A Tailored Internet of Things Lighting Solution to Support Circadian

Rhythms and Wellbeing for People Living with Dementia

Kate Turley

, Joseph Rafferty

, Raymond Bond

, Assumpta Ryan², Maurice Mulvenna

and Lloyd Crawford³

School of Computing, Ulster University, 2-24 York St., Belfast, N. Ireland

School of Nursing and Pandemic Science, Ulster University, Northland Rd, Derry, N. Ireland

³Chroma Lighting, 213-215 Donegal Rd, Belfast, N. Ireland

Keywords: Dementia, Wellbeing, Dynamic Lighting, Circadian Rhythm, Body Clock, Sensing, IoT, Digital Health.

Abstract: Light is a requirement for setting and maintaining the body’s circadian rhythm, however our knowledge of

the spectral content, timing and duration of lighting exposure for the indoors is not well defined. For people

living with dementia, this knowledge gap is important to address since they experience more heavily disrupted

circadian rhythms, which can heighten symptoms of sundowning, agitation, low mood and poor sleep quality.

This paper focuses on the required design aspects for a dynamic lighting and sensing device tailored towards

supporting the wellbeing of people living with dementia. The authors discuss the current understanding of

lighting for health, identify the gaps to be addressed and propose the design and research protocol for an

indoor lighting and sensing solution. The device is currently deployed within a care home and analysis of

results is forthcoming.

1 INTRODUCTION

On a global scale, the ageing population is increasing

at a rapid rate. Moreover, it is commonly reported that

within these populations, the prevalence of dementia

is more significant than in other age brackets

(Livingston et al. 2020). This contributes to the fact

that there are approximately 50 million people living

with dementia at present, forecasted to more than

triple by 2050.

The diagnosis itself is associated with certain

behavioural and psychological symptoms which can

contribute to decreased wellbeing (Finkel et al. 1997).

Common of these symptoms are expressions of

agitation and sundowning; the latter referring to the

increase in neuropsychiatric symptoms which

contribute to evening restlessness (Canevelli et al.

2016). As a result, this evening restlessness offsets

the body’s circadian rhythm; the 24-hour harmonic

cycle responsible for many of the body’s essential

functions (Hastings et al. 2007). Of these essential

functions, the circadian rhythm is dominantly

responsible for controlling hormone balance

(impacting mood), regulating the body temperature,

regulating sleep-wake cycles, and influencing rest-

activity patterns (Hastings et al. 2007). Therefore

disproportionate influence over mood, agitation and

sleep-wake cycles, as common in dementia, can put

pressure on informal caregivers when looking after

their close relations. This makes it more likely for

admittance to care facilities (Fillit et al. 2021,

Fishbein et al. 2021). As a result, it is estimated that

the total cost of dementia to the global economy is $1

trillion (Livingston et al. 2020).

This combined social and economic strain of a

dementia diagnosis can be attributed to the fact that

to date there is no cure for dementia. Therefore, the

best attempt to confront this challenge is to find

optimal ways to alleviate symptoms in dementia. In

turn, this will contribute to enhancing their wellbeing,

and possibly reducing the rate of admissions to care

facilities. A common solution to attenuating these

symptoms is to use pharmaceuticals such as sedatives

(ARUK, 2022). However, research has demonstrated

that these solutions are indirectly targeting symptoms

such as agitation and restless nights by prescribing a

‘one size fits all’ solution, and unnecessarily

increasing drowsiness in favour of reduced agitation

(ARUK, 2022). This can therefore lead to reduced

wellbeing which counteracts any initial efforts made.

In contrast, this paper focuses on devising a non-

190

Turley, K., Rafferty, J., Bond, R., Ryan, A., Mulvenna, M. and Crawford, L.

A Tailored Internet of Things Lighting Solution to Support Circadian Rhythms and Wellbeing for People Living with Dementia.

DOI: 10.5220/0012650100003699

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2024), pages 190-197

ISBN: 978-989-758-700-9; ISSN: 2184-4984

pharmaceutical solution to improve wellbeing for

people living with dementia; the adoption of circadian

lighting. This is ‘daylight-mimicking’ indoor

lighting, designed to support the body’s circadian

rhythm and positively impact activity metrics such as

mood, agitation, sleep-wake cycles, hormone balance

and social interactions. This paper represents a work

in progress report which outlines the design of the

technical architecture and summarizes the research

protocol of the study.

2 OUTLINE OF OBJECTIVES

In order to address the research problem, several

objectives have been formulated:

1. Design a novel digital health technology capable of

both monitoring and improving wellbeing for people

with dementia; consisting of circadian luminaires

(actuators) and sensing devices.

2. Develop and evaluate algorithms acting on the raw

sensor data, to produce relevant metrics on individual

activity profiles, since this is expected to be impacted

through the circadian lighting.

