whether
any of the significant changes from the monitoring data could
be linked with information gathered by the care professional.
This information was extracted from interviews, emails, andmemos exchanged with the care professional and informationfrom the care recipient’s medical file.
We assumed that significant changes calculated from themonitoring data would be coherent with real-life information,
thereby validating the potential of this approach in a clinicalsetting. Moreover, because the care professional may lackcontinuous and reliable information, we expected thatmonitoring reports would detect changes in activities of dailyliving that the care professional would not have been aware of.

Results
For each of the main activities monitored (sleep, outing, lowactivity, cooking activities, and hygiene), the results are provided
in 2 sections. The first section presents statistical analyses andvalues for significant changes in the monitoring reports (seeTable 1 for means and 95% CI of repeated measurements). The
second section compares each significant change withinformation gathered by the care professional to explore
concurrent validity. For each value, the mean and SD arepresented.

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Table 1. Means and 95% CI of repeated measurements for sleeping outings, cooking activities, hygiene, and low mobility during the monitoring period.

a

Month

Sleep, mean (95% CI)

Outings, mean (95% CI)

Cooking activities, mean
(95% CI)

Bathroom usage, mean
(95% CI)

Low mobility, mean
(95% CI)1

8.36 (7.93 to 8.80)

3.11 (2.49 to 3.72)

1.50 (1.28 to 1.72)

1.96 (1.71 to 2.20)

3.96 (3.18 to 4.74)

2

8.53 (8.09 to 8.97)

3.65 (3.04 to 4.27)

1.24 (1.02 to 1.46)

2.22 (1.92 to 2.53)

3.88 (3.10 to 4.66)

3

8.66 (8.24 to 9.10)

3.15 (2.54 to 3.77)

1.17 (0.95 to 1.39)a2.07 (1.79 to 2.35)

3.74 (2.96 to 4.52)

49.08 (8.60 to 9.57)a2.73 (2.06 to 3.41)

1.02 (0.78 to 1.27)a

1.66 (1.39 to 1.93)

3.93 (3.06 to 4.80)

58.71 (8.27 to 9.15)

2.54 (1.93 to 3.16)

0.77 (0.55 to 0.99)a

1.66 (1.41 to 1.90)

4.12 (3.31 to 4.92)

6

9.37 (8.88 to 9.85)a

1.22 (0.60 to 1.85)a

0.73 (0.49 to 0.97)a

1.76 (1.51 to 2.01)

4.63 (3.76 to 5.51)

7

8.96 (8.48 to 9.43)

1.40 (0.76 to 2.05)a0.80 (0.57 to 1.02)a

1.65 (1.40 to 1.91)

5.94 (5.11 to 6.77)a8

9.42 (8.96 to 9.88)a

1.40 (0.78 to 2.01)a

1.10 (0.88 to 1.32)a1.84 (1.60 to 2.09)

4.08 (3.25 to 4.91)

99.90 (9.42 to 10.39)a0.57 (−0.06 to 1.21)a

1.03 (0.80 to 1.26)a

1.87 (1.62 to 2.13)

5.38 (4.55 to 6.21)a10

9.77 (9.30 to 10.24)a

1.56 (0.94 to 2.18)a

1.14 (0.91 to 1.36)a

1.47 (1.22 to 1.72)a4.21 (3.37 to 5.06)

11

9.74 (9.30 to 10.19)a

2.05 (1.44 to 2.67)a

1.71 (1.49 to 1.93)

1.62 (1.37 to 1.86)

3.83 (3.03 to 4.62)

12

9.72 (9.24 to 10.20)a

1.85 (1.24 to 2.47)a

1.76 (1.54 to 1.98)

1.72 (1.47 to 1.96)

4.84 (4.06 to 5.62)

13

10.11 (9.65 to 10.57)a

1.17 (0.53 to 1.80)a

1.23 (1.00 to 1.46)

1.56 (1.29 to 1.83)a

4.29 (3.49 to 5.10)

14

10.18 (9.74 to 10.62)a

0.95 (0.33 to 1.56)a

1.05 (0.83 to 1.27)a

1.36 (1.12 to 1.61)a3.43 (2.64 to 4.21)

Statistically significant (P<.05) changes compared with the first period.

Sleep Habits
Monitoring Report
Over the course of monitoring, Lisette gradually spent moretime resting (month effect: F13, 397.98=7.05; P<.001). This trend
became steady and significant from the eighth month onward.
In the last month, sleep was detected for 10.18 (SD 1.64) hours,

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which represents a 22% increase in resting time when comparedwith the baseline (8.37, SD 1.27 hours; Figure 2). In addition,
the monitoring data suggested that she more frequently woke
up later in the morning toward the end of the monitoring period.
The data showed that she woke up between 7:25 AM and 7:56
AM during the first month but between 7:33 AM and 10:30 AM
over the last month of monitoring. There were no indications
that she woke up more frequently during the night.

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Lussier et al

Figure 2. Evolution of estimated means (SE) for hours of sleep detected during the monitoring period. The gray zones highlight statistically significant(P<.05) periods when compared with the first period.

Clinical Observation
Interestingly, Lisette did not mention to the clinician that she
was feeling more tired or sleeping longer than before. However,the personal care assistant responsible for giving her medicationeach morning (a different health professional, not the one incharge of coordination and treatment plan) noted that she had
to wake her up more often when visiting.

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Outings
Monitoring Report
Overall, Lisette gradually went outside for shorter periods(month effect: F13, 443.00=8.70; P<.001; see Figure 3, top-left).
This change became steady and significant from the sixth monthonward. The reports highlight that Lisette spent 68% less timeoutside when compared with the baseline (3.10, SD 2.80 hours)
and to the last month (0.97, SD 0.90 hours). Another noticeable
decline in outings was observed specifically in December (0.35,
SD 0.23 hours).

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Lussier et al

Figure 3. Evolution of estimated means (SE) for outings, bathroom usage, low mobility, and cooking activities during the monitoring period. The grayzones highlight statistically significant (P<.05) periods when compared with the first period.

Clinical Observation
Trends in outings were corroborated by Lisette, who mentioned
to the care professional that she had lost interest in the social
activities held at her building because she said she disliked the
new hosts in charge of the activities. She also mentioned that
she had stopped attending the dance activities on weekends
because she did not appreciate the change in the style of music
played. There was no way for the clinician to verify that
information. As for the decline observed for the month ofDecember, it was noted in her medical record that Lisette had
influenza in December and was quite incapacitated by it, which
would most likely account for her staying at home.

Cooking Activities
Monitoring Report
Over time, a significant decline in cooking-related activities
was observed (month effect: F13, 424.81=7.83; P<.001; see Figure
3, top-right). However, the time spent on these activities did
not follow a steady decline. A first significant decline wasobserved in the third month, and the decline continued gradually
up to the sixth month, the lowest point reached (42.24, SD 26.40m), representing a 53% decline when compared with the baseline(90.00, SD 50.00 m). Cooking activities slowly increased overthe 11th to 13th month, reaching a value comparable with thatof the first month. Finally, cooking activities started decreasing
again during the last month (62.40, SD 30.61 m), a 31% decline
compared with the first month.
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The stove was used on about 16% of days during the monitoring
period (the oven with or without the burners, 13%, and only theburners, 3%). On days the stove was used, burners were used
for an average of 10 (SD 11) min and the oven for 15 (SD 10)
min. Only one instance of dangerous stove use was identified:
Lisette left the apartment while the stove was switched on andthen came back but only turned it off the next morning. The
microwave was used on 32% of days for an average of 6 (SD6) min per day. On one occasion, the microwave was in use for
20 min nonstop, which was a clear outlier for Lisette. Other
than that, no unusual behaviors were detected.