3. Deploy and evaluate the solution in a care home

environment in order to observe the impacts of the

lighting on activity for people living with dementia.

4. Make use of these observed changes in activity in

order to better actuate/tailor the circadian lighting

output to better align circadian rhythms.

5. Assess the impact to wellbeing/circadian rhythm

through the sensor-generated activity profiles, from

interviews and validated wellbeing questionnaires.

6. Assess the acceptability of the digital health

solution for supporting wellbeing and circadian

rhythms in dementia.

7. Generate a conclusion on the capacity for

alleviation of symptoms of dementia and strain on

care staff in order to remark on the initial research

problem.

3 STATE OF THE ART

Circling briefly back to circadian rhythms, it is well

established that the principle excitor for controlling

this rhythm is light (Berson 2002). Due to consistent

day/night intervals of light and dark, early human

ancestors were subconsciously attuned to wake with

the rising sun and sleep with the setting sun (Wright

et al. 2013). This light/dark cycle has manifested

itself into what is now known as the ‘typical’

circadian rhythm. This process occurs through

receival of light signals in the retina, which prompts

the brain to instantiate the controlled release of the

hormones (melatonin and cortisol) that influence our

mood, sleep-wake cycles and activity patterns

mentioned earlier. Melatonin is known for inducing

sleepiness and cortisol is known for inducing

alertness (Sato et al. 2014). Therefore with a ‘typical’

circadian rhythm, these hormones are optimally

balanced to lend itself to better sleep-wake cycles and

fewer night time disturbances (Figueiro et al. 2020).

However, with dementia, a more irregular rhythm

means that these hormones are no longer

synchronised with the circadian rhythm, and high

levels of cortisol may dominate bed-times, creating a

detrimental impact on sleep-wake cycles and mood.

This contrast in typical and atypical circadian

rhythms is demonstrated in Figure 1.

Figure 1: The difference between the hormone release of

both typical and atypical circadian rhythms. Note how an

irregular rhythm would result in poorer sleep/wake cycles

(Sato et al. 2014).

Therefore exposing people living with dementia

to circadian lighting is likely to alleviate their

symptoms and improve their wellbeing. There are

many studies which have conducted this type of

research before, generating positive impact to

wellbeing (Sust et al. 2012, van Lieushout-van Da l et

al. 2019, Bromundt et al. 2019, Figueiro et al. 2020).

However, common within this research area is the

agreement on what is not yet known. This stems from

the fact that the ‘typical’ circadian rhythm differs

somewhat for every individual (Skeldon et al. 2017).

An individual’s response to lighting will depend on

many factors, such as age, chronotype, previous

A Tailored Internet of Things Lighting Solution to Support Circadian Rhythms and Wellbeing for People Living with Dementia

191

lighting exposures, gender, dementia type and state of

progression, amongst others (Skeldon et al. 2017).

Therefore in order to provide a tailored lighting

solution designed to optimise wellbeing for any

individual, it is paramount to find a way to monitor

the impact to both the circadian rhythm and wellbeing

that the lighting is responsible for. It is also

commonly reported that the parameters of the lighting

for benefiting wellbeing are not fully understood.

These parameters refer to the timings of lighting

exposure, the duration of the exposure, the intensity

of the lighting, and the colour temperature of the

lighting (van Lieushout-van Dal et al. 2019, Brown et

al. 2022).

As a result, this study has an initial focus on

designing a novel technical architecture which can

facilitate the collection of data which will help

uncover these unknowns in the present literature. The

overarching aim is that the architecture can generate

the necessary health informatics to understand the

circadian rhythm of each individual, while

simultaneously actuating and monitoring the lighting

output over time. Therefore along with the luminaire,

the architecture makes use of an environmental

sensing device designed to track the activity of an

individual in an unobtrusive manner. This is essential

within dementia cohorts since their vulnerable status

makes enforcement of wearables unfeasible in the

long-term (Harper et al. 2020). The sensor is capable

of tracking the current location of an individual, their

activity patterns, and their sleep-wake cycles. When

initially trialling this technology, the added social

wellbeing parameters such as mood and social

interactions will be documented using the validated

‘QUALIDEM’ scale, to support the quantitative

sensor data (Ettema, T.P. et al. 2007).

Lastly, this novel architecture will cater for the

collection of lighting and circadian-related activity

metrics in order to infer the relationship between

them. The collection of demographic information

through the mobile application will also help to draw

any individual and group-based homogeneities to the

lighting response. Once there is an accepted

understanding of the circadian response to lighting

over time, it becomes possible to make data-driven

changes to the circadian lighting in order to best

support any particular individual. This therefore

creates a lighting/sensing feedback loop which

outputs the optimal lighting based on the circadian

health metrics of the individual. Due to the novel

architecture, this feedback communication channel is

built-in to the design; a concept not explored in this

field to date. An outline of the main contributions to

‘state-of-the- art’ status are summarized in Table 1.