Clinical Observation
The patterns related to cooking activities were supported byreal-life information. Notably, when the care professional
received monthly reports illustrating raw data, she noticed achange in pattern. Considering her concern that Lisette was at
risk of malnutrition, she used that information to discuss
Lisette’s eating habits with her son during the summer (exact
date unknown, but between the fifth and seventh month). This
conversation led to a change in the caregiver’s behaviors: now
more aware that his mother preferred homemade meals to readyor frozen meals, and to increase meal intake, the son said hewould prepare more homemade meals for her. He and his wife
would also come more often to cook for her and put leftovers
in the refrigerator. This decision correlated with increased
detection of cooking-related activities in Lisette’s home forseveral months after this exchange. Therefore, the increase intime spent cooking at that time is a result of the compensatory
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JMIR MEDICAL INFORMATICS
behaviors by family members (ie, family cooking at Lisette’s
home and Lisette eating more regularly because the food she
liked was easily available). However, this compensatory
behavior either did not last or was not sufficient support, as
Lisette’s cooking-related activities again declined in the last 2
months before her hospitalization and the end of monitoring.
Overall, the infrequent use of the microwave and stove wasconsistent with the care professional’s suspicions that Lisette
might not be eating hot meals daily (as indicated by theaccumulation of prepared meals in the refrigerator). Concerning
the dangerous use of the stove, a member of the research teamcontacted Lisette shortly thereafter to verify if the sensor was
defective. She said she forgot the burner on and burned her
fudge during the night. The care professional was informed ofthe event. As for the microwave, the care professionalquestioned Lisette about this incident a couple of weeks later.
She answered that she probably meant to set 2 min but added
a 0, resulting in 20 min instead.

HygieneMonitoring Report
The monitoring results suggest that hygiene-related activities
declined significantly over time (month effect: F13, 40658=2.94;
P<.001), reaching a 30% decline in time spent in the bathroom
when comparing the baseline (117.36, SD 56.80 m) with thelast month (82.98, SD 25.80 m; see Figure 3, bottom-left). This
trend first became significant during the 10th month but
remained steadily significant only from the 13th month onward.
Activation of the clothing drawers’ sensors remained stable
over time.

Clinical Observation
As changes in hygiene were detected late in the study, the care
professional did not have many opportunities to gather
information on this aspect before Lisette was hospitalized andtransferred. However, she mentioned that a decline in hygiene
would not be surprising, considering the rapid decline of her
cognitive state in the month preceding her transfer. Moreover,post hoc analyses suggest that the longest bathroom hygiene
periods tended to occur before outings. Therefore, a decline inhygiene-related activities would be consistent with Lisette’s
confirmed abandonment of some social activities.

Low Mobility
Monitoring Report
The average period of low mobility remained quite stable overtime (month effect: nonsignificant.; see Figure 3, bottom-right).
The only 2 significant increases in periods of low mobility
occurred in the seventh month (5.27, SD 3.21 hours; P<.001)
and the ninth month (4.69, SD 1.65 hours; P=.02) comparedwith baseline (3.96, SD 1.98 hours).

Clinical Observation
Although there was no clear explanation available for theincrease in inactivity in the seventh month, Lisette’s medical
record reported that she was sick with influenza during the ninth
month, which could explain the fewer outings and moreinactivity being detected in the home.
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Discussion
Principal Findings
This study describes a longitudinal single-case study in a
Canadian public home care setting. The objective was to
examine the concurrent validity of AAL monitoring reports andcare professional descriptions of real-life changes in activities
of daily living experienced by an older adult diagnosed withAD (ie, Lisette). Lisette’s care professional received monthly
monitoring reports of sleep, low mobility, outings, cooking, andhygiene-related activities. In the monitoring reports, algorithms
and linear mixed models for repeated measures were used tohighlight significant changes in Lisette’s daily activities.
Highlights from the reports were then juxtaposed to information
gathered by the care professional to determine if they concurred.
Lisette's health and life events were gathered from her medical
file and from interviews, emails, and telephone exchanges withher care professional.
As expected, statistically significant changes in daily routine
were detected over the 490 days of monitoring. Throughinterviews conducted with the care professional, it was possible
to conclude that monitoring report trends were consistent withthe clinical information collected when staff visited the carerecipient. In fact, a priori interrogations and observations madeby the care professional were indeed reflected in the monitoring
reports. For instance, the care professionals believed that Lisette
was not cooking complex meals, and this was later confirmedby the monitoring report. In addition, the outcome of subsequentinterventions was observable in the monitoring report. Thisoccurred when the care professional invited the family toparticipate more in meal preparation, which led to a detectablechange in routine. Interestingly, in addition to validating theinitial hypotheses, the monitoring reports drew attention tocertain unforeseen or unexpected changes that were thentriangulated with other information gathered by the clinician.
On occasion, this led to discussions with the care recipient. For
example, when there was a temporary decline in outings andan increase in low-mobility periods during the month ofDecember, the care professional asked Lisette about it. She thenlearned that Lisette had caught the flu that month, which was
supported by information in her medical file. Another example
is the gradual increase in the time spent sleeping, which Lisette
was not aware of (or denied) but was corroborated by a personalcare assistant. As such, by combining information from themonitoring report with comments made by another care worker,
the care professional was able to gain a better understanding ofLisette’s situation that would have otherwise been overlooked,
unattainable, or ambiguous.