Table 1: Summary of the research problems and the

devised solutions.

Research problem Devised solution

No knowledge of exact

timing, duration,

intensity and colour

temperature of lighting

that individuals are

exposed to (van

Lieushout-van Dal et al.

2019, Brown et al.

2022).

The architecture supports

networked luminaires and

sensors with a scalable IoT

design. Knowledge of an

individual’s location and

corresponding luminaire

spectral output in relation to the

height of an individual is

possible. This is how we

determine the lighting

exposures throughout the day

(Brown et al. 2022).

Vulnerable cohort status

means collecting long-

term data with wearables

is difficult to implement

and maintain (Harper et

al. 2020).

Unobtrusive sensor design

means that longitudinal data

collection is possible. Mains

connected device also removes

burden associated with device

recharge, calibration, and of

becoming forgetful to use the

device.

Limited knowledge of

influence of dementia

type and state of

progression on

lighting/circadian

rhythms (Skeldon et al.

2017, van Lieushout-van

Dal et al. 2019, Brown et

al. 2022).

Long-term database collation

with lighting/activity/

demographic information over

time lends itself to pattern

mining and new discoveries

The unique element of this research begins with

the novel architecture. This data-driven feedback

system allows for automated tailoring of circadian

lighting based on an individual’s circadian-related

activity over time. This design concept has not yet

been developed for the research area of circadian

rhythms.

In addition, the unobtrusive design

(environmentally fixed) of the technology facilitates

long-term data collection without the need for

constant intervention to charge the device or re-

calibrate it. This facilitates a better capacity for

database growth. This enriched database with metrics

on activity, mood, sleep-wake cycles, lighting

exposures, and demographic information will

therefore provide the fundamental building blocks to

uncovering the largely unknown relationship between

these above factors (Skeldon et al. 2017, van

Lieushout-van Dal et al. 2019, Brown et al. 2022).

This will therefore contribute to new knowledge in

the field.

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4 METHODOLOGY

The methodology focuses on trialling the lighting and

sensing technologies within a care home over 16

weeks. This data collection will consist of validated

wellbeing scales (QUALIDEM), interviews and

sensor analytics in order to form an overall

assessment of the circadian lighting for this cohort.

Therefore, conducting this research requires a

real- life environment such as a care home to evaluate

the digital health technology. This will take place in a

care home in Belfast, Northern Ireland. This research

proposal has undergone ethical review and been

approved by the Office for Research Ethics

Committees Northern Ireland (ORECNI). The initial

project objective is to design and build the

architecture. Preliminary work has been published on

this by (Turley et al., 2021).

The unobtrusive digital health technology is

deployed in a care home environment. The luminaires

and sensors are in a mesh network where real-time

sensor data is sent to a third party cloud server over a

Bluetooth Low Energy (BLE) gateway. Luminaire

output data is also transferred in this manner, but at a

lower frequency (per 15 minutes). The processing,

storage and application data are all managed from a

local server. Real-time sensor data is filtered and

transferred to an Influx database. At pre- defined

intervals, the processing block will fetch data from

this database and generate metrics to produce

information on activity, sleep/wake cycles and

location trajectories.

Again, at a specified time interval which is

informed by known circadian rhythm adaptation

times (van Lieushout-van Dal et al. 2019), the rule-

based circadian logic will be invoked. This assesses

if the circadian lighting is having a positive, negative,

or null effect on wellbeing. Depending on the

outcome, the lighting will remain as it is or be

marginally tailored (in colour temperature and

intensity). This occurs via REST API to the Bluetooth

mesh network connecting the care home devices. It

should be noted these lighting changes are informed

by insights from the literature. For example, increased

blue wavelength light during the day may alleviate

disrupted sleep (Hanford et al. 2013), so if the sensor

detects a resident who experiences frequent night-

time disturbances, the blue component of the lighting

may be increased during the day to try and help

improve their sleep at night. This will be monitored

over the next time interval and assessed for efficacy.

As and when the circadian-related metrics are

updated in Influx, they will be presented on a

dashboard. This dashboard is integrated into an

overall mobile application. This application then acts

as an outlet which provides both client-side

demographic information (stored separately) and the

matching circadian-related metrics per individual. It

will then act as a support to care staff when planning

their daily or weekly routines. A summary of this

architecture is seen in Figure 2.

As an aside, there is a preliminary ‘value-add’

feature of fall detection being trialled, which

consumes real time sensor data and uses rule-based

threshold techniques to determine if a fall has

occurred, in order to alert the carer’s mobile

application.