Comparison With Prior Work
This study is innovative because monitoring reports were
designed in collaboration with care professionals to specificallyaddress their requirements and the care professional’s need for
information that would enable the best client support possible
were carefully examined. Moreover, this technology was
implemented in concert with the head of service to be included
harmoniously with actual current services from the public healthcare system. Importantly, the evolution of daily routine observed
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JMIR MEDICAL INFORMATICSin this case study was highly consistent with the current
literature on daily activities in AD. For instance, sleep
disturbance is prevalent and predictive of cognitive decline inolder adults and in those with neurocognitive disorders [42].
Three studies using AAL monitoring technology to monitor
sleep found that sleep quality and sleep hygiene measures wererelated to mild cognitive disorders in older adults [43-45]. Moreprecisely, it is suggested that AD is not associated with more
sleeping time but with less sleep efficiency (ie, lower percentageof time in bed spent asleep) [46]. With the sensors used in thiscase study, it was not possible to distinguish the time spent
sleeping from the time spent lying down in bed. Nonetheless,although Lisette did not report any change in her sleep routine
when asked, it is possible to assume that her sleep quality
decreased because she needed to spend more time lying downin her bedroom as the disease progressed. The decrease in timespent outside the home was partially explained by Lisette, who
mentioned withdrawing from social activities. She justified thisbehavior by referring to different changes in the way theactivities were held. However, this behavioral evolution is also
consistent with several studies showing that older adults withcognitive impairment reduce their engagement in social activities
as their cognition declines [47-50]. Anosognosia (ie, a lack ofawareness of deficit) is often observed early in the course ofAD [51], so it is possible that Lisette withdrew from social
activities because of cognitive decline but without being awareof that change. It is also at this stage that agitation, confusion,and distress episodes, such as the one Lisette experienced justbefore being moved out of her apartment, occur more frequently[52]. Therefore, anxiety and distress might have kept her fromgoing out. Finally, the decline in hygiene occurring last in thetimeline is also consistent with the literature. Indeed, while adecline in the ability to perform basic activities of daily life isminimal in the early stages of AD, moderate stages bringincipient difficulties with this sphere [53]. For example, at thisstage, the care recipient should be able to shower alone but may
forget to do so regularly. Therefore, the decline in hygiene wasconsistent within the context of progressing dementia and could
be considered an indicator of worsening symptoms.
In comparison with other similar studies on AAL and clinicalreasoning, our study is also in line with the study by Rantz etal [41]. In their study, Rantz et al [41] showed that monitoring
the increase in activity level in the bathroom could support thedetection of early signs of urinary tract infection by a nurse. To
our knowledge, our study is however the first to document theuse of daily living monitoring over a long period, in relation toother clinical data to support the clinical reasoning of healthcare providers. Monitoring data related to activities of dailyliving have great potential to support the work of home careproviders via telehealth modalities. Faced with unprecedented
challenges in allocating resources, in urban and remote rural
areas, vast countries such as Canada could use AAL tools tobetter allocate services to the right person, at the right moment,
ensuring more equitable and sustainable use of public healthcare capacities [54].

Limitations
Although the results of this case study are promising, further
replications among care recipients with similar medical
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Lussier et al
conditions and in a variety of environments are necessary. The
limitations of the monitoring technology used must also be
addressed. First, it was impossible to accurately determine
whether more than one occupant was in the home at the sametime. Therefore, visitors’ activities were averaged in the datareports. The multioccupant dilemma must be further explored
to develop a simple but accurate solution. This solution should
not require wearable sensors, as poor compliance has beenreported with this technique in older adults with cognitive
deficits [55]. Nevertheless, this study focused on the question,“Has the activity been done?” rather than “Who is doing theactivity?” because our participant was living alone. From theperspective of the care professional using the monitoring reports
to decide if any additional services are needed or not, the maininterest is in knowing that the activity is being carried out
regularly, regardless of who has done it; a decision taking into
account the support given by the caregivers, not in abstractionof it. Furthermore, care professionals are particularly interested
in AAL monitoring technology for care recipients who live
alone with minimal caregiver support as social isolation is amajor risk factor for security and reliable sources of information
are often lacking in this context [27]. Finally, monitoring reports
are not a substitute for a functional, performance-based
evaluation and should not be used.