The participants will take part in 16 weeks of

observation; 4 weeks of baseline measurements

(static lighting output) preceding 12 weeks of a

between-subjects experimental design. The

experimental group will receive circadian lighting for

12 weeks and the control group will receive the

existing lighting in the care home for 12 weeks. The

circadian lighting will begin as a general profile, set

in accordance with lighting parameters used in other

literature using circadian lighting on these cohorts

(Figueiro et al. 2020). It will gradually rise in

intensity from early morning to afternoon and fall

again by evening. The colour temperature will be cool

from 09:00-14:30 to promote active days and

gradually become warmer in the evenings to promote

restful nights. Depending on the outcome of

the

circadian lighting logic outlined in Figure 2, the

lighting will be tweaked according to the activity

profile picked up by the sensor. Completion of

QUALIDEM questionnaires will be required by care

staff once a week, and short interviews with carers

will be conducted four times throughout the study

duration. At the end of the 16 weeks, individual and

group analyses of the data will take place, in order to

inform the relationship between circadian lighting,

demographics, and activity profiles. A conclusive

report supported by the data will then be shared with

the care home team and researchers alike. This should

state the demonstrated impact to wellbeing (if any),

and outline future milestones for this research field.

A flow schematic of the study protocol is summarized

in Figure 3.

An overall assessment of wellbeing is deduced from

combined analyses of qualitative (QUALIDEM/

interview originated) and quantitative (sensor

originated) data.

A comparison of a week 4 (last control week for

experimental group) to week 16 behaviour profile

between those who have experienced static lighting

and those who have experience circadian lighting will

be conducted. Key indicators to explore will be the

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193

Figure 2: Overall IoT architecture of the digital health solution. Note that that processing, storage and application layers are

all managed locally. Push-based actuation occurs both out to luminaires and to mobile application alerting panel.

frequency of night time disturbances, phase-advance

or delay of sleep onset/wake times, duration of sleep,

changes to mood and social interactions, amongst

others. In unison, these wellbeing parameters will

give an insight into the overall status or ‘wellbeing

score’ at both the beginning and end of the study. In

turn, this will give an insight into the initial efficacy

of this digital health technology for its outlined

purpose; supporting wellbeing for people living with

dementia.

The acceptability of the digital health solution is

supported by the fact that the research is conducted

within a care facility environment. This makes it

possible to access feedback from both people living

with dementia making most use of the lighting, and

also care staff making most use of the mobile

application.

Feedback will be collected at multiple intervals

throughout the study. This will be with people with

dementia, care staff and family members/close

relations. Subject areas such as perception of

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Figure 3: Flow schematic of the study protocol over a 16 week period. It highlights the study measures, collection

techniques and expected outcomes.

circadian lighting, impacts to activity, overall

consensus of circadian impact, acceptance of

technology design and , and willingness to adopt this

technology in the future will be presented.

For future work, any suggestions for improvement

will be integrated as best as possible into the digital

health solution and workshops arranged for re-

evaluation purposes.

5 EXPECTED OUTCOMES

Care staff will be presented with an overall high-level

dashboard which highlights the locations of the

individual residents as labelled by room name. These

type of metrics have been reported to be highly useful

by care staff (Hall et al. 2017). This dashboard will

have a link to another dashboard named ‘Resident

overview’, which accumulates the finer-grain detail

of the circadian metrics per individual room.

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From accessing these health informatics for every

individual, care staff can use this data to support their

delivery of care. For example, care staff could look at

the latest ‘wake time’ for all residents in order to

determine when the breakfast can finish, in order to

better plan schedules for the following week.

In terms of contribution to knowledge, the metrics

outlined will be critical to understanding the

progression of the individual circadian rhythm over

time. When combined with the lighting exposure and

demographic data also collected in the study, insight

into the relationship between circadian rhythms,

lighting and wellbeing will hopefully become more

apparent.

In addition, the information and can provide

insight into whether the circadian lighting is

benefitting wellbeing overall in relation to the

previous static lighting in place in the care home. As

supported by current literature on circadian lighting

on dementia cohorts (Sust et al. 2012, van Lieushout-

van Dal et al. 2019, Bromundt et al. 2019, Figueiro et

al. 2020), it is expected that the parameters of

wellbeing impacted by the circadian rhythm will

improve.

6 STAGE OF THE RESEARCH

This research has already received the necessary

ethical approval, as referenced in section 4.

At present, the novel IoT architecture for the

digital health solution has been designed and

developed (Turley et al. 2021). Alongside the

software, this includes the hardware; luminaires and

sensors. The algorithms for deducing circadian-

related metrics have also been developed. These

algorithms are in the testing phases whereby ground

truth metrics are being compared to the sensor

processed metrics. The dashboard platform has also

been set up and deployed on a local server.

The research is currently underway in the care

home and results of the trial are expected to be

analysed and published in the coming months.

ACKNOWLEDGEMENTS

This research is undertaken through an industry-

academic partnership and supported by the Royal

Commission for the Exhibition of 1851.

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