Conclusions
Although living at home presents several benefits for older
adults [4,22,23], dealing with the increase in the incidence ofself-neglect also poses great challenges for the health caresystem. This increases the demand for better resource allocationand innovative strategies to attenuate the estimated impact on
health care expenditures. With the technology boom of the past
decades, AAL monitoring systems are among the mostpromising tools to support the outcomes of individuals whose
cognitive declines limit their effective participation in daily
activities. AAL may improve the ability of older adults to copeat home and handle tasks and needs, which are the basis of thewell-known aging-in-place design for living. Moreover, global
efforts to cope with the COVID-19 pandemic of 2020 haveacutely highlighted that it is crucial to have a system of healthand social services that allow for remote monitoring of fragile
older adults living alone and isolated under conditions of social
distancing [56].
In this paper, we showed how an AAL monitoring system, using
customized wireless sensors, was relevant during the assessmentof home care services for an older woman with AD at risk of
self-neglect. We were able to monitor the care recipient’s routineof basic (hygiene, sleep) and instrumental (cooking, outing)activities of daily living. We showed that nonintrusive AAL
monitoring can identify and present the relevant trends in daily
routines in data format. This continuously gathered information
can then be integrated with other sources of information to help
care professionals manage risks and develop tailoredintervention plans.
Replication of this study will be required to strengthen thefindings of this study. Future studies also need to consider the
economic benefits of accessible AAL monitoring for clinicaldecision making in public and private health services. SuchJMIR Med Inform 2020 | vol. 8 | iss. 11 | e20215 | p. 12(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
efforts are driven by a pressing need to deliver efficient homecare services, enhance the quality of life of home care recipients,
and reduce the burden on informal caregivers. We believe these

Lussier et al
results show that with further development and broader
implementation, AAL monitoring systems will become essential
tools in the promotion of aging in place.

Acknowledgments
This study was supported by the Centre de recherche de l’Institut universitaire de gériatrie de Montréal–Comité aviseur pour larecherche clinique and the Canadian Institutes of Health Research –Natural Sciences and Engineering Research Council. ML
was supported by a postdoctoral award from the Fonds de la recherche du Québec—Santé (FRQS). NB was supported by aresearch scholar award from FRQS. The authors would like to thank Marie-Michèle Hachée for her valuable contribution to datacollection.

Conflicts of Interest
None declared.

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Abbreviations
AAL: ambient assisted living
AD: Alzheimer disease
FRQS: Fonds de la recherche du Québec—Santé
PIR: passive infrared

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Edited by C Lovis; submitted 09.07.20; peer-reviewed by J Li, N Mohammad Gholi Mezerji; comments to author 28.07.20; revised
version received 10.08.20; accepted 26.09.20; published 13.11.20
Please cite as:
Lussier M, Aboujaoudé A, Couture M, Moreau M, Laliberté C, Giroux S, Pigot H, Gaboury S, Bouchard K, Belchior P, Bottari C,
Paré G, Consel C, Bier N
Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report
JMIR Med Inform 2020;8(11):e20215
URL: https://medinform.jmir.org/2020/11/e20215
doi: 10.2196/20215
PMID: 33185555©Maxime Lussier, Aline Aboujaoudé, Mélanie Couture, Maxim Moreau, Catherine Laliberté, Sylvain Giroux, Hélène Pigot,
Sébastien Gaboury, Kévin Bouchard, Patricia Belchior, Carolina Bottari, Guy Paré, Charles Consel, Nathalie Bier. Originallypublished in JMIR Medical Informatics (http://medinform.jmir.org), 13.11.2020. This is an open-access article distributed under
the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, isproperly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as wellas this copyright and license information must be included.

